From 8ded6408c87a9acc25732a35e9568637a9605aaf Mon Sep 17 00:00:00 2001
From: andvagapov <86461996+andvagapov@users.noreply.github.com>
Date: Fri, 25 Jun 2021 09:43:21 +0300
Subject: [PATCH 1/4] =?UTF-8?q?=D0=92=D1=8B=D0=BF=D0=BE=D0=BB=D0=BD=D0=B5?=
 =?UTF-8?q?=D0=BD=D0=B0=201=20=D0=BB=D0=B0=D0=B1=D0=B0?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
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---
 .../lab1_3 (5).ipynb"                         | 5352 +++++++++++++++++
 1 file changed, 5352 insertions(+)
 create mode 100644 "ml/lb1/\320\222\320\260\320\263\320\260\320\277\320\276\320\262/lab1_3 (5).ipynb"

diff --git "a/ml/lb1/\320\222\320\260\320\263\320\260\320\277\320\276\320\262/lab1_3 (5).ipynb" "b/ml/lb1/\320\222\320\260\320\263\320\260\320\277\320\276\320\262/lab1_3 (5).ipynb"
new file mode 100644
index 0000000..24054b8
--- /dev/null
+++ "b/ml/lb1/\320\222\320\260\320\263\320\260\320\277\320\276\320\262/lab1_3 (5).ipynb"	
@@ -0,0 +1,5352 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "id": "vkCoLck9lErz"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "H848iYKnoGMT"
+   },
+   "source": [
+    "# Массивы NumPy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "s-kILeHIlbZN",
+    "outputId": "547845c4-a64a-4728-f26a-9f489045c10d"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vec = np.array([1,2,3])\n",
+    "vec.ndim "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "8ZgVPvqdlklr",
+    "outputId": "26795931-3d45-4648-a7bb-ddd3f036928a"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mat = np.array([[1,2,3],[4,5,6]])\n",
+    "mat.ndim "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "2q57HdX3lvBT",
+    "outputId": "677d5488-ee13-4009-dd85-dad9919e5d15"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3,)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vec.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 35
+    },
+    "id": "qpIG5Qm3l5IE",
+    "outputId": "12d53ebb-c6fd-49a7-cc71-7c5503a16c66"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'int32'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mat.dtype.name "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ei_BzZIml9C7",
+    "outputId": "d58b96a1-6d20-4e0b-d59b-c0425b196fc0"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mat.itemsize "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Создание массивов"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "a2mFdyuCmLUz",
+    "outputId": "fe8050c4-42e0-4eeb-e9af-e76864aa5ed4"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 2, 3])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A = np.array([1, 2, 3]) \n",
+    "A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "hC8j1VZdmSrK",
+    "outputId": "22d718fa-12e9-4f40-bb77-2b282e866edd",
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1., 2., 3.])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A = np.array([1, 2, 3], dtype=float) \n",
+    "A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "zquz8XgcmYVa",
+    "outputId": "2fbe2d0a-122e-4a8b-ba4f-ee09ca09b349"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 2, 3],\n",
+       "       [4, 5, 6]])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "B = np.array([[1,2,3],[4,5,6]])\n",
+    "B"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Создание массивов специального вида"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "47g5kC0Zmmgc",
+    "outputId": "b4305f66-e9c1-41aa-96da-07b1d78d6707"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0., 0., 0.])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.zeros((3)) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "DSzH4O7Gm1Pr",
+    "outputId": "e42e029c-01d2-442b-8e86-7026c6e78136"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1., 1., 1., 1.],\n",
+       "       [1., 1., 1., 1.],\n",
+       "       [1., 1., 1., 1.]])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.ones((3, 4)) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "8BxSLNaEnCKr",
+    "outputId": "0f5bbb59-6aa3-48fd-de40-03e47e14abb3"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1., 0., 0.],\n",
+       "       [0., 1., 0.],\n",
+       "       [0., 0., 1.]])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.identity(3) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "6ncYJf5DneBq",
+    "outputId": "98a14f64-c1dd-419e-e689-18af925a1293"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 7.55642701e-311, -5.37804471e-310,  1.96930938e+243,\n",
+       "         3.65875398e+233,  1.17523295e+214],\n",
+       "       [-5.45592196e-310,  1.55533306e+161,  1.27581013e+232,\n",
+       "         2.68901231e+161, -5.49348129e-310]])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.empty((2, 5)) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "qehyDUqUoegk"
+   },
+   "source": [
+    "### Создание последовательностей"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "GxBPtN8qolZO",
+    "outputId": "0f270c86-b8e9-464b-c7bc-134c60391da4"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 2,  5,  8, 11, 14, 17])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.arange(2, 20, 3) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Ej7dmZJRopn4",
+    "outputId": "9d10c924-0a2f-4e16-d420-2aad445d0249"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2.5, 3.4, 4.3, 5.2, 6.1, 7. , 7.9])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.arange(2.5, 8.7, 0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "dzd0Z41cozVz",
+    "outputId": "d7ae4fe6-3ed0-479d-b682-94b5561a5c7a"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 2.        ,  3.23076923,  4.46153846,  5.69230769,  6.92307692,\n",
+       "        8.15384615,  9.38461538, 10.61538462, 11.84615385, 13.07692308,\n",
+       "       14.30769231, 15.53846154, 16.76923077, 18.        ])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.linspace(2, 18, 14)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "TnOxT5a4o30L",
+    "outputId": "3a9a54ea-1171-4e22-cd48-45efa53228ec"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 1, 2],\n",
+       "       [3, 4, 5],\n",
+       "       [6, 7, 8]])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.arange(9).reshape(3, 3) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "4IsX2FWhpRpz",
+    "outputId": "8802669e-8b0c-411c-e63a-357d44851507"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 1, 2, 3],\n",
+       "       [4, 5, 6, 7]])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "С = np.arange(8).reshape(2, -1)  \n",
+    "С"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 1, 2],\n",
+       "       [3, 4, 5]])"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "C = np.arange(6).reshape(2, -1)\n",
+    "C"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "-rPMYzu4pg5j",
+    "outputId": "938cac8c-2816-4669-bc2c-5a91cbd1aa6d"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 3],\n",
+       "       [1, 4],\n",
+       "       [2, 5]])"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "C.T "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "_zMeMNHfqBIq",
+    "outputId": "cc03ad28-d920-4ce3-8fe9-6b529bbfad66"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0,  1,  2,  0,  1,  4],\n",
+       "       [ 3,  4,  5,  9, 16, 25]])"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A = np.arange(6).reshape(2, -1)\n",
+    "np.hstack((A, A**2)) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ubk0VbvcqOzr",
+    "outputId": "9ad79459-fab5-4577-98ca-e9735570b99e"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0,  1,  2],\n",
+       "       [ 3,  4,  5],\n",
+       "       [ 0,  1,  4],\n",
+       "       [ 9, 16, 25]])"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.vstack((A, A**2)) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "PE4KDqvBqViK",
+    "outputId": "036a2d48-6107-46d0-ff71-98c5e99d7185"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0,  1,  2,  0,  1,  4],\n",
+       "       [ 3,  4,  5,  9, 16, 25]])"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.concatenate((A, A**2), axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "JLbS2xv_rBeb",
+    "outputId": "5b5e7b4e-c834-4649-f2e1-713247d7b50e"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 1, 2, 0, 1, 2],\n",
+       "       [0, 1, 2, 0, 1, 2]])"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A = np.arange(3)\n",
+    "np.tile(A, (2, 2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "GCwo92V5rr6p"
+   },
+   "source": [
+    "## Базовые операции"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "id": "bPj5TI5kr03V"
+   },
+   "outputs": [],
+   "source": [
+    "A = np.arange(9).reshape(3, 3)\n",
+    "B = np.arange(1, 10).reshape(3, 3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "AZPW4cQ-r8nz",
+    "outputId": "de5a3ab9-09c0-4d00-d8a2-401f046d9cb8"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0 1 2]\n",
+      " [3 4 5]\n",
+      " [6 7 8]]\n",
+      "[[1 2 3]\n",
+      " [4 5 6]\n",
+      " [7 8 9]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(A)\n",
+    "print(B)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "37V9YTf4sHOj",
+    "outputId": "0ff71bda-a910-4c99-d904-e565b41bc3ad"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1,  3,  5],\n",
+       "       [ 7,  9, 11],\n",
+       "       [13, 15, 17]])"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A + B"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "JwMeQNvKsKVq",
+    "outputId": "ab3fe51b-8d1c-4d2e-e5b7-815a6bb18609"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.        , 0.5       , 0.66666667],\n",
+       "       [0.75      , 0.8       , 0.83333333],\n",
+       "       [0.85714286, 0.875     , 0.88888889]])"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A * 1.0 / B"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "dKauehNWsNYa",
+    "outputId": "72d1715f-c878-45d6-c38c-23a03182a06a"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 2, 3],\n",
+       "       [4, 5, 6],\n",
+       "       [7, 8, 9]])"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A + 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "9--NC41bsSZe",
+    "outputId": "34d27d30-c7ed-4ae5-fa3c-7659a27d35ce"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0,  3,  6],\n",
+       "       [ 9, 12, 15],\n",
+       "       [18, 21, 24]])"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "3 * A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "UNW3iyersT-0",
+    "outputId": "d179cf19-60bc-4110-cf8e-644628cc0d81"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0,  1,  4],\n",
+       "       [ 9, 16, 25],\n",
+       "       [36, 49, 64]], dtype=int32)"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A ** 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "wSnQz8XRsVYj",
+    "outputId": "3474a1ee-a8cc-44c4-a2b4-a7f0d603f035"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0,  2,  6],\n",
+       "       [12, 20, 30],\n",
+       "       [42, 56, 72]])"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A * B "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "7skVtabvsXCi",
+    "outputId": "b2f3ad69-b355-4159-ba53-2236b4e68f43"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 18,  21,  24],\n",
+       "       [ 54,  66,  78],\n",
+       "       [ 90, 111, 132]])"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A.dot(B) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "5KR3jRkosZfa",
+    "outputId": "97f14a8c-f0c3-423a-d2ab-e630c8ea82ce"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0,  1,  2],\n",
+       "       [10, 11, 12],\n",
+       "       [20, 21, 22],\n",
+       "       [30, 31, 32]])"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.tile(np.arange(0, 40, 10), (3, 1)).T + np.array([0, 1, 2]) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "FMuaT5GsswvD",
+    "outputId": "d72e255a-d867-4298-8e60-ee6c763dd9f0"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1.00000000e+00, 2.71828183e+00, 7.38905610e+00],\n",
+       "       [2.00855369e+01, 5.45981500e+01, 1.48413159e+02],\n",
+       "       [4.03428793e+02, 1.09663316e+03, 2.98095799e+03]])"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.exp(A) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "39VPMDEjs1JY",
+    "outputId": "218b9857-15fb-44ee-d801-dea78c29f681"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 1, 2],\n",
+       "       [3, 4, 5],\n",
+       "       [6, 7, 8]])"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "iqHhs5YKs99z",
+    "outputId": "513e8b93-e7d5-4171-f5d1-672ac41e0263"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A.min() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "hke6fi9ts-1P",
+    "outputId": "c6faca38-4144-434d-f896-e9985fbfc619"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([6, 7, 8])"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A.max(axis=0) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "4BzXoXuVtBKb",
+    "outputId": "65c7ca20-133d-47d3-fb00-81dcb15d4264"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 3, 12, 21])"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A.sum(axis=1) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "XZ568K7vtE2N"
+   },
+   "source": [
+    "## Индексация"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "id": "7WHogsQmtQtj"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = np.arange(10)\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "HjdZ9EP4tUFD",
+    "outputId": "6d675ce8-c90f-4c14-e75e-30b59e32132c"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2, 3, 4])"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[2:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "LWJdC6IFtVs8",
+    "outputId": "9e298476-933f-4735-b2e7-68dc77bc43f3"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([3, 5, 7])"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[3:8:2] # от, до, шаг"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "kK4ybpuotZ87",
+    "outputId": "c42de9da-58ea-4f8b-ee15-6afeb24e7f2a"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8],\n",
+       "       [ 9, 10, 11, 12, 13, 14, 15, 16, 17],\n",
+       "       [18, 19, 20, 21, 22, 23, 24, 25, 26],\n",
+       "       [27, 28, 29, 30, 31, 32, 33, 34, 35],\n",
+       "       [36, 37, 38, 39, 40, 41, 42, 43, 44],\n",
+       "       [45, 46, 47, 48, 49, 50, 51, 52, 53],\n",
+       "       [54, 55, 56, 57, 58, 59, 60, 61, 62],\n",
+       "       [63, 64, 65, 66, 67, 68, 69, 70, 71],\n",
+       "       [72, 73, 74, 75, 76, 77, 78, 79, 80]])"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A = np.arange(81).reshape(9, -1)\n",
+    "A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "In6KN9-mtkGj",
+    "outputId": "cbf4cf0b-aa89-4996-a2d5-5c7b46bb41a8"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[18, 19, 20, 21, 22, 23, 24, 25, 26],\n",
+       "       [27, 28, 29, 30, 31, 32, 33, 34, 35]])"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A[2:4]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "qJHbt99Rto17",
+    "outputId": "5a3576e2-2c97-4546-bbf0-fee91cacc92a"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 2,  3],\n",
+       "       [11, 12],\n",
+       "       [20, 21],\n",
+       "       [29, 30],\n",
+       "       [38, 39],\n",
+       "       [47, 48],\n",
+       "       [56, 57],\n",
+       "       [65, 66],\n",
+       "       [74, 75]])"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a\n",
+    "A[:, 2:4]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "5xHijrtUt0N6",
+    "outputId": "967e3070-6ca4-4619-b93b-bf5918bffa0b"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[20, 21],\n",
+       "       [29, 30]])"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A[2:4, 2:4]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "xNlvFaFot-yy",
+    "outputId": "fc609746-2aab-4b6f-eff5-2a1c9da7f585"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([72, 73, 74, 75, 76, 77, 78, 79, 80])"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A[-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "G9yl7pu7uVDR",
+    "outputId": "37172528-05a0-4e70-9bed-646d3889353f"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8],\n",
+       "       [ 9, 10, 11, 12, 13, 14, 15, 16, 17],\n",
+       "       [18, 19, 20, 21, 22, 23, 24, 25, 26],\n",
+       "       [27, 28, 29, 30, 31, 32, 33, 34, 35],\n",
+       "       [36, 37, 38, 39, 40, 41, 42, 43, 44],\n",
+       "       [45, 46, 47, 48, 49, 50, 51, 52, 53],\n",
+       "       [54, 55, 56, 57, 58, 59, 60, 61, 62],\n",
+       "       [63, 64, 65, 66, 67, 68, 69, 70, 71],\n",
+       "       [72, 73, 74, 75, 76, 77, 78, 79, 80]])"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A = np.arange(81).reshape(9, -1) \n",
+    "A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "GuaCmfQiuBTr",
+    "outputId": "5bf05c9d-fb4d-48db-b5b8-b3943a4ee547"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([18, 37, 48])"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A[[2, 4, 5], [0, 1, 3]] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "xZgEANeguSKy",
+    "outputId": "656173ba-9fb8-4bf5-e9a5-5dda2a206870"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10])"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A = np.arange(11)\n",
+    "A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "x61032R5uq3L",
+    "outputId": "d8dd7f1b-56b2-4d67-a6f2-b6c3120b1009"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([3, 8])"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A[A % 5 == 3] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "yeGnxDXxutXy",
+    "outputId": "ff1f4662-8a7a-41f5-be8f-730e03a6257d"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0,  1,  2,  4,  5,  6,  9, 10])"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A[np.logical_and(A != 7, A % 5 != 3)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "aGETWdA6u6Aj",
+    "outputId": "c80a00bc-9645-4c8f-b0e9-aba3d6a30ae8"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0,  1,  2,  4,  5,  6,  9, 10])"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A[(A != 7) & (A % 5 != 3)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "id": "n59r2_uEu_OL"
+   },
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "A_quick_arr = np.random.normal(size = (1000000))\n",
+    "B_quick_arr = np.random.normal(size = (1000000))\n",
+    "A_slow_list, B_slow_list = list(A_quick_arr), list(B_quick_arr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "224 ms ± 3.76 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit -n 10 -c\n",
+    "ans = 0\n",
+    "for i in range(len(A_slow_list)):\n",
+    "    ans += A_slow_list[i] * B_slow_list[i]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "241 ms ± 2.48 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit -n 10 -c\n",
+    "ans = sum([A_slow_list[i] * B_slow_list[i] for i in range(1000000)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3.68 ms ± 232 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit -n 10 -c\n",
+    "ans = np.sum(A_quick_arr * B_quick_arr)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pandas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0     5\n",
+       "1     6\n",
+       "2     7\n",
+       "3     8\n",
+       "4     9\n",
+       "5    10\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "my_series = pd.Series([5, 6, 7, 8, 9, 10]) \n",
+    "my_series"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RangeIndex(start=0, stop=6, step=1)"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_series.index "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 5,  6,  7,  8,  9, 10], dtype=int64)"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_series.values "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_series[4]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_series2 = pd.Series([5, 6, 7, 8, 9, 10], index=['a', 'b', 'c', 'd', 'e', 'f']) \n",
+    "my_series2['f']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a     5\n",
+       "b     6\n",
+       "f    10\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_series2[['a', 'b', 'f']] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a    0\n",
+       "b    0\n",
+       "c    7\n",
+       "d    8\n",
+       "e    9\n",
+       "f    0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_series2[['a', 'b', 'f']] = 0\n",
+    "my_series2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "c    7\n",
+       "d    8\n",
+       "e    9\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_series2[my_series2 > 0] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "c    14\n",
+       "d    16\n",
+       "e    18\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_series2[my_series2 > 0]*2 "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a    5\n",
+       "b    6\n",
+       "c    7\n",
+       "d    8\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_series3 = pd.Series({'a':5, 'b':6, 'c':7, 'd':8})\n",
+    "my_series3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "letters\n",
+       "a    5\n",
+       "b    6\n",
+       "c    7\n",
+       "d    8\n",
+       "Name: numbers, dtype: int64"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_series3.name = 'numbers' \n",
+    "my_series3.index.name = 'letters'\n",
+    "my_series3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "A    5\n",
+       "B    6\n",
+       "C    7\n",
+       "D    8\n",
+       "Name: numbers, dtype: int64"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_series3.index = ['A', 'B', 'C', 'D'] \n",
+    "my_series3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# DataFrame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>population</th>\n",
+       "      <th>square</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Kazahstan</td>\n",
+       "      <td>17.04</td>\n",
+       "      <td>2724902</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Russia</td>\n",
+       "      <td>143.50</td>\n",
+       "      <td>17125191</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Belarus</td>\n",
+       "      <td>9.50</td>\n",
+       "      <td>207600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Ukraine</td>\n",
+       "      <td>45.50</td>\n",
+       "      <td>603628</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     country  population    square\n",
+       "0  Kazahstan       17.04   2724902\n",
+       "1     Russia      143.50  17125191\n",
+       "2    Belarus        9.50    207600\n",
+       "3    Ukraine       45.50    603628"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df =pd. DataFrame({'country' : ['Kazahstan', 'Russia', 'Belarus', 'Ukraine'], 'population' : [17.04, 143.5, 9.5, 45.5], 'square' : [2724902, 17125191, 207600, 603628]})\n",
+    "df "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    Kazahstan\n",
+       "1       Russia\n",
+       "2      Belarus\n",
+       "3      Ukraine\n",
+       "Name: country, dtype: object"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['country'] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['country', 'population', 'square'], dtype='object')"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RangeIndex(start=0, stop=4, step=1)"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.index "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>population</th>\n",
+       "      <th>square</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>KZ</th>\n",
+       "      <td>Kazahstan</td>\n",
+       "      <td>17.04</td>\n",
+       "      <td>2724902</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RU</th>\n",
+       "      <td>Russia</td>\n",
+       "      <td>143.50</td>\n",
+       "      <td>17125191</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BY</th>\n",
+       "      <td>Belarus</td>\n",
+       "      <td>9.50</td>\n",
+       "      <td>207600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UA</th>\n",
+       "      <td>Ukraine</td>\n",
+       "      <td>45.50</td>\n",
+       "      <td>603628</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      country  population    square\n",
+       "KZ  Kazahstan       17.04   2724902\n",
+       "RU     Russia      143.50  17125191\n",
+       "BY    Belarus        9.50    207600\n",
+       "UA    Ukraine       45.50    603628"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd. DataFrame({'country' : ['Kazahstan', 'Russia', 'Belarus', 'Ukraine'], 'population' : [17.04, 143.5, 9.5, 45.5], 'square' : [2724902, 17125191, 207600, 603628]}, index=['KZ', 'RU', 'BY', 'UA'])\n",
+    "df "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>population</th>\n",
+       "      <th>square</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Country Code</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>KZ</th>\n",
+       "      <td>Kazahstan</td>\n",
+       "      <td>17.04</td>\n",
+       "      <td>2724902</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RU</th>\n",
+       "      <td>Russia</td>\n",
+       "      <td>143.50</td>\n",
+       "      <td>17125191</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BY</th>\n",
+       "      <td>Belarus</td>\n",
+       "      <td>9.50</td>\n",
+       "      <td>207600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UA</th>\n",
+       "      <td>Ukraine</td>\n",
+       "      <td>45.50</td>\n",
+       "      <td>603628</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                country  population    square\n",
+       "Country Code                                 \n",
+       "KZ            Kazahstan       17.04   2724902\n",
+       "RU               Russia      143.50  17125191\n",
+       "BY              Belarus        9.50    207600\n",
+       "UA              Ukraine       45.50    603628"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.index = ['KZ', 'RU', 'BY', 'UA']\n",
+    "df.index.name = 'Country Code'\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Country Code\n",
+       "KZ    Kazahstan\n",
+       "RU       Russia\n",
+       "BY      Belarus\n",
+       "UA      Ukraine\n",
+       "Name: country, dtype: object"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['country']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "country       Kazahstan\n",
+       "population        17.04\n",
+       "square          2724902\n",
+       "Name: KZ, dtype: object"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.loc['KZ'] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "country       Kazahstan\n",
+       "population        17.04\n",
+       "square          2724902\n",
+       "Name: KZ, dtype: object"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.iloc[0] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Country Code\n",
+       "KZ     17.04\n",
+       "RU    143.50\n",
+       "Name: population, dtype: float64"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.loc[['KZ', 'RU'], 'population']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>population</th>\n",
+       "      <th>square</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Country Code</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>KZ</th>\n",
+       "      <td>Kazahstan</td>\n",
+       "      <td>17.04</td>\n",
+       "      <td>2724902</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RU</th>\n",
+       "      <td>Russia</td>\n",
+       "      <td>143.50</td>\n",
+       "      <td>17125191</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BY</th>\n",
+       "      <td>Belarus</td>\n",
+       "      <td>9.50</td>\n",
+       "      <td>207600</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                country  population    square\n",
+       "Country Code                                 \n",
+       "KZ            Kazahstan       17.04   2724902\n",
+       "RU               Russia      143.50  17125191\n",
+       "BY              Belarus        9.50    207600"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.loc['KZ': 'BY', :]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>square</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Country Code</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>KZ</th>\n",
+       "      <td>Kazahstan</td>\n",
+       "      <td>2724902</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RU</th>\n",
+       "      <td>Russia</td>\n",
+       "      <td>17125191</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UA</th>\n",
+       "      <td>Ukraine</td>\n",
+       "      <td>603628</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                country    square\n",
+       "Country Code                     \n",
+       "KZ            Kazahstan   2724902\n",
+       "RU               Russia  17125191\n",
+       "UA              Ukraine    603628"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df.population>10][['country', 'square']] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country Code</th>\n",
+       "      <th>country</th>\n",
+       "      <th>population</th>\n",
+       "      <th>square</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>KZ</td>\n",
+       "      <td>Kazahstan</td>\n",
+       "      <td>17.04</td>\n",
+       "      <td>2724902</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>RU</td>\n",
+       "      <td>Russia</td>\n",
+       "      <td>143.50</td>\n",
+       "      <td>17125191</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>BY</td>\n",
+       "      <td>Belarus</td>\n",
+       "      <td>9.50</td>\n",
+       "      <td>207600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>UA</td>\n",
+       "      <td>Ukraine</td>\n",
+       "      <td>45.50</td>\n",
+       "      <td>603628</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Country Code    country  population    square\n",
+       "0           KZ  Kazahstan       17.04   2724902\n",
+       "1           RU     Russia      143.50  17125191\n",
+       "2           BY    Belarus        9.50    207600\n",
+       "3           UA    Ukraine       45.50    603628"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>population</th>\n",
+       "      <th>square</th>\n",
+       "      <th>density</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Country Code</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>KZ</th>\n",
+       "      <td>Kazahstan</td>\n",
+       "      <td>17.04</td>\n",
+       "      <td>2724902</td>\n",
+       "      <td>6.253436</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RU</th>\n",
+       "      <td>Russia</td>\n",
+       "      <td>143.50</td>\n",
+       "      <td>17125191</td>\n",
+       "      <td>8.379469</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BY</th>\n",
+       "      <td>Belarus</td>\n",
+       "      <td>9.50</td>\n",
+       "      <td>207600</td>\n",
+       "      <td>45.761079</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UA</th>\n",
+       "      <td>Ukraine</td>\n",
+       "      <td>45.50</td>\n",
+       "      <td>603628</td>\n",
+       "      <td>75.377550</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                country  population    square    density\n",
+       "Country Code                                            \n",
+       "KZ            Kazahstan       17.04   2724902   6.253436\n",
+       "RU               Russia      143.50  17125191   8.379469\n",
+       "BY              Belarus        9.50    207600  45.761079\n",
+       "UA              Ukraine       45.50    603628  75.377550"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['density'] = df['population'] / df['square'] * 1000000 \n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>population</th>\n",
+       "      <th>square</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Country Code</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>KZ</th>\n",
+       "      <td>Kazahstan</td>\n",
+       "      <td>17.04</td>\n",
+       "      <td>2724902</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RU</th>\n",
