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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Download the train.csv file and save it to the same location as your iPython Notebooks.\n",
+    "\n",
+    "## About the file\n",
+    "\n",
+    "```\n",
+    "Columns\n",
+    "PassengerId    type should be integers\n",
+    "Survived       Survived or Not\n",
+    "Pclass         Class of Travel\n",
+    "Name           Name of Passenger\n",
+    "Sex            Gender\n",
+    "Age\n",
+    "SibSp          Number of Sibling/Spouse aboard\n",
+    "Parch          Number of Parent/Child aboard\n",
+    "Ticket\n",
+    "Fare\n",
+    "Cabin\n",
+    "Embarked       The port in which a passenger has embarked. C - Cherbourg, S - Southampton, Q = Queenstown\n",
+    "\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "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": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Now let's open it with pandas\n",
+    "import pandas as pd\n",
+    "from pandas import Series,DataFrame\n",
+    "\n",
+    "# Set up the Titanic csv file as a DataFrame\n",
+    "titanic_df = pd.read_csv('Titanic_Train/titanic_train/train.csv')\n",
+    "\n",
+    "# Let's see a preview of the data\n",
+    "titanic_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 891 entries, 0 to 890\n",
+      "Data columns (total 12 columns):\n",
+      "PassengerId    891 non-null int64\n",
+      "Survived       891 non-null int64\n",
+      "Pclass         891 non-null int64\n",
+      "Name           891 non-null object\n",
+      "Sex            891 non-null object\n",
+      "Age            714 non-null float64\n",
+      "SibSp          891 non-null int64\n",
+      "Parch          891 non-null int64\n",
+      "Ticket         891 non-null object\n",
+      "Fare           891 non-null float64\n",
+      "Cabin          204 non-null object\n",
+      "Embarked       889 non-null object\n",
+      "dtypes: float64(2), int64(5), object(5)\n",
+      "memory usage: 83.6+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We could also get overall info for the dataset\n",
+    "titanic_df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "All good data analysis projects begin with trying to answer questions. Now that we know what column category data we have let's think of some questions or insights we would like to obtain from the data. So here's a list of questions we'll try to answer using our new data analysis skills!\n",
+    "\n",
+    "First some basic questions:\n",
+    "\n",
+    "1. Who were the passengers on the Titanic? (Ages,Gender,Class,..etc)\n",
+    "\n",
+    "2. What deck were the passengers on and how does that relate to their class?\n",
+    "\n",
+    "3. Where did the passengers come from?\n",
+    "\n",
+    "4. Who was alone and who was with family?\n",
+    "\n",
+    "Then we'll dig deeper, with a broader question:\n",
+    "\n",
+    "5. What factors helped someone survive the sinking?\n",
+    "\n",
+    "So let's start with the first question: **Who were the passengers on the titanic?**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python version 3.7.0 (default, Jun 28 2018, 07:39:16) \n",
+      "[Clang 4.0.1 (tags/RELEASE_401/final)]\n",
+      "Pandas version 0.23.4\n",
+      "Seaborn version0.9.0\n",
+      "Matplotlib version2.2.3\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Let's import what we'll need for the analysis and visualization\n",
+    "#import numpy as np\n",
+    "#import matplotlib.pyplot as plt\n",
+    "#import seaborn as sns\n",
+    "#%matplotlib inline\n",
+    "\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd \n",
+    "import sys \n",
+    "import seaborn as sns\n",
+    "# import matplotlib \n",
+    "%matplotlib inline\n",
+    "\n",
+    "print('Python version ' + sys.version)\n",
+    "print('Pandas version ' + pd.__version__)\n",
+    "print('Seaborn version' + sns.__version__)\n",