+       "      <td>Russia</td>\n",
+       "      <td>143.50</td>\n",
+       "      <td>17125191</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BY</th>\n",
+       "      <td>Belarus</td>\n",
+       "      <td>9.50</td>\n",
+       "      <td>207600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UA</th>\n",
+       "      <td>Ukraine</td>\n",
+       "      <td>45.50</td>\n",
+       "      <td>603628</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                country  population    square\n",
+       "Country Code                                 \n",
+       "KZ            Kazahstan       17.04   2724902\n",
+       "RU               Russia      143.50  17125191\n",
+       "BY              Belarus        9.50    207600\n",
+       "UA              Ukraine       45.50    603628"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.drop(['density'], axis = 'columns') "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>population</th>\n",
+       "      <th>square</th>\n",
+       "      <th>density</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Country Code</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>KZ</th>\n",
+       "      <td>Kazahstan</td>\n",
+       "      <td>17.04</td>\n",
+       "      <td>2724902</td>\n",
+       "      <td>6.253436</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RU</th>\n",
+       "      <td>Russia</td>\n",
+       "      <td>143.50</td>\n",
+       "      <td>17125191</td>\n",
+       "      <td>8.379469</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BY</th>\n",
+       "      <td>Belarus</td>\n",
+       "      <td>9.50</td>\n",
+       "      <td>207600</td>\n",
+       "      <td>45.761079</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UA</th>\n",
+       "      <td>Ukraine</td>\n",
+       "      <td>45.50</td>\n",
+       "      <td>603628</td>\n",
+       "      <td>75.377550</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                country  population    square    density\n",
+       "Country Code                                            \n",
+       "KZ            Kazahstan       17.04   2724902   6.253436\n",
+       "RU               Russia      143.50  17125191   8.379469\n",
+       "BY              Belarus        9.50    207600  45.761079\n",
+       "UA              Ukraine       45.50    603628  75.377550"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['density'] = df['population'] / df['square'] * 1000000 \n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>population</th>\n",
+       "      <th>square</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Country Code</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>KZ</th>\n",
+       "      <td>Kazahstan</td>\n",
+       "      <td>17.04</td>\n",
+       "      <td>2724902</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RU</th>\n",
+       "      <td>Russia</td>\n",
+       "      <td>143.50</td>\n",
+       "      <td>17125191</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BY</th>\n",
+       "      <td>Belarus</td>\n",
+       "      <td>9.50</td>\n",
+       "      <td>207600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UA</th>\n",
+       "      <td>Ukraine</td>\n",
+       "      <td>45.50</td>\n",
+       "      <td>603628</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                country  population    square\n",
+       "Country Code                                 \n",
+       "KZ            Kazahstan       17.04   2724902\n",
+       "RU               Russia      143.50  17125191\n",
+       "BY              Belarus        9.50    207600\n",
+       "UA              Ukraine       45.50    603628"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "del df['density'] \n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>population</th>\n",
+       "      <th>square</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Country Code</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>KZ</th>\n",
+       "      <td>Kazahstan</td>\n",
+       "      <td>17.04</td>\n",
+       "      <td>2724902</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RU</th>\n",
+       "      <td>Russia</td>\n",
+       "      <td>143.50</td>\n",
+       "      <td>17125191</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BY</th>\n",
+       "      <td>Belarus</td>\n",
+       "      <td>9.50</td>\n",
+       "      <td>207600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UA</th>\n",
+       "      <td>Ukraine</td>\n",
+       "      <td>45.50</td>\n",
+       "      <td>603628</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                country  population    square\n",
+       "Country Code                                 \n",
+       "KZ            Kazahstan       17.04   2724902\n",
+       "RU               Russia      143.50  17125191\n",
+       "BY              Belarus        9.50    207600\n",
+       "UA              Ukraine       45.50    603628"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = df.rename(columns={'Country Code': 'country_code'}) \n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Группировка и агрегирование в pandas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   PassengerId  Survived  Pclass  \\\n",
+      "0            1         0       3   \n",
+      "1            2         1       1   \n",
+      "2            3         1       3   \n",
+      "3            4         1       1   \n",
+      "4            5         0       3   \n",
+      "\n",
+      "                                                Name     Sex   Age  SibSp  \\\n",
+      "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
+      "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
+      "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
+      "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
+      "4                           Allen, Mr. William Henry    male  35.0      0   \n",
+      "\n",
+      "   Parch            Ticket     Fare Cabin Embarked  \n",
+      "0      0         A/5 21171   7.2500   NaN        S  \n",
+      "1      0          PC 17599  71.2833   C85        C  \n",
+      "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
+      "3      0            113803  53.1000  C123        S  \n",
+      "4      0            373450   8.0500   NaN        S  \n"
+     ]
+    }
+   ],
+   "source": [
+    "titanic_df = pd.read_csv('titanic.csv')\n",
+    "print(titanic_df.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sex     Survived\n",
+      "female  0            81\n",
+      "        1           233\n",
+      "male    0           468\n",
+      "        1           109\n",
+      "Name: PassengerId, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(titanic_df.groupby(['Sex', 'Survived'])['PassengerId'].count()) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pclass  Survived\n",
+      "1       0            80\n",
+      "        1           136\n",
+      "2       0            97\n",
+      "        1            87\n",
+      "3       0           372\n",
+      "        1           119\n",
+      "Name: PassengerId, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(titanic_df.groupby(['Pclass', 'Survived'])['PassengerId'].count())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Cabin</th>\n",
+       "      <th>Embarked</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Braund, Mr. Owen Harris</td>\n",
+       "      <td>male</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/5 21171</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
+       "      <td>female</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17599</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>C85</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Heikkinen, Miss. Laina</td>\n",
+       "      <td>female</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>STON/O2. 3101282</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>113803</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>C123</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Allen, Mr. William Henry</td>\n",
+       "      <td>male</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>373450</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>886</th>\n",
+       "      <td>887</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Montvila, Rev. Juozas</td>\n",
+       "      <td>male</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>211536</td>\n",
+       "      <td>13.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>887</th>\n",
+       "      <td>888</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Graham, Miss. Margaret Edith</td>\n",
+       "      <td>female</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>112053</td>\n",
+       "      <td>30.0000</td>\n",
+       "      <td>B42</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>888</th>\n",
+       "      <td>889</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
+       "      <td>female</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>W./C. 6607</td>\n",
+       "      <td>23.4500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>889</th>\n",
+       "      <td>890</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Behr, Mr. Karl Howell</td>\n",
+       "      <td>male</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>111369</td>\n",
+       "      <td>30.0000</td>\n",
+       "      <td>C148</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>890</th>\n",
+       "      <td>891</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Dooley, Mr. Patrick</td>\n",
+       "      <td>male</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>370376</td>\n",
+       "      <td>7.7500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>891 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     PassengerId  Survived  Pclass  \\\n",
+       "0              1         0       3   \n",
+       "1              2         1       1   \n",
+       "2              3         1       3   \n",
+       "3              4         1       1   \n",
+       "4              5         0       3   \n",
+       "..           ...       ...     ...   \n",
+       "886          887         0       2   \n",
+       "887          888         1       1   \n",
+       "888          889         0       3   \n",
+       "889          890         1       1   \n",
+       "890          891         0       3   \n",
+       "\n",
+       "                                                  Name     Sex   Age  SibSp  \\\n",
+       "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
+       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
+       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
+       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
+       "4                             Allen, Mr. William Henry    male  35.0      0   \n",
+       "..                                                 ...     ...   ...    ...   \n",
+       "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
+       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
+       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
+       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
+       "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
+       "\n",
+       "     Parch            Ticket     Fare Cabin Embarked  \n",
+       "0        0         A/5 21171   7.2500   NaN        S  \n",
+       "1        0          PC 17599  71.2833   C85        C  \n",
+       "2        0  STON/O2. 3101282   7.9250   NaN        S  \n",
+       "3        0            113803  53.1000  C123        S  \n",
+       "4        0            373450   8.0500   NaN        S  \n",
+       "..     ...               ...      ...   ...      ...  \n",
+       "886      0            211536  13.0000   NaN        S  \n",
+       "887      0            112053  30.0000   B42        S  \n",
+       "888      2        W./C. 6607  23.4500   NaN        S  \n",
+       "889      0            111369  30.0000  C148        C  \n",
+       "890      0            370376   7.7500   NaN        Q  \n",
+       "\n",
+       "[891 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pass_data = pd.read_csv('titanic.csv') \n",
+    "pass_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Cabin</th>\n",
+       "      <th>Embarked</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Braund, Mr. Owen Harris</td>\n",
+       "      <td>male</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/5 21171</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
+       "      <td>female</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17599</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>C85</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Heikkinen, Miss. Laina</td>\n",
+       "      <td>female</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>STON/O2. 3101282</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   PassengerId  Survived  Pclass  \\\n",
+       "0            1         0       3   \n",
+       "1            2         1       1   \n",
+       "2            3         1       3   \n",
+       "\n",
+       "                                                Name     Sex   Age  SibSp  \\\n",
+       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
+       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
+       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
+       "\n",
+       "   Parch            Ticket     Fare Cabin Embarked  \n",
+       "0      0         A/5 21171   7.2500   NaN        S  \n",
+       "1      0          PC 17599  71.2833   C85        C  \n",
+       "2      0  STON/O2. 3101282   7.9250   NaN        S  "
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pass_data.head(3) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
+       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pass_data.columns "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Cabin</th>\n",
+       "      <th>Embarked</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Heikkinen, Miss. Laina</td>\n",
+       "      <td>female</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>STON/O2. 3101282</td>\n",
+       "      <td>7.925</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>113803</td>\n",
+       "      <td>53.100</td>\n",
+       "      <td>C123</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Allen, Mr. William Henry</td>\n",
+       "      <td>male</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>373450</td>\n",
+       "      <td>8.050</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   PassengerId  Survived  Pclass  \\\n",
+       "2            3         1       3   \n",
+       "3            4         1       1   \n",
+       "4            5         0       3   \n",
+       "\n",
+       "                                           Name     Sex   Age  SibSp  Parch  \\\n",
+       "2                        Heikkinen, Miss. Laina  female  26.0      0      0   \n",
+       "3  Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1      0   \n",
+       "4                      Allen, Mr. William Henry    male  35.0      0      0   \n",
+       "\n",
+       "             Ticket    Fare Cabin Embarked  \n",
+       "2  STON/O2. 3101282   7.925   NaN        S  \n",
+       "3            113803  53.100  C123        S  \n",
+       "4            373450   8.050   NaN        S  "
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pass_data[2:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
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+       "      <td>1</td>\n",
+       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Heikkinen, Miss. Laina</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Allen, Mr. William Henry</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Survived  Pclass                                               Name\n",
+       "1         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...\n",
+       "2         1       3                             Heikkinen, Miss. Laina\n",
+       "3         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)\n",
+       "4         0       3                           Allen, Mr. William Henry"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pass_data.iloc[1:5, 1:4] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0                              Braund, Mr. Owen Harris\n",
+       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...\n",
+       "2                               Heikkinen, Miss. Laina\n",
+       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)\n",
+       "4                             Allen, Mr. William Henry\n",
+       "Name: Name, dtype: object"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pass_data['Name'].head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "<table border=\"1\" class=\"dataframe\">\n",
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+       "      <th>Name</th>\n",
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+       "      <th>Parch</th>\n",
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+       "  </thead>\n",
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+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
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+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Heikkinen, Miss. Laina</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Allen, Mr. William Henry</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                Name     Sex  Parch\n",
+       "0                            Braund, Mr. Owen Harris    male      0\n",
+       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female      0\n",
+       "2                             Heikkinen, Miss. Laina  female      0\n",
+       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female      0\n",
+       "4                           Allen, Mr. William Henry    male      0"
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pass_data[['Name', 'Sex', 'Parch']].head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Cabin</th>\n",
+       "      <th>Embarked</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
+       "      <td>female</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17599</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>C85</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Heikkinen, Miss. Laina</td>\n",
+       "      <td>female</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>STON/O2. 3101282</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>113803</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>C123</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>9</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>347742</td>\n",
+       "      <td>11.1333</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>237736</td>\n",
+       "      <td>30.0708</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   PassengerId  Survived  Pclass  \\\n",
+       "1            2         1       1   \n",
+       "2            3         1       3   \n",
+       "3            4         1       1   \n",
+       "8            9         1       3   \n",
+       "9           10         1       2   \n",
+       "\n",
+       "                                                Name     Sex   Age  SibSp  \\\n",
+       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
+       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
+       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
+       "8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
+       "9                Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \n",
+       "\n",
+       "   Parch            Ticket     Fare Cabin Embarked  \n",
+       "1      0          PC 17599  71.2833   C85        C  \n",
+       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
+       "3      0            113803  53.1000  C123        S  \n",
+       "8      2            347742  11.1333   NaN        S  \n",
+       "9      0            237736  30.0708   NaN        C  "
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pass_data[pass_data['Sex'] == 'female'].head() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Cabin</th>\n",
+       "      <th>Embarked</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Braund, Mr. Owen Harris</td>\n",
+       "      <td>male</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/5 21171</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Allen, Mr. William Henry</td>\n",
+       "      <td>male</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>373450</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Moran, Mr. James</td>\n",
+       "      <td>male</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>330877</td>\n",
+       "      <td>8.4583</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>McCarthy, Mr. Timothy J</td>\n",
+       "      <td>male</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>17463</td>\n",
+       "      <td>51.8625</td>\n",
+       "      <td>E46</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Palsson, Master. Gosta Leonard</td>\n",
+       "      <td>male</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>349909</td>\n",
+       "      <td>21.0750</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   PassengerId  Survived  Pclass                            Name   Sex   Age  \\\n",
+       "0            1         0       3         Braund, Mr. Owen Harris  male  22.0   \n",
+       "4            5         0       3        Allen, Mr. William Henry  male  35.0   \n",
+       "5            6         0       3                Moran, Mr. James  male   NaN   \n",
+       "6            7         0       1         McCarthy, Mr. Timothy J  male  54.0   \n",
+       "7            8         0       3  Palsson, Master. Gosta Leonard  male   2.0   \n",
+       "\n",
+       "   SibSp  Parch     Ticket     Fare Cabin Embarked  \n",
+       "0      1      0  A/5 21171   7.2500   NaN        S  \n",
+       "4      0      0     373450   8.0500   NaN        S  \n",
+       "5      0      0     330877   8.4583   NaN        Q  \n",
+       "6      0      0      17463  51.8625   E46        S  \n",
+       "7      3      1     349909  21.0750   NaN        S  "
+      ]
+     },
+     "execution_count": 100,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pass_data[(pass_data['Sex'] == 'female') & (pass_data['Age'] >= 60) | (pass_data['Sex'] == 'male')].head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(25, 12)"
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pass_data[(pass_data.Sex == 'female') & (pass_data.Age > 18) & (pass_data.Age < 25) & (pass_data.SibSp == 0) & (pass_data.Parch == 0)].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pass_data.Age.hist(bins = 30) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Редактирование DataFrame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Cabin</th>\n",
+       "      <th>Embarked</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Braund, Mr. Owen Harris</td>\n",
+       "      <td>male</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/5 21171</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
+       "      <td>female</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17599</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>C85</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Heikkinen, Miss. Laina</td>\n",
+       "      <td>female</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>STON/O2. 3101282</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>113803</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>C123</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Allen, Mr. William Henry</td>\n",
+       "      <td>male</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>373450</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   PassengerId  Survived  Pclass  \\\n",
+       "0            1         0       3   \n",
+       "1            2         1       1   \n",
+       "2            3         1       3   \n",
+       "3            4         1       1   \n",
+       "4            5         0       3   \n",
+       "\n",
+       "                                                Name     Sex   Age  SibSp  \\\n",
+       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
+       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
+       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
+       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
+       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
+       "\n",
+       "   Parch            Ticket     Fare Cabin Embarked  \n",
+       "0      0         A/5 21171   7.2500   NaN        S  \n",
+       "1      0          PC 17599  71.2833   C85        C  \n",
+       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
+       "3      0            113803  53.1000  C123        S  \n",
+       "4      0            373450   8.0500   NaN        S  "
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pass_data.rename(columns={'Sex': 'Sex'}, inplace = True) \n",
+    "pass_data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       Braund\n",
+       "1      Cumings\n",
+       "2    Heikkinen\n",
+       "3     Futrelle\n",
+       "4        Allen\n",
+       "Name: Name, dtype: object"
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def get_last_name(name):\n",
+    "    return name.split(',')[0].strip()\n",
+    "last_names = pass_data['Name'].apply(get_last_name)\n",
+    "last_names.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Cabin</th>\n",
+       "      <th>Embarked</th>\n",
+       "      <th>Last_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Braund, Mr. Owen Harris</td>\n",
+       "      <td>male</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/5 21171</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>Braund</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
+       "      <td>female</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17599</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>C85</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Cumings</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Heikkinen, Miss. Laina</td>\n",
+       "      <td>female</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>STON/O2. 3101282</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>Heikkinen</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>113803</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>C123</td>\n",
+       "      <td>S</td>\n",
+       "      <td>Futrelle</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Allen, Mr. William Henry</td>\n",
+       "      <td>male</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>373450</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>Allen</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   PassengerId  Survived  Pclass  \\\n",
+       "0            1         0       3   \n",
+       "1            2         1       1   \n",
+       "2            3         1       3   \n",
+       "3            4         1       1   \n",
+       "4            5         0       3   \n",
+       "\n",
+       "                                                Name     Sex   Age  SibSp  \\\n",
+       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
+       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
+       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
+       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
+       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
+       "\n",
+       "   Parch            Ticket     Fare Cabin Embarked  Last_name  \n",
+       "0      0         A/5 21171   7.2500   NaN        S     Braund  \n",
+       "1      0          PC 17599  71.2833   C85        C    Cumings  \n",
+       "2      0  STON/O2. 3101282   7.9250   NaN        S  Heikkinen  \n",
+       "3      0            113803  53.1000  C123        S   Futrelle  \n",
+       "4      0            373450   8.0500   NaN        S      Allen  "
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pass_data['Last_name'] = last_names \n",
+    "pass_data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Cabin</th>\n",
+       "      <th>Embarked</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Braund, Mr. Owen Harris</td>\n",
+       "      <td>male</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/5 21171</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
+       "      <td>female</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17599</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>C85</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Heikkinen, Miss. Laina</td>\n",
+       "      <td>female</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>STON/O2. 3101282</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>113803</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>C123</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Allen, Mr. William Henry</td>\n",
+       "      <td>male</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>373450</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   PassengerId  Survived  Pclass  \\\n",
+       "0            1         0       3   \n",
+       "1            2         1       1   \n",
+       "2            3         1       3   \n",
+       "3            4         1       1   \n",
+       "4            5         0       3   \n",
+       "\n",
+       "                                                Name     Sex   Age  SibSp  \\\n",
+       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
+       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
+       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
+       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
+       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
+       "\n",
+       "   Parch            Ticket     Fare Cabin Embarked  \n",
+       "0      0         A/5 21171   7.2500   NaN        S  \n",
+       "1      0          PC 17599  71.2833   C85        C  \n",
+       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
+       "3      0            113803  53.1000  C123        S  \n",
+       "4      0            373450   8.0500   NaN        S  "
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pass_data.drop('Last_name', axis = 1, inplace=True) \n",
+    "pass_data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0     True\n",
+       "1    False\n",
+       "2     True\n",
+       "3    False\n",
+       "4     True\n",
+       "Name: Cabin, dtype: bool"
+      ]
+     },
+     "execution_count": 107,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pass_data['Cabin'].isnull().head() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Cabin</th>\n",
+       "      <th>Embarked</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
+       "      <td>female</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17599</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>C85</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>113803</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>C123</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>McCarthy, Mr. Timothy J</td>\n",
+       "      <td>male</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>17463</td>\n",
+       "      <td>51.8625</td>\n",
+       "      <td>E46</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>11</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
+       "      <td>female</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>PP 9549</td>\n",
+       "      <td>16.7000</td>\n",
+       "      <td>G6</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>12</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Bonnell, Miss. Elizabeth</td>\n",
+       "      <td>female</td>\n",
+       "      <td>58.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>113783</td>\n",
+       "      <td>26.5500</td>\n",
+       "      <td>C103</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    PassengerId  Survived  Pclass  \\\n",
+       "1             2         1       1   \n",
+       "3             4         1       1   \n",
+       "6             7         0       1   \n",
+       "10           11         1       3   \n",
+       "11           12         1       1   \n",
+       "\n",
+       "                                                 Name     Sex   Age  SibSp  \\\n",
+       "1   Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
+       "3        Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
+       "6                             McCarthy, Mr. Timothy J    male  54.0      0   \n",
+       "10                    Sandstrom, Miss. Marguerite Rut  female   4.0      1   \n",
+       "11                           Bonnell, Miss. Elizabeth  female  58.0      0   \n",
+       "\n",
+       "    Parch    Ticket     Fare Cabin Embarked  \n",
+       "1       0  PC 17599  71.2833   C85        C  \n",
+       "3       0    113803  53.1000  C123        S  \n",
+       "6       0     17463  51.8625   E46        S  \n",
+       "10      1   PP 9549  16.7000    G6        S  \n",
+       "11      0    113783  26.5500  C103        S  "
+      ]
+     },
+     "execution_count": 108,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pass_data[pass_data['Cabin'].notnull()].head() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
+       "    .dataframe thead th {\n",
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+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Sex</th>\n",
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+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Cabin</th>\n",
+       "      <th>Embarked</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>263</th>\n",
+       "      <td>264</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Harrison, Mr. William</td>\n",
+       "      <td>male</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>0.0</td>\n",
+       "      <td>B94</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
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+       "      <td>634</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Parr, Mr. William Henry Marsh</td>\n",
+       "      <td>male</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>112052</td>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>806</th>\n",
+       "      <td>807</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Andrews, Mr. Thomas Jr</td>\n",
+       "      <td>male</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>112050</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>A36</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>815</th>\n",
+       "      <td>816</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Fry, Mr. Richard</td>\n",
+       "      <td>male</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>112058</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>B102</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>822</th>\n",
+       "      <td>823</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Reuchlin, Jonkheer. John George</td>\n",
+       "      <td>male</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>19972</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     PassengerId  Survived  Pclass                             Name   Sex  \\\n",
+       "263          264         0       1            Harrison, Mr. William  male   \n",
+       "633          634         0       1    Parr, Mr. William Henry Marsh  male   \n",
+       "806          807         0       1           Andrews, Mr. Thomas Jr  male   \n",
+       "815          816         0       1                 Fry, Mr. Richard  male   \n",
+       "822          823         0       1  Reuchlin, Jonkheer. John George  male   \n",
+       "\n",
+       "      Age  SibSp  Parch  Ticket  Fare Cabin Embarked  \n",
+       "263  40.0      0      0  112059   0.0   B94        S  \n",
+       "633   NaN      0      0  112052   0.0   NaN        S  \n",
+       "806  39.0      0      0  112050   0.0   A36        S  \n",
+       "815   NaN      0      0  112058   0.0  B102        S  \n",
+       "822  38.0      0      0   19972   0.0   NaN        S  "
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pass_data.sort_values(by=['Pclass', 'Fare'], ascending = True).head() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Cabin</th>\n",
+       "      <th>Embarked</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>258</th>\n",
+       "      <td>259</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Ward, Miss. Anna</td>\n",
+       "      <td>female</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17755</td>\n",
+       "      <td>512.3292</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>679</th>\n",
+       "      <td>680</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cardeza, Mr. Thomas Drake Martinez</td>\n",
+       "      <td>male</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>PC 17755</td>\n",
+       "      <td>512.3292</td>\n",
+       "      <td>B51 B53 B55</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>737</th>\n",
+       "      <td>738</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Lesurer, Mr. Gustave J</td>\n",
+       "      <td>male</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17755</td>\n",
+       "      <td>512.3292</td>\n",
+       "      <td>B101</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>28</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Fortune, Mr. Charles Alexander</td>\n",
+       "      <td>male</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>19950</td>\n",
+       "      <td>263.0000</td>\n",
+       "      <td>C23 C25 C27</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>88</th>\n",
+       "      <td>89</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Fortune, Miss. Mabel Helen</td>\n",
+       "      <td>female</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>19950</td>\n",
+       "      <td>263.0000</td>\n",
+       "      <td>C23 C25 C27</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     PassengerId  Survived  Pclass                                Name  \\\n",
+       "258          259         1       1                    Ward, Miss. Anna   \n",
+       "679          680         1       1  Cardeza, Mr. Thomas Drake Martinez   \n",
+       "737          738         1       1              Lesurer, Mr. Gustave J   \n",
+       "27            28         0       1      Fortune, Mr. Charles Alexander   \n",
+       "88            89         1       1          Fortune, Miss. Mabel Helen   \n",
+       "\n",
+       "        Sex   Age  SibSp  Parch    Ticket      Fare        Cabin Embarked  \n",
+       "258  female  35.0      0      0  PC 17755  512.3292          NaN        C  \n",
+       "679    male  36.0      0      1  PC 17755  512.3292  B51 B53 B55        C  \n",
+       "737    male  35.0      0      0  PC 17755  512.3292         B101        C  \n",
+       "27     male  19.0      3      2     19950  263.0000  C23 C25 C27        S  \n",
+       "88   female  23.0      3      2     19950  263.0000  C23 C25 C27        S  "
+      ]
+     },
+     "execution_count": 110,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pass_data.sort_values(by=['Pclass', 'Fare'], ascending = [True, False]).head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Группировка данных"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x000001388EB1E0A0>"
+      ]
+     },
+     "execution_count": 111,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pass_data.groupby('Sex') "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Sex     Pclass\n",
+       "female  3         144\n",
+       "        1          94\n",
+       "        2          76\n",
+       "male    3         347\n",
+       "        1         122\n",
+       "        2         108\n",
+       "Name: Pclass, dtype: int64"
+      ]
+     },
+     "execution_count": 112,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pass_data.groupby('Sex')['Pclass'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Pclass</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>216.0</td>\n",
+       "      <td>84.154687</td>\n",
+       "      <td>78.380373</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>30.92395</td>\n",
+       "      <td>60.2875</td>\n",
+       "      <td>93.5</td>\n",
+       "      <td>512.3292</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>184.0</td>\n",
+       "      <td>20.662183</td>\n",
+       "      <td>13.417399</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>13.00000</td>\n",
+       "      <td>14.2500</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>73.5000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>491.0</td>\n",
+       "      <td>13.675550</td>\n",
+       "      <td>11.778142</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.75000</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>15.5</td>\n",
+       "      <td>69.5500</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        count       mean        std  min       25%      50%   75%       max\n",
+       "Pclass                                                                     \n",
+       "1       216.0  84.154687  78.380373  0.0  30.92395  60.2875  93.5  512.3292\n",
+       "2       184.0  20.662183  13.417399  0.0  13.00000  14.2500  26.0   73.5000\n",
+       "3       491.0  13.675550  11.778142  0.0   7.75000   8.0500  15.5   69.5500"
+      ]
+     },
+     "execution_count": 113,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pass_data.groupby('Pclass')['Fare'].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Sex\n",
+       "female    27.915709\n",
+       "male      30.726645\n",
+       "Name: Age, dtype: float64"
+      ]
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pass_data.groupby('Sex')['Age'].mean() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Sex\n",
+       "female    0.742038\n",
+       "male      0.188908\n",
+       "Name: Survived, dtype: float64"
+      ]
+     },
+     "execution_count": 115,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pass_data.groupby('Sex')['Survived'].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Pclass\n",
+       "1    0.629630\n",
+       "2    0.472826\n",
+       "3    0.242363\n",
+       "Name: Survived, dtype: float64"
+      ]
+     },
+     "execution_count": 116,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pass_data.groupby('Pclass')['Survived'].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Survived</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>female</td>\n",
+       "      <td>0.742038</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>male</td>\n",
+       "      <td>0.188908</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Sex  Survived\n",
+       "0  female  0.742038\n",
+       "1    male  0.188908"
+      ]
+     },
+     "execution_count": 117,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pass_data.groupby('Sex', as_index = False)['Survived'].mean() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "collapsed_sections": [],
+   "name": "Машинное обучение л.р. 1.ipynb",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

From 05b02e2d94a44bf1e9c1c25abd07ccc2bdb1013a Mon Sep 17 00:00:00 2001
From: andvagapov <86461996+andvagapov@users.noreply.github.com>
Date: Fri, 25 Jun 2021 09:44:23 +0300
Subject: [PATCH 2/4] =?UTF-8?q?=D0=92=D1=8B=D0=BF=D0=BE=D0=BB=D0=BD=D0=B5?=
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---
 .../Lab2 (6).ipynb"                           | 3412 +++++++++++++++++
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diff --git "a/ml/lb2/\320\222\320\260\320\263\320\260\320\277\320\276\320\262/Lab2 (6).ipynb" "b/ml/lb2/\320\222\320\260\320\263\320\260\320\277\320\276\320\262/Lab2 (6).ipynb"
new file mode 100644
index 0000000..415b9b1
--- /dev/null
+++ "b/ml/lb2/\320\222\320\260\320\263\320\260\320\277\320\276\320\262/Lab2 (6).ipynb"	
@@ -0,0 +1,3412 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "mobile-biology",
+   "metadata": {},
+   "source": [
+    "# Лабораторная работа №2\n",
+    "\n",
+    "## Вариант №3\n",
+    "\n",
+    "### Выполнил студент группы 19-ИВТ-3 Вагапов Андрей\n",
+    "\n",
+    "Задание: Вторичный рынок машин\n",
+    "\n",
+    "1. Удалите столбцы, ценность которых для оценки стоимости машины низка.\n",
+    "2. Удалите повторяющиеся строки, строки содержащие пропуски в данных. Выведите размер набора данных до и после удаления.\n",
+    "3. Удалите данные в строках, выходящие за некоторые пределы (год регистрации, цена, мощность двигателя). Выведите размер набора данных до и после удаления. Для оценки диапазонов значений признаков используйте BoxPlot.\n",
+    "4. Заполнить пропущенные данные в строковых полях.\n",
+    "5. Постройте гистограмму по маркам автомобилей, типам кузова и используемому топливу.\n",
+    "6. Добавьте в данные новый признак, который представляет собой длину названия автомобиля.\n",
+    "7. Постройте карту корреляций между признаками. Выведите также числовые значения признаков.\n",
+    "8. Определите коэффициент корреляции Пирсона и Спирмена между стоимостью автомобиля и типом кузова. Оцените значение p-value. Постройте плотность распределения признаков.\n",
+    "9. Добавьте в набор данных признак, являющийся суммой двух других признаков.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "wrong-edition",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from scipy import stats\n",
+    "%matplotlib inline\n",
+    "cars_df = pd.read_csv('autos.csv', encoding='iso-8859-1')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "divine-suggestion",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
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+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>dateCrawled</th>\n",
+       "      <th>name</th>\n",
+       "      <th>seller</th>\n",
+       "      <th>offerType</th>\n",
+       "      <th>price</th>\n",
+       "      <th>abtest</th>\n",
+       "      <th>vehicleType</th>\n",
+       "      <th>yearOfRegistration</th>\n",
+       "      <th>gearbox</th>\n",
+       "      <th>powerPS</th>\n",
+       "      <th>model</th>\n",
+       "      <th>kilometer</th>\n",
+       "      <th>monthOfRegistration</th>\n",
+       "      <th>fuelType</th>\n",
+       "      <th>brand</th>\n",
+       "      <th>notRepairedDamage</th>\n",
+       "      <th>dateCreated</th>\n",
+       "      <th>nrOfPictures</th>\n",
+       "      <th>postalCode</th>\n",
+       "      <th>lastSeen</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2016-03-24 11:52:17</td>\n",
+       "      <td>Golf_3_1.6</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>480</td>\n",
+       "      <td>test</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1993</td>\n",
+       "      <td>manuell</td>\n",
+       "      <td>0</td>\n",
+       "      <td>golf</td>\n",
+       "      <td>150000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>benzin</td>\n",
+       "      <td>volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016-03-24 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>70435</td>\n",
+       "      <td>2016-04-07 03:16:57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2016-03-24 10:58:45</td>\n",
+       "      <td>A5_Sportback_2.7_Tdi</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>18300</td>\n",
+       "      <td>test</td>\n",
+       "      <td>coupe</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>manuell</td>\n",
+       "      <td>190</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>125000</td>\n",
+       "      <td>5</td>\n",
+       "      <td>diesel</td>\n",
+       "      <td>audi</td>\n",
+       "      <td>ja</td>\n",
+       "      <td>2016-03-24 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>66954</td>\n",
+       "      <td>2016-04-07 01:46:50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2016-03-14 12:52:21</td>\n",
+       "      <td>Jeep_Grand_Cherokee_\"Overland\"</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>9800</td>\n",
+       "      <td>test</td>\n",
+       "      <td>suv</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>automatik</td>\n",
+       "      <td>163</td>\n",
+       "      <td>grand</td>\n",
+       "      <td>125000</td>\n",
+       "      <td>8</td>\n",
+       "      <td>diesel</td>\n",
+       "      <td>jeep</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016-03-14 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>90480</td>\n",
+       "      <td>2016-04-05 12:47:46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2016-03-17 16:54:04</td>\n",
+       "      <td>GOLF_4_1_4__3TÜRER</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>test</td>\n",
+       "      <td>kleinwagen</td>\n",
+       "      <td>2001</td>\n",
+       "      <td>manuell</td>\n",
+       "      <td>75</td>\n",
+       "      <td>golf</td>\n",
+       "      <td>150000</td>\n",
+       "      <td>6</td>\n",
+       "      <td>benzin</td>\n",
+       "      <td>volkswagen</td>\n",
+       "      <td>nein</td>\n",
+       "      <td>2016-03-17 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>91074</td>\n",
+       "      <td>2016-03-17 17:40:17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2016-03-31 17:25:20</td>\n",
+       "      <td>Skoda_Fabia_1.4_TDI_PD_Classic</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>test</td>\n",
+       "      <td>kleinwagen</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>manuell</td>\n",
+       "      <td>69</td>\n",
+       "      <td>fabia</td>\n",
+       "      <td>90000</td>\n",
+       "      <td>7</td>\n",
+       "      <td>diesel</td>\n",
+       "      <td>skoda</td>\n",
+       "      <td>nein</td>\n",
+       "      <td>2016-03-31 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>60437</td>\n",
+       "      <td>2016-04-06 10:17:21</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           dateCrawled                            name  seller offerType  \\\n",
+       "0  2016-03-24 11:52:17                      Golf_3_1.6  privat   Angebot   \n",
+       "1  2016-03-24 10:58:45            A5_Sportback_2.7_Tdi  privat   Angebot   \n",
+       "2  2016-03-14 12:52:21  Jeep_Grand_Cherokee_\"Overland\"  privat   Angebot   \n",
+       "3  2016-03-17 16:54:04              GOLF_4_1_4__3TÜRER  privat   Angebot   \n",
+       "4  2016-03-31 17:25:20  Skoda_Fabia_1.4_TDI_PD_Classic  privat   Angebot   \n",
+       "\n",
+       "   price abtest vehicleType  yearOfRegistration    gearbox  powerPS  model  \\\n",
+       "0    480   test         NaN                1993    manuell        0   golf   \n",
+       "1  18300   test       coupe                2011    manuell      190    NaN   \n",
+       "2   9800   test         suv                2004  automatik      163  grand   \n",
+       "3   1500   test  kleinwagen                2001    manuell       75   golf   \n",
+       "4   3600   test  kleinwagen                2008    manuell       69  fabia   \n",
+       "\n",
+       "   kilometer  monthOfRegistration fuelType       brand notRepairedDamage  \\\n",
+       "0     150000                    0   benzin  volkswagen               NaN   \n",
+       "1     125000                    5   diesel        audi                ja   \n",
+       "2     125000                    8   diesel        jeep               NaN   \n",
+       "3     150000                    6   benzin  volkswagen              nein   \n",
+       "4      90000                    7   diesel       skoda              nein   \n",
+       "\n",
+       "           dateCreated  nrOfPictures  postalCode             lastSeen  \n",
+       "0  2016-03-24 00:00:00             0       70435  2016-04-07 03:16:57  \n",
+       "1  2016-03-24 00:00:00             0       66954  2016-04-07 01:46:50  \n",
+       "2  2016-03-14 00:00:00             0       90480  2016-04-05 12:47:46  \n",
+       "3  2016-03-17 00:00:00             0       91074  2016-03-17 17:40:17  \n",
+       "4  2016-03-31 00:00:00             0       60437  2016-04-06 10:17:21  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cars_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "guilty-functionality",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>dateCrawled</th>\n",
+       "      <th>name</th>\n",
+       "      <th>seller</th>\n",
+       "      <th>offerType</th>\n",
+       "      <th>price</th>\n",
+       "      <th>abtest</th>\n",
+       "      <th>vehicleType</th>\n",
+       "      <th>yearOfRegistration</th>\n",
+       "      <th>gearbox</th>\n",
+       "      <th>powerPS</th>\n",
+       "      <th>model</th>\n",
+       "      <th>kilometer</th>\n",
+       "      <th>monthOfRegistration</th>\n",
+       "      <th>fuelType</th>\n",
+       "      <th>brand</th>\n",
+       "      <th>notRepairedDamage</th>\n",
+       "      <th>dateCreated</th>\n",
+       "      <th>nrOfPictures</th>\n",
+       "      <th>postalCode</th>\n",
+       "      <th>lastSeen</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>371523</th>\n",
+       "      <td>2016-03-14 17:48:27</td>\n",
+       "      <td>Suche_t4___vito_ab_6_sitze</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>2200</td>\n",
+       "      <td>test</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2005</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>20000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sonstige_autos</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016-03-14 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>39576</td>\n",
+       "      <td>2016-04-06 00:46:52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>371524</th>\n",
+       "      <td>2016-03-05 19:56:21</td>\n",
+       "      <td>Smart_smart_leistungssteigerung_100ps</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>1199</td>\n",
+       "      <td>test</td>\n",
+       "      <td>cabrio</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>automatik</td>\n",
+       "      <td>101</td>\n",
+       "      <td>fortwo</td>\n",
+       "      <td>125000</td>\n",
+       "      <td>3</td>\n",
+       "      <td>benzin</td>\n",
+       "      <td>smart</td>\n",
+       "      <td>nein</td>\n",
+       "      <td>2016-03-05 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>26135</td>\n",
+       "      <td>2016-03-11 18:17:12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>371525</th>\n",
+       "      <td>2016-03-19 18:57:12</td>\n",
+       "      <td>Volkswagen_Multivan_T4_TDI_7DC_UY2</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>9200</td>\n",
+       "      <td>test</td>\n",
+       "      <td>bus</td>\n",
+       "      <td>1996</td>\n",
+       "      <td>manuell</td>\n",
+       "      <td>102</td>\n",
+       "      <td>transporter</td>\n",
+       "      <td>150000</td>\n",
+       "      <td>3</td>\n",
+       "      <td>diesel</td>\n",
+       "      <td>volkswagen</td>\n",
+       "      <td>nein</td>\n",
+       "      <td>2016-03-19 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>87439</td>\n",
+       "      <td>2016-04-07 07:15:26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>371526</th>\n",
+       "      <td>2016-03-20 19:41:08</td>\n",
+       "      <td>VW_Golf_Kombi_1_9l_TDI</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>3400</td>\n",
+       "      <td>test</td>\n",
+       "      <td>kombi</td>\n",
+       "      <td>2002</td>\n",
+       "      <td>manuell</td>\n",
+       "      <td>100</td>\n",
+       "      <td>golf</td>\n",
+       "      <td>150000</td>\n",
+       "      <td>6</td>\n",
+       "      <td>diesel</td>\n",
+       "      <td>volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016-03-20 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>40764</td>\n",
+       "      <td>2016-03-24 12:45:21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>371527</th>\n",
+       "      <td>2016-03-07 19:39:19</td>\n",
+       "      <td>BMW_M135i_vollausgestattet_NP_52.720____Euro</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>28990</td>\n",
+       "      <td>control</td>\n",
+       "      <td>limousine</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>manuell</td>\n",
+       "      <td>320</td>\n",
+       "      <td>m_reihe</td>\n",
+       "      <td>50000</td>\n",
+       "      <td>8</td>\n",
+       "      <td>benzin</td>\n",
+       "      <td>bmw</td>\n",
+       "      <td>nein</td>\n",
+       "      <td>2016-03-07 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>73326</td>\n",
+       "      <td>2016-03-22 03:17:10</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                dateCrawled                                          name  \\\n",
+       "371523  2016-03-14 17:48:27                    Suche_t4___vito_ab_6_sitze   \n",
+       "371524  2016-03-05 19:56:21         Smart_smart_leistungssteigerung_100ps   \n",
+       "371525  2016-03-19 18:57:12            Volkswagen_Multivan_T4_TDI_7DC_UY2   \n",
+       "371526  2016-03-20 19:41:08                        VW_Golf_Kombi_1_9l_TDI   \n",
+       "371527  2016-03-07 19:39:19  BMW_M135i_vollausgestattet_NP_52.720____Euro   \n",
+       "\n",
+       "        seller offerType  price   abtest vehicleType  yearOfRegistration  \\\n",
+       "371523  privat   Angebot   2200     test         NaN                2005   \n",
+       "371524  privat   Angebot   1199     test      cabrio                2000   \n",
+       "371525  privat   Angebot   9200     test         bus                1996   \n",
+       "371526  privat   Angebot   3400     test       kombi                2002   \n",
+       "371527  privat   Angebot  28990  control   limousine                2013   \n",
+       "\n",
+       "          gearbox  powerPS        model  kilometer  monthOfRegistration  \\\n",
+       "371523        NaN        0          NaN      20000                    1   \n",
+       "371524  automatik      101       fortwo     125000                    3   \n",
+       "371525    manuell      102  transporter     150000                    3   \n",
+       "371526    manuell      100         golf     150000                    6   \n",
+       "371527    manuell      320      m_reihe      50000                    8   \n",
+       "\n",
+       "       fuelType           brand notRepairedDamage          dateCreated  \\\n",
+       "371523      NaN  sonstige_autos               NaN  2016-03-14 00:00:00   \n",
+       "371524   benzin           smart              nein  2016-03-05 00:00:00   \n",
+       "371525   diesel      volkswagen              nein  2016-03-19 00:00:00   \n",
+       "371526   diesel      volkswagen               NaN  2016-03-20 00:00:00   \n",
+       "371527   benzin             bmw              nein  2016-03-07 00:00:00   \n",
+       "\n",
+       "        nrOfPictures  postalCode             lastSeen  \n",
+       "371523             0       39576  2016-04-06 00:46:52  \n",
+       "371524             0       26135  2016-03-11 18:17:12  \n",
+       "371525             0       87439  2016-04-07 07:15:26  \n",
+       "371526             0       40764  2016-03-24 12:45:21  \n",
+       "371527             0       73326  2016-03-22 03:17:10  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cars_df.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "detected-space",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 371528 entries, 0 to 371527\n",
+      "Data columns (total 20 columns):\n",
+      " #   Column               Non-Null Count   Dtype \n",
+      "---  ------               --------------   ----- \n",
+      " 0   dateCrawled          371528 non-null  object\n",
+      " 1   name                 371528 non-null  object\n",
+      " 2   seller               371528 non-null  object\n",
+      " 3   offerType            371528 non-null  object\n",
+      " 4   price                371528 non-null  int64 \n",
+      " 5   abtest               371528 non-null  object\n",
+      " 6   vehicleType          333659 non-null  object\n",
+      " 7   yearOfRegistration   371528 non-null  int64 \n",
+      " 8   gearbox              351319 non-null  object\n",
+      " 9   powerPS              371528 non-null  int64 \n",
+      " 10  model                351044 non-null  object\n",
+      " 11  kilometer            371528 non-null  int64 \n",
+      " 12  monthOfRegistration  371528 non-null  int64 \n",
+      " 13  fuelType             338142 non-null  object\n",
+      " 14  brand                371528 non-null  object\n",
+      " 15  notRepairedDamage    299468 non-null  object\n",
+      " 16  dateCreated          371528 non-null  object\n",
+      " 17  nrOfPictures         371528 non-null  int64 \n",
+      " 18  postalCode           371528 non-null  int64 \n",
+      " 19  lastSeen             371528 non-null  object\n",
+      "dtypes: int64(7), object(13)\n",
+      "memory usage: 56.7+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "cars_df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "divided-corruption",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>price</th>\n",
+       "      <th>yearOfRegistration</th>\n",
+       "      <th>powerPS</th>\n",
+       "      <th>kilometer</th>\n",
+       "      <th>monthOfRegistration</th>\n",
+       "      <th>nrOfPictures</th>\n",
+       "      <th>postalCode</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>3.715280e+05</td>\n",
+       "      <td>371528.000000</td>\n",
+       "      <td>371528.000000</td>\n",
+       "      <td>371528.000000</td>\n",
+       "      <td>371528.000000</td>\n",
+       "      <td>371528.0</td>\n",
+       "      <td>371528.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>1.729514e+04</td>\n",
+       "      <td>2004.577997</td>\n",
+       "      <td>115.549477</td>\n",
+       "      <td>125618.688228</td>\n",
+       "      <td>5.734445</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>50820.66764</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>3.587954e+06</td>\n",
+       "      <td>92.866598</td>\n",
+       "      <td>192.139578</td>\n",
+       "      <td>40112.337051</td>\n",
+       "      <td>3.712412</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>25799.08247</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>1000.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5000.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1067.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>1.150000e+03</td>\n",
+       "      <td>1999.000000</td>\n",
+       "      <td>70.000000</td>\n",
+       "      <td>125000.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>30459.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>2.950000e+03</td>\n",
+       "      <td>2003.000000</td>\n",
+       "      <td>105.000000</td>\n",
+       "      <td>150000.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>49610.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>7.200000e+03</td>\n",
+       "      <td>2008.000000</td>\n",
+       "      <td>150.000000</td>\n",
+       "      <td>150000.000000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>71546.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>2.147484e+09</td>\n",
+       "      <td>9999.000000</td>\n",
+       "      <td>20000.000000</td>\n",
+       "      <td>150000.000000</td>\n",
+       "      <td>12.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>99998.00000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              price  yearOfRegistration        powerPS      kilometer  \\\n",
+       "count  3.715280e+05       371528.000000  371528.000000  371528.000000   \n",
+       "mean   1.729514e+04         2004.577997     115.549477  125618.688228   \n",
+       "std    3.587954e+06           92.866598     192.139578   40112.337051   \n",
+       "min    0.000000e+00         1000.000000       0.000000    5000.000000   \n",
+       "25%    1.150000e+03         1999.000000      70.000000  125000.000000   \n",
+       "50%    2.950000e+03         2003.000000     105.000000  150000.000000   \n",