+    "print('Matplotlib version' + matplotlib.__version__)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 简单统计男女比例，我们data数据选择titanic_df,然后选择其中的Sex字段作为X轴，其中kind : {point, bar, count, box, violin, strip}一共六种方式，我们选count，有的版本似乎不需要选择kind=count"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/lw/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
+      "  warnings.warn(msg)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x1a1f00efd0>"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Let's first check gender\n",
+    "\n",
+    "\n",
+    "sns.factorplot('Sex',data=titanic_df,kind='count')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 为了更细化，我们显示以Pclass作为X轴，统计每个等级中的男女比例："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/lw/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
+      "  warnings.warn(msg)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x1a1f033e48>"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Now let's seperate the genders by classes, remember we can use the 'hue' arguement here!\n",
+    "sns.factorplot('Pclass',data=titanic_df,kind='count')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/lw/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
+      "  warnings.warn(msg)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x1a1f7202e8>"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 430.5x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.factorplot('Pclass',data=titanic_df,kind=\"count\",hue=\"Sex\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "我们也可以将男女分为男，女，小孩，为原有数据库新增一个字段\n",
+    "\n",
+    "定义一个函数，判断男，女，小孩"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# We'll treat anyone as under 16 as a child, and then use the apply technique with a function to create a new column\n",
+    "\n",
+    "# Revisit Lecture 45 for a refresher on how to do this.\n",
+    "\n",
+    "# First let's make a function to sort through the sex \n",
+    "def male_female_child(passenger):\n",
+    "    # Take the Age and Sex\n",
+    "    age,sex = passenger\n",
+    "    # Compare the age, otherwise leave the sex\n",
+    "    if age < 16:\n",
+    "        return 'child'\n",
+    "    else:\n",
+    "        return sex\n",
+    "    \n",
+    "# 创造一个新的列，person\n",
+    "# 将Age，Sex传给male_female_child\n",
+    "# male_female_child（Age，Sex）\n",
+    "# person  如果 age <16  child  如果age >=16 原来是male 就是male 原来是female就是female\n",
+    "# We'll define a new column called 'person', remember to specify axis=1 for columns and not index\n",
+    "titanic_df['person'] = titanic_df[['Age','Sex']].apply(male_female_child,axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "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>person</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>male</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>female</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>female</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>female</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>male</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",
+       "      <td>male</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",
+       "      <td>male</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",
+       "      <td>child</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",
+       "      <td>female</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",
+       "      <td>child</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",
+       "5            6         0       3   \n",
+       "6            7         0       1   \n",
+       "7            8         0       3   \n",
+       "8            9         1       3   \n",
+       "9           10         1       2   \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",
+       "5                                   Moran, Mr. James    male   NaN      0   \n",
+       "6                            McCarthy, Mr. Timothy J    male  54.0      0   \n",