+       "75%    7.200000e+03         2008.000000     150.000000  150000.000000   \n",
+       "max    2.147484e+09         9999.000000   20000.000000  150000.000000   \n",
+       "\n",
+       "       monthOfRegistration  nrOfPictures    postalCode  \n",
+       "count        371528.000000      371528.0  371528.00000  \n",
+       "mean              5.734445           0.0   50820.66764  \n",
+       "std               3.712412           0.0   25799.08247  \n",
+       "min               0.000000           0.0    1067.00000  \n",
+       "25%               3.000000           0.0   30459.00000  \n",
+       "50%               6.000000           0.0   49610.00000  \n",
+       "75%               9.000000           0.0   71546.00000  \n",
+       "max              12.000000           0.0   99998.00000  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cars_df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "sharp-thumbnail",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>dateCrawled</th>\n",
+       "      <th>name</th>\n",
+       "      <th>seller</th>\n",
+       "      <th>offerType</th>\n",
+       "      <th>price</th>\n",
+       "      <th>abtest</th>\n",
+       "      <th>vehicleType</th>\n",
+       "      <th>yearOfRegistration</th>\n",
+       "      <th>gearbox</th>\n",
+       "      <th>powerPS</th>\n",
+       "      <th>model</th>\n",
+       "      <th>kilometer</th>\n",
+       "      <th>monthOfRegistration</th>\n",
+       "      <th>fuelType</th>\n",
+       "      <th>brand</th>\n",
+       "      <th>notRepairedDamage</th>\n",
+       "      <th>dateCreated</th>\n",
+       "      <th>nrOfPictures</th>\n",
+       "      <th>postalCode</th>\n",
+       "      <th>lastSeen</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2016-03-17 16:54:04</td>\n",
+       "      <td>GOLF_4_1_4__3TÜRER</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>test</td>\n",
+       "      <td>kleinwagen</td>\n",
+       "      <td>2001</td>\n",
+       "      <td>manuell</td>\n",
+       "      <td>75</td>\n",
+       "      <td>golf</td>\n",
+       "      <td>150000</td>\n",
+       "      <td>6</td>\n",
+       "      <td>benzin</td>\n",
+       "      <td>volkswagen</td>\n",
+       "      <td>nein</td>\n",
+       "      <td>2016-03-17 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>91074</td>\n",
+       "      <td>2016-03-17 17:40:17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2016-03-31 17:25:20</td>\n",
+       "      <td>Skoda_Fabia_1.4_TDI_PD_Classic</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>test</td>\n",
+       "      <td>kleinwagen</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>manuell</td>\n",
+       "      <td>69</td>\n",
+       "      <td>fabia</td>\n",
+       "      <td>90000</td>\n",
+       "      <td>7</td>\n",
+       "      <td>diesel</td>\n",
+       "      <td>skoda</td>\n",
+       "      <td>nein</td>\n",
+       "      <td>2016-03-31 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>60437</td>\n",
+       "      <td>2016-04-06 10:17:21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2016-04-04 17:36:23</td>\n",
+       "      <td>BMW_316i___e36_Limousine___Bastlerfahrzeug__Ex...</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>650</td>\n",
+       "      <td>test</td>\n",
+       "      <td>limousine</td>\n",
+       "      <td>1995</td>\n",
+       "      <td>manuell</td>\n",
+       "      <td>102</td>\n",
+       "      <td>3er</td>\n",
+       "      <td>150000</td>\n",
+       "      <td>10</td>\n",
+       "      <td>benzin</td>\n",
+       "      <td>bmw</td>\n",
+       "      <td>ja</td>\n",
+       "      <td>2016-04-04 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>33775</td>\n",
+       "      <td>2016-04-06 19:17:07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2016-04-01 20:48:51</td>\n",
+       "      <td>Peugeot_206_CC_110_Platinum</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>2200</td>\n",
+       "      <td>test</td>\n",
+       "      <td>cabrio</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>manuell</td>\n",
+       "      <td>109</td>\n",
+       "      <td>2_reihe</td>\n",
+       "      <td>150000</td>\n",
+       "      <td>8</td>\n",
+       "      <td>benzin</td>\n",
+       "      <td>peugeot</td>\n",
+       "      <td>nein</td>\n",
+       "      <td>2016-04-01 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>67112</td>\n",
+       "      <td>2016-04-05 18:18:39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2016-03-21 18:54:38</td>\n",
+       "      <td>VW_Derby_Bj_80__Scheunenfund</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>0</td>\n",
+       "      <td>test</td>\n",
+       "      <td>limousine</td>\n",
+       "      <td>1980</td>\n",
+       "      <td>manuell</td>\n",
+       "      <td>50</td>\n",
+       "      <td>andere</td>\n",
+       "      <td>40000</td>\n",
+       "      <td>7</td>\n",
+       "      <td>benzin</td>\n",
+       "      <td>volkswagen</td>\n",
+       "      <td>nein</td>\n",
+       "      <td>2016-03-21 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>19348</td>\n",
+       "      <td>2016-03-25 16:47:58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>371520</th>\n",
+       "      <td>2016-03-19 19:53:49</td>\n",
+       "      <td>turbo_defekt</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>3200</td>\n",
+       "      <td>control</td>\n",
+       "      <td>limousine</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>manuell</td>\n",
+       "      <td>225</td>\n",
+       "      <td>leon</td>\n",
+       "      <td>150000</td>\n",
+       "      <td>5</td>\n",
+       "      <td>benzin</td>\n",
+       "      <td>seat</td>\n",
+       "      <td>ja</td>\n",
+       "      <td>2016-03-19 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>96465</td>\n",
+       "      <td>2016-03-19 20:44:43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>371521</th>\n",
+       "      <td>2016-03-27 20:36:20</td>\n",
+       "      <td>Opel_Zafira_1.6_Elegance_TÜV_12/16</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>1150</td>\n",
+       "      <td>control</td>\n",
+       "      <td>bus</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>manuell</td>\n",
+       "      <td>0</td>\n",
+       "      <td>zafira</td>\n",
+       "      <td>150000</td>\n",
+       "      <td>3</td>\n",
+       "      <td>benzin</td>\n",
+       "      <td>opel</td>\n",
+       "      <td>nein</td>\n",
+       "      <td>2016-03-27 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>26624</td>\n",
+       "      <td>2016-03-29 10:17:23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>371524</th>\n",
+       "      <td>2016-03-05 19:56:21</td>\n",
+       "      <td>Smart_smart_leistungssteigerung_100ps</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>1199</td>\n",
+       "      <td>test</td>\n",
+       "      <td>cabrio</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>automatik</td>\n",
+       "      <td>101</td>\n",
+       "      <td>fortwo</td>\n",
+       "      <td>125000</td>\n",
+       "      <td>3</td>\n",
+       "      <td>benzin</td>\n",
+       "      <td>smart</td>\n",
+       "      <td>nein</td>\n",
+       "      <td>2016-03-05 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>26135</td>\n",
+       "      <td>2016-03-11 18:17:12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>371525</th>\n",
+       "      <td>2016-03-19 18:57:12</td>\n",
+       "      <td>Volkswagen_Multivan_T4_TDI_7DC_UY2</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>9200</td>\n",
+       "      <td>test</td>\n",
+       "      <td>bus</td>\n",
+       "      <td>1996</td>\n",
+       "      <td>manuell</td>\n",
+       "      <td>102</td>\n",
+       "      <td>transporter</td>\n",
+       "      <td>150000</td>\n",
+       "      <td>3</td>\n",
+       "      <td>diesel</td>\n",
+       "      <td>volkswagen</td>\n",
+       "      <td>nein</td>\n",
+       "      <td>2016-03-19 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>87439</td>\n",
+       "      <td>2016-04-07 07:15:26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>371527</th>\n",
+       "      <td>2016-03-07 19:39:19</td>\n",
+       "      <td>BMW_M135i_vollausgestattet_NP_52.720____Euro</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>28990</td>\n",
+       "      <td>control</td>\n",
+       "      <td>limousine</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>manuell</td>\n",
+       "      <td>320</td>\n",
+       "      <td>m_reihe</td>\n",
+       "      <td>50000</td>\n",
+       "      <td>8</td>\n",
+       "      <td>benzin</td>\n",
+       "      <td>bmw</td>\n",
+       "      <td>nein</td>\n",
+       "      <td>2016-03-07 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>73326</td>\n",
+       "      <td>2016-03-22 03:17:10</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>260956 rows × 20 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                dateCrawled  \\\n",
+       "3       2016-03-17 16:54:04   \n",
+       "4       2016-03-31 17:25:20   \n",
+       "5       2016-04-04 17:36:23   \n",
+       "6       2016-04-01 20:48:51   \n",
+       "7       2016-03-21 18:54:38   \n",
+       "...                     ...   \n",
+       "371520  2016-03-19 19:53:49   \n",
+       "371521  2016-03-27 20:36:20   \n",
+       "371524  2016-03-05 19:56:21   \n",
+       "371525  2016-03-19 18:57:12   \n",
+       "371527  2016-03-07 19:39:19   \n",
+       "\n",
+       "                                                     name  seller offerType  \\\n",
+       "3                                      GOLF_4_1_4__3TÜRER  privat   Angebot   \n",
+       "4                          Skoda_Fabia_1.4_TDI_PD_Classic  privat   Angebot   \n",
+       "5       BMW_316i___e36_Limousine___Bastlerfahrzeug__Ex...  privat   Angebot   \n",
+       "6                             Peugeot_206_CC_110_Platinum  privat   Angebot   \n",
+       "7                            VW_Derby_Bj_80__Scheunenfund  privat   Angebot   \n",
+       "...                                                   ...     ...       ...   \n",
+       "371520                                       turbo_defekt  privat   Angebot   \n",
+       "371521                 Opel_Zafira_1.6_Elegance_TÜV_12/16  privat   Angebot   \n",
+       "371524              Smart_smart_leistungssteigerung_100ps  privat   Angebot   \n",
+       "371525                 Volkswagen_Multivan_T4_TDI_7DC_UY2  privat   Angebot   \n",
+       "371527       BMW_M135i_vollausgestattet_NP_52.720____Euro  privat   Angebot   \n",
+       "\n",
+       "        price   abtest vehicleType  yearOfRegistration    gearbox  powerPS  \\\n",
+       "3        1500     test  kleinwagen                2001    manuell       75   \n",
+       "4        3600     test  kleinwagen                2008    manuell       69   \n",
+       "5         650     test   limousine                1995    manuell      102   \n",
+       "6        2200     test      cabrio                2004    manuell      109   \n",
+       "7           0     test   limousine                1980    manuell       50   \n",
+       "...       ...      ...         ...                 ...        ...      ...   \n",
+       "371520   3200  control   limousine                2004    manuell      225   \n",
+       "371521   1150  control         bus                2000    manuell        0   \n",
+       "371524   1199     test      cabrio                2000  automatik      101   \n",
+       "371525   9200     test         bus                1996    manuell      102   \n",
+       "371527  28990  control   limousine                2013    manuell      320   \n",
+       "\n",
+       "              model  kilometer  monthOfRegistration fuelType       brand  \\\n",
+       "3              golf     150000                    6   benzin  volkswagen   \n",
+       "4             fabia      90000                    7   diesel       skoda   \n",
+       "5               3er     150000                   10   benzin         bmw   \n",
+       "6           2_reihe     150000                    8   benzin     peugeot   \n",
+       "7            andere      40000                    7   benzin  volkswagen   \n",
+       "...             ...        ...                  ...      ...         ...   \n",
+       "371520         leon     150000                    5   benzin        seat   \n",
+       "371521       zafira     150000                    3   benzin        opel   \n",
+       "371524       fortwo     125000                    3   benzin       smart   \n",
+       "371525  transporter     150000                    3   diesel  volkswagen   \n",
+       "371527      m_reihe      50000                    8   benzin         bmw   \n",
+       "\n",
+       "       notRepairedDamage          dateCreated  nrOfPictures  postalCode  \\\n",
+       "3                   nein  2016-03-17 00:00:00             0       91074   \n",
+       "4                   nein  2016-03-31 00:00:00             0       60437   \n",
+       "5                     ja  2016-04-04 00:00:00             0       33775   \n",
+       "6                   nein  2016-04-01 00:00:00             0       67112   \n",
+       "7                   nein  2016-03-21 00:00:00             0       19348   \n",
+       "...                  ...                  ...           ...         ...   \n",
+       "371520                ja  2016-03-19 00:00:00             0       96465   \n",
+       "371521              nein  2016-03-27 00:00:00             0       26624   \n",
+       "371524              nein  2016-03-05 00:00:00             0       26135   \n",
+       "371525              nein  2016-03-19 00:00:00             0       87439   \n",
+       "371527              nein  2016-03-07 00:00:00             0       73326   \n",
+       "\n",
+       "                   lastSeen  \n",
+       "3       2016-03-17 17:40:17  \n",
+       "4       2016-04-06 10:17:21  \n",
+       "5       2016-04-06 19:17:07  \n",
+       "6       2016-04-05 18:18:39  \n",
+       "7       2016-03-25 16:47:58  \n",
+       "...                     ...  \n",
+       "371520  2016-03-19 20:44:43  \n",
+       "371521  2016-03-29 10:17:23  \n",
+       "371524  2016-03-11 18:17:12  \n",
+       "371525  2016-04-07 07:15:26  \n",
+       "371527  2016-03-22 03:17:10  \n",
+       "\n",
+       "[260956 rows x 20 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cars_df.dropna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "difficult-sunset",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(371528, 20)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cars_df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "warming-constraint",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>dateCrawled</th>\n",
+       "      <th>name</th>\n",
+       "      <th>seller</th>\n",
+       "      <th>offerType</th>\n",
+       "      <th>price</th>\n",
+       "      <th>abtest</th>\n",
+       "      <th>vehicleType</th>\n",
+       "      <th>yearOfRegistration</th>\n",
+       "      <th>gearbox</th>\n",
+       "      <th>powerPS</th>\n",
+       "      <th>model</th>\n",
+       "      <th>kilometer</th>\n",
+       "      <th>monthOfRegistration</th>\n",
+       "      <th>fuelType</th>\n",
+       "      <th>brand</th>\n",
+       "      <th>notRepairedDamage</th>\n",
+       "      <th>dateCreated</th>\n",
+       "      <th>nrOfPictures</th>\n",
+       "      <th>postalCode</th>\n",
+       "      <th>lastSeen</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2016-03-24 11:52:17</td>\n",
+       "      <td>Golf_3_1.6</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>480</td>\n",
+       "      <td>test</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1993</td>\n",
+       "      <td>manuell</td>\n",
+       "      <td>0</td>\n",
+       "      <td>golf</td>\n",
+       "      <td>150000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>benzin</td>\n",
+       "      <td>volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016-03-24 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>70435</td>\n",
+       "      <td>2016-04-07 03:16:57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2016-03-24 10:58:45</td>\n",
+       "      <td>A5_Sportback_2.7_Tdi</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>18300</td>\n",
+       "      <td>test</td>\n",
+       "      <td>coupe</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>manuell</td>\n",
+       "      <td>190</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>125000</td>\n",
+       "      <td>5</td>\n",
+       "      <td>diesel</td>\n",
+       "      <td>audi</td>\n",
+       "      <td>ja</td>\n",
+       "      <td>2016-03-24 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>66954</td>\n",
+       "      <td>2016-04-07 01:46:50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2016-03-14 12:52:21</td>\n",
+       "      <td>Jeep_Grand_Cherokee_\"Overland\"</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>9800</td>\n",
+       "      <td>test</td>\n",
+       "      <td>suv</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>automatik</td>\n",
+       "      <td>163</td>\n",
+       "      <td>grand</td>\n",
+       "      <td>125000</td>\n",
+       "      <td>8</td>\n",
+       "      <td>diesel</td>\n",
+       "      <td>jeep</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016-03-14 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>90480</td>\n",
+       "      <td>2016-04-05 12:47:46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2016-03-17 16:54:04</td>\n",
+       "      <td>GOLF_4_1_4__3TÜRER</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>test</td>\n",
+       "      <td>kleinwagen</td>\n",
+       "      <td>2001</td>\n",
+       "      <td>manuell</td>\n",
+       "      <td>75</td>\n",
+       "      <td>golf</td>\n",
+       "      <td>150000</td>\n",
+       "      <td>6</td>\n",
+       "      <td>benzin</td>\n",
+       "      <td>volkswagen</td>\n",
+       "      <td>nein</td>\n",
+       "      <td>2016-03-17 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>91074</td>\n",
+       "      <td>2016-03-17 17:40:17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2016-03-31 17:25:20</td>\n",
+       "      <td>Skoda_Fabia_1.4_TDI_PD_Classic</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>test</td>\n",
+       "      <td>kleinwagen</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>manuell</td>\n",
+       "      <td>69</td>\n",
+       "      <td>fabia</td>\n",
+       "      <td>90000</td>\n",
+       "      <td>7</td>\n",
+       "      <td>diesel</td>\n",
+       "      <td>skoda</td>\n",
+       "      <td>nein</td>\n",
+       "      <td>2016-03-31 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>60437</td>\n",
+       "      <td>2016-04-06 10:17:21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>371523</th>\n",
+       "      <td>2016-03-14 17:48:27</td>\n",
+       "      <td>Suche_t4___vito_ab_6_sitze</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>2200</td>\n",
+       "      <td>test</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2005</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>20000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>sonstige_autos</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016-03-14 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>39576</td>\n",
+       "      <td>2016-04-06 00:46:52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>371524</th>\n",
+       "      <td>2016-03-05 19:56:21</td>\n",
+       "      <td>Smart_smart_leistungssteigerung_100ps</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>1199</td>\n",
+       "      <td>test</td>\n",
+       "      <td>cabrio</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>automatik</td>\n",
+       "      <td>101</td>\n",
+       "      <td>fortwo</td>\n",
+       "      <td>125000</td>\n",
+       "      <td>3</td>\n",
+       "      <td>benzin</td>\n",
+       "      <td>smart</td>\n",
+       "      <td>nein</td>\n",
+       "      <td>2016-03-05 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>26135</td>\n",
+       "      <td>2016-03-11 18:17:12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>371525</th>\n",
+       "      <td>2016-03-19 18:57:12</td>\n",
+       "      <td>Volkswagen_Multivan_T4_TDI_7DC_UY2</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>9200</td>\n",
+       "      <td>test</td>\n",
+       "      <td>bus</td>\n",
+       "      <td>1996</td>\n",
+       "      <td>manuell</td>\n",
+       "      <td>102</td>\n",
+       "      <td>transporter</td>\n",
+       "      <td>150000</td>\n",
+       "      <td>3</td>\n",
+       "      <td>diesel</td>\n",
+       "      <td>volkswagen</td>\n",
+       "      <td>nein</td>\n",
+       "      <td>2016-03-19 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>87439</td>\n",
+       "      <td>2016-04-07 07:15:26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>371526</th>\n",
+       "      <td>2016-03-20 19:41:08</td>\n",
+       "      <td>VW_Golf_Kombi_1_9l_TDI</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>3400</td>\n",
+       "      <td>test</td>\n",
+       "      <td>kombi</td>\n",
+       "      <td>2002</td>\n",
+       "      <td>manuell</td>\n",
+       "      <td>100</td>\n",
+       "      <td>golf</td>\n",
+       "      <td>150000</td>\n",
+       "      <td>6</td>\n",
+       "      <td>diesel</td>\n",
+       "      <td>volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016-03-20 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>40764</td>\n",
+       "      <td>2016-03-24 12:45:21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>371527</th>\n",
+       "      <td>2016-03-07 19:39:19</td>\n",
+       "      <td>BMW_M135i_vollausgestattet_NP_52.720____Euro</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>28990</td>\n",
+       "      <td>control</td>\n",
+       "      <td>limousine</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>manuell</td>\n",
+       "      <td>320</td>\n",
+       "      <td>m_reihe</td>\n",
+       "      <td>50000</td>\n",
+       "      <td>8</td>\n",
+       "      <td>benzin</td>\n",
+       "      <td>bmw</td>\n",
+       "      <td>nein</td>\n",
+       "      <td>2016-03-07 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>73326</td>\n",
+       "      <td>2016-03-22 03:17:10</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>371524 rows × 20 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                dateCrawled                                          name  \\\n",
+       "0       2016-03-24 11:52:17                                    Golf_3_1.6   \n",
+       "1       2016-03-24 10:58:45                          A5_Sportback_2.7_Tdi   \n",
+       "2       2016-03-14 12:52:21                Jeep_Grand_Cherokee_\"Overland\"   \n",
+       "3       2016-03-17 16:54:04                            GOLF_4_1_4__3TÜRER   \n",
+       "4       2016-03-31 17:25:20                Skoda_Fabia_1.4_TDI_PD_Classic   \n",
+       "...                     ...                                           ...   \n",
+       "371523  2016-03-14 17:48:27                    Suche_t4___vito_ab_6_sitze   \n",
+       "371524  2016-03-05 19:56:21         Smart_smart_leistungssteigerung_100ps   \n",
+       "371525  2016-03-19 18:57:12            Volkswagen_Multivan_T4_TDI_7DC_UY2   \n",
+       "371526  2016-03-20 19:41:08                        VW_Golf_Kombi_1_9l_TDI   \n",
+       "371527  2016-03-07 19:39:19  BMW_M135i_vollausgestattet_NP_52.720____Euro   \n",
+       "\n",
+       "        seller offerType  price   abtest vehicleType  yearOfRegistration  \\\n",
+       "0       privat   Angebot    480     test         NaN                1993   \n",
+       "1       privat   Angebot  18300     test       coupe                2011   \n",
+       "2       privat   Angebot   9800     test         suv                2004   \n",
+       "3       privat   Angebot   1500     test  kleinwagen                2001   \n",
+       "4       privat   Angebot   3600     test  kleinwagen                2008   \n",
+       "...        ...       ...    ...      ...         ...                 ...   \n",
+       "371523  privat   Angebot   2200     test         NaN                2005   \n",
+       "371524  privat   Angebot   1199     test      cabrio                2000   \n",
+       "371525  privat   Angebot   9200     test         bus                1996   \n",
+       "371526  privat   Angebot   3400     test       kombi                2002   \n",
+       "371527  privat   Angebot  28990  control   limousine                2013   \n",
+       "\n",
+       "          gearbox  powerPS        model  kilometer  monthOfRegistration  \\\n",
+       "0         manuell        0         golf     150000                    0   \n",
+       "1         manuell      190          NaN     125000                    5   \n",
+       "2       automatik      163        grand     125000                    8   \n",
+       "3         manuell       75         golf     150000                    6   \n",
+       "4         manuell       69        fabia      90000                    7   \n",
+       "...           ...      ...          ...        ...                  ...   \n",
+       "371523        NaN        0          NaN      20000                    1   \n",
+       "371524  automatik      101       fortwo     125000                    3   \n",
+       "371525    manuell      102  transporter     150000                    3   \n",
+       "371526    manuell      100         golf     150000                    6   \n",
+       "371527    manuell      320      m_reihe      50000                    8   \n",
+       "\n",
+       "       fuelType           brand notRepairedDamage          dateCreated  \\\n",
+       "0        benzin      volkswagen               NaN  2016-03-24 00:00:00   \n",
+       "1        diesel            audi                ja  2016-03-24 00:00:00   \n",
+       "2        diesel            jeep               NaN  2016-03-14 00:00:00   \n",
+       "3        benzin      volkswagen              nein  2016-03-17 00:00:00   \n",
+       "4        diesel           skoda              nein  2016-03-31 00:00:00   \n",
+       "...         ...             ...               ...                  ...   \n",
+       "371523      NaN  sonstige_autos               NaN  2016-03-14 00:00:00   \n",
+       "371524   benzin           smart              nein  2016-03-05 00:00:00   \n",
+       "371525   diesel      volkswagen              nein  2016-03-19 00:00:00   \n",
+       "371526   diesel      volkswagen               NaN  2016-03-20 00:00:00   \n",
+       "371527   benzin             bmw              nein  2016-03-07 00:00:00   \n",
+       "\n",
+       "        nrOfPictures  postalCode             lastSeen  \n",
+       "0                  0       70435  2016-04-07 03:16:57  \n",
+       "1                  0       66954  2016-04-07 01:46:50  \n",
+       "2                  0       90480  2016-04-05 12:47:46  \n",
+       "3                  0       91074  2016-03-17 17:40:17  \n",
+       "4                  0       60437  2016-04-06 10:17:21  \n",
+       "...              ...         ...                  ...  \n",
+       "371523             0       39576  2016-04-06 00:46:52  \n",
+       "371524             0       26135  2016-03-11 18:17:12  \n",
+       "371525             0       87439  2016-04-07 07:15:26  \n",
+       "371526             0       40764  2016-03-24 12:45:21  \n",
+       "371527             0       73326  2016-03-22 03:17:10  \n",
+       "\n",
+       "[371524 rows x 20 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cars_df.drop_duplicates()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "engaging-recycling",
+   "metadata": {},
+   "source": [
+    "### 1. Удалите столбцы, ценность которых для оценки стоимости машины низка."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "phantom-captain",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "evaluation_df = cars_df.drop(['dateCrawled','seller','offerType','abtest','dateCreated','nrOfPictures','postalCode','lastSeen'], axis='columns')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "departmental-colony",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>price</th>\n",
+       "      <th>vehicleType</th>\n",
+       "      <th>yearOfRegistration</th>\n",
+       "      <th>gearbox</th>\n",
+       "      <th>powerPS</th>\n",
+       "      <th>model</th>\n",
+       "      <th>kilometer</th>\n",
+       "      <th>monthOfRegistration</th>\n",
+       "      <th>fuelType</th>\n",
+       "      <th>brand</th>\n",
+       "      <th>notRepairedDamage</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Golf_3_1.6</td>\n",
+       "      <td>480</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1993</td>\n",
+       "      <td>manuell</td>\n",
+       "      <td>0</td>\n",
+       "      <td>golf</td>\n",
+       "      <td>150000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>benzin</td>\n",
+       "      <td>volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>A5_Sportback_2.7_Tdi</td>\n",
+       "      <td>18300</td>\n",
+       "      <td>coupe</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>manuell</td>\n",
+       "      <td>190</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>125000</td>\n",
+       "      <td>5</td>\n",
+       "      <td>diesel</td>\n",
+       "      <td>audi</td>\n",
+       "      <td>ja</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Jeep_Grand_Cherokee_\"Overland\"</td>\n",
+       "      <td>9800</td>\n",
+       "      <td>suv</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>automatik</td>\n",
+       "      <td>163</td>\n",
+       "      <td>grand</td>\n",
+       "      <td>125000</td>\n",
+       "      <td>8</td>\n",
+       "      <td>diesel</td>\n",
+       "      <td>jeep</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>GOLF_4_1_4__3TÜRER</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>kleinwagen</td>\n",
+       "      <td>2001</td>\n",
+       "      <td>manuell</td>\n",
+       "      <td>75</td>\n",
+       "      <td>golf</td>\n",
+       "      <td>150000</td>\n",
+       "      <td>6</td>\n",
+       "      <td>benzin</td>\n",
+       "      <td>volkswagen</td>\n",
+       "      <td>nein</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Skoda_Fabia_1.4_TDI_PD_Classic</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>kleinwagen</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>manuell</td>\n",
+       "      <td>69</td>\n",
+       "      <td>fabia</td>\n",
+       "      <td>90000</td>\n",
+       "      <td>7</td>\n",
+       "      <td>diesel</td>\n",
+       "      <td>skoda</td>\n",
+       "      <td>nein</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                             name  price vehicleType  yearOfRegistration  \\\n",
+       "0                      Golf_3_1.6    480         NaN                1993   \n",
+       "1            A5_Sportback_2.7_Tdi  18300       coupe                2011   \n",
+       "2  Jeep_Grand_Cherokee_\"Overland\"   9800         suv                2004   \n",
+       "3              GOLF_4_1_4__3TÜRER   1500  kleinwagen                2001   \n",
+       "4  Skoda_Fabia_1.4_TDI_PD_Classic   3600  kleinwagen                2008   \n",
+       "\n",
+       "     gearbox  powerPS  model  kilometer  monthOfRegistration fuelType  \\\n",
+       "0    manuell        0   golf     150000                    0   benzin   \n",
+       "1    manuell      190    NaN     125000                    5   diesel   \n",
+       "2  automatik      163  grand     125000                    8   diesel   \n",
+       "3    manuell       75   golf     150000                    6   benzin   \n",
+       "4    manuell       69  fabia      90000                    7   diesel   \n",
+       "\n",
+       "        brand notRepairedDamage  \n",
+       "0  volkswagen               NaN  \n",
+       "1        audi                ja  \n",
+       "2        jeep               NaN  \n",
+       "3  volkswagen              nein  \n",
+       "4       skoda              nein  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "evaluation_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "english-leisure",
+   "metadata": {},
+   "source": [
+    "### 2. Удалите повторяющиеся строки, строки содержащие пропуски в данных. Выведите размер набора данных до и после удаления."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "temporal-container",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Размер набора данных до удаления повторяющихся строк 371528\n",
+      "Размер данных после удаления повторяющихся строк 362770\n",
+      "Размер набора данных после удаления дубликатов и пустых строк 254921\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Размер набора данных до удаления повторяющихся строк\",evaluation_df.shape[0])\n",
+    "deldupl_df = cars_df.drop_duplicates(['name','price','vehicleType','yearOfRegistration'\n",
+    "                         ,'gearbox','powerPS','model','kilometer','monthOfRegistration','fuelType'\n",
+    "                         ,'notRepairedDamage'])\n",
+    "print(\"Размер данных после удаления повторяющихся строк\",deldupl_df.shape[0])\n",
+    "deldupl_null = deldupl_df.dropna()\n",
+    "print(\"Размер набора данных после удаления дубликатов и пустых строк\",deldupl_null.shape[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "suspected-maine",
+   "metadata": {},
+   "source": [
+    "### 3. Удалите данные в строках, выходящие за некоторые пределы (год регистрации, цена, мощность двигателя). Выведите размер набора данных до и после удаления. Для оценки диапазонов значений признаков используйте BoxPlot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "blond-crest",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "boxplot1 = deldupl_null.boxplot(column=['yearOfRegistration'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "detected-drilling",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "boxplot2 = deldupl_null.boxplot(column=['price'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "opponent-wildlife",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "boxplot3 = deldupl_null.boxplot(column=['powerPS'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "saved-poison",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Размер набора данных до применения фильтра 254921\n",
+      "Размер набора данных после применения фильтра 3124\n"
+     ]
+    }
+   ],
+   "source": [
+    "autocars_df  = deldupl_null[\n",
+    "    (deldupl_null.yearOfRegistration <= 2015)&\n",
+    "    (deldupl_null.yearOfRegistration >= 1980)&\n",
+    "    (deldupl_null.price >= 0)&\n",
+    "    (deldupl_null.price <= 0.5)&\n",
+    "    (deldupl_null.powerPS >= 0)&\n",
+    "    (deldupl_null.powerPS <= 10000)\n",
+    "]\n",
+    "print(\"Размер набора данных до применения фильтра\", deldupl_null.shape[0]) \n",
+    "print(\"Размер набора данных после применения фильтра\", autocars_df.shape[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "needed-seating",
+   "metadata": {},
+   "source": [
+    "### 4. Заполнить пропущенные данные в строковых полях."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "aggressive-election",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dateCrawled                0\n",
+       "name                       0\n",
+       "seller                     0\n",
+       "offerType                  0\n",
+       "price                      0\n",
+       "abtest                     0\n",
+       "vehicleType            37380\n",
+       "yearOfRegistration         0\n",
+       "gearbox                19102\n",
+       "powerPS                    0\n",
+       "model                  20260\n",
+       "kilometer                  0\n",
+       "monthOfRegistration        0\n",
+       "fuelType               33047\n",
+       "brand                      0\n",
+       "notRepairedDamage      70197\n",
+       "dateCreated                0\n",
+       "nrOfPictures               0\n",
+       "postalCode                 0\n",
+       "lastSeen                   0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "deldupl_df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "reverse-dollar",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>dateCrawled</th>\n",
+       "      <th>name</th>\n",
+       "      <th>seller</th>\n",
+       "      <th>offerType</th>\n",
+       "      <th>price</th>\n",
+       "      <th>abtest</th>\n",
+       "      <th>vehicleType</th>\n",
+       "      <th>yearOfRegistration</th>\n",
+       "      <th>gearbox</th>\n",
+       "      <th>powerPS</th>\n",
+       "      <th>model</th>\n",
+       "      <th>kilometer</th>\n",
+       "      <th>monthOfRegistration</th>\n",
+       "      <th>fuelType</th>\n",
+       "      <th>brand</th>\n",
+       "      <th>notRepairedDamage</th>\n",
+       "      <th>dateCreated</th>\n",
+       "      <th>nrOfPictures</th>\n",
+       "      <th>postalCode</th>\n",
+       "      <th>lastSeen</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2016-03-24 11:52:17</td>\n",
+       "      <td>Golf_3_1.6</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>480</td>\n",
+       "      <td>test</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1993</td>\n",
+       "      <td>manuell</td>\n",
+       "      <td>0</td>\n",
+       "      <td>golf</td>\n",
+       "      <td>150000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>benzin</td>\n",
+       "      <td>volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016-03-24 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>70435</td>\n",
+       "      <td>2016-04-07 03:16:57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2016-03-24 10:58:45</td>\n",
+       "      <td>A5_Sportback_2.7_Tdi</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>18300</td>\n",
+       "      <td>test</td>\n",
+       "      <td>coupe</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>manuell</td>\n",
+       "      <td>190</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>125000</td>\n",
+       "      <td>5</td>\n",
+       "      <td>diesel</td>\n",
+       "      <td>audi</td>\n",
+       "      <td>ja</td>\n",
+       "      <td>2016-03-24 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>66954</td>\n",
+       "      <td>2016-04-07 01:46:50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2016-03-14 12:52:21</td>\n",
+       "      <td>Jeep_Grand_Cherokee_\"Overland\"</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>9800</td>\n",
+       "      <td>test</td>\n",
+       "      <td>suv</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>automatik</td>\n",
+       "      <td>163</td>\n",
+       "      <td>grand</td>\n",
+       "      <td>125000</td>\n",
+       "      <td>8</td>\n",
+       "      <td>diesel</td>\n",
+       "      <td>jeep</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016-03-14 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>90480</td>\n",
+       "      <td>2016-04-05 12:47:46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2016-03-17 16:54:04</td>\n",
+       "      <td>GOLF_4_1_4__3TÜRER</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>test</td>\n",
+       "      <td>kleinwagen</td>\n",
+       "      <td>2001</td>\n",
+       "      <td>manuell</td>\n",
+       "      <td>75</td>\n",
+       "      <td>golf</td>\n",
+       "      <td>150000</td>\n",
+       "      <td>6</td>\n",
+       "      <td>benzin</td>\n",
+       "      <td>volkswagen</td>\n",
+       "      <td>nein</td>\n",
+       "      <td>2016-03-17 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>91074</td>\n",
+       "      <td>2016-03-17 17:40:17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2016-03-31 17:25:20</td>\n",
+       "      <td>Skoda_Fabia_1.4_TDI_PD_Classic</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>test</td>\n",
+       "      <td>kleinwagen</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>manuell</td>\n",
+       "      <td>69</td>\n",
+       "      <td>fabia</td>\n",
+       "      <td>90000</td>\n",
+       "      <td>7</td>\n",
+       "      <td>diesel</td>\n",
+       "      <td>skoda</td>\n",
+       "      <td>nein</td>\n",
+       "      <td>2016-03-31 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>60437</td>\n",
+       "      <td>2016-04-06 10:17:21</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           dateCrawled                            name  seller offerType  \\\n",
+       "0  2016-03-24 11:52:17                      Golf_3_1.6  privat   Angebot   \n",
+       "1  2016-03-24 10:58:45            A5_Sportback_2.7_Tdi  privat   Angebot   \n",
+       "2  2016-03-14 12:52:21  Jeep_Grand_Cherokee_\"Overland\"  privat   Angebot   \n",
+       "3  2016-03-17 16:54:04              GOLF_4_1_4__3TÜRER  privat   Angebot   \n",
+       "4  2016-03-31 17:25:20  Skoda_Fabia_1.4_TDI_PD_Classic  privat   Angebot   \n",
+       "\n",
+       "   price abtest vehicleType  yearOfRegistration    gearbox  powerPS  model  \\\n",
+       "0    480   test         NaN                1993    manuell        0   golf   \n",
+       "1  18300   test       coupe                2011    manuell      190    NaN   \n",
+       "2   9800   test         suv                2004  automatik      163  grand   \n",
+       "3   1500   test  kleinwagen                2001    manuell       75   golf   \n",
+       "4   3600   test  kleinwagen                2008    manuell       69  fabia   \n",
+       "\n",
+       "   kilometer  monthOfRegistration fuelType       brand notRepairedDamage  \\\n",
+       "0     150000                    0   benzin  volkswagen               NaN   \n",
+       "1     125000                    5   diesel        audi                ja   \n",
+       "2     125000                    8   diesel        jeep               NaN   \n",
+       "3     150000                    6   benzin  volkswagen              nein   \n",
+       "4      90000                    7   diesel       skoda              nein   \n",
+       "\n",
+       "           dateCreated  nrOfPictures  postalCode             lastSeen  \n",
+       "0  2016-03-24 00:00:00             0       70435  2016-04-07 03:16:57  \n",
+       "1  2016-03-24 00:00:00             0       66954  2016-04-07 01:46:50  \n",
+       "2  2016-03-14 00:00:00             0       90480  2016-04-05 12:47:46  \n",
+       "3  2016-03-17 00:00:00             0       91074  2016-03-17 17:40:17  \n",
+       "4  2016-03-31 00:00:00             0       60437  2016-04-06 10:17:21  "
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "deldupl_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "rational-harris",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "D:\\мо\\WPy64-3920\\python-3.9.2.amd64\\lib\\site-packages\\pandas\\core\\series.py:4463: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  return super().fillna(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>dateCrawled</th>\n",
+       "      <th>name</th>\n",
+       "      <th>seller</th>\n",
+       "      <th>offerType</th>\n",
+       "      <th>price</th>\n",
+       "      <th>abtest</th>\n",
+       "      <th>vehicleType</th>\n",
+       "      <th>yearOfRegistration</th>\n",
+       "      <th>gearbox</th>\n",
+       "      <th>powerPS</th>\n",
+       "      <th>model</th>\n",
+       "      <th>kilometer</th>\n",
+       "      <th>monthOfRegistration</th>\n",
+       "      <th>fuelType</th>\n",
+       "      <th>brand</th>\n",
+       "      <th>notRepairedDamage</th>\n",
+       "      <th>dateCreated</th>\n",
+       "      <th>nrOfPictures</th>\n",
+       "      <th>postalCode</th>\n",
+       "      <th>lastSeen</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2016-03-24 11:52:17</td>\n",
+       "      <td>Golf_3_1.6</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>480</td>\n",
+       "      <td>test</td>\n",
+       "      <td>not_empty</td>\n",
+       "      <td>1993</td>\n",
+       "      <td>manuell</td>\n",
+       "      <td>0</td>\n",
+       "      <td>golf</td>\n",
+       "      <td>150000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>benzin</td>\n",
+       "      <td>volkswagen</td>\n",
+       "      <td>not_empty</td>\n",
+       "      <td>2016-03-24 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>70435</td>\n",
+       "      <td>2016-04-07 03:16:57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2016-03-24 10:58:45</td>\n",
+       "      <td>A5_Sportback_2.7_Tdi</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>18300</td>\n",
+       "      <td>test</td>\n",
+       "      <td>coupe</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>manuell</td>\n",
+       "      <td>190</td>\n",
+       "      <td>not_empty</td>\n",
+       "      <td>125000</td>\n",
+       "      <td>5</td>\n",
+       "      <td>diesel</td>\n",
+       "      <td>audi</td>\n",
+       "      <td>ja</td>\n",
+       "      <td>2016-03-24 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>66954</td>\n",
+       "      <td>2016-04-07 01:46:50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2016-03-14 12:52:21</td>\n",
+       "      <td>Jeep_Grand_Cherokee_\"Overland\"</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>9800</td>\n",
+       "      <td>test</td>\n",
+       "      <td>suv</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>automatik</td>\n",
+       "      <td>163</td>\n",
+       "      <td>grand</td>\n",
+       "      <td>125000</td>\n",
+       "      <td>8</td>\n",
+       "      <td>diesel</td>\n",
+       "      <td>jeep</td>\n",
+       "      <td>not_empty</td>\n",
+       "      <td>2016-03-14 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>90480</td>\n",
+       "      <td>2016-04-05 12:47:46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2016-03-17 16:54:04</td>\n",
+       "      <td>GOLF_4_1_4__3TÜRER</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>test</td>\n",
+       "      <td>kleinwagen</td>\n",
+       "      <td>2001</td>\n",
+       "      <td>manuell</td>\n",
+       "      <td>75</td>\n",
+       "      <td>golf</td>\n",
+       "      <td>150000</td>\n",
+       "      <td>6</td>\n",
+       "      <td>benzin</td>\n",
+       "      <td>volkswagen</td>\n",
+       "      <td>nein</td>\n",
+       "      <td>2016-03-17 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>91074</td>\n",
+       "      <td>2016-03-17 17:40:17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2016-03-31 17:25:20</td>\n",
+       "      <td>Skoda_Fabia_1.4_TDI_PD_Classic</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>test</td>\n",
+       "      <td>kleinwagen</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>manuell</td>\n",
+       "      <td>69</td>\n",
+       "      <td>fabia</td>\n",
+       "      <td>90000</td>\n",
+       "      <td>7</td>\n",
+       "      <td>diesel</td>\n",
+       "      <td>skoda</td>\n",
+       "      <td>nein</td>\n",
+       "      <td>2016-03-31 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>60437</td>\n",
+       "      <td>2016-04-06 10:17:21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>371523</th>\n",
+       "      <td>2016-03-14 17:48:27</td>\n",
+       "      <td>Suche_t4___vito_ab_6_sitze</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>2200</td>\n",
+       "      <td>test</td>\n",
+       "      <td>not_empty</td>\n",
+       "      <td>2005</td>\n",
+       "      <td>not_empty</td>\n",
+       "      <td>0</td>\n",
+       "      <td>not_empty</td>\n",
+       "      <td>20000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>not_empty</td>\n",
+       "      <td>sonstige_autos</td>\n",
+       "      <td>not_empty</td>\n",
+       "      <td>2016-03-14 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>39576</td>\n",
+       "      <td>2016-04-06 00:46:52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>371524</th>\n",
+       "      <td>2016-03-05 19:56:21</td>\n",
+       "      <td>Smart_smart_leistungssteigerung_100ps</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>1199</td>\n",
+       "      <td>test</td>\n",
+       "      <td>cabrio</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>automatik</td>\n",
+       "      <td>101</td>\n",
+       "      <td>fortwo</td>\n",
+       "      <td>125000</td>\n",
+       "      <td>3</td>\n",
+       "      <td>benzin</td>\n",
+       "      <td>smart</td>\n",
+       "      <td>nein</td>\n",
+       "      <td>2016-03-05 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>26135</td>\n",
+       "      <td>2016-03-11 18:17:12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>371525</th>\n",
+       "      <td>2016-03-19 18:57:12</td>\n",
+       "      <td>Volkswagen_Multivan_T4_TDI_7DC_UY2</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>9200</td>\n",
+       "      <td>test</td>\n",
+       "      <td>bus</td>\n",
+       "      <td>1996</td>\n",
+       "      <td>manuell</td>\n",
+       "      <td>102</td>\n",
+       "      <td>transporter</td>\n",
+       "      <td>150000</td>\n",
+       "      <td>3</td>\n",
+       "      <td>diesel</td>\n",
+       "      <td>volkswagen</td>\n",
+       "      <td>nein</td>\n",
+       "      <td>2016-03-19 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>87439</td>\n",
+       "      <td>2016-04-07 07:15:26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>371526</th>\n",
+       "      <td>2016-03-20 19:41:08</td>\n",
+       "      <td>VW_Golf_Kombi_1_9l_TDI</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>3400</td>\n",
+       "      <td>test</td>\n",
+       "      <td>kombi</td>\n",
+       "      <td>2002</td>\n",
+       "      <td>manuell</td>\n",
+       "      <td>100</td>\n",
+       "      <td>golf</td>\n",
+       "      <td>150000</td>\n",
+       "      <td>6</td>\n",
+       "      <td>diesel</td>\n",
+       "      <td>volkswagen</td>\n",
+       "      <td>not_empty</td>\n",
+       "      <td>2016-03-20 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>40764</td>\n",
+       "      <td>2016-03-24 12:45:21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>371527</th>\n",
+       "      <td>2016-03-07 19:39:19</td>\n",
+       "      <td>BMW_M135i_vollausgestattet_NP_52.720____Euro</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>28990</td>\n",
+       "      <td>control</td>\n",
+       "      <td>limousine</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>manuell</td>\n",
+       "      <td>320</td>\n",
+       "      <td>m_reihe</td>\n",
+       "      <td>50000</td>\n",
+       "      <td>8</td>\n",
+       "      <td>benzin</td>\n",
+       "      <td>bmw</td>\n",
+       "      <td>nein</td>\n",
+       "      <td>2016-03-07 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>73326</td>\n",
+       "      <td>2016-03-22 03:17:10</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>362770 rows × 20 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                dateCrawled                                          name  \\\n",
+       "0       2016-03-24 11:52:17                                    Golf_3_1.6   \n",
+       "1       2016-03-24 10:58:45                          A5_Sportback_2.7_Tdi   \n",
+       "2       2016-03-14 12:52:21                Jeep_Grand_Cherokee_\"Overland\"   \n",
+       "3       2016-03-17 16:54:04                            GOLF_4_1_4__3TÜRER   \n",
+       "4       2016-03-31 17:25:20                Skoda_Fabia_1.4_TDI_PD_Classic   \n",
+       "...                     ...                                           ...   \n",
+       "371523  2016-03-14 17:48:27                    Suche_t4___vito_ab_6_sitze   \n",
+       "371524  2016-03-05 19:56:21         Smart_smart_leistungssteigerung_100ps   \n",
+       "371525  2016-03-19 18:57:12            Volkswagen_Multivan_T4_TDI_7DC_UY2   \n",
+       "371526  2016-03-20 19:41:08                        VW_Golf_Kombi_1_9l_TDI   \n",
+       "371527  2016-03-07 19:39:19  BMW_M135i_vollausgestattet_NP_52.720____Euro   \n",
+       "\n",
+       "        seller offerType  price   abtest vehicleType  yearOfRegistration  \\\n",
+       "0       privat   Angebot    480     test   not_empty                1993   \n",
+       "1       privat   Angebot  18300     test       coupe                2011   \n",
+       "2       privat   Angebot   9800     test         suv                2004   \n",
+       "3       privat   Angebot   1500     test  kleinwagen                2001   \n",
+       "4       privat   Angebot   3600     test  kleinwagen                2008   \n",
+       "...        ...       ...    ...      ...         ...                 ...   \n",
+       "371523  privat   Angebot   2200     test   not_empty                2005   \n",
+       "371524  privat   Angebot   1199     test      cabrio                2000   \n",
+       "371525  privat   Angebot   9200     test         bus                1996   \n",
+       "371526  privat   Angebot   3400     test       kombi                2002   \n",
+       "371527  privat   Angebot  28990  control   limousine                2013   \n",
+       "\n",
+       "          gearbox  powerPS        model  kilometer  monthOfRegistration  \\\n",
+       "0         manuell        0         golf     150000                    0   \n",
+       "1         manuell      190    not_empty     125000                    5   \n",
+       "2       automatik      163        grand     125000                    8   \n",
+       "3         manuell       75         golf     150000                    6   \n",
+       "4         manuell       69        fabia      90000                    7   \n",
+       "...           ...      ...          ...        ...                  ...   \n",
+       "371523  not_empty        0    not_empty      20000                    1   \n",
+       "371524  automatik      101       fortwo     125000                    3   \n",
+       "371525    manuell      102  transporter     150000                    3   \n",
+       "371526    manuell      100         golf     150000                    6   \n",
+       "371527    manuell      320      m_reihe      50000                    8   \n",
+       "\n",
+       "         fuelType           brand notRepairedDamage          dateCreated  \\\n",
+       "0          benzin      volkswagen         not_empty  2016-03-24 00:00:00   \n",
+       "1          diesel            audi                ja  2016-03-24 00:00:00   \n",
+       "2          diesel            jeep         not_empty  2016-03-14 00:00:00   \n",
+       "3          benzin      volkswagen              nein  2016-03-17 00:00:00   \n",
+       "4          diesel           skoda              nein  2016-03-31 00:00:00   \n",
+       "...           ...             ...               ...                  ...   \n",
+       "371523  not_empty  sonstige_autos         not_empty  2016-03-14 00:00:00   \n",
+       "371524     benzin           smart              nein  2016-03-05 00:00:00   \n",
+       "371525     diesel      volkswagen              nein  2016-03-19 00:00:00   \n",
+       "371526     diesel      volkswagen         not_empty  2016-03-20 00:00:00   \n",
+       "371527     benzin             bmw              nein  2016-03-07 00:00:00   \n",
+       "\n",
+       "        nrOfPictures  postalCode             lastSeen  \n",
+       "0                  0       70435  2016-04-07 03:16:57  \n",
+       "1                  0       66954  2016-04-07 01:46:50  \n",
+       "2                  0       90480  2016-04-05 12:47:46  \n",
+       "3                  0       91074  2016-03-17 17:40:17  \n",
+       "4                  0       60437  2016-04-06 10:17:21  \n",
+       "...              ...         ...                  ...  \n",
+       "371523             0       39576  2016-04-06 00:46:52  \n",
+       "371524             0       26135  2016-03-11 18:17:12  \n",
+       "371525             0       87439  2016-04-07 07:15:26  \n",
+       "371526             0       40764  2016-03-24 12:45:21  \n",
+       "371527             0       73326  2016-03-22 03:17:10  \n",
+       "\n",
+       "[362770 rows x 20 columns]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "not_empty_df = deldupl_df\n",
+    "not_empty_df['name'].fillna(value='not_empty', inplace=True)\n",
+    "not_empty_df['seller'].fillna(value='not_empty', inplace=True)\n",
+    "not_empty_df['offerType'].fillna(value='not_empty', inplace=True)\n",
+    "not_empty_df['abtest'].fillna(value='not_empty', inplace=True)\n",
+    "not_empty_df['vehicleType'].fillna(value='not_empty', inplace=True)\n",
+    "not_empty_df['gearbox'].fillna(value='not_empty', inplace=True)\n",
+    "not_empty_df['model'].fillna(value='not_empty', inplace=True)\n",
+    "not_empty_df['fuelType'].fillna(value='not_empty', inplace=True)\n",
+    "not_empty_df['brand'].fillna(value='not_emptye', inplace=True)\n",
+    "not_empty_df['notRepairedDamage'].fillna(value='not_empty', inplace=True)\n",
+    "\n",
+    "not_empty_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "frequent-combination",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dateCrawled            0\n",
+       "name                   0\n",
+       "seller                 0\n",
+       "offerType              0\n",
+       "price                  0\n",
+       "abtest                 0\n",
+       "vehicleType            0\n",
+       "yearOfRegistration     0\n",
+       "gearbox                0\n",
+       "powerPS                0\n",
+       "model                  0\n",
+       "kilometer              0\n",
+       "monthOfRegistration    0\n",
+       "fuelType               0\n",
+       "brand                  0\n",
+       "notRepairedDamage      0\n",
+       "dateCreated            0\n",
+       "nrOfPictures           0\n",
+       "postalCode             0\n",
+       "lastSeen               0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "not_empty_df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "accessible-champion",
+   "metadata": {},
+   "source": [
+    "### 5. Постройте гистограмму по маркам автомобилей, типам кузова и используемому топливу."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "assigned-township",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "deldupl_df.brand.hist(xrot=80,bins=15,xlabelsize=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "authorized-variety",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "deldupl_df.vehicleType.hist(xrot=50,bins=15,xlabelsize=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "opening-walker",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "deldupl_df.fuelType.hist(xrot=35,bins=15,xlabelsize=12)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "committed-margin",
+   "metadata": {},
+   "source": [
+    "### 6. Добавьте в данные новый признак, который представляет собой длину названия автомобиля."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "southeast-medicine",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>dateCrawled</th>\n",
+       "      <th>name</th>\n",
+       "      <th>seller</th>\n",
+       "      <th>offerType</th>\n",
+       "      <th>price</th>\n",
+       "      <th>abtest</th>\n",
+       "      <th>vehicleType</th>\n",
+       "      <th>yearOfRegistration</th>\n",
+       "      <th>gearbox</th>\n",
+       "      <th>powerPS</th>\n",
+       "      <th>...</th>\n",
+       "      <th>kilometer</th>\n",
+       "      <th>monthOfRegistration</th>\n",
+       "      <th>fuelType</th>\n",
+       "      <th>brand</th>\n",
+       "      <th>notRepairedDamage</th>\n",
+       "      <th>dateCreated</th>\n",
+       "      <th>nrOfPictures</th>\n",
+       "      <th>postalCode</th>\n",
+       "      <th>lastSeen</th>\n",
+       "      <th>len_of_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2016-03-24 11:52:17</td>\n",
+       "      <td>Golf_3_1.6</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>480</td>\n",
+       "      <td>test</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1993</td>\n",
+       "      <td>manuell</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>150000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>benzin</td>\n",
+       "      <td>volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016-03-24 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>70435</td>\n",
+       "      <td>2016-04-07 03:16:57</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2016-03-24 10:58:45</td>\n",
+       "      <td>A5_Sportback_2.7_Tdi</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>18300</td>\n",
+       "      <td>test</td>\n",
+       "      <td>coupe</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>manuell</td>\n",
+       "      <td>190</td>\n",
+       "      <td>...</td>\n",
+       "      <td>125000</td>\n",
+       "      <td>5</td>\n",
+       "      <td>diesel</td>\n",
+       "      <td>audi</td>\n",
+       "      <td>ja</td>\n",
+       "      <td>2016-03-24 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>66954</td>\n",
+       "      <td>2016-04-07 01:46:50</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2016-03-14 12:52:21</td>\n",
+       "      <td>Jeep_Grand_Cherokee_\"Overland\"</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>9800</td>\n",
+       "      <td>test</td>\n",
+       "      <td>suv</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>automatik</td>\n",
+       "      <td>163</td>\n",
+       "      <td>...</td>\n",
+       "      <td>125000</td>\n",
+       "      <td>8</td>\n",
+       "      <td>diesel</td>\n",
+       "      <td>jeep</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016-03-14 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>90480</td>\n",
+       "      <td>2016-04-05 12:47:46</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2016-03-17 16:54:04</td>\n",
+       "      <td>GOLF_4_1_4__3TÜRER</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>test</td>\n",
+       "      <td>kleinwagen</td>\n",
+       "      <td>2001</td>\n",
+       "      <td>manuell</td>\n",
+       "      <td>75</td>\n",
+       "      <td>...</td>\n",
+       "      <td>150000</td>\n",
+       "      <td>6</td>\n",
+       "      <td>benzin</td>\n",
+       "      <td>volkswagen</td>\n",
+       "      <td>nein</td>\n",
+       "      <td>2016-03-17 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>91074</td>\n",
+       "      <td>2016-03-17 17:40:17</td>\n",
+       "      <td>18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2016-03-31 17:25:20</td>\n",
+       "      <td>Skoda_Fabia_1.4_TDI_PD_Classic</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>test</td>\n",
+       "      <td>kleinwagen</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>manuell</td>\n",
+       "      <td>69</td>\n",
+       "      <td>...</td>\n",
+       "      <td>90000</td>\n",
+       "      <td>7</td>\n",
+       "      <td>diesel</td>\n",
+       "      <td>skoda</td>\n",
+       "      <td>nein</td>\n",
+       "      <td>2016-03-31 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>60437</td>\n",
+       "      <td>2016-04-06 10:17:21</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           dateCrawled                            name  seller offerType  \\\n",
+       "0  2016-03-24 11:52:17                      Golf_3_1.6  privat   Angebot   \n",
+       "1  2016-03-24 10:58:45            A5_Sportback_2.7_Tdi  privat   Angebot   \n",
+       "2  2016-03-14 12:52:21  Jeep_Grand_Cherokee_\"Overland\"  privat   Angebot   \n",
+       "3  2016-03-17 16:54:04              GOLF_4_1_4__3TÜRER  privat   Angebot   \n",
+       "4  2016-03-31 17:25:20  Skoda_Fabia_1.4_TDI_PD_Classic  privat   Angebot   \n",
+       "\n",