+       "7                     Palsson, Master. Gosta Leonard    male   2.0      3   \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  person  \n",
+       "0      0         A/5 21171   7.2500   NaN        S    male  \n",
+       "1      0          PC 17599  71.2833   C85        C  female  \n",
+       "2      0  STON/O2. 3101282   7.9250   NaN        S  female  \n",
+       "3      0            113803  53.1000  C123        S  female  \n",
+       "4      0            373450   8.0500   NaN        S    male  \n",
+       "5      0            330877   8.4583   NaN        Q    male  \n",
+       "6      0             17463  51.8625   E46        S    male  \n",
+       "7      1            349909  21.0750   NaN        S   child  \n",
+       "8      2            347742  11.1333   NaN        S  female  \n",
+       "9      0            237736  30.0708   NaN        C   child  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's see if this worked, check out the first ten rows\n",
+    "titanic_df[0:10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Excellent! Now we have seperated the passengers between female,male,and child. This will be important later on beacuse of the famous \"Women and children first policy\"!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/lw/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
+      "  warnings.warn(msg)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x1a1f033320>"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 430.5x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Let's try the factorplot again!\n",
+    "sns.factorplot('Pclass',data=titanic_df,hue='person',kind='count')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Interesting, quite a bit of children in 3rd class and not so many in 1st! How about we create a distribution of the ages to get a more precise picture of the who the passengers were."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "简要查看各年龄段的发布，将年龄段的间距分为70段，默认10段，你当然可以分得更细或者更系数"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1e1cd5f8>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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/JCIeqLJ9rnpDfeAi4pyIeCgidjclV0Qcioj9EfFwRMxU85qwLVdFxD0R8Z1qX3tjQ3JdWr1Wcz8/iYj3NSTbn1T7/YGIuKv6e+j7PtbYQm/YpQU+DWyaN287sCcz1wN7qulBOwlsy8zXAlcB76leoyZk+xlwTWZeBlwObIqIq4APAR+psh0HtgwhG8CtwMG26abkmszMy9tOcWvCtvwY8KXM/A3gMlqv29BzZeaj1Wt1ObAR+F/gC8POFhFrgT8GxjPz9bROGrmJQexjmdnIH+CNwJfbpm8DbhtinjHgQNv0o8Ca6vYa4NEGvGa7gN9rWjbgZcCDwG/R+nDFuQtt4wHmWUfrD/0aYDcQDcl1CLhg3ryhbkvgFcD3qN5va0quBXK+GfjPJmTj+U/Rr6Z14slu4C2D2Mcae4RO8y8tMJqZRwCq3xcNM0xEjAFXAA/QkGzVsMbDwDHgfuC7wDOZebJaZFjb9KPAnwP/V02/siG5EvhKROyrPmENw9+WrwZ+AHyqGqL6RESsaECu+W4C7qpuDzVbZh4G/h54EjgCnAD2MYB9rMmFHgvM85ScBUTECPAvwPsy8yfDzjMnM3+ZrX8Or6N1MbfXLrTYIDNFxNuBY5m5r332AosOY1+7OjPfQGuY8T0R8btDyDDfucAbgH/MzCuAZxnOsM9pVWPR7wD+edhZAKox++uBS4BXAStobdP5er6PNbnQu7q0wBAdjYg1ANXvY8MIEREvolXmn8nMzzcp25zMfAaYpjXOvyoi5j7/MIxtejXwjog4ROsKodfQOmIfdi4y8+nq9zFaY8FXMvxt+RTwVGY+UE3fQ6vgh52r3VuBBzPzaDU97GxvAr6XmT/IzF8Anwd+mwHsY00u9KZfWuBeYHN1ezOt8euBiogA7gQOZuaHG5btwohYVd0+j9ZOfhDYC7xzWNky87bMXJeZY7T2qa9m5s3DzhURKyLi5XO3aY0JH2DI2zIz/wf4fkRcWs26Fvj2sHPN8y6eH26B4Wd7ErgqIl5W/Y3OvWb938eG+UZGF28uvA34b1pjr381xBx30RoL+wWtI5YttMZd9wCPVb9XDyHX79D6Z9u3gIern7c1JNtvAg9V2Q4A76/mvxr4OvA4rX8iv2SI23UC2N2EXNXzf7P6eWRuf2/ItrwcmKm25b8C5zchV5XtZcCPgJVt84aeDfgg8J1q3/8n4CWD2Mf8pKgkFaLJQy6SpBosdEkqhIUuSYWw0CWpEBa6JBXCQpekQljoklQIC12SCvH/UF+7c9LWqqUAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Quick way to create a histogram using pandas\n",
+    "titanic_df['Age'].hist(bins=70)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "查看“Person”字段的数量统计"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "male      537\n",
+       "female    271\n",
+       "child      83\n",
+       "Name: person, dtype: int64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# We could also get a quick overall comparison of male,female,child\n",
+    "titanic_df['person'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "统计不同年龄段，各类别的分布趋势，核密度统计方式\n",