+       "   price abtest vehicleType  yearOfRegistration    gearbox  powerPS  ...  \\\n",
+       "0    480   test         NaN                1993    manuell        0  ...   \n",
+       "1  18300   test       coupe                2011    manuell      190  ...   \n",
+       "2   9800   test         suv                2004  automatik      163  ...   \n",
+       "3   1500   test  kleinwagen                2001    manuell       75  ...   \n",
+       "4   3600   test  kleinwagen                2008    manuell       69  ...   \n",
+       "\n",
+       "  kilometer  monthOfRegistration  fuelType       brand notRepairedDamage  \\\n",
+       "0    150000                    0    benzin  volkswagen               NaN   \n",
+       "1    125000                    5    diesel        audi                ja   \n",
+       "2    125000                    8    diesel        jeep               NaN   \n",
+       "3    150000                    6    benzin  volkswagen              nein   \n",
+       "4     90000                    7    diesel       skoda              nein   \n",
+       "\n",
+       "           dateCreated nrOfPictures  postalCode             lastSeen  \\\n",
+       "0  2016-03-24 00:00:00            0       70435  2016-04-07 03:16:57   \n",
+       "1  2016-03-24 00:00:00            0       66954  2016-04-07 01:46:50   \n",
+       "2  2016-03-14 00:00:00            0       90480  2016-04-05 12:47:46   \n",
+       "3  2016-03-17 00:00:00            0       91074  2016-03-17 17:40:17   \n",
+       "4  2016-03-31 00:00:00            0       60437  2016-04-06 10:17:21   \n",
+       "\n",
+       "  len_of_name  \n",
+       "0          10  \n",
+       "1          20  \n",
+       "2          30  \n",
+       "3          18  \n",
+       "4          30  \n",
+       "\n",
+       "[5 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def get_len_name(name):\n",
+    "    return str(len(name.split('\\n')[0].strip()))\n",
+    "    \n",
+    "list_len = cars_df['name'].apply(get_len_name)\n",
+    "\n",
+    "cars_df['len_of_name'] = list_len\n",
+    "cars_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "weighted-highland",
+   "metadata": {},
+   "source": [
+    "### 7. Постройте карту корреляций между признаками. Выведите также числовые значения признаков."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "senior-sailing",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>price</th>\n",
+       "      <th>yearOfRegistration</th>\n",
+       "      <th>powerPS</th>\n",
+       "      <th>kilometer</th>\n",
+       "      <th>monthOfRegistration</th>\n",
+       "      <th>nrOfPictures</th>\n",
+       "      <th>postalCode</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>price</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.000184</td>\n",
+       "      <td>0.006852</td>\n",
+       "      <td>-0.000622</td>\n",
+       "      <td>-0.002853</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.003223</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>yearOfRegistration</th>\n",
+       "      <td>-0.000184</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000454</td>\n",
+       "      <td>-0.055157</td>\n",
+       "      <td>-0.011581</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.001386</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>powerPS</th>\n",
+       "      <td>0.006852</td>\n",
+       "      <td>0.000454</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.008877</td>\n",
+       "      <td>0.044365</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.027794</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>kilometer</th>\n",
+       "      <td>-0.000622</td>\n",
+       "      <td>-0.055157</td>\n",
+       "      <td>-0.008877</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.002872</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.029992</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>monthOfRegistration</th>\n",
+       "      <td>-0.002853</td>\n",
+       "      <td>-0.011581</td>\n",
+       "      <td>0.044365</td>\n",
+       "      <td>0.002872</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.016596</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>nrOfPictures</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>postalCode</th>\n",
+       "      <td>-0.003223</td>\n",
+       "      <td>-0.001386</td>\n",
+       "      <td>0.027794</td>\n",
+       "      <td>-0.029992</td>\n",
+       "      <td>0.016596</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        price  yearOfRegistration   powerPS  kilometer  \\\n",
+       "price                1.000000           -0.000184  0.006852  -0.000622   \n",
+       "yearOfRegistration  -0.000184            1.000000  0.000454  -0.055157   \n",
+       "powerPS              0.006852            0.000454  1.000000  -0.008877   \n",
+       "kilometer           -0.000622           -0.055157 -0.008877   1.000000   \n",
+       "monthOfRegistration -0.002853           -0.011581  0.044365   0.002872   \n",
+       "nrOfPictures              NaN                 NaN       NaN        NaN   \n",
+       "postalCode          -0.003223           -0.001386  0.027794  -0.029992   \n",
+       "\n",
+       "                     monthOfRegistration  nrOfPictures  postalCode  \n",
+       "price                          -0.002853           NaN   -0.003223  \n",
+       "yearOfRegistration             -0.011581           NaN   -0.001386  \n",
+       "powerPS                         0.044365           NaN    0.027794  \n",
+       "kilometer                       0.002872           NaN   -0.029992  \n",
+       "monthOfRegistration             1.000000           NaN    0.016596  \n",
+       "nrOfPictures                         NaN           NaN         NaN  \n",
+       "postalCode                      0.016596           NaN    1.000000  "
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.heatmap(deldupl_df.corr(), annot=True,fmt=\".3f\")\n",
+    "deldupl_df.corr()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "objective-cooking",
+   "metadata": {},
+   "source": [
+    "### 8. Определите коэффициент корреляции Пирсона и Спирмена между стоимостью автомобиля и типом кузова. Оцените значение p-value. Постройте плотность распределения признаков."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "pointed-victor",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.stats import pearsonr\n",
+    "from scipy.stats import spearmanr\n",
+    "from sklearn.preprocessing import LabelEncoder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "posted-arctic",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "label_encoder = LabelEncoder()\n",
+    "vehicleCoder = pd.Series(label_encoder.fit_transform(deldupl_df['vehicleType']))\n",
+    "coefficientpearson = pearsonr(deldupl_df['price'],vehicleCoder)\n",
+    "coefficientspearman = spearmanr(deldupl_df['price'],vehicleCoder)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "rotary-reservation",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Коэффициент корреляции Пирсона:  (-0.004096348099478991, 0.013615589695521369)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Коэффициент корреляции Пирсона: \",coefficientpearson)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "suburban-repeat",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Коэффициент корреляции Спирмена:  SpearmanrResult(correlation=-0.0579966487892841, pvalue=8.838847580174968e-268)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Коэффициент корреляции Спирмена: \",coefficientspearman)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "technological-reading",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:ylabel='Density'>"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEQCAYAAAC9VHPBAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAS6ElEQVR4nO3df7DddX3n8efLJKButHRJqmwSCbVpMdMBKle0dTulP6wJtmWs6xTUOjK6LLPgtLu1I9vdae2PnbbbXad1BNNMm6H2h7QurI1uVqqugjtqS2gBCRhNoZVr3HIpKkqFGHjvH+eEPdx8770n8X5zzs3n+Zg5k/P9fD/nc9/5Qs7rfr+f749UFZKkdj1t0gVIkibLIJCkxhkEktQ4g0CSGmcQSFLjDAJJatyKDIIku5I8kOSuZRrvN5PcNXz91HKMKUkrxYoMAuA6YNtyDJTkFcALgfOAFwM/n+TZyzG2JK0EKzIIquoW4KHRtiTPT/LBJLcl+XiSs8ccbitwc1UdrqpHgDtYppCRpJVgRQbBAnYCb66q84G3ANeO+bk7gO1JnplkHfCDwKaeapSkqbN60gUshyRrge8D3pvkSPOpw3U/CfxKx8e+UFUvr6q/SPIi4BPAHPBJ4HD/VUvSdMhKvddQks3AB6rqu4fH9PdX1RnLMO6fAH9UVXu+2bEkaSU4KQ4NVdXDwH1JXg2QgXPH+WySVUlOH74/BzgH+IveipWkKbMi9wiSvAe4EFgH/APwS8D/Bt4FnAGsAa6vqq5DQvPHejrw18PFh4Erqur25a9akqbTigwCSdLyOSkODUmSjt+KO2to3bp1tXnz5kmXIUkrym233fZgVa3vWrfigmDz5s3s3bt30mVI0oqS5O8XWuehIUlqnEEgSY0zCCSpcQaBJDXOIJCkxhkEktQ4g0CSGmcQqFNVccNtszz6jccnXYqknhkE6vTxzz3Iz733Dn59zz2TLkVSzwwCdfrqo4Nn88x97bEJVyKpbwaBJDXOIJCkxhkEktQ4g0CSGmcQSFLjDAJJapxBIEmNMwgkqXEGgSQ1ziCQpMYZBJLUOINAkhpnEEhS4wwCSWqcQSBJjTMIJKlxBoEkNc4gkKTGGQSS1LjegiDJriQPJLlrgfVJ8o4kB5LcmeSFfdUiSVpYn3sE1wHbFlm/HdgyfF0OvKvHWnSMipp0CZJOkN6CoKpuAR5apMvFwLtr4FPAaUnO6KseSVK3Sc4RbADuH1meHbYdJcnlSfYm2Ts3N3dCimtdyKRLkHSCTDIIur5pOo9HVNXOqpqpqpn169f3XJYktWWSQTALbBpZ3ggcnFAtktSsSQbBbuD1w7OHXgJ8paq+OMF6JKlJq/saOMl7gAuBdUlmgV8C1gBU1Q5gD3ARcAD4J+CyvmqRJC2styCoqkuXWF/AlX39fEnSeLyyWJIaZxBIUuMMAklqnEEgSY0zCCSpcQaBJDXOIJCkxhkEktQ4g0CSGmcQSFLjDAJJapxBIEmNMwgkqXEGgSQ1ziCQpMYZBJLUOINAkhpnEEhS4wwCSWqcQSBJjTMIJKlxBoEkNc4gkKTGGQSS1DiDQJIaZxBIUuN6DYIk25LsT3IgydUd678lyfuT3JFkX5LL+qxHknS03oIgySrgGmA7sBW4NMnWed2uBO6uqnOBC4H/luSUvmqSJB2tzz2CC4ADVXVvVR0CrgcuntengGclCbAWeAg43GNNkqR5+gyCDcD9I8uzw7ZR7wReABwEPg38TFU9MX+gJJcn2Ztk79zcXF/1SlKT+gyCdLTVvOWXA7cD/wI4D3hnkmcf9aGqnVU1U1Uz69evX+461aGO+k8l6WTVZxDMAptGljcy+M1/1GXAjTVwALgPOLvHmiRJ8/QZBLcCW5KcNZwAvgTYPa/P54EfBkjyHOC7gHt7rEljSucOnaST0eq+Bq6qw0muAm4CVgG7qmpfkiuG63cAvwpcl+TTDA4lvbWqHuyrJknS0XoLAoCq2gPsmde2Y+T9QeBH+6xBkrQ4ryxWJyeLpXYYBJLUOINAnZwsltphEEhS4wwCSWqcQaBOThZL7TAIJKlxBoE6OVkstcMgkKTGGQSS1DiDQJ2cLJbaYRBIUuMMAnVyslhqh0EgSY0zCCSpcQaBJDXOIJCkxhkEktS4sYIgyQ1JXpHE4JCkk8y4X+zvAl4DfC7JbyQ5u8eaJEkn0FhBUFUfrqrXAi8E/g74UJJPJLksyZo+C5Qk9WvsQz1JTgfeALwJ+BvgdxgEw4d6qUySdEKsHqdTkhuBs4E/BH68qr44XPWnSfb2VZwkqX9jBQHwe1W1Z7QhyalV9VhVzfRQlyTpBBn30NCvdbR9cjkLkSRNxqJ7BEmeC2wAnpHke+DJO5E9G3hmz7VJkk6ApQ4NvZzBBPFG4O0j7V8FfmGpwZNsYzCpvIrB4aXf6OhzIfDbwBrgwar6gaXLliQtl0WDoKr+APiDJK+qqhuOZeAkq4BrgJcBs8CtSXZX1d0jfU4DrgW2VdXnk3zbsf4FJEnfnKUODb2uqv4I2Jzk389fX1Vv7/jYERcAB6rq3uFY1wMXA3eP9HkNcGNVfX443gPHWL964hPKpHYsNVn8z4Z/rgWe1fFazAbg/pHl2WHbqO8EvjXJx5LcluT1XQMluTzJ3iR75+bmlvixkqRjsdShod8d/vnLxzF21yOu5v+auRo4H/hh4BnAJ5N8qqo+O6+OncBOgJmZGX9VPQF8QpnUjnFvOvdfkjw7yZokH0nyYJLXLfGxWWDTyPJG4GBHnw9W1SNV9SBwC3DuuMVLkr55415H8KNV9TDwYwy+vL8T+PklPnMrsCXJWUlOAS4Bds/r8+fA9ydZneSZwIuBe8auXpL0TRv3yuIjN5a7CHhPVT2ULH7ooKoOJ7kKuInB6aO7qmpfkiuG63dU1T1JPgjcCTzB4BTTu47nL6Ll5WSx1I5xg+D9ST4DfB34t0nWA48u9aHhbSn2zGvbMW/5t4DfGrMOSdIyG/c21FcD3wvMVNU3gEcYnAqqk5STxVI7xt0jAHgBg+sJRj/z7mWuR5J0go17G+o/BJ4P3A48PmwuDAJJWvHG3SOYAbZWlTOIjXCyWGrHuKeP3gU8t89CJEmTMe4ewTrg7iR/BTx2pLGqfqKXqjRxThZL7Rg3CN7WZxGSpMkZKwiq6uYkZwJbqurDw6uAV/VbmiTpRBj3XkP/GvjvwO8OmzYA7+upJk0BJ4uldow7WXwl8FLgYYCq+hzgQ2Qk6SQwbhA8VlWHjiwMLyrzV0ZJOgmMGwQ3J/kFBg+xfxnwXuD9/ZWlSfOKEakd4wbB1cAc8Gng3zC4kdx/6qsoSdKJM+5ZQ08keR/wvqryWZENcIdAaseiewQZeFuSB4HPAPuTzCX5xRNTniSpb0sdGvpZBmcLvaiqTq+qf87gKWIvTfLv+i5OktS/pYLg9cClVXXfkYaquhd43XCdTlLeX1Bqx1JBsGb4UPmnGM4TrOnoL0laYZYKgkPHuU6StEIsddbQuUke7mgP8PQe6pEknWCLBkFVeWM5STrJjXtBmRrjXLHUDoNAkhpnEKiTt6GW2mEQSFLjeg2CJNuS7E9yIMnVi/R7UZLHk/yrPuuRJB2ttyBIsgq4BtgObAUuTbJ1gX6/CdzUVy06dk4WS+3oc4/gAuBAVd07fKjN9cDFHf3eDNwAPNBjLZKkBfQZBBuA+0eWZ4dtT0qyAXglsKPHOiRJi+gzCNLRNv+Aw28Db62qxxcdKLk8yd4ke+fmfBzCieChIakdYz2Y5jjNAptGljcCB+f1mQGuTwKwDrgoyeGqet9op6raCewEmJmZ8StKkpZRn0FwK7AlyVnAF4BLgNeMdqiqs468T3Id8IH5IaDJMG2ldvQWBFV1OMlVDM4GWgXsqqp9Sa4YrndeQJKmQJ97BFTVHgYPuh9t6wyAqnpDn7VIkrp5ZbE6+YQyqR0GgSQ1ziBQJ/cHpHYYBJLUOINAkhpnEKibx4akZhgEktQ4g0CdfEKZ1A6DQJIaZxBIUuMMAnXywmKpHQaBJDXOIFAndwikdhgEktQ4g0CSGmcQqJOTxVI7DAJJapxBoE5eWSy1wyCQpMYZBJLUOINAnZwsltphEEhS4wwCdXKHQGqHQSBJjTMIJKlxBoG6OVssNaPXIEiyLcn+JAeSXN2x/rVJ7hy+PpHk3D7rkSQdrbcgSLIKuAbYDmwFLk2ydV63+4AfqKpzgF8FdvZVj46N+wNSO/rcI7gAOFBV91bVIeB64OLRDlX1iar60nDxU8DGHuuRJHXoMwg2APePLM8O2xbyRuB/da1IcnmSvUn2zs3NLWOJkqQ+gyAdbZ1HHJL8IIMgeGvX+qraWVUzVTWzfv36ZSxRC3GuWGrH6h7HngU2jSxvBA7O75TkHOD3gO1V9Y891iNJ6tDnHsGtwJYkZyU5BbgE2D3aIcnzgBuBn66qz/ZYi45RuUsgNaO3PYKqOpzkKuAmYBWwq6r2JbliuH4H8IvA6cC1SQAOV9VMXzVJko7W56EhqmoPsGde246R928C3tRnDZKkxXllsTp5YEhqh0EgSY0zCNTJuWKpHQaBJDXOIJCkxhkE6uSRIakdBoEkNc4gUCevLJbaYRBIUuMMAklqnEEgSY0zCCSpcQaBJDXOIFAnTxqS2mEQSFLjDAJ1Kq8tlpphEEhS4wwCSWqcQaBOi00Wf+mRQ5zztpv4m89/6cQVJKk3BoGO2V/e9488/Ohhrv3Y3066FEnLwCBQJ6eKpXYYBDpumXQBkpaFQaDjYARIJxODQJ0Wv7LYA0fSycQg0KL2fPr/HtV2+AmDQDqZGATqNHpl8eHHn3jKukOHn5jfXdIK1msQJNmWZH+SA0mu7lifJO8Yrr8zyQv7rEfH56FHDj1l+TGDQDqp9BYESVYB1wDbga3ApUm2zuu2HdgyfF0OvKuvenT85r722JPvd99xkL994GsTrEbSclvd49gXAAeq6l6AJNcDFwN3j/S5GHh3DZ6U/qkkpyU5o6q+uNzF3PzZOX7tA3cv3VHAU/cCXvGO/8N3fNtavn7ocb7w5a8/2f7he/6Bl7395kmUJzXpp160iTd9/7cv+7h9BsEG4P6R5VngxWP02QA8JQiSXM5gj4HnPe95x1XM2lNXs+U5a4/rs63a+3df4vnr1/K0p8G3PGMNAA8/+g3O23Qat9//Zf7ld6wjnkkqnTDr1p7ay7h9BkHXV8T8003G6UNV7QR2AszMzBzXKSvnn/mtnH/m+cfzUUk6qfU5WTwLbBpZ3ggcPI4+kqQe9RkEtwJbkpyV5BTgEmD3vD67gdcPzx56CfCVPuYHJEkL6+3QUFUdTnIVcBOwCthVVfuSXDFcvwPYA1wEHAD+Cbisr3okSd36nCOgqvYw+LIfbdsx8r6AK/usQZK0OK8slqTGGQSS1DiDQJIaZxBIUuNSi994fuokmQP+fpmHXQc8uMxjnizcNt3cLgtz2yxsktvmzKpa37VixQVBH5LsraqZSdcxjdw23dwuC3PbLGxat42HhiSpcQaBJDXOIBjYOekCppjbppvbZWFum4VN5bZxjkCSGucegSQ1ziCQpMYZBECSVyfZl+SJJFN3atckJNmWZH+SA0munnQ90yLJriQPJLlr0rVMmySbknw0yT3Df08/M+mapkWSpyf5qyR3DLfNL0+6plEGwcBdwE8Ct0y6kGmQZBVwDbAd2ApcmmTrZKuaGtcB2yZdxJQ6DPxcVb0AeAlwpf/fPOkx4Ieq6lzgPGDb8BksU8EgAKrqnqraP+k6psgFwIGqureqDgHXAxdPuKapUFW3AA9Nuo5pVFVfrKq/Hr7/KnAPg2eQN68GvjZcXDN8Tc2ZOgaBumwA7h9ZnsV/0DoGSTYD3wP85YRLmRpJViW5HXgA+FBVTc226fXBNNMkyYeB53as+o9V9ecnup4pl462qfntRdMtyVrgBuBnq+rhSdczLarqceC8JKcB/yPJd1fVVMw1NRMEVfUjk65hBZkFNo0sbwQOTqgWrSBJ1jAIgT+uqhsnXc80qqovJ/kYg7mmqQgCDw2py63AliRnJTkFuATYPeGaNOWSBPh94J6qevuk65kmSdYP9wRI8gzgR4DPTLSoEQYBkOSVSWaB7wX+Z5KbJl3TJFXVYeAq4CYGE35/VlX7JlvVdEjyHuCTwHclmU3yxknXNEVeCvw08ENJbh++Lpp0UVPiDOCjSe5k8IvWh6rqAxOu6UneYkKSGucegSQ1ziCQpMYZBJLUOINAkhpnEEjSFDuWGx0mOTPJR5LcmeRjSTaO8zMMAkmabtcx/o0O/yvw7qo6B/gV4NfH+ZBBIElTrOtGh0men+SDSW5L8vEkZw9XbQU+Mnz/Uca8WaRBIEkrz07gzVV1PvAW4Nph+x3Aq4bvXwk8K8npSw3WzL2GJOlkMLyp3/cB7x3c1QOAU4d/vgV4Z5I3MHi+yhcYPCdiUQaBJK0sTwO+XFXnzV9RVQcZPGTrSGC8qqq+Ms6AkqQVYnhr7/uSvBoGN/tLcu7w/bokR77X/wOwa5wxDQJJmmIL3OjwtcAbk9wB7OP/TwpfCOxP8lngOcB/HutneNM5SWqbewSS1DiDQJIaZxBIUuMMAklqnEEgSY0zCCSpcQaBJDXu/wEhNeAwuAE4ywAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sPrice = pd.Series(deldupl_df['price'])\n",
+    "sPrice.plot.kde()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "modular-standard",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "p-value удовлетворительное\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "if (coefficientspearman[1]<0.05):\n",
+    "    print(\"p-value удовлетворительное\")\n",
+    "else:\n",
+    "    print(\"p-value неудовлетворительное\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "relative-western",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "deldupl_df['vehicleType'].value_counts().plot.barh()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "available-citation",
+   "metadata": {},
+   "source": [
+    "### 9. Добавьте в набор данных признак, являющийся суммой двух других признаков."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "developed-examination",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>dateCrawled</th>\n",
+       "      <th>name</th>\n",
+       "      <th>seller</th>\n",
+       "      <th>offerType</th>\n",
+       "      <th>price</th>\n",
+       "      <th>abtest</th>\n",
+       "      <th>vehicleType</th>\n",
+       "      <th>yearOfRegistration</th>\n",
+       "      <th>gearbox</th>\n",
+       "      <th>powerPS</th>\n",
+       "      <th>...</th>\n",
+       "      <th>monthOfRegistration</th>\n",
+       "      <th>fuelType</th>\n",
+       "      <th>brand</th>\n",
+       "      <th>notRepairedDamage</th>\n",
+       "      <th>dateCreated</th>\n",
+       "      <th>nrOfPictures</th>\n",
+       "      <th>postalCode</th>\n",
+       "      <th>lastSeen</th>\n",
+       "      <th>len_of_name</th>\n",
+       "      <th>sum</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2016-03-24 11:52:17</td>\n",
+       "      <td>Golf_3_1.6</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>480</td>\n",
+       "      <td>test</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1993</td>\n",
+       "      <td>manuell</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>benzin</td>\n",
+       "      <td>volkswagen</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016-03-24 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>70435</td>\n",
+       "      <td>2016-04-07 03:16:57</td>\n",
+       "      <td>10</td>\n",
+       "      <td>480</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2016-03-24 10:58:45</td>\n",
+       "      <td>A5_Sportback_2.7_Tdi</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>18300</td>\n",
+       "      <td>test</td>\n",
+       "      <td>coupe</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>manuell</td>\n",
+       "      <td>190</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5</td>\n",
+       "      <td>diesel</td>\n",
+       "      <td>audi</td>\n",
+       "      <td>ja</td>\n",
+       "      <td>2016-03-24 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>66954</td>\n",
+       "      <td>2016-04-07 01:46:50</td>\n",
+       "      <td>20</td>\n",
+       "      <td>18490</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2016-03-14 12:52:21</td>\n",
+       "      <td>Jeep_Grand_Cherokee_\"Overland\"</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>9800</td>\n",
+       "      <td>test</td>\n",
+       "      <td>suv</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>automatik</td>\n",
+       "      <td>163</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8</td>\n",
+       "      <td>diesel</td>\n",
+       "      <td>jeep</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016-03-14 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>90480</td>\n",
+       "      <td>2016-04-05 12:47:46</td>\n",
+       "      <td>30</td>\n",
+       "      <td>9963</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2016-03-17 16:54:04</td>\n",
+       "      <td>GOLF_4_1_4__3TÜRER</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>test</td>\n",
+       "      <td>kleinwagen</td>\n",
+       "      <td>2001</td>\n",
+       "      <td>manuell</td>\n",
+       "      <td>75</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6</td>\n",
+       "      <td>benzin</td>\n",
+       "      <td>volkswagen</td>\n",
+       "      <td>nein</td>\n",
+       "      <td>2016-03-17 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>91074</td>\n",
+       "      <td>2016-03-17 17:40:17</td>\n",
+       "      <td>18</td>\n",
+       "      <td>1575</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2016-03-31 17:25:20</td>\n",
+       "      <td>Skoda_Fabia_1.4_TDI_PD_Classic</td>\n",
+       "      <td>privat</td>\n",
+       "      <td>Angebot</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>test</td>\n",
+       "      <td>kleinwagen</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>manuell</td>\n",
+       "      <td>69</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7</td>\n",
+       "      <td>diesel</td>\n",
+       "      <td>skoda</td>\n",
+       "      <td>nein</td>\n",
+       "      <td>2016-03-31 00:00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>60437</td>\n",
+       "      <td>2016-04-06 10:17:21</td>\n",
+       "      <td>30</td>\n",
+       "      <td>3669</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 22 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           dateCrawled                            name  seller offerType  \\\n",
+       "0  2016-03-24 11:52:17                      Golf_3_1.6  privat   Angebot   \n",
+       "1  2016-03-24 10:58:45            A5_Sportback_2.7_Tdi  privat   Angebot   \n",
+       "2  2016-03-14 12:52:21  Jeep_Grand_Cherokee_\"Overland\"  privat   Angebot   \n",
+       "3  2016-03-17 16:54:04              GOLF_4_1_4__3TÜRER  privat   Angebot   \n",
+       "4  2016-03-31 17:25:20  Skoda_Fabia_1.4_TDI_PD_Classic  privat   Angebot   \n",
+       "\n",
+       "   price abtest vehicleType  yearOfRegistration    gearbox  powerPS  ...  \\\n",
+       "0    480   test         NaN                1993    manuell        0  ...   \n",
+       "1  18300   test       coupe                2011    manuell      190  ...   \n",
+       "2   9800   test         suv                2004  automatik      163  ...   \n",
+       "3   1500   test  kleinwagen                2001    manuell       75  ...   \n",
+       "4   3600   test  kleinwagen                2008    manuell       69  ...   \n",
+       "\n",
+       "  monthOfRegistration  fuelType       brand notRepairedDamage  \\\n",
+       "0                   0    benzin  volkswagen               NaN   \n",
+       "1                   5    diesel        audi                ja   \n",
+       "2                   8    diesel        jeep               NaN   \n",
+       "3                   6    benzin  volkswagen              nein   \n",
+       "4                   7    diesel       skoda              nein   \n",
+       "\n",
+       "           dateCreated nrOfPictures postalCode             lastSeen  \\\n",
+       "0  2016-03-24 00:00:00            0      70435  2016-04-07 03:16:57   \n",
+       "1  2016-03-24 00:00:00            0      66954  2016-04-07 01:46:50   \n",
+       "2  2016-03-14 00:00:00            0      90480  2016-04-05 12:47:46   \n",
+       "3  2016-03-17 00:00:00            0      91074  2016-03-17 17:40:17   \n",
+       "4  2016-03-31 00:00:00            0      60437  2016-04-06 10:17:21   \n",
+       "\n",
+       "   len_of_name    sum  \n",
+       "0           10    480  \n",
+       "1           20  18490  \n",
+       "2           30   9963  \n",
+       "3           18   1575  \n",
+       "4           30   3669  \n",
+       "\n",
+       "[5 rows x 22 columns]"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cars_df['sum'] = cars_df['price'] + cars_df['powerPS']\n",
+    "cars_df.head()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From ff9f58405ad3479ee02cabbc2620f9daf000c9c7 Mon Sep 17 00:00:00 2001
From: andvagapov <86461996+andvagapov@users.noreply.github.com>
Date: Fri, 25 Jun 2021 09:45:08 +0300
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@@ -0,0 +1,1227 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ФИО студента: Вагапов Андрей Андреевич\n",
+    "\n",
+    "# Номер группы: 19-ИВТ-3\n",
+    "\n",
+    "# Вариант: 6\n",
+    "\n",
+    "# Задание: Occupancy\n",
+    "\n",
+    "1.\tИзучить набор данных. Создать описание набора данных на русском языке. Описать признаки, используемые в наборе данных (включить полученные описания в отчёт).\n",
+    "2.\tУдалите дубликаты строк в наборе данных; приведите размер набора данных до и после данной операции;\n",
+    "3.\tОцените сбалансированность данных по классам (постройте гистограмму). Используйте полученную информацию при выборе метрики оценки качества классификации (PR или ROC кривая)\n",
+    "4.\tВыполните масштабирование количественных признаков; Постройте диаграммы BoxPlot для признаков до и после масштабирования. Выберите способ масштабирования (например, нормализацию или стандартизацию);\n",
+    "5.\tВыполните замену категориальных признаков; выберите и обоснуйте способ замены;\n",
+    "6.\tОцените корреляцию между признаков и удалите те признаки, которые коррелируют с наибольшим числом других (удалять признаки нужно только для линейных методов классификации);\n",
+    "7.\tЗаполните пропущенные значения в данных;\n",
+    "8.\tРешите поставленную задачу классификации в соответствии с заданием. При подборе параметров классификатора используйте метод GridSearchCV и перекрёстную проверку (изучите возможные для изменения параметры классификации). Определите схему построения многоклассового классификатора, используемую по умолчанию (опишите используемую схему кодирования, обоснуйте свой выбор). Постройте, если это возможно, многоклассовую классификацию на основе схем «один-против-всех» и «все-против-всех». Оцените точность классификации для каждой их схем. Постройте кривые PR и ROC (для каждого из классов должны быть построены отдельные кривые, а также кривые для микро и макроусреднения метрик качества). Для линейного классификатора используйте регуляризацию. \n",
+    "9.\tСравните кривые для классификаторов, указанных в задании, сделайте выводы.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 1 пункт\n",
+    "<b>Изучить набор данных. Создать описание набора данных на русском языке. Описать признаки, используемые в наборе данных</b>\n",
+    "\n",
+    "1. date time - <b>Дата-время (год - месяц - день - час : минута : секунда)</b> \n",
+    "2. Temperature - <b>Температура (по Цельсию)</b>\n",
+    "3. Relative Humidity - <b>Относительная влажность (%)</b>\n",
+    "4. Light - <b>Свет (в люксах)</b>\n",
+    "5. CO2 - <b>Углекислый газ  (в ppm)</b>\n",
+    "6. Humidity Ratio - <b>Удельная влажность</b>\n",
+    "7. Occupancy - <b>Занятость (0 - незанятый статус, 1 - занятый статус)</b>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Humidity</th>\n",
+       "      <th>Light</th>\n",
+       "      <th>CO2</th>\n",
+       "      <th>HumidityRatio</th>\n",
+       "      <th>Occupancy</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2015-02-04 17:51:00</td>\n",
+       "      <td>23.18</td>\n",
+       "      <td>27.2720</td>\n",
+       "      <td>426.0</td>\n",
+       "      <td>721.25</td>\n",
+       "      <td>0.004793</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2015-02-04 17:51:59</td>\n",
+       "      <td>23.15</td>\n",
+       "      <td>27.2675</td>\n",
+       "      <td>429.5</td>\n",
+       "      <td>714.00</td>\n",
+       "      <td>0.004783</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2015-02-04 17:53:00</td>\n",
+       "      <td>23.15</td>\n",
+       "      <td>27.2450</td>\n",
+       "      <td>426.0</td>\n",
+       "      <td>713.50</td>\n",
+       "      <td>0.004779</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2015-02-04 17:54:00</td>\n",
+       "      <td>23.15</td>\n",
+       "      <td>27.2000</td>\n",
+       "      <td>426.0</td>\n",
+       "      <td>708.25</td>\n",
+       "      <td>0.004772</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2015-02-04 17:55:00</td>\n",
+       "      <td>23.10</td>\n",
+       "      <td>27.2000</td>\n",
+       "      <td>426.0</td>\n",
+       "      <td>704.50</td>\n",
+       "      <td>0.004757</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  date  Temperature  Humidity  Light     CO2  HumidityRatio  \\\n",
+       "1  2015-02-04 17:51:00        23.18   27.2720  426.0  721.25       0.004793   \n",
+       "2  2015-02-04 17:51:59        23.15   27.2675  429.5  714.00       0.004783   \n",
+       "3  2015-02-04 17:53:00        23.15   27.2450  426.0  713.50       0.004779   \n",
+       "4  2015-02-04 17:54:00        23.15   27.2000  426.0  708.25       0.004772   \n",
+       "5  2015-02-04 17:55:00        23.10   27.2000  426.0  704.50       0.004757   \n",
+       "\n",
+       "   Occupancy  \n",
+       "1          1  \n",
+       "2          1  \n",
+       "3          1  \n",
+       "4          1  \n",
+       "5          1  "
+      ]
+     },
+     "execution_count": 155,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "import sklearn as skl\n",
+    "from sklearn import preprocessing\n",
+    "from sklearn.preprocessing import label_binarize\n",
+    "from sklearn.metrics import accuracy_score, precision_recall_curve, average_precision_score, roc_curve, auc\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "from sklearn.calibration import CalibratedClassifierCV\n",
+    "\n",
+    "DATA_TIME = 'date'\n",
+    "TEMPERATURE = 'Temperature'\n",
+    "RELATIVE_HUMIDITY = 'Humidity'\n",
+    "LIGHT = 'Light'\n",
+    "CO2 = 'CO2'\n",
+    "HUMIDITY_RATIO = 'HumidityRatio'\n",
+    "OCCUPANCY = 'Occupancy'\n",
+    "df = pd.read_csv('datatraining.txt')\n",
+    "df_pandas = pd.DataFrame(df)\n",
+    "df_pandas.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['date', 'Temperature', 'Humidity', 'Light', 'CO2', 'HumidityRatio',\n",
+       "       'Occupancy'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 156,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(8143, 7)"
+      ]
+     },
+     "execution_count": 157,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_pandas.shape\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 2 пункт\n",
+    "<b>Удалите дубликаты строк в наборе данных; приведите размер набора данных до и после данной операции</b>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "До удаления дубликатов: (8143, 7)\n",
+      "После удаления дубликатов: (8143, 7)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"До удаления дубликатов:\", df_pandas.shape)\n",
+    "df_pandas = df_pandas.drop_duplicates()\n",
+    "print(\"После удаления дубликатов:\", df_pandas.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Дубликаты не найдены "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 3 пункт\n",
+    "<b>Оцените сбалансированность данных по классам (постройте гистограмму). Используйте полученную информацию при выборе метрики оценки качества классификации (PR или ROC кривая)</b>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='Occupancy', ylabel='count'>"
+      ]
+     },
+     "execution_count": 159,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(data=df_pandas, x=OCCUPANCY)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Объем данных по каждому классу отличается. Классы несбалансированы. ROC-кривую, поскольку она дает адекватную оценку качества работы алгоритма, где классы несбалансированы. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 4 пункт\n",
+    "\n",
+    "<b>Выполните масштабирование количественных признаков; Постройте диаграммы BoxPlot для признаков до и после масштабирования. Выберите способ масштабирования</b> "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 160,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "quan_atrib = [TEMPERATURE, RELATIVE_HUMIDITY, LIGHT, CO2, HUMIDITY_RATIO]\n",
+    "sns.boxplot(data = df_pandas[quan_atrib])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<b>Способ масшатбирования - нормализация</b>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 161,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_pandas[quan_atrib] = preprocessing.MinMaxScaler().fit_transform(df_pandas[quan_atrib].values.astype(float))\n",
+    "sns.boxplot(data = df_pandas[quan_atrib])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 5 пункт\n",
+    "<b>Выполните замену категориальных признаков; выберите и обоснуйте способ замены</b>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Замена не требуется, так как категориальные признаки отсутсвуют"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 6 пункт \n",
+    "<b>Оцените корреляцию между признаков и удалите те признаки, которые коррелируют с наибольшим числом других (удалять признаки нужно только для линейных методов классификации)</b>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 162,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_corr = df_pandas.corr()\n",
+    "df_corr = df.drop('Occupancy', axis=1).corr()\n",
+    "sns.heatmap(df_corr, annot = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 7 пункт\n",
+    "<b> Заполните пропущенные значения в данных </b>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "date             8143\n",
+       "Temperature      8143\n",
+       "Humidity         8143\n",
+       "Light            8143\n",
+       "CO2              8143\n",
+       "HumidityRatio    8143\n",
+       "Occupancy        8143\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 163,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_pandas.count()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Пропусков нет"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 8 пункт\n",
+    "<b>Решите поставленную задачу классификации в соответствии с заданием. При подборе параметров классификатора используйте метод GridSearchCV и перекрёстную проверку (изучите возможные для изменения параметры классификации). Определите схему построения многоклассового классификатора, используемую по умолчанию (опишите используемую схему кодирования, обоснуйте свой выбор). Постройте, если это возможно, многоклассовую классификацию на основе схем «один-против-всех» и «все-против-всех». Оцените точность классификации для каждой их схем. Постройте кривые PR и ROC (для каждого из классов должны быть построены отдельные кривые, а также кривые для микро и макроусреднения метрик качества). Для линейного классификатора используйте регуляризацию.</b>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Humidity</th>\n",
+       "      <th>Light</th>\n",
+       "      <th>CO2</th>\n",
+       "      <th>HumidityRatio</th>\n",
+       "      <th>Occupancy</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2015-02-11 14:48:00</td>\n",
+       "      <td>21.7600</td>\n",
+       "      <td>31.133333</td>\n",
+       "      <td>437.333333</td>\n",
+       "      <td>1029.666667</td>\n",
+       "      <td>0.005021</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2015-02-11 14:49:00</td>\n",
+       "      <td>21.7900</td>\n",
+       "      <td>31.000000</td>\n",
+       "      <td>437.333333</td>\n",
+       "      <td>1000.000000</td>\n",
+       "      <td>0.005009</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2015-02-11 14:50:00</td>\n",
+       "      <td>21.7675</td>\n",
+       "      <td>31.122500</td>\n",
+       "      <td>434.000000</td>\n",
+       "      <td>1003.750000</td>\n",
+       "      <td>0.005022</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2015-02-11 14:51:00</td>\n",
+       "      <td>21.7675</td>\n",
+       "      <td>31.122500</td>\n",
+       "      <td>439.000000</td>\n",
+       "      <td>1009.500000</td>\n",
+       "      <td>0.005022</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2015-02-11 14:51:59</td>\n",
+       "      <td>21.7900</td>\n",
+       "      <td>31.133333</td>\n",
+       "      <td>437.333333</td>\n",
+       "      <td>1005.666667</td>\n",
+       "      <td>0.005030</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  date  Temperature   Humidity       Light          CO2  \\\n",
+       "1  2015-02-11 14:48:00      21.7600  31.133333  437.333333  1029.666667   \n",
+       "2  2015-02-11 14:49:00      21.7900  31.000000  437.333333  1000.000000   \n",
+       "3  2015-02-11 14:50:00      21.7675  31.122500  434.000000  1003.750000   \n",
+       "4  2015-02-11 14:51:00      21.7675  31.122500  439.000000  1009.500000   \n",
+       "5  2015-02-11 14:51:59      21.7900  31.133333  437.333333  1005.666667   \n",
+       "\n",
+       "   HumidityRatio  Occupancy  \n",
+       "1       0.005021          1  \n",
+       "2       0.005009          1  \n",
+       "3       0.005022          1  \n",
+       "4       0.005022          1  \n",
+       "5       0.005030          1  "
+      ]
+     },
+     "execution_count": 164,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_test = pd.read_csv('datatest2.txt')\n",
+    "df_test.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Нормализуем"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.boxplot(data = df_test[quan_atrib])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Humidity</th>\n",
+       "      <th>Light</th>\n",
+       "      <th>CO2</th>\n",
+       "      <th>HumidityRatio</th>\n",
+       "      <th>Occupancy</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2015-02-11 14:48:00</td>\n",
+       "      <td>0.462168</td>\n",
+       "      <td>0.525565</td>\n",
+       "      <td>0.276618</td>\n",
+       "      <td>0.342373</td>\n",
+       "      <td>0.700223</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2015-02-11 14:49:00</td>\n",
+       "      <td>0.468303</td>\n",
+       "      <td>0.518004</td>\n",
+       "      <td>0.276618</td>\n",
+       "      <td>0.323736</td>\n",
+       "      <td>0.695239</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2015-02-11 14:50:00</td>\n",
+       "      <td>0.463701</td>\n",
+       "      <td>0.524950</td>\n",
+       "      <td>0.274510</td>\n",
+       "      <td>0.326092</td>\n",
+       "      <td>0.700447</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2015-02-11 14:51:00</td>\n",
+       "      <td>0.463701</td>\n",
+       "      <td>0.524950</td>\n",
+       "      <td>0.277672</td>\n",
+       "      <td>0.329704</td>\n",
+       "      <td>0.700447</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2015-02-11 14:51:59</td>\n",
+       "      <td>0.468303</td>\n",
+       "      <td>0.525565</td>\n",
+       "      <td>0.276618</td>\n",
+       "      <td>0.327296</td>\n",
+       "      <td>0.703947</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  date  Temperature  Humidity     Light       CO2  \\\n",
+       "1  2015-02-11 14:48:00     0.462168  0.525565  0.276618  0.342373   \n",
+       "2  2015-02-11 14:49:00     0.468303  0.518004  0.276618  0.323736   \n",
+       "3  2015-02-11 14:50:00     0.463701  0.524950  0.274510  0.326092   \n",
+       "4  2015-02-11 14:51:00     0.463701  0.524950  0.277672  0.329704   \n",
+       "5  2015-02-11 14:51:59     0.468303  0.525565  0.276618  0.327296   \n",
+       "\n",
+       "   HumidityRatio  Occupancy  \n",
+       "1       0.700223          1  \n",
+       "2       0.695239          1  \n",
+       "3       0.700447          1  \n",
+       "4       0.700447          1  \n",
+       "5       0.703947          1  "
+      ]
+     },
+     "execution_count": 166,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numerical_columns = [i for i in df_test.columns[1:6] if df_test[i].dtype.name != 'object']\n",
+    "categorial_columns = [i for i in df_test.columns[1:6] if df_test[i].dtype.name == 'object']\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "scaler = MinMaxScaler()\n",
+    "df_test[numerical_columns] = scaler.fit_transform(df_test[numerical_columns])\n",
+    "df_test.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "metadata": {
+    "tags": [
+     "outputPrepend"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.boxplot(data = df_test[quan_atrib])\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(9752, 7)"
+      ]
+     },
+     "execution_count": 168,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_test.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "date             9752\n",
+       "Temperature      9752\n",
+       "Humidity         9752\n",
+       "Light            9752\n",
+       "CO2              9752\n",
+       "HumidityRatio    9752\n",
+       "Occupancy        9752\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 169,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_test.count()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": [
+     "outputPrepend"
+    ]
+   },
+   "source": [
+    "### Реализуем классификацию методом k ближайших соседй"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train, y_train = df_pandas[numerical_columns + ['Occupancy']].drop('Occupancy', axis=1), df_pandas['Occupancy']\n",
+    "x_test, y_test = df_test[numerical_columns + ['Occupancy']].drop('Occupancy', axis=1), df_test['Occupancy']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "best train score:  0.9328218726008781 | at neighbors number:  44\n",
+      "test score:  0.9487284659557014\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "knn = KNeighborsClassifier()\n",
+    "\n",
+    "#найдем оптимальные параметры для классификатора\n",
+    "#n_neighbors - количество соседей\n",
+    "knn_grid = GridSearchCV(knn, dict(n_neighbors = [x for x in range(1,50)]))\n",
+    "knn_grid.fit(x_train, y_train)\n",
+    "selected_n_neighbors = knn_grid.best_estimator_.n_neighbors\n",
+    "best_score = knn_grid.best_score_\n",
+    "print('best train score: ', best_score,'| at neighbors number: ', selected_n_neighbors)\n",
+    "\n",
+    "#создаем модель knn по нашим оптимальным данным\n",
+    "knn = KNeighborsClassifier(n_neighbors=selected_n_neighbors)\n",
+    "#тренируем ее\n",
+    "knn.fit(x_train, y_train)\n",
+    "#смотрим в каком проценте случаев классификатор дает верный результат\n",
+    "best_score  = np.mean(y_test  == knn.predict(x_test))\n",
+    "print('test score: ', best_score)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Линейный классификатор (персептрон)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "best score: 0.9766651131292015 | alpha: 0.05263157894736842 | max_iter: 3\n",
+      "test score:  0.9638022969647252\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "D:\\мо\\WPy64-3920\\python-3.9.2.amd64\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:574: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+      "  warnings.warn(\"Maximum number of iteration reached before \"\n",
+      "D:\\мо\\WPy64-3920\\python-3.9.2.amd64\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:574: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+      "  warnings.warn(\"Maximum number of iteration reached before \"\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Perceptron\n",
+    "slf = Perceptron()\n",
+    "#найдем оптимальные параметры для классификатора\n",
+    "#alfa - Константа, которая умножает член регуляризации\n",
+    "#max_iter - Максимальное количество проходов над данными обучения (иначе говоря, эпохи)\n",
+    "paramsearch = GridSearchCV(estimator=slf, param_grid=dict(alpha=np.linspace(0,1,20)[1:],max_iter=[x for x in range(1,50)]), n_jobs=-1)\n",
+    "paramsearch.fit(x_train, y_train)\n",
+    "best_score = paramsearch.best_score_\n",
+    "selected_alpha = paramsearch.best_estimator_.alpha\n",
+    "selected_max_iter = paramsearch.best_estimator_.max_iter\n",
+    "print('best score:',best_score,'| alpha:',selected_alpha, '| max_iter:', selected_max_iter)\n",
+    "\n",
+    "#создаем модель персептрона по нашим оптимальным данным\n",
+    "perceptron = skl.linear_model.Perceptron(alpha=selected_alpha,max_iter=selected_max_iter)\n",
+    "#тренируем ее\n",
+    "perceptron.fit(x_train, y_train)\n",
+    "#смотрим в каком проценте случаев классификатор дает верный результат\n",
+    "test_score  = np.mean(y_test == perceptron.predict(x_test))\n",
+    "print('test score: ', test_score)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Для построения ROC и PR кривых необходимо получить вероятности принадлежности примера к одному из классов, а затем варьировать границу выбора класса. Вероятности можно получить с помощью метода predict_proba"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "D:\\мо\\WPy64-3920\\python-3.9.2.amd64\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:574: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+      "  warnings.warn(\"Maximum number of iteration reached before \"\n",
+      "D:\\мо\\WPy64-3920\\python-3.9.2.amd64\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:574: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+      "  warnings.warn(\"Maximum number of iteration reached before \"\n",
+      "D:\\мо\\WPy64-3920\\python-3.9.2.amd64\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:574: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+      "  warnings.warn(\"Maximum number of iteration reached before \"\n",
+      "D:\\мо\\WPy64-3920\\python-3.9.2.amd64\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:574: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+      "  warnings.warn(\"Maximum number of iteration reached before \"\n",
+      "D:\\мо\\WPy64-3920\\python-3.9.2.amd64\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:574: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+      "  warnings.warn(\"Maximum number of iteration reached before \"\n",
+      "D:\\мо\\WPy64-3920\\python-3.9.2.amd64\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:574: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+      "  warnings.warn(\"Maximum number of iteration reached before \"\n",
+      "D:\\мо\\WPy64-3920\\python-3.9.2.amd64\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:574: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+      "  warnings.warn(\"Maximum number of iteration reached before \"\n",
+      "D:\\мо\\WPy64-3920\\python-3.9.2.amd64\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:574: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+      "  warnings.warn(\"Maximum number of iteration reached before \"\n",
+      "D:\\мо\\WPy64-3920\\python-3.9.2.amd64\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:574: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+      "  warnings.warn(\"Maximum number of iteration reached before \"\n",
+      "D:\\мо\\WPy64-3920\\python-3.9.2.amd64\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:574: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+      "  warnings.warn(\"Maximum number of iteration reached before \"\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 720x576 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.calibration import CalibratedClassifierCV\n",
+    "plt.figure(figsize=(10,8))\n",
+    "y_test_invers = []\n",
+    "for i, y in enumerate(y_test.reset_index(drop=True)):\n",
+    "    y_test_invers.append(1-y)\n",
+    "    \n",
+    "probas_knn = knn.fit(x_train, y_train).predict_proba(x_test)\n",
+    "\n",
+    "#Откалибруем линейный классификатор\n",
+    "#cv - кратность перекрестной проверки\n",
+    "#method - метод калибровки, так как у нас записей больше 1000 то рекомендуется использовать isotonic\n",
+    "#         если же данных << 1000 то sigmoid\n",
+    "clf_isotonic = CalibratedClassifierCV(perceptron, cv=10, method='isotonic')\n",
+    "clf_isotonic.fit(x_train, y_train)\n",
+    "probas_slf = clf_isotonic.predict_proba(x_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### PR кривые"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# K соседей\n",
+    "precision, recall, thresholds = precision_recall_curve(y_test_invers, probas_knn[:,0])\n",
+    "aps_knn0 = average_precision_score(y_test_invers,probas_knn[:,0])\n",
+    "plt.plot(recall, precision, label='KNeighborsClassifier PR (ср. тч. прог. = %f)' % (aps_knn0)) \n",
+    "\n",
+    "# Персептрон\n",
+    "precision, recall, thresholds = precision_recall_curve(y_test_invers, probas_slf[:,0])\n",
+    "aps_ptron0 = average_precision_score(y_test_invers,probas_slf[:,0])\n",
+    "plt.plot(recall, precision, label='PerceptronClassifier PR (ср. тч. прог. = %f)' % (aps_ptron0)) \n",
+    "\n",
+    "# вывод графика\n",
+    "plt.xlabel('Recall (полнота)')\n",
+    "plt.ylabel('Precision (точность)')\n",
+    "plt.xlim([0.0, 1.0])\n",
+    "plt.ylim([0.0, 1.0])\n",
+    "plt.legend(loc=4, fontsize='medium')\n",
+    "plt.title('Occupancy = 0')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,8))\n",
+    "\n",
+    "# K соседей\n",
+    "precision, recall, thresholds = precision_recall_curve(y_test, probas_knn[:,1])\n",
+    "aps_knn1 = average_precision_score(y_test,probas_knn[:,1])\n",
+    "plt.plot(recall, precision, label='KNeighborsClassifier PR (ср. тч. прог.= %f)' % (aps_knn1)) \n",
+    "\n",
+    "# Персептрон\n",
+    "precision, recall, thresholds = precision_recall_curve(y_test, probas_slf[:,1])\n",
+    "aps_ptron1 = average_precision_score(y_test,probas_slf[:,1])\n",
+    "plt.plot(recall, precision, label='PerceptronClassifier PR (ср. тч. прог. = %f)' % (aps_ptron1)) \n",
+    "\n",
+    "# вывод графика\n",
+    "plt.xlabel('Recall (полнота)')\n",
+    "plt.ylabel('Precision (точность)')\n",
+    "plt.xlim([0.0, 1.0])\n",
+    "plt.ylim([0.0, 1.0])\n",
+    "plt.legend(loc=4, fontsize='medium')\n",
+    "plt.title('Occupancy = 1')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### ROC графики"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#TPR тоже самое что и Precision, те точноть\n",
+    "plt.figure(figsize=(10,8))\n",
+    "\n",
+    "# K соседей\n",
+    "tpr, fpr, thresholds = roc_curve(y_test, probas_knn[:,0])\n",
+    "roc_auc  = auc(fpr, tpr)\n",
+    "plt.plot(fpr, tpr, label='KNeighborsClassifier ROC (ср. тч. прог. = %f)' % (roc_auc)) \n",
+    "\n",
+    "# Персептрон\n",
+    "tpr, fpr, thresholds = roc_curve(y_test, probas_slf[:,0])\n",
+    "roc_auc  = auc(fpr, tpr)\n",
+    "plt.plot(fpr, tpr, label='PerceptronClassifier ROC (ср. тч. прог. = %f)' % (roc_auc))   \n",
+    "\n",
+    "# вывод графика\n",
+    "plt.plot([0, 1], [0, 1], 'k--')\n",
+    "plt.xlim([0.0, 1.0])\n",
+    "plt.ylim([0.0, 1.0])\n",
+    "plt.xlabel('False Positive Rate')\n",
+    "plt.ylabel('True Positive Rate (Precision / Точность)')\n",
+    "plt.legend(loc=4, fontsize='medium')\n",
+    "plt.title('Occupancy = 0')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,8))\n",
+    "\n",
+    "# K соседей\n",
+    "fpr, tpr, thresholds = roc_curve(y_test, probas_knn[:,1])\n",
+    "roc_auc  = auc(fpr, tpr)\n",
+    "plt.plot(fpr, tpr, label='KNeighborsClassifier ROC (ср. тч. прог. = %f)' % (roc_auc))\n",
+    "\n",
+    "# PerceptronClassifier\n",
+    "fpr, tpr, thresholds = roc_curve(y_test, probas_slf[:,1])\n",
+    "roc_auc  = auc(fpr, tpr)\n",
+    "plt.plot(fpr, tpr, label='PerceptronClassifier ROC (ср. тч. прог. = %f)' % (roc_auc)) \n",
+    "\n",
+    "# вывод графика\n",
+    "plt.plot([0, 1], [0, 1], 'k--')\n",
+    "plt.xlim([0.0, 1.0])\n",
+    "plt.ylim([0.0, 1.0])\n",
+    "plt.xlabel('False Positive Rate')\n",
+    "plt.ylabel('True Positive Rate (Precision / Точность)')\n",
+    "plt.legend(loc=4, fontsize='medium')\n",
+    "plt.title('Occupancy = 1')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 9.Сравните кривые для классификаторов, указанных в задании, сделайте выводы.\n",
+    "Данные классификаторы решают задачу классификации с высокой вероятностью, что является хорошим результатом, т.к. классов всего 2. Персептрон дает лучший результат, благодаря хорошо подобранным параметрам и многослойной сетке. Поэтому задача классификации была решена корректно."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From f0b4531acd074e959d77439314d4e8a1ff8be9e2 Mon Sep 17 00:00:00 2001
From: andvagapov <86461996+andvagapov@users.noreply.github.com>
Date: Fri, 25 Jun 2021 09:45:35 +0300
Subject: [PATCH 4/4] =?UTF-8?q?=D0=92=D1=8B=D0=BF=D0=BE=D0=BB=D0=BD=D0=B5?=
 =?UTF-8?q?=D0=BD=D0=B0=204=20=D0=BB=D0=B0=D0=B1=D0=B0?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .../lab4.ipynb"                               | 1869 +++++++++++++++++
 1 file changed, 1869 insertions(+)
 create mode 100644 "ml/lb4/\320\222\320\260\320\263\320\260\320\277\320\276\320\262/lab4.ipynb"

diff --git "a/ml/lb4/\320\222\320\260\320\263\320\260\320\277\320\276\320\262/lab4.ipynb" "b/ml/lb4/\320\222\320\260\320\263\320\260\320\277\320\276\320\262/lab4.ipynb"
new file mode 100644
index 0000000..5fd46ed
--- /dev/null
+++ "b/ml/lb4/\320\222\320\260\320\263\320\260\320\277\320\276\320\262/lab4.ipynb"
@@ -0,0 +1,1869 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Лабораторная работа №4. Регрессионный анализ данных"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Вариант 10\n",
+    "## Вагапов Андрей 19-ивт-3\n",
+    "### Цель лабораторной работы – получение навыков работы с методами регрессии\n",
+    "Задания:\n",
+    "\n",
+    "1.\tИзучить набор данных. Создать описание набора данных на русском языке. Описать признаки, используемые в наборе данных (включить полученные описания в отчёт).\n",
+    "2.\tУдалите дубликаты строк в наборе данных; приведите размер набора данных до и после данной операции;\n",
+    "3.\tВыполните масштабирование количественных признаков; Постройте диаграммы BoxPlot для признаков до и после масштабирования. Выберите способ масштабирования (например, нормализацию или стандартизацию);\n",
+    "4.\tВыполните замену категориальных признаков; выберите и обоснуйте способ замены;\n",
+    "5.\tОцените корреляцию между признаков и удалите те признаки, которые коррелируют с наибольшим числом других;\n",
+    "6.\tЗаполните пропущенные значения в данных;\n",
+    "7.\tРешите поставленную задачу регрессии в соответствии с заданием. При подборе параметров метода принятия решения (метода регрессии) используйте перекрёстную проверку (изучите возможные для изменения параметры метода регрессии).  Вычислите точность решения задачи, вычислив разницу между реальным значением и предсказанным. Вычислите коэффициент корреляции (Пирсона, Спирмена) между реальным значением и предсказанным с учётом p-value. \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import sklearn as sk\n",
+    "import seaborn as sn\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.stats import *\n",
+    "from  sklearn import linear_model\n",
+    "\n",
+    "from pylab import rcParams\n",
+    "from scipy import interp\n",
+    "from sklearn.preprocessing import MinMaxScaler, LabelEncoder\n",
+    "from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV\n",
+    "\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>date</th>\n",
+       "      <th>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>sqft_lot</th>\n",
+       "      <th>floors</th>\n",
+       "      <th>waterfront</th>\n",
+       "      <th>view</th>\n",
+       "      <th>...</th>\n",
+       "      <th>grade</th>\n",
+       "      <th>sqft_above</th>\n",
+       "      <th>sqft_basement</th>\n",
+       "      <th>yr_built</th>\n",
+       "      <th>yr_renovated</th>\n",
+       "      <th>zipcode</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "      <th>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7129300520</td>\n",
+       "      <td>20141013T000000</td>\n",
+       "      <td>221900.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>5650</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1955</td>\n",
+       "      <td>0</td>\n",
+       "      <td>98178</td>\n",
+       "      <td>47.5112</td>\n",
+       "      <td>-122.257</td>\n",
+       "      <td>1340</td>\n",
+       "      <td>5650</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>6414100192</td>\n",
+       "      <td>20141209T000000</td>\n",
+       "      <td>538000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>2570</td>\n",
+       "      <td>7242</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2170</td>\n",
+       "      <td>400</td>\n",
+       "      <td>1951</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>98125</td>\n",
+       "      <td>47.7210</td>\n",
+       "      <td>-122.319</td>\n",
+       "      <td>1690</td>\n",
+       "      <td>7639</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5631500400</td>\n",
+       "      <td>20150225T000000</td>\n",
+       "      <td>180000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>770</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6</td>\n",
+       "      <td>770</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1933</td>\n",
+       "      <td>0</td>\n",
+       "      <td>98028</td>\n",
+       "      <td>47.7379</td>\n",
+       "      <td>-122.233</td>\n",
+       "      <td>2720</td>\n",
+       "      <td>8062</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2487200875</td>\n",
+       "      <td>20141209T000000</td>\n",
+       "      <td>604000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>1960</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1050</td>\n",
+       "      <td>910</td>\n",
+       "      <td>1965</td>\n",
+       "      <td>0</td>\n",
+       "      <td>98136</td>\n",
+       "      <td>47.5208</td>\n",
+       "      <td>-122.393</td>\n",
+       "      <td>1360</td>\n",
+       "      <td>5000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1954400510</td>\n",
+       "      <td>20150218T000000</td>\n",
+       "      <td>510000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>8080</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1987</td>\n",
+       "      <td>0</td>\n",
+       "      <td>98074</td>\n",
+       "      <td>47.6168</td>\n",
+       "      <td>-122.045</td>\n",
+       "      <td>1800</td>\n",
+       "      <td>7503</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21608</th>\n",
+       "      <td>263000018</td>\n",
+       "      <td>20140521T000000</td>\n",
+       "      <td>360000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>1530</td>\n",
+       "      <td>1131</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1530</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>0</td>\n",
+       "      <td>98103</td>\n",
+       "      <td>47.6993</td>\n",
+       "      <td>-122.346</td>\n",
+       "      <td>1530</td>\n",
+       "      <td>1509</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21609</th>\n",
+       "      <td>6600060120</td>\n",
+       "      <td>20150223T000000</td>\n",
+       "      <td>400000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>2310</td>\n",
+       "      <td>5813</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2310</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>0</td>\n",
+       "      <td>98146</td>\n",
+       "      <td>47.5107</td>\n",
+       "      <td>-122.362</td>\n",
+       "      <td>1830</td>\n",
+       "      <td>7200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21610</th>\n",
+       "      <td>1523300141</td>\n",
+       "      <td>20140623T000000</td>\n",
+       "      <td>402101.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>1350</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>0</td>\n",
+       "      <td>98144</td>\n",
+       "      <td>47.5944</td>\n",
+       "      <td>-122.299</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>2007</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21611</th>\n",
+       "      <td>291310100</td>\n",
+       "      <td>20150116T000000</td>\n",
+       "      <td>400000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>1600</td>\n",
+       "      <td>2388</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1600</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>0</td>\n",
+       "      <td>98027</td>\n",
+       "      <td>47.5345</td>\n",
+       "      <td>-122.069</td>\n",
+       "      <td>1410</td>\n",
+       "      <td>1287</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21612</th>\n",
+       "      <td>1523300157</td>\n",
+       "      <td>20141015T000000</td>\n",
+       "      <td>325000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>1076</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>0</td>\n",
+       "      <td>98144</td>\n",
+       "      <td>47.5941</td>\n",
+       "      <td>-122.299</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>1357</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>21613 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               id             date     price  bedrooms  bathrooms  \\\n",
+       "0      7129300520  20141013T000000  221900.0         3       1.00   \n",
+       "1      6414100192  20141209T000000  538000.0         3       2.25   \n",
+       "2      5631500400  20150225T000000  180000.0         2       1.00   \n",
+       "3      2487200875  20141209T000000  604000.0         4       3.00   \n",
+       "4      1954400510  20150218T000000  510000.0         3       2.00   \n",
+       "...           ...              ...       ...       ...        ...   \n",
+       "21608   263000018  20140521T000000  360000.0         3       2.50   \n",
+       "21609  6600060120  20150223T000000  400000.0         4       2.50   \n",
+       "21610  1523300141  20140623T000000  402101.0         2       0.75   \n",
+       "21611   291310100  20150116T000000  400000.0         3       2.50   \n",
+       "21612  1523300157  20141015T000000  325000.0         2       0.75   \n",
+       "\n",
+       "       sqft_living  sqft_lot  floors  waterfront  view  ...  grade  \\\n",
+       "0             1180      5650     1.0           0     0  ...      7   \n",
+       "1             2570      7242     2.0           0     0  ...      7   \n",
+       "2              770     10000     1.0           0     0  ...      6   \n",
+       "3             1960      5000     1.0           0     0  ...      7   \n",
+       "4             1680      8080     1.0           0     0  ...      8   \n",
+       "...            ...       ...     ...         ...   ...  ...    ...   \n",
+       "21608         1530      1131     3.0           0     0  ...      8   \n",
+       "21609         2310      5813     2.0           0     0  ...      8   \n",
+       "21610         1020      1350     2.0           0     0  ...      7   \n",
+       "21611         1600      2388     2.0           0     0  ...      8   \n",
+       "21612         1020      1076     2.0           0     0  ...      7   \n",
+       "\n",
+       "       sqft_above  sqft_basement  yr_built  yr_renovated  zipcode      lat  \\\n",
+       "0            1180              0      1955             0    98178  47.5112   \n",
+       "1            2170            400      1951          1991    98125  47.7210   \n",
+       "2             770              0      1933             0    98028  47.7379   \n",
+       "3            1050            910      1965             0    98136  47.5208   \n",
+       "4            1680              0      1987             0    98074  47.6168   \n",
+       "...           ...            ...       ...           ...      ...      ...   \n",
+       "21608        1530              0      2009             0    98103  47.6993   \n",
+       "21609        2310              0      2014             0    98146  47.5107   \n",
+       "21610        1020              0      2009             0    98144  47.5944   \n",
+       "21611        1600              0      2004             0    98027  47.5345   \n",
+       "21612        1020              0      2008             0    98144  47.5941   \n",
+       "\n",
+       "          long  sqft_living15  sqft_lot15  \n",
+       "0     -122.257           1340        5650  \n",
+       "1     -122.319           1690        7639  \n",
+       "2     -122.233           2720        8062  \n",
+       "3     -122.393           1360        5000  \n",
+       "4     -122.045           1800        7503  \n",
+       "...        ...            ...         ...  \n",
+       "21608 -122.346           1530        1509  \n",
+       "21609 -122.362           1830        7200  \n",
+       "21610 -122.299           1020        2007  \n",
+       "21611 -122.069           1410        1287  \n",
+       "21612 -122.299           1020        1357  \n",
+       "\n",
+       "[21613 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "house = pd.read_csv(\"kc_house_data.csv\")\n",
+    "house"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Задание 1\n",
+    "##### Изучить набор данных. Создать описание набора данных на русском языке. Описать признаки, используемые в наборе данных (включить полученные описания в отчёт)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Этот набор данных содержит цены продажи домов в округе Кинг, включая Сиэтл. Он включает в себя дома, проданные в период с мая 2014 по май 2015 года.\n",
+    "\n",
+    "Это отличный набор данных для оценки простых регрессионных моделей.\n",
+    "\n",
+    "    id - Уникальный идентификатор для каждого проданного дома\n",
+    "    дата - Дата продажи дома\n",
+    "    цена - Цена каждого проданного дома\n",
+    "    спальни - Количество спален\n",
+    "    ванные комнаты - Количество ванных комнат, где .5 приходится на комнату с туалетом, но без душа\n",
+    "    sqft_living - Квадратные метры квартир внутренняя жилая площадь\n",
+    "    sqft_lot - Квадратные метры земельных площадей\n",
+    "    этажи - Количество этажей\n",
+    "    прибрежный - фиктивная переменная, находится ли квартира была с видом на набережную или нет\n",
+    "    вид - индекс от 0 до 4 насколько хорош тот или иной вид имущества\n",