+    "\n",
+    "注：核密度估计，参考：http://www.lifelaf.com/blog/?p=723\n",
+    "\n",
+    "注：hue代表除row，col之外的第三维度，等级，不同的类型不同的颜色\n",
+    "\n",
+    "Palette代表调色板\n",
+    "\n",
+    "### 使用Facet函数创建plot，以“Sex”字段区分等级，aspect=4代表宽度为之前的4倍"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/lw/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x1a1e3a26d8>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 934.5x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "# Set the figure equal to a facetgrid with the \n",
+    "# pandas dataframe as its data source, set the hue, \n",
+    "# and change the aspect ratio.\n",
+    "fig = sns.FacetGrid(titanic_df, hue=\"Sex\",aspect=4)\n",
+    "\n",
+    "# Next use map to plot all the possible kdeplots for the 'Age' column by the hue choice\n",
+    "fig.map(sns.kdeplot,'Age',shade= True)\n",
+    "\n",
+    "# Set the x max limit by the oldest passenger\n",
+    "oldest = titanic_df['Age'].max()\n",
+    "\n",
+    "#Since we know no one can be negative years old set the x lower limit at 0\n",
+    "fig.set(xlim=(0,oldest))\n",
+    "\n",
+    "#Finally add a legend\n",
+    "fig.add_legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/lw/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x1a1e4f2be0>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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MBhqy2UdDLoSmj023zy9+PtZlDHmBjgDPVD3D4l2L6Y50c0bRGdww5Qbm5M7RUGYRERERkYGjH9t9NORCaMHEAvvEq0/EuoxhoznYzKs7XuXl6pdpDjYzNm0sV46/kosqLiI1LjXW5YmIiIiIjDQKoX005ELomKlj7MMvPhzrMoadYDjIkj1LeG3na1Q1VeFz+zi/7HwWjF/AjOwZ6h0VEREREekf+mHdR0MuhE6aMck++NyDsS5jWNvevJ3Xdr7G0j1L6Qh1MCZ1DAsmLFDvqIiIiIhI3w3pEGqM8Vhrh/S6jgqhI1hnqJNle5fxWvVrVDU7vaMXlF3AjVNuZGz62FiXJyIiIiIyHA14CDXGlAHPAkuBWcAm4HpgEvCfQBIQAG601u4xxrwKvAWcBiwCdgA/BMJAk7V2njHGD/wWmAOEgK9ba18xxtwIXAIkAGOAhdbabw3k/XkG8uQSW36Pn3lF85hXNO9A7+iz257liS1PcFbxWfzLtH9hevb0WJcpIiIiIiIfNAH4vLX2TWPM/cC/A5cDl1pra40xVwE/Aj4XbZ9mrT0DwBizBjjPWrvLGJMW/f7fAay104wxE4HnjTHjo9/NxAm7XcBGY8yvrbXVA3VjCqGjRGlKKddPvp7Lx17OSzte4qUdL/FK9SucmHciX531VWbmzIx1iSIiIiIiclC1tfbN6Pu/At8FpgIvROd7cQN7erR/pMf7N4E/GWMeBf4R3Xc68GsAa+37xpjtwP4Q+pK1tgnAGLMeKAUUQqV/JPuSuWzsZZxfdv6BntHrnrmO80rP4/+c8H8oTi6OdYkiIiIiIgKHPzfZAqyz1p5ylPZtBw609kvGmLnAhcBKY8xMjj2MuKvH+zADnBNdA3lyGbr8Hj/nlZ3Hf5z+H1wy5hJe3fkqlz5+KT9/5+c0dTXFujwRERERkdGuxBizP3BeAywBsvfvM8Z4jTFTjnSgMWaMtXaptfYHOM+OFgOvA5+Ofj8eKAE2DvA9HJFC6Cjn9/i5bOxl/Pj0HzM3fy4Prn+QSx6/hKe3Ps1Qm7RKRERERGQU2QDcYIxZDWTgDKW9ErjLGLMKWAmcepRjf2aMWWOMWYsTPlcB/w24o8+LPoIzqVHXUY4fUJodVw6xvXk7f173Z7Y1b+PUglO55eRbNERXREREROSgwZod90lr7dSBvlYsqCdUDlGaUsotJ9/CtROvZXnNci574jLuW3Mf3ZHuWJcmIiIiIiIjgHpC5ajqO+t5aMNDLN+3nIkZE7n91NuZlDkp1mWJiIiIiMTSgPeEjnTqCZWjyvBn8OVZX+amGTexp3UP1zx1Db9a/iu6wjEZOi4iIiIiIiOAQqgc15y8Odx5+p2cUnAKf1zzR65YdAXLa5bHuiwRERERERmGFEKlVxK9iXxu6uf4xgnfoK27jRuevYE7l9xJa7A11qWJiIiIiMgwohAqH8qUrCncfurtzC+dz6MbH+WihRexcPNCIjYS69JERERERGQYUAiVD83v8XPNxGv4/snfJ92fzg/e+gHXPnUtq2pXxbo0ERERERH5CIwxZxpjnhyMaymEykdWllrGzSfdzBemfYHdrbv5zNOf4Vuvf4uqpqpYlyYiIiIiIkOUJ9YFyPBmjOGUglOYlTOLp6qe4sXtL/Lctuc4v+x8bphyA5MzJ8e6RBERERGRUcEYUwY8CywGTgZWAQ8AtwE5wKejTe8G4oEO4LPW2o2HnScR+DUwDScz3mqtfaLf6uzNOqHGmPOBXwFu4I/W2p8c9n0c8CBwAlAHXGWt3dbj+xJgfbT4nx/rWlondHhr7mrmmW3P8Fr1a3SGO5mdM5tPjvsk80vnk+BNiHV5IiIiIiJ9ddx1Qsu+89TdwMx+vu7KbT+58GvHahANoZXALGAd8A5OEP08cAnwWeB6oN1aGzLGfBz4N2vtFcaYM4FvWmsvMsb8GFhvrf2rMSYNWAbMsta29ceNHLcn1BjjBn4DzAd2Au8YYxZZa9f3aPZ5oMFaO9YYczVwF3BVj+9/CTzTHwXL0JYSl8JVE67i4oqLeX3X67xW/Rq3vHkLdy65k9MKT+Os4rM4Me9E8hPzMUbr/IqIiIiI9LMqa+0aAGPMOuAla601xqwByoBU4M/GmHGABbxHOMe5wCXGmG9GP/uBEmBDfxTYm+G4JwGV1tqtAMaYh4FLcXo297sUuDX6/u/APcYYE73Zy4CtQL+kZhkeErwJnF92PueVnsfmxs0s2bOE5TXLeWnHSwBkx2czM2cmM7JnMCljEhVpFWT6MxVMRURERGTYO16P5QDr6vE+0uNzBCf/3QG8Yq29PNpz+uoRzmGAKw4fpttfehNCC4HqHp93AnOP1ibardsEZBpjOoBv4/SifpOjMMZ8EfgiQF5hXq+Ll6HPGMP49PGMTx9PZFKE6pZqKhsq2dK0hZX7VvLC9hcOtE3xpVCRWsGYtDGUp5ZTkVpBRVoF+Yn5uIzm0BIRERER6QepwK7o+xuP0uY54CvGmK9EOxZnWWtX9FcBvQmhR+qaOvxB0qO1uQ34pbW29Vg9XNbaPwB/AOeZ0F7UJMOQy7