+    "    состояние - индекс от 1 до 5 при условии, квартиры,\n",
+    "    класс - индекс от 1 до 13, где 1-3 дотягивает строительства и дизайна, 7 имеет средний уровень проектирования и строительства, и 11-13 имеют высокий уровень качества строительства и дизайна.\n",
+    "    sqft_above - Площадь внутреннего жилого пространства, находящегося над уровнем земли\n",
+    "    sqft_basement - Площадь внутреннего жилого пространства, которое находится ниже уровня\n",
+    "    земли yr_built - Год, когда дом был первоначально построен\n",
+    "    yr_renovated - Год последней реконструкции дома\n",
+    "    zipcode - Какая площадь zipcode находится в доме\n",
+    "    lat - Lattitude\n",
+    "    long - Longitude\n",
+    "    sqft_living15 - Площадь внутреннего жилого пространства для ближайших 15 соседей\n",
+    "    sqft_lot15 - Площадь земельных участков ближайших 15 соседей"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Задание 2\n",
+    "##### Удалите дубликаты строк в наборе данных; приведите размер набора данных до и после данной операции;"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of rows before deleting clones:  21613\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Number of rows before deleting clones: \", house.shape[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "house.drop_duplicates(keep = 'first', inplace = True) #Убираем дубликаты"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of rows after deleting clones:  21613\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Number of rows after deleting clones: \", house.shape[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Задание 3\n",
+    "##### Выполните масштабирование количественных признаков; Постройте диаграммы BoxPlot для признаков до и после масштабирования. Выберите способ масштабирования (например, нормализацию или стандартизацию);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rcParams[\"figure.figsize\"]=10, 7"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "        18, 19, 20]),\n",
+       " [Text(1, 0, 'id'),\n",
+       "  Text(2, 0, 'price'),\n",
+       "  Text(3, 0, 'bedrooms'),\n",
+       "  Text(4, 0, 'bathrooms'),\n",
+       "  Text(5, 0, 'sqft_living'),\n",
+       "  Text(6, 0, 'sqft_lot'),\n",
+       "  Text(7, 0, 'floors'),\n",
+       "  Text(8, 0, 'waterfront'),\n",
+       "  Text(9, 0, 'view'),\n",
+       "  Text(10, 0, 'condition'),\n",
+       "  Text(11, 0, 'grade'),\n",
+       "  Text(12, 0, 'sqft_above'),\n",
+       "  Text(13, 0, 'sqft_basement'),\n",
+       "  Text(14, 0, 'yr_built'),\n",
+       "  Text(15, 0, 'yr_renovated'),\n",
+       "  Text(16, 0, 'zipcode'),\n",
+       "  Text(17, 0, 'lat'),\n",
+       "  Text(18, 0, 'long'),\n",
+       "  Text(19, 0, 'sqft_living15'),\n",
+       "  Text(20, 0, 'sqft_lot15')])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "house.boxplot()\n",
+    "plt.xticks(rotation=60)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['price',\n",
+       " 'bedrooms',\n",
+       " 'bathrooms',\n",
+       " 'sqft_living',\n",
+       " 'sqft_lot',\n",
+       " 'floors',\n",
+       " 'waterfront',\n",
+       " 'view',\n",
+       " 'condition',\n",
+       " 'grade',\n",
+       " 'sqft_above',\n",
+       " 'sqft_basement',\n",
+       " 'yr_built',\n",
+       " 'yr_renovated',\n",
+       " 'zipcode',\n",
+       " 'lat',\n",
+       " 'long',\n",
+       " 'sqft_living15',\n",
+       " 'sqft_lot15']"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numerical = []\n",
+    "for i in house.columns:\n",
+    "    if (house[i].dtype == np.float64 or house[i].dtype == np.int64):\n",
+    "        numerical.append(house[i].name)\n",
+    "numerical = numerical[1:]\n",
+    "numerical"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sn.boxplot(data = house[numerical], width=1, orient = 'h')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "scaler = MinMaxScaler()\n",
+    "house[numerical] = scaler.fit_transform(house[numerical].values.astype(float)) # Нормализация\n",
+    "sn.boxplot(data = house[numerical], width=1, orient = 'h')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Задание 4\n",
+    "##### Выполните замену категориальных признаков;"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['id', 'date']"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "categorical = ['id']\n",
+    "for i in house.columns:\n",
+    "    if (house[i].dtype != np.float64 and house[i].dtype != np.int64):\n",
+    "        categorical.append(house[i].name)\n",
+    "categorical"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>date</th>\n",
+       "      <th>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>sqft_lot</th>\n",
+       "      <th>floors</th>\n",
+       "      <th>waterfront</th>\n",
+       "      <th>view</th>\n",
+       "      <th>...</th>\n",
+       "      <th>grade</th>\n",
+       "      <th>sqft_above</th>\n",
+       "      <th>sqft_basement</th>\n",
+       "      <th>yr_built</th>\n",
+       "      <th>yr_renovated</th>\n",
+       "      <th>zipcode</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "      <th>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>15339</td>\n",
+       "      <td>164</td>\n",
+       "      <td>0.019266</td>\n",
+       "      <td>0.090909</td>\n",
+       "      <td>0.12500</td>\n",
+       "      <td>0.067170</td>\n",
+       "      <td>0.003108</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.097588</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.478261</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.893939</td>\n",
+       "      <td>0.571498</td>\n",
+       "      <td>0.217608</td>\n",
+       "      <td>0.161934</td>\n",
+       "      <td>0.005742</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>14466</td>\n",
+       "      <td>220</td>\n",
+       "      <td>0.060721</td>\n",
+       "      <td>0.090909</td>\n",
+       "      <td>0.28125</td>\n",
+       "      <td>0.172075</td>\n",
+       "      <td>0.004072</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.206140</td>\n",
+       "      <td>0.082988</td>\n",
+       "      <td>0.443478</td>\n",
+       "      <td>0.988089</td>\n",
+       "      <td>0.626263</td>\n",
+       "      <td>0.908959</td>\n",
+       "      <td>0.166113</td>\n",
+       "      <td>0.222165</td>\n",
+       "      <td>0.008027</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>13503</td>\n",
+       "      <td>290</td>\n",
+       "      <td>0.013770</td>\n",
+       "      <td>0.060606</td>\n",
+       "      <td>0.12500</td>\n",
+       "      <td>0.036226</td>\n",
+       "      <td>0.005743</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.416667</td>\n",
+       "      <td>0.052632</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.286957</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.136364</td>\n",
+       "      <td>0.936143</td>\n",
+       "      <td>0.237542</td>\n",
+       "      <td>0.399415</td>\n",
+       "      <td>0.008513</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>6371</td>\n",
+       "      <td>220</td>\n",
+       "      <td>0.069377</td>\n",
+       "      <td>0.121212</td>\n",
+       "      <td>0.37500</td>\n",
+       "      <td>0.126038</td>\n",
+       "      <td>0.002714</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.083333</td>\n",
+       "      <td>0.188797</td>\n",
+       "      <td>0.565217</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.681818</td>\n",
+       "      <td>0.586939</td>\n",
+       "      <td>0.104651</td>\n",
+       "      <td>0.165376</td>\n",
+       "      <td>0.004996</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4955</td>\n",
+       "      <td>283</td>\n",
+       "      <td>0.057049</td>\n",
+       "      <td>0.090909</td>\n",
+       "      <td>0.25000</td>\n",
+       "      <td>0.104906</td>\n",
+       "      <td>0.004579</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.583333</td>\n",
+       "      <td>0.152412</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.756522</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.368687</td>\n",
+       "      <td>0.741354</td>\n",
+       "      <td>0.393688</td>\n",
+       "      <td>0.241094</td>\n",
+       "      <td>0.007871</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21608</th>\n",
+       "      <td>574</td>\n",
+       "      <td>19</td>\n",
+       "      <td>0.037377</td>\n",
+       "      <td>0.090909</td>\n",
+       "      <td>0.31250</td>\n",
+       "      <td>0.093585</td>\n",
+       "      <td>0.000370</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.583333</td>\n",
+       "      <td>0.135965</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.947826</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.515152</td>\n",
+       "      <td>0.874055</td>\n",
+       "      <td>0.143688</td>\n",
+       "      <td>0.194631</td>\n",
+       "      <td>0.000986</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21609</th>\n",
+       "      <td>14564</td>\n",
+       "      <td>288</td>\n",
+       "      <td>0.042623</td>\n",
+       "      <td>0.121212</td>\n",
+       "      <td>0.31250</td>\n",
+       "      <td>0.152453</td>\n",
+       "      <td>0.003206</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.583333</td>\n",
+       "      <td>0.221491</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.991304</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.732323</td>\n",
+       "      <td>0.570693</td>\n",
+       "      <td>0.130399</td>\n",
+       "      <td>0.246257</td>\n",
+       "      <td>0.007523</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21610</th>\n",
+       "      <td>3621</td>\n",
+       "      <td>52</td>\n",
+       "      <td>0.042898</td>\n",
+       "      <td>0.060606</td>\n",
+       "      <td>0.09375</td>\n",
+       "      <td>0.055094</td>\n",
+       "      <td>0.000503</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.080044</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.947826</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.722222</td>\n",
+       "      <td>0.705324</td>\n",
+       "      <td>0.182724</td>\n",
+       "      <td>0.106866</td>\n",
+       "      <td>0.001558</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21611</th>\n",
+       "      <td>629</td>\n",
+       "      <td>252</td>\n",
+       "      <td>0.042623</td>\n",
+       "      <td>0.090909</td>\n",
+       "      <td>0.31250</td>\n",
+       "      <td>0.098868</td>\n",
+       "      <td>0.001132</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.583333</td>\n",
+       "      <td>0.143640</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.904348</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.131313</td>\n",
+       "      <td>0.608975</td>\n",
+       "      <td>0.373754</td>\n",
+       "      <td>0.173980</td>\n",
+       "      <td>0.000731</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21612</th>\n",
+       "      <td>3622</td>\n",
+       "      <td>166</td>\n",
+       "      <td>0.032787</td>\n",
+       "      <td>0.060606</td>\n",
+       "      <td>0.09375</td>\n",
+       "      <td>0.055094</td>\n",
+       "      <td>0.000337</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.080044</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.939130</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.722222</td>\n",
+       "      <td>0.704842</td>\n",
+       "      <td>0.182724</td>\n",
+       "      <td>0.106866</td>\n",
+       "      <td>0.000811</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>21613 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          id  date     price  bedrooms  bathrooms  sqft_living  sqft_lot  \\\n",
+       "0      15339   164  0.019266  0.090909    0.12500     0.067170  0.003108   \n",
+       "1      14466   220  0.060721  0.090909    0.28125     0.172075  0.004072   \n",
+       "2      13503   290  0.013770  0.060606    0.12500     0.036226  0.005743   \n",
+       "3       6371   220  0.069377  0.121212    0.37500     0.126038  0.002714   \n",
+       "4       4955   283  0.057049  0.090909    0.25000     0.104906  0.004579   \n",
+       "...      ...   ...       ...       ...        ...          ...       ...   \n",
+       "21608    574    19  0.037377  0.090909    0.31250     0.093585  0.000370   \n",
+       "21609  14564   288  0.042623  0.121212    0.31250     0.152453  0.003206   \n",
+       "21610   3621    52  0.042898  0.060606    0.09375     0.055094  0.000503   \n",
+       "21611    629   252  0.042623  0.090909    0.31250     0.098868  0.001132   \n",
+       "21612   3622   166  0.032787  0.060606    0.09375     0.055094  0.000337   \n",
+       "\n",
+       "       floors  waterfront  view  ...     grade  sqft_above  sqft_basement  \\\n",
+       "0         0.0         0.0   0.0  ...  0.500000    0.097588       0.000000   \n",
+       "1         0.4         0.0   0.0  ...  0.500000    0.206140       0.082988   \n",
+       "2         0.0         0.0   0.0  ...  0.416667    0.052632       0.000000   \n",
+       "3         0.0         0.0   0.0  ...  0.500000    0.083333       0.188797   \n",
+       "4         0.0         0.0   0.0  ...  0.583333    0.152412       0.000000   \n",
+       "...       ...         ...   ...  ...       ...         ...            ...   \n",
+       "21608     0.8         0.0   0.0  ...  0.583333    0.135965       0.000000   \n",
+       "21609     0.4         0.0   0.0  ...  0.583333    0.221491       0.000000   \n",
+       "21610     0.4         0.0   0.0  ...  0.500000    0.080044       0.000000   \n",
+       "21611     0.4         0.0   0.0  ...  0.583333    0.143640       0.000000   \n",
+       "21612     0.4         0.0   0.0  ...  0.500000    0.080044       0.000000   \n",
+       "\n",
+       "       yr_built  yr_renovated   zipcode       lat      long  sqft_living15  \\\n",
+       "0      0.478261      0.000000  0.893939  0.571498  0.217608       0.161934   \n",
+       "1      0.443478      0.988089  0.626263  0.908959  0.166113       0.222165   \n",
+       "2      0.286957      0.000000  0.136364  0.936143  0.237542       0.399415   \n",
+       "3      0.565217      0.000000  0.681818  0.586939  0.104651       0.165376   \n",
+       "4      0.756522      0.000000  0.368687  0.741354  0.393688       0.241094   \n",
+       "...         ...           ...       ...       ...       ...            ...   \n",
+       "21608  0.947826      0.000000  0.515152  0.874055  0.143688       0.194631   \n",
+       "21609  0.991304      0.000000  0.732323  0.570693  0.130399       0.246257   \n",
+       "21610  0.947826      0.000000  0.722222  0.705324  0.182724       0.106866   \n",
+       "21611  0.904348      0.000000  0.131313  0.608975  0.373754       0.173980   \n",
+       "21612  0.939130      0.000000  0.722222  0.704842  0.182724       0.106866   \n",
+       "\n",
+       "       sqft_lot15  \n",
+       "0        0.005742  \n",
+       "1        0.008027  \n",
+       "2        0.008513  \n",
+       "3        0.004996  \n",
+       "4        0.007871  \n",
+       "...           ...  \n",
+       "21608    0.000986  \n",
+       "21609    0.007523  \n",
+       "21610    0.001558  \n",
+       "21611    0.000731  \n",
+       "21612    0.000811  \n",
+       "\n",
+       "[21613 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "encoder = LabelEncoder()\n",
+    "for i in categorical:\n",
+    "    house[i] = encoder.fit_transform(house[i])\n",
+    "house"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Задание 5\n",
+    "##### Оцените корреляцию между признаков и удалите те признаки, которые коррелируют с наибольшим числом других;"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "id              -0.016534\n",
+       "date            -0.004649\n",
+       "price            1.000000\n",
+       "bedrooms         0.308350\n",
+       "bathrooms        0.525138\n",
+       "sqft_living      0.702035\n",
+       "sqft_lot         0.089661\n",
+       "floors           0.256794\n",
+       "waterfront       0.266369\n",
+       "view             0.397293\n",
+       "condition        0.036362\n",
+       "grade            0.667434\n",
+       "sqft_above       0.605567\n",
+       "sqft_basement    0.323816\n",
+       "yr_built         0.054012\n",
+       "yr_renovated     0.126434\n",
+       "zipcode         -0.053203\n",
+       "lat              0.307003\n",
+       "long             0.021626\n",
+       "sqft_living15    0.585379\n",
+       "sqft_lot15       0.082447\n",
+       "Name: price, dtype: float64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sn.heatmap(house.corr())\n",
+    "house.corr()['price']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "id                            : weak corr!!!\n",
+      "date                          : weak corr!!!\n",
+      "price                         :   1.000000\n",
+      "bedrooms                      :   0.308350\n",
+      "bathrooms                     :   0.525138\n",
+      "sqft_living                   :   0.702035\n",
+      "sqft_lot                      : weak corr!!!\n",
+      "floors                        :   0.256794\n",
+      "waterfront                    :   0.266369\n",
+      "view                          :   0.397293\n",
+      "condition                     : weak corr!!!\n",
+      "grade                         :   0.667434\n",
+      "sqft_above                    :   0.605567\n",
+      "sqft_basement                 :   0.323816\n",
+      "yr_built                      : weak corr!!!\n",
+      "yr_renovated                  : weak corr!!!\n",
+      "zipcode                       : weak corr!!!\n",
+      "lat                           :   0.307003\n",
+      "long                          : weak corr!!!\n",
+      "sqft_living15                 :   0.585379\n",
+      "sqft_lot15                    : weak corr!!!\n"
+     ]
+    }
+   ],
+   "source": [
+    "for name, val in zip(house.columns, house.corr()['price']):\n",
+    "    print(f\"{name:<30}: \", end='')\n",
+    "    print(f'{val:>10.6f}') if abs(val) > 0.25 else print('weak corr!!!')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "id                            :  -0.016534\n",
+      "date                          :  -0.004649\n",
+      "price                         : strong corr!!!\n",
+      "bedrooms                      : strong corr!!!\n",
+      "bathrooms                     : strong corr!!!\n",
+      "sqft_living                   : strong corr!!!\n",
+      "sqft_lot                      :   0.089661\n",
+      "floors                        : strong corr!!!\n",
+      "waterfront                    : strong corr!!!\n",
+      "view                          : strong corr!!!\n",
+      "condition                     :   0.036362\n",
+      "grade                         : strong corr!!!\n",
+      "sqft_above                    : strong corr!!!\n",
+      "sqft_basement                 : strong corr!!!\n",
+      "yr_built                      :   0.054012\n",
+      "yr_renovated                  :   0.126434\n",
+      "zipcode                       :  -0.053203\n",
+      "lat                           : strong corr!!!\n",
+      "long                          :   0.021626\n",
+      "sqft_living15                 : strong corr!!!\n",
+      "sqft_lot15                    :   0.082447\n",
+      "['sqft_lot', 'condition', 'yr_built', 'yr_renovated', 'zipcode', 'long', 'sqft_lot15']\n"
+     ]
+    }
+   ],
+   "source": [
+    "cols = []\n",
+    "for name, val in zip(house.columns, house.corr()['price']):\n",
+    "    print(f\"{name:<30}: \", end='')\n",
+    "    print(f'{val:>10.6f}') if abs(val) < 0.25 else print('strong corr!!!')\n",
+    "    \n",
+    "    cols.append(name) if abs(val) < 0.25 else \"\"\n",
+    "cols=cols[2:]\n",
+    "print(cols)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>date</th>\n",
+       "      <th>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>floors</th>\n",
+       "      <th>waterfront</th>\n",
+       "      <th>view</th>\n",
+       "      <th>grade</th>\n",
+       "      <th>sqft_above</th>\n",
+       "      <th>sqft_basement</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>sqft_living15</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>15339</td>\n",
+       "      <td>164</td>\n",
+       "      <td>0.019266</td>\n",
+       "      <td>0.090909</td>\n",
+       "      <td>0.12500</td>\n",
+       "      <td>0.067170</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.097588</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.571498</td>\n",
+       "      <td>0.161934</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>14466</td>\n",
+       "      <td>220</td>\n",
+       "      <td>0.060721</td>\n",
+       "      <td>0.090909</td>\n",
+       "      <td>0.28125</td>\n",
+       "      <td>0.172075</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.206140</td>\n",
+       "      <td>0.082988</td>\n",
+       "      <td>0.908959</td>\n",
+       "      <td>0.222165</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>13503</td>\n",
+       "      <td>290</td>\n",
+       "      <td>0.013770</td>\n",
+       "      <td>0.060606</td>\n",
+       "      <td>0.12500</td>\n",
+       "      <td>0.036226</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.416667</td>\n",
+       "      <td>0.052632</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.936143</td>\n",
+       "      <td>0.399415</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>6371</td>\n",
+       "      <td>220</td>\n",
+       "      <td>0.069377</td>\n",
+       "      <td>0.121212</td>\n",
+       "      <td>0.37500</td>\n",
+       "      <td>0.126038</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.083333</td>\n",
+       "      <td>0.188797</td>\n",
+       "      <td>0.586939</td>\n",
+       "      <td>0.165376</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4955</td>\n",
+       "      <td>283</td>\n",
+       "      <td>0.057049</td>\n",
+       "      <td>0.090909</td>\n",
+       "      <td>0.25000</td>\n",
+       "      <td>0.104906</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.583333</td>\n",
+       "      <td>0.152412</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.741354</td>\n",
+       "      <td>0.241094</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21608</th>\n",
+       "      <td>574</td>\n",
+       "      <td>19</td>\n",
+       "      <td>0.037377</td>\n",
+       "      <td>0.090909</td>\n",
+       "      <td>0.31250</td>\n",
+       "      <td>0.093585</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.583333</td>\n",
+       "      <td>0.135965</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.874055</td>\n",
+       "      <td>0.194631</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21609</th>\n",
+       "      <td>14564</td>\n",
+       "      <td>288</td>\n",
+       "      <td>0.042623</td>\n",
+       "      <td>0.121212</td>\n",
+       "      <td>0.31250</td>\n",
+       "      <td>0.152453</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.583333</td>\n",
+       "      <td>0.221491</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.570693</td>\n",
+       "      <td>0.246257</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21610</th>\n",
+       "      <td>3621</td>\n",
+       "      <td>52</td>\n",
+       "      <td>0.042898</td>\n",
+       "      <td>0.060606</td>\n",
+       "      <td>0.09375</td>\n",
+       "      <td>0.055094</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.080044</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.705324</td>\n",
+       "      <td>0.106866</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21611</th>\n",
+       "      <td>629</td>\n",
+       "      <td>252</td>\n",
+       "      <td>0.042623</td>\n",
+       "      <td>0.090909</td>\n",
+       "      <td>0.31250</td>\n",
+       "      <td>0.098868</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.583333</td>\n",
+       "      <td>0.143640</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.608975</td>\n",
+       "      <td>0.173980</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21612</th>\n",
+       "      <td>3622</td>\n",
+       "      <td>166</td>\n",
+       "      <td>0.032787</td>\n",
+       "      <td>0.060606</td>\n",
+       "      <td>0.09375</td>\n",
+       "      <td>0.055094</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.080044</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.704842</td>\n",
+       "      <td>0.106866</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>21613 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          id  date     price  bedrooms  bathrooms  sqft_living  floors  \\\n",
+       "0      15339   164  0.019266  0.090909    0.12500     0.067170     0.0   \n",
+       "1      14466   220  0.060721  0.090909    0.28125     0.172075     0.4   \n",
+       "2      13503   290  0.013770  0.060606    0.12500     0.036226     0.0   \n",
+       "3       6371   220  0.069377  0.121212    0.37500     0.126038     0.0   \n",
+       "4       4955   283  0.057049  0.090909    0.25000     0.104906     0.0   \n",
+       "...      ...   ...       ...       ...        ...          ...     ...   \n",
+       "21608    574    19  0.037377  0.090909    0.31250     0.093585     0.8   \n",
+       "21609  14564   288  0.042623  0.121212    0.31250     0.152453     0.4   \n",
+       "21610   3621    52  0.042898  0.060606    0.09375     0.055094     0.4   \n",
+       "21611    629   252  0.042623  0.090909    0.31250     0.098868     0.4   \n",
+       "21612   3622   166  0.032787  0.060606    0.09375     0.055094     0.4   \n",
+       "\n",
+       "       waterfront  view     grade  sqft_above  sqft_basement       lat  \\\n",
+       "0             0.0   0.0  0.500000    0.097588       0.000000  0.571498   \n",
+       "1             0.0   0.0  0.500000    0.206140       0.082988  0.908959   \n",
+       "2             0.0   0.0  0.416667    0.052632       0.000000  0.936143   \n",
+       "3             0.0   0.0  0.500000    0.083333       0.188797  0.586939   \n",
+       "4             0.0   0.0  0.583333    0.152412       0.000000  0.741354   \n",
+       "...           ...   ...       ...         ...            ...       ...   \n",
+       "21608         0.0   0.0  0.583333    0.135965       0.000000  0.874055   \n",
+       "21609         0.0   0.0  0.583333    0.221491       0.000000  0.570693   \n",
+       "21610         0.0   0.0  0.500000    0.080044       0.000000  0.705324   \n",
+       "21611         0.0   0.0  0.583333    0.143640       0.000000  0.608975   \n",
+       "21612         0.0   0.0  0.500000    0.080044       0.000000  0.704842   \n",
+       "\n",
+       "       sqft_living15  \n",
+       "0           0.161934  \n",
+       "1           0.222165  \n",
+       "2           0.399415  \n",
+       "3           0.165376  \n",
+       "4           0.241094  \n",
+       "...              ...  \n",
+       "21608       0.194631  \n",
+       "21609       0.246257  \n",
+       "21610       0.106866  \n",
+       "21611       0.173980  \n",
+       "21612       0.106866  \n",
+       "\n",
+       "[21613 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "house.drop(cols, axis=1, inplace=True)\n",
+    "house"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Задание 6\n",
+    "##### Заполните пропущенные значения в данных;\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "id               0\n",
+       "date             0\n",
+       "price            0\n",
+       "bedrooms         0\n",
+       "bathrooms        0\n",
+       "sqft_living      0\n",
+       "floors           0\n",
+       "waterfront       0\n",
+       "view             0\n",
+       "grade            0\n",
+       "sqft_above       0\n",
+       "sqft_basement    0\n",
+       "lat              0\n",
+       "sqft_living15    0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "house.isna().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Пропущенных значений нет"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Задание 7\n",
+    "##### Решите поставленную задачу регрессии в соответствии с заданием. При подборе параметров метода принятия решения (метода регрессии) используйте перекрёстную проверку (изучите возможные для изменения параметры метода регрессии). Вычислите точность решения задачи, вычислив разницу между реальным значением и предсказанным. Вычислите коэффициент корреляции (Пирсона, Спирмена) между реальным значением и предсказанным с учётом p-value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Lasso(alpha=0.1)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from  sklearn import linear_model\n",
+    "# линейная регрессия, LASSO регуляризатор.\n",
+    "x, y = house.drop(['price', 'date'], axis=1), house['price']\n",
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.3, random_state = 123)\n",
+    "\n",
+    "reg = linear_model.Lasso (alpha = 0.1)\n",
+    "reg.fit (x_train, y_train) # Вычисляем оптимальные значения"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-1.26138531e-07,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
+       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
+       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00])"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.coef_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.06081472 0.06068783 0.05984119 0.06123766 0.0602056  0.06136898\n",
+      " 0.06176846 0.0618816  0.06090352 0.06215734]\n"
+     ]
+    }
+   ],
+   "source": [
+    "xt = reg.predict(x_test)\n",
+    "print(xt[:10])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5506     0.060000\n",
+      "9279     0.043934\n",
+      "16034    0.092787\n",
+      "6608     0.120656\n",
+      "20359    0.026754\n",
+      "8798     0.013115\n",
+      "10035    0.080525\n",
+      "13321    0.026230\n",
+      "15842    0.051148\n",
+      "12119    0.056525\n",
+      "Name: price, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(y_test[:10])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5506     0.000815\n",
+      "9279     0.016753\n",
+      "16034   -0.032946\n",
+      "6608    -0.059418\n",
+      "20359    0.033452\n",
+      "8798     0.048254\n",
+      "10035   -0.018756\n",
+      "13321    0.035652\n",
+      "15842    0.009756\n",
+      "12119    0.005633\n",
+      "Name: price, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(xt[:10]-y_test[:10])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.06222583423075206"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.intercept_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# линейная регрессия без использования регуляризаторов;\n",
+    "x, y = house.drop(['price','date' ], axis=1), house['price']\n",
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.3, random_state = 123)\n",
+    "\n",
+    "reg = linear_model.LinearRegression()\n",
+    "\n",
+    "reg.fit(x_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-1.08963613e-07, -1.02642038e-01, -6.55608472e-03,  1.25559247e-01,\n",
+       "       -1.06380304e-02,  8.47949706e-02,  3.48565991e-02,  1.36639216e-01,\n",
+       "        1.42910023e-01,  7.47553146e-02,  5.52860562e-02,  1.52070983e-03])"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.coef_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-0.08326686045108067"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg.intercept_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.016352244805927955\n",
+      "0.0015439396422982205\n"
+     ]
+    }
+   ],
+   "source": [
+    "corr={}\n",
+    "corr['pearson'], _ = stats.pearsonr(xt,y_test) #коэффициент корреляции\n",
+    "corr['spearman'], _ = stats.spearmanr(xt,y_test)\n",
+    "print(corr['pearson'])\n",
+    "print(corr['spearman'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}