goTSmlNKWUczgHgKauJna27GR32272tO5hd9tuXtj+As3B5gPHxXvimZo1ldk5szmt8DSmZ03H7XLH6jZERERERIazn+IMx/068PJR2tyBM3nRauMEuW3ARf1VwHEnJjLGnIIzodB50c83A1hr/6NHm+eibd42xniAvUA28DpQHG2WhtMF/ANr7T1Hu54mJhKAlmALe9r2sLt1N7tad1HZWEl1czURImT4Mzi/7Hw+NeFTjEkbE+tSRURERGR00fNjfdSbntB3gHHGmHKcbturgWsPa7MIuAF4G7gSeNk66fZj+xsYY24FWo8VQEX2S/Ylk+xLZnz6+AP72rvbWRNYw3s17/Hopkd56P2HOCX/FL4y6ytMy54Ww2pFRERERKS3jhtCo894fhlnXLAbuN9au84YczvwrrV2EXAf8BdjTCVQjxNURfpVgjeBuflzmZs/l+ZgM4t3Lua57c9x7dPXMr90PjefdDPZCdmxLlNERERERI6hV+uEDiYNx5UPoyPUwfPbnufpqqfxe/x8+8Rvc8mYSzTLroiIiIgMFP3Q7CNNOSrDWrwnnkvHXsptp95GfmI+t7x5Cz9864d0hbuOf7CIiIiIiAw6hVAZEfIS8/jWid/i4oqLWVi5kBueuYFARyDWZYmIiIiIyGF6MzGRyLDgMi4uH3c5Zall/GH1H7jhmRu499x7KUgqiHVpcphgKEJ7MER32GKtJWIhbC2RiCUS/exxGTxug9ftwut24XO78LoNbpfRcGsRERGRYUzPhMqIVNlYya/e+xVJviTuP+9+SlJKYl3SyGItdDYSbq6hvnY3zY31tLc20dnaQLC9mVBHM6HONoLBIJFwNzYcgnA3NuIEz5B1Eabn5qYDHx02jg58dBJHh/XRhp9mEmm2CTSTQLNNpJUEfD4vSXGeQze/85oa7yUz0Ud6ou8DrxkJPlwuBVgRERHpkyH9Y8IY81Xg34Dl1tpPD8D5b8VZ9eTnH/kcCqEyUu1o3sEv3vsFyd5k/vKJv5CTkBPrkoaPUBc0bIeGKsJ1VTTt3kR3oArTWoOvs5bE7nq8hI56eMQagsZLGDdh4yGMG2vcRIwLF+AmgouIE0Gt8+qJdOM+xjl7ancl0eRKo9GVTr1JpdamUWtT2RdJYVd3Mru6k6i1aQRIJYj3wHEelyE7OY78VD95qX5yU/zkR1+L0uMpzkggOylOPa0iIiJyLEP6h4Ix5n3gAmtt1QCd/1b6GEI1HFdGrJKUEr42+2v87J2f8a8v/Ct/Ov9PpMalxrqsoSUSgYYq2LsGatbRvXs14T1riGvbjcH5A5UbiLNx1NgcamwGTWYiHd5UwnGp4E/DG59MfHwCfr+fBH8CiQl+vL44MB/hkfNICFckiCscxIS7cIU7cYfacXe34wq14Q614e5ux93dQkKwiZSuJiqCW/AEm3CH2p1zuIC4g6fs8iTT7Muh3pPHPlc2u2wW29syqGxM56WONKq7k7E9Ho/3e10UpydQkpFAcYbzWp6VSEV2IkXpCbjVkyoiIiJDlDHmd0AFsMgY8zAwBpiGk/tutdY+YYy5EbgM52feVOAXgA+4DugCPmGtrTfGfAH4YvS7SuA6a237YdcbA/wGyAbagS9Ya98/bp3qCZWRbkPdBn65/JfMypnF7+f/Hq/Le/yDRqr2eqheBtVLsDuWYHevxBXqACCMYWukgA22hC2RAmo9uUQSc/Cn5pKdmkJhsiEvAZJ9Mb6HozDhIJ5gM55gI+5gE56uZjzBJjzBBrwddXg7A/g663CH2g45LuLy0ubPo8mXT607m2pyqQzlsL4rmxVtGdR1H0y0PreLsqwExuYkMSbb2cbmOJvf6x7sWxYREZHYOP5fpG9NvRuY2c/XXcmtTV87XiNjzDZgDvB1YL219q/GmDRgGTALWADcEn3vxwmY37bW/s4Y80tgu7X2bmNMprW2LnrOO4Eaa+2ve/aEGmNeAr5krd1sjJkL/Ie19uzj1aieUBnxJmVO4sYpN/LHNX/kp8t+yvdO/l6sSxo8HQ2w9VXY+ip2+9uYwEYAwrjZQBnvhOax3pay012CL72I8gwf5SkwJwUy/LEt/cOybh/d8Vl0x2cds50r1I63IxANpYED7zM6A+S2bmJWV+PBxm7oSsikMb6EvZ5CtpHPxmAOq7dlct/adNqtE1DdxlCencjEvGQm5acwMS+ZCXnJFKbFa2iviIiIxMq5wCXGmG9GP/uB/ROlvGKtbQFajDFNwD+j+9cA06Pvp0bDZxqQBDzX8+TGmCTgVOBvPX7vxNELCqEyKpxacCo7W3by8MaHGZ8xngXjF8S6pIERicCelVD5ElS+gN35LsaG6XQlsNKO543uT/GeHc8O3xjGZsQxLRPOzoTiJBgtWSniSaAruYSu5CNPVmXCnfja9+Fr30tc+1587XtIat/L5ObFzAj2CKhx0O7PZV9cGdvdJazvzufdLbn8fnUOzSQCkBLvYVpBKlOLUplW6GwlGQkKpiIiIiNdL3osB4EBrrDWbjxkp9Nj2dVjV6TH5wgHM+KfgMustauiQ3jPPOz8LqDRWvuhe3wVQmXUuHL8lVS3VPOTpT9hWtY0JmZMjHVJ/SMShu1vwrrHYcMiaKsFYIdvDM9GLubZ4EzWmzFMyXIzNxc+lwUFiaMndH5Y1u0/EFJbDvvOFWrH116Dr31vNKTuIbd1FyUtKzgjEnQa+aHdl82euDK2mGLWBPJZsi2H/w0X0EwiKX4PUwtTmV6UxqwSZ8tJHmbdziIiIjIcPAd8xRjzFWutNcbMstau+BDHJwN7jDFe4NPArp5fWmubjTFVxpgF1tq/Geev7NOttauOd2KFUBk1XMbFF6Z/gdveuo1vvvZNHrnoERK9ibEu66M5QvAMu+NY7ZvFQ6EFvByaRlc4lTk5cG4e3JwNCaP4Udj+EvEk0JlSTmdK+aFf2Ajejlri2nbhb91JXNsuCtp2Ud62mnPDXeAFvNDszaHKU87a2mKWbCvkuUgJ22wuBWkJzCpJj25pTClIIc6jZ0xFRESkT+4A7gZWRwPiNuCiD3H894GlwHacYbrJR2jzaeC3xphbcH7xPAwcN4RqYiIZdTbWb+Rn7/yMCysu5Mcf+3Gsy/lw6rbAir/CqoegZS8RdxybEmbz59aTeLxjBm6vn3kFcHoBTMkAz0eYoFb6kY3g7QwQ17qLuLad+Ft24G/dQVzbLoyNABA0frZ5ylgTKmZFsIj1kVKq3GVMKMnjpPJM5pZnMKskjQSf/mYoIiIyRGg8WR8phMqo9Hjl4yzasoi7z7qbc0rOiXU5xxZsh/VPwIq/wPY3scbF7pRZPBQ8nfsbZhA0fmbnwDlFMDcXfOpAG/JMOOj0mrZsx9+6w3lt2XFg5t4Ihl0mn5WhElZHKthABZG8GUwfW8Lc8gxOKEsnxa+ubRERkRhRCO0jhVAZlUKREHcuuZPW7laeuPQJ0vxpsS7pgwKVsOz3sOp/oauF7sR8FvvP5M7a09gSzKAsGc4phjMLh99MtnIE1uLtDER7S7fjb9lOXHMVcZ2BA0222nzWRMpZa8tpSptM8SZqAAAgAElEQVRKxtgTmTm2mJPKM8hIHKJr54iIiIw8CqF9pBAqo1Z1SzW3v30755aey0/P+Gmsy3FYC1tehqW/g83PY10e6nJP48GuM/nNnnFYXJyeD5dWwIQ0TS40GriDzcQ3V+FvrsLXtBVfUxWJ3XUARKyhyuax2lawN2Ei3uLZlE49hZMmlJAar55SERGRAaJfYH2kECqj2qIti3i88nHuPvNuzimN4bDc7g5Y9TAs+S0ENmL9abyfdS4/qj+bxfUpJPvgghK4sAyy4mNXpgwN7mAT8dFQGqmrIqm1itRwPeAE0y22gF3x44nkzyJn4smMn3EavvikGFctIiIyYiiE9pFCqIxqoUiIHy39ES3BFh6/9HHS/emDW0BXC7xzH7x9D7TVEskYw9K0T/Dd7SdS1eahJBkuKYezCsGveWnkGDxdjXibqmjbV4W7YSs5XVVk2QYAQtbFbl8Z7dkzSBt7MrmTTsHkTAa3ektFREQ+AoXQPlIIlVFvZ8tObnv7tsEdltteD0t/7wy77Wwkkj+Ll1Iu54eVY9ndBuPT4JrxcGKOhtzKRxdsbaB291bCgS1ktG9hQmQracaZ/ChofLSkTcJfdhKJ5SdBwWzIqACXplQWERE5Dv066yOFUBHgn1v+ycLKhdxz9j2cUXzGwF2orQ7e+hW880cIthEuOpnnki7njs0l7GmzTEx3wucJ2Qqf0v/2tVm27dlHZ+0Wklq2MMFuZZqpIt4EAej2puAqnIW76AQonA2FJ0BKQYyrFhERGXKG1a80Y8yfgCettX8/bH8B8F/W2iuNMWcC37TWfmAdUWPMNmCOtTZw+HcflQb4iQAXlF/A0r1L+fHSH3NS/knEe/r5wcvOJnj7N86w22A7kbKP8VzS5dy6Po+adsukdMtNU2GWwqcMoJxEQ87YXBibS8SeyrZmeK42TH3NLuKbtzAltJWZVVuYsG0xHsIA2KRcTGE0lBbMhoJZkJAR4zsRERGRvrLW7gaujMW1FUJFAI/Lw3WTruOud+7i3tX38tXZX+2fEwfbYNkfYPHd0NmILT2NJdlX8YO1OWxujDAx3fLV6TAjS+FTBpfLQEUqVKS6YWwJXeES1tWdxR9rYe2+IElt25nh2sJJrVs5YctKcjc+ffDg9HKnl3R/MM2fAb6E2N2MiIiIHGCMuR74JmCB1UAYmGeM+TqQB3zLWvt3Y0wZTg/p1MOOzwT+F8gGljEAPb8KoSJREzImcFrBaTyw7gEuqriIirSKj36ycDe89yd47S5oq4WiOWwsuZbvbyhk2cYwRYkRvjsHTs1T+JShIc4Ns3OcjSk+Ah3jWFE7jodr4dsBMME2prqqODepilNCWymrfJW4tdFRPcYNOROdQLo/nGriIxERGcWm/Xna3cDMfj7tyjU3rPnasRoYY6YA3wNOs9YGjDEZwH8C+cDpwERgEfD3o5+FHwKLrbW3G2MuBL7YL9X3oBAq0sOC8QtYWbuSO5bcwf3n3Y/5sAnRWnj/SXjhB1C/FXKnsWf2N7hzSwVPvRwiLS7MTdPgvBLwaP4XGcKy4mF+ibOFLWxpSmT5vqn8PTCVOwLOvlJvI1dmbuMM/1bGRrYSv/4JzIq/OCfw+CFvmhNKC2Y7wTRjjCY+EhERGVhnA3/f//ymtbY++nv2cWttBFhvjMk9zjnmAZ+MHv+UMaahv4tUCBXpISUuhSvGXcGD6x/kya1PcvGYi3t/8M534bnvQfUSSCuhY94PuHvvNO57sRuPK8S14+HyMZCg/9fJMOM2zozN49Pg6vHQ3g2rArC8No2HAjP5xV7nD73FSXBpcT0fT9zKJLuVuMZKZ0TA0t85J4pLcZ4p3T+Md//ERxoOICIiI8zxeiwHkMEZhnu4rsPaHM+Azl6rn8Mih5lXNI/Fuxbz83d/zhnFZ5DiSzn2AQ3b4MVbYd1CiE/HnvzvPOU6izsWd1PT3s38Yrh+ImT4B6N6kYGX4IVT8p0NYE8bvFcLK2rhvqoM7gll4DZzmJXr5owJhvnpexgf3oKrbhPUbYHtiyHiTHxEUm6PYbyznPea+EhEROSjeglYaIz5pbW2Ljoc98N6Hfg0cKcx5gIgvV8rRCFU5ANcxsV1k6/jjrfv4L+W/xe3nHzLkRt2tcLi/4S37nF6cmZcw5bCS7llieHt3UHGpML/Ow0m6fe0jHD5iXBRIlxUBt0R2NgA7+2DlYEw//ke/IJs0uKyOa3wdM4Y6+Fj+RHyg9shsAnqNsO+dbDpmYMnTC87dBhv/gzwJcbq9kRERIYNa+06Y8yPgNeMMWFgxUc4zW3A/xpjlgOvATv6s0bQOqEiR/XQhod4acdLPHThQ0zN6jFpWCQCqx+BF38IrTVQcRbt02/gVxuSuG91EL/H6fk8v9QZxigymjV1wYqA00u6ohbqOp3949JdfKzIw7wiD3Pz3cTbdqirhMBmqNsEgUpo2+c0Ni7Innhw7dKC2ZA7RRMfiYhIrOgXXh/1KoQaY84HfgW4gT9aa39y2PdxwIPACUAdcJW1dpsxZj7wE8AHBIH/Z619+VjXUgiVoaIj1MH3Fn+PgsQCHrrwIdwut/Pc59Pfgt3vQdYE7In/wtMtY7n9rU5q2i3zi+GzkyA1LtbViww91sL2loNDd9fWOT2nPjfMzXfzsSIPHyvyMCHDhcsY6GhwQmlgkxNQ6zY7a+4CuOMOTny0/xnTzLGa+EhERAaDQmgfHTeEGmPcwCZgPrATeAe4xlq7vkebm4Dp1tovGWOuBi631l5ljJkF1FhrdxtjpgLPWWsLj3U9hVAZSpbtWcbvVv+Om2d8hWu3rYSV/wMJmTD7eirT5/HDt4K8uStMRQrcNE1Db0U+jM4QrK13AunyWtjR4uxPi4MT850e0rn5HiZnunC7jJNiW2ucUBrYDPWVTo9pqMM5MC7Zmfho/zDewhMgpVATH4mISH/Tf1j6qDch9BTgVmvtedHPNwNYa/+jR5vnom3eNsZ4gL1Atu1xcuPMDRwACqy1PWdnOoRCqAwlNhzinte/y+bOfSzavY/sCZfQNmkBv17j5o+rg/jdcN0EuKBMQ29F+irQASsDTg/p2npnwiOAJC/MyfMwt8DN3Hw307LcePf/Hy4Shqbqgz2m9ZVQXwWRkPN9Ys6hs/EWauIjERHpM/3q66PeTExUCFT3+LwTmHu0NtbakDGmCcjECZ37XQGsOFIANcZ8kegiqHmFeb0uXmQgJe9eRckbv+aOlp18sqiAn009mzOTPs3t/+hkb1uY+cVw4ySn10ZE+i4rHj5e7GzghNK19U4oXVcf4tVqJ1jGe+CEXDcnRXtLZ+SU4k8vg3HznQPDQSeIHpj4aD1seo4Ds82nlR46jDd/BsQlDfr9ioiIjFa9CaFHSvqHd58es40xZgpwF3DukS5grf0D8AdwekJ7UZPIgPG2Bihe8nsyK1+hKyGD4OzrOC3YzjONb/PY6tWUusfxs9NgsjpTRAZUVjycWehsAI1dsK4O1tTD2rowv9wVxuI8Uzozx82sHDezct3MzvGQkz0BsiccPFkwOvFRXXQo7/bFsO4fznfG5bQtiC4TU3gC5EwBj2/Q71lERGQ06E0I3QkU9/hcBOw+Spud0eG4qUA9gDGmCFgIXG+t3dLnikUGiAl3k7vmHxS8+1dMpJtdE85lW/k5/G1jOk9t9BFfvoncksf5adnXiPdoVk6RwZYWB6cVOBtASxDWHegpDXPf6jCh6J8x8xMNs3OdUDozx83kzHgS8qdD/vSDJ9w/8VHdZuf1/X/Cyr8637njIHeqE0rzosdlTwKvFvwVERHpq948E+rBmZjoHGAXzsRE11pr1/Vo8+/AtB4TE33SWvspY0waztoyt1trH+tNQXomVGIhpfo9Shb/mvimnTTkTWXHlEt5raGAP69Ipq7Dw5z8ZiYXr2Jhy1NcmfFxrsz4eKxLFpHDBMOwtRneb3DWKt3YADXROYtcBsakupiW7WZqtotpWW4mZ7lJ9PYYyLN/4qP9oTSwCeq3Qnd79CQeyBrvDN/Nm+7Mzps3DeLTBv9mRUQklvRMaB/1domWTwB34yzRcr+19kfGmNuBd621i4wxfuAvwCycHtCrrbVbjTG3ADcDm3uc7lxr7b6jXUshVAaTr6WG4rd+S0bVYjoTs9gx7TLWx0/j/uUprK6JIz+pi8snBChLcxY3/EfDy2zs2MbPSv4v+b6sGFcvIsdT3wmbGqGyCSobYUsT1EdnJjBARZqLSZkuJmW4mZjpYmKGm4Ikg9k/o66NQMteJ4zu3xqqoL3u4EXSSp0w2jOcphRoVl4RkZFL/8D3Ua9C6GBSCJXB4OruJG/Vo+SveBiLZc/4+WwrPZPHNqax6P1EvG7LeRV1nFzYjLvHsoOt4Xb+e9/fGOcv4XsFnz/4Q1VEho36zoOhtLLJWbt0b/vB75N9MDHDzcQMF+PS3YxNdzE2zUV2Qo9w2tHwwWDatIsD0yEkZEDejGhv6XTInQyZ4/ScqYjIyKAfgH2kECqji7Wkb32D4rd/R1zrPuoLZrJj8sUsbsjjgeUp1HW4OSGvhU+MrSM5LnzEU7zbtp5nmt7kX3Ou4KyUEwf5BkRkILR3w7YW2NYMVc3O++3N0BY62CbZB2PTXIxNdzM2zUVpiouSVOc10WucYbsN2w4Lp9sh0u2cwOWBjDFOIM2ZDDmTnNf0MnC5Y3HbIiLy0SiE9pFCqIwa8XVbKXnzN6TsXkV7SgE7pl3GxrgJ3L88mVV7/eQndXHZhADl0aG3R2Ot5a91T1HTXc/PS79Opid1kO5ARAaTtc7Q3eoWqG6FHS2ws9V533DYYmOZfkNpqqE0xU1JiqE0JRpSkyJkBXdhmnZAww5o3A6NO6BlDwd6TT1xkD3RCaTZE50ta5wzzNfdm/kDRURkkCmE9pFCqIx47s5mCt/5Mznr/0nYG8/OiedTXXgK/3g/hX9uTMTjspxbUc8phU2HDL09lvpQM3+ofYyp8WP4Vv6NGpYrMsq0dsOeNmcY75422NMOe6OvgY5D1zFL8EBxNJSWprgoSDIUxQcpt7vI7a4mqW07pnGHE07beyyv7fJCRrkzGVLWeCeYZo2HzLGaDElEJLb0w6+PFEJl5IqEyd7wFEXLHsAdbGVf2ansnHA+b9dm8KcVyQTaPccdenssy1rX8lzz2/xbzgLOSDlhAG5ARIaj7rAzK+/h4XRvNLQGI4e297khL9FQmOSiIqGdye7dVLj2UBDeRUZwNwntu3G37oZIj3+nErMPBtKMiuhWDunlEJc0uDcsIjL6KIT2kUKojEhJu1dT+uY9JNRtpTlrLDumXsZGW8KfViazJjrr7WXjA5SnH3vo7bFYa3mw7kkCoUZ+XvJ1Mjwp/XgHIjISWQvNQdjX4fSY1nZGXzsg0Om81nXAYTmVVG+ImfEBZsTtYbx7D6XsJje0m7Su3fi6mw9tnJjtPHu6P5hmVDjhNKMc4tM1a6+ISN/pH9I+UgiVEcXXvJeipX8kc8urdMWnUz31ErZnzOCRtSm8sCWeeE+Ej1c0fKiht8dSF2riD7WPMT1+PP8v/3oNyxWRPgtHnGdRazsOBtTaTiecBjqd2X3rOw8G1WTaKTE1jHHXMNlXw3jPPoqpITdSQ0qo7pBzW18yJr0E0sogreSDm4b5ioj0hn7w9ZFmPJARwd3VQv7yh8hdsxAw7JpwLjsrzuaZqjQeXZJEZ8hwSlEz8yvqSfQe3sfw0WV6UjkreQ4vNC/l1ZZ3NVuuiPSZ2wXZ8c52NOGIMzlSoBPqOhKo6ywn0FnOkk54sgPqupwA64oEKTH7KDN7KTU1FIdqqQgGKK5dTZ59Cb89dDRI2JeMSSvBlV4G6aUfDKl+TcQmIiJ9pxAqw5oJB8lZu4iC9/6KO9hGoHgOOydewNLGXB58MZndLR7GZ7Rz0bgAeUndA1LDSYlT2dxZzQO1i5jgL6PAlz0g1xER2c/tgqx4ZyP9yG2shZZuH4GOIuo6iwh0wvudsLgD6jqhrsMS6molPVRLkQlQZGopCtVS3FFLac0qCs2LxHPoNMDd3mTCKcW4M0rxZkRDamoxpBU7s/lquK+IiPSChuPK8GQjZGx5jcKl9+Nv2UNT9niqp1zMmlApD61xnvvMTghy0bg6Jma2D/hvouZwG/fW/oMcTwZ3FN+E1+jvOyIyPHSGnFAa6DHkt64TAu2W7q4W/J0BUrtrKTS1FJtaCk2AQhOg2NSSaA7tSQ254wklF+HOKMWTUYpJK3ECamq0JzUpRyFVREYC/UPWRwqhMrxYS+qOpRQtu5+Euq20p+RTPeViNvin8L9rklm600+iN8w5ZQ2cXNSEpx+e++ytTZ3beaT+ec5NPYXPZV86eBcWERlgoYjzHOr+sLqvA/a1Wdra2/B01OLvCpAdcXpTe4bUVNN2yHnCLh+hpAJMutOTag4M9S12XpPzweWO0V2KiPSaQmgfqbtGhgdrSd61gqJlD5C0bwOdiVlsnX0tmzLm8Oi6FF6pisfntswvr+djJY34PYP/x5Xx/lJOSZzO801vM85fwseSZw16DSIiA8HjgpwEZzvIAElAEtaW09oNNe2wvQPeaXeCaktbO672AHFdATL3h9SGAEWNOynatoIsc+jMvhHjJpSYh0nv2ZPaY8hvShF4fIN45yIiMhAUQmXIS9qzhsJlfyJlzyqC8WlUzVhAVe5cFm5M5dklCUQsnFbcxNlljST5Pvx6n/3p7JQT2dNdy737HqPEl0tpXEFM6xERGQzGQLLP2cYeMsFuAlAClNDaDfvaYW8HrGl31lJtaOvC1R7A1xkgY39IbQpQ1BygaMf75JgGXBz8o6LFEErIwZVegjujLBpOew75LQbvMWZ0EhGRIUHDcWVospaU6ncoWP4QyXvX0h2XzO7xH2dTzqk8vjmNF7fEEwwbZue3ML+8gYz4UKwrPqA13M59tY/jdXn4UdGXSfMkx7okEZEhr70b9rY7val7oyE10BaC9jp8nQFyrDOBUqEJUEiAEnctedThPmxV1XB8ZnSG39JDn0dNK3ZCq19rOotIn2k4bh8phMrQYiOkVy0mf/lDJAYqCcansWfMmazLOo2Fm9N5pSqeiIWZua2cVdZAbuLAzHjbV3uCAf5c90+KfXn8sPCLxLk0fExE5KOyFpqCB0Pq/q22PUykvYG4zgB5HHwmtcgEKI2GVB+H/nciEpeKSSs59HnUAz2qJZrhV0R6Q/9I9JFCqAwJJtRF5uaXyFv1N+Ibq+lMzGbPuLNZmXIS/9iYxps7/BjgxIJmzihtJHMI9XwezcaObfyt4QVOSJjM/83/NG6jyTZERAZC2DoTJx3oRd2/tUUIdTQR3xWIhtNoSHUFKHUHKLAB/Bw6w2/Ek4BNLcaVUYo5sPxMycHhvok54BrEWe9EZChSCO0jhVCJKW9rLTnrFpG9/im8Xc20pxSwa9w5vOY6kae3JLFidxxet2VuYTPzShpJjYvtM58f1rLWtTzX/DanJ8/ippwFuIx+uIiIDLbusDNR0v5wurcDajucZWiCna3EdwXIp8dwXxOg2OV8TuEoM/ymleDJLMPVc3bf1GJnhl+3ptwQGeEUQvtI/0rK4LOWxJoN5K75BxlbXwcsjXlT2VY8jyeapvLsqkT2tnpI9oU4p7yB04qaSPRFjnvaoeikpKl02W5ebXkXv/HxuexLFURFRAaZ1w2FSc52KAMkE7bJNHaVO8G0A97pgGc6naDa3tGOrzNAUjBAQTSgFjXUUtS4i8Ltq8g2TYecMYKbNn8uXYmF2JRC3OlFxGeW4M8sxqQWQUohJGRoyK+IjGoKoTJoPB2NZG56kaz3nyGhYTshbzx7x8xjReYZLNxZzOtv+ukKuyhN7eCaKXVMy2kd1HU+B8rpSTPptiFebF5K0HbzrzlXaGiuiMgQ4jaQ6Xc20g//1pnhNxQpObBW6p4uWN8FDV3Q2hHE3REgvitAUneA9FAt+W0BCtsDFASqyKUBrzl0FE8XcTR6smiJy6UjPo/uxDwiyYWY1CJ86UXEZ5eQkp5NWkIcvpHwH0IRkcMohMrAioRJrX6XrI3PkrbtbVyREK3ppayf/CkWRU7lpeo0Nq/14XVFmJnbyilFTRSlBGNddb8yxnBW8hy8xs2rLe/REeniK7lX43N5Y12aiIj00pHXSgXwAQXRzZlEqbUb6rqgsgsaOiIE25twddYR11lPfHcdyd31pEXqyGirJ6d1KTm1DXjMoSN+2m0c1TaDfSaTOlcWDd4cWny5tPtz6UzII5SQjzsxneR4L0lxHlL8XpL9HpL8HpL9+/c5n+O9box6XkVkCNEzodL/bISkvevJ2PIa6Vtfw9deT7cviX2FJ/BS3Md4vKaCVXvjCFtDflIXs/NamFPQQqJ3eA65/TCWta7l+ea3KY8r5Jv5N5Dh0VIBIiKjmbXQGYrQ1dpIuK0OOupxddTh66onPlhHUncdqeEG0iL1h6yZCtBlvdTYNGpIp8ams886rzU2/cC+GptOp0kgye85JJjuD6qJcR4nvMb12KKfD/nO7yHR58HtUpgVQc+E9plCqPQPGyGx5n0ytrxKxtbX8LXVEXF5qM+exHtJc3m0dQ5v7kqiM+QiNS7ErLwWZuW2kp88sno9e2Nj53Yeb3iFRJefr+Rdw+T4iliXJCIiQ10kjCfYiLezDm9nPZ6uBrxdDbi7GnB1NuDpasQXbMAb7vjAoZ0mnkZPJvWuTOpMOjU2gxqbzp5IGjtDqVSHUtkZSqWL4y8nFu91HwipSYcHWP9hrz32J8c5PbXJ0QCsYcYyzCmE9pFCqHxk7q5WUnYtJ3XHMlJ3LMPXXk/E5aE2cyJvx53I39rm8M6+VIJhF35PhGnZrczKa6EivZPR/ofUmu56Hqt/kfpwM5eln8UVGefg0XOiIiLSR65QB56uhgMh1XltPLDP09WIt6seV+SD62x3e1Po8OfQHpdDqy+LJk8Wje5MGtyZ1JkMam0atTaVlrCbjmCYju4wHcEwnd0H37d3hwlHjv/bMs7jOhBID4TTA0H14L4U/8F9SQdCrLM/zuPSMGOJFf0Pr48UQqX3ImES6raQsnMFqTuWkrR3HS4b/v/t3XuMHWd5x/HvMzPnthd7s7t2Gl/qOMQkDm7IDQqEohBugSJCaVqSXoSqUP6hAqpWCKpKLVRIRapaUEsrUEgLFSWFAK0VtUkplwICQgiJwUnACYmTOLZjJ2vv/Zwzl6d/zOzuWV/wJmufOev9faTRzPuemd13/fic2d/OjSRqsG/txXwvvJzPTbyE3UfXAnBOPWb76AyXjE5zwTmzZ8VNhk6ndhZz5/h32TW7h83Vc/nDdW/jhY0tZQ9LRETOdu6EyfRxYXU+sDbH8qOurSOYH3+pTFJZQ9wYpd04t5ivI26so11fR7s+ymx1lIlomHFbw2zizLYTZuZC6vyUdLQTZuN8ea5vNj71I9kqoS1cD9vIQ+zAceG1M9RWitciBorA21fV9bLyvOg/zTIphMpJWRrTf3gPg/t3MXDgJwwe3E0Y56f5PNu3kfuql3Jn6zLuGN9OM4swnC1rm2wfnWH76Azn9rd1B/ol+OnsXu6a+C6T6TS/NngFNwy/lvWV4bKHJSIiq51nhO3JhZDaPkrUGi/mR4na4/nUOkqYNo/f3ELi+sh8SI3r6xYCa2MdcX20WF5PVulftG2W+XwwXRRY47y9KLAWIXZ2/vWUmVa+zql+yw0Dmz+tuDOkzl0z+4uOzM7dCGqgGhGs9lO8Vh8VfJkUQiWXpTSOPE7/4T30Hd6Tz599lCDNr9l8pnYeD0Yv5Fvxdu6Y3MFBH8ZwNg622To0y9ahJluHZlfs8zzL1srafHvyPu6ZfgDHefWal/CmoVeyobqu7KGJiIicUpA0F4fTRfO55TzAnujoaho1FoXU/MjqKEl9mLg+QlwbJi7aSXUNLOGZ25l7fqpwx9HVuQA70xFg55abcdpxxDbvn24lnOrsYjPor0YnDap91ZBGtZhXQhrVkL5ialSiheXi9b5qRL2iU417nIqzTAqhq41n1CYPUj/yBI2xx6kffYLG2F76xh6bD5ytoMZj4RZ2ZRfwzebF3J1ezBhrqIYZGwdb84Fzy9om9ai3/v+sdOPpFN+ZvI9dM3tIybi0sY1r1lzFVf2X6JEuIiKy8nlGGE91hNQjJwitxTyZPvGXsJC4dk4eTusjRVAdLYLqMEl9hLgIr0ltmKQyCMHzu++Cu9NKso4jrwuhdS7Udp5WvNC/sH4zyWgnz+2P9AbUqyF9i0JrVITUY/o61mucJNz2FSG4XsmDbiU0hdzl0T/eMimEnm3cCdtTVKcOU5s8SG3iINXJg9QmDlAZP0B9cj9RunBH2jEb4lE28ONkC/enF7Dbt/KY/xID1YwNg202DLTYMNhiw2CbkUa86m8o1C1T6Qz3Tj/Ertk9jKdT1K3KpX3buLz/Yi6qn895lVHtPERE5KxmWUzYniSKJ/J5e3x+HsWThO0JovYEYTyZz5OZE34dx0irgyTVIZLqWpLaEEltbUe76KuuXXi9mq/jYe20/CyZO+0ko5VktOI0nycpzTjvaydp/qieOO9fvF7RF3csH/O1kiXcDKpTaEa9GtCoLATTRvWYeSWk3rHcqHaue+Jt68csn8WP9Dlrf7BuWVIINbPrgI8DIXCLu//1Ma/XgM8CVwLPAm93973Fax8EbgZS4D3uftcv+l4KoYtZmuQfuK0pwuYkUSufwtYkQXMSn54gmB6jMv0s9Zln6GsdoeKLH3syQ40nfD1PZut53NfzsG/i4Wwjj9t5VPpqjDbajPbFjDRiRvti1vfFDNZOfUMAOfPcnb3t/Tw4+ygPN59kMsv/KtwfNLiwvpkLa5vZWF3P+sow6yvnMBj0K5yKiMiqlIfWCaL2JGFcBNT2JGEyTRhPFdN00Z6ebxsnP0qZhnXS6hrSSj9pNEBaGciX56ZooZ1F/d/KHzIAAAtMSURBVItfjzrXHcCDKmfqZhlJli2E1LgItEmaB984o1kE17kg3D6undFOF7ZZaC8E4+eYcwGoRnNhNTgu6NaikGoYUI0CalE+z5fDhb4woFYJOtYLO9YrtgkD6pWAahhSiYwwMCpBQBQalTAgDIwoOO1HfvXL1jJFp1rBzELgE8DrgH3APWa2090f7FjtZuCIu19oZjcCHwXebmaXADcCLwI2AP9rZi909xWRcCxtE7ansSzNJ09ptRI8TecnsgTPUjzN8uU0hSyFuXmWYmmCpW0sbmFJC4tbBGmLIMmnMG0RJi3CrEWUtqilszTSKfrSKRp+/IX+nSa9wSEf4kk/h6d5wfzDqp+xIZ6NhpmsDuO1BmsbKWtrCWtqCSO1lBfUY9bUDunIZo8zM7bWNrK1thF353ByhKfah3gqPsRT7cP8eGbPopsu1KzKUDRIf1CnP2jQCOr0BTVCCwkt4C1D1zBaGSrt5xERETlTPKiQ1EdI6iPPYaOMIGkSJlOLgmkYT3WE12mCtFk8/maM6sz+fJuiL8iW/szzLKyRBTWyqJiHNbKwjkdzy7X5dTyskUX1hW3CGh5WcAvxoGMehLhFeBAd3xdFeKWzr4JbkIdhC3CsuL42zPvJX3MzYGEdx0gzaKXQSp126rTSjHYy14Zm4nlwTTPiIujOhdyFdkycNJluZhxNM2ayCs0sIE4z4tSLeb58ukVBHlCj0IiCgKhYrgQBYZgH1UoYzL9eKeZhYASBEVp+I6swMD75+1ed9vGtNqcMocBLgUfc/VEAM7sNuB7oDKHXA39ZLN8O/IPlf264HrjN3VvAY2b2SPH1vneybxZYQF+l77n+HGfEmse/z+b//vMz8rWbXqFJlVlqxXKNGapF3zCTtolJG2Da+pgOBmiGfbSjfIor/SSVBl5rUKuEDNSc/oozUHMGqxnnVZzqosrOZf6IpZVcetVaRrmQbfPtdtbm2fgoY/E4Y/FRxpJxppIZmlmTiWyWQ/FRWlmLzJ2MjDecey3VxpoSfwIREZHelBXT8U9QPdWGCUHSJEhm8lBaTNaxHCSz+QGJrI2lbYL55RjL2gRpTBRPYM02lsVFf6tYN8bS1i88YrtS7X3dLUxuef1x/e5Okvl8IG0n2aKAOrfcLtpJmh+9TTInzbyYZ6QZpNlC/8Jrne0Tr9eKM2Y8f+5t5k7m+V2bsx67lHGlWkoi2Qg82dHeB/zqydZx98TMxoGRov/7x2y78dhvYGbvAt5VNFs7RnfsXtLopZtGgWfKHoQs8pxr8gZ+5wwNRTrovdJ7VJPeo5r0JtWl95zdNfnQb5c9gufF3s9ud99R9jhWsqWE0BOdsHnsnwBOts5StsXdPwV8CsDMfujuOsbdY1SX3qOa9CbVpfeoJr1HNelNqkvvUU16k5n9sOwxrHSnfshSfvRyc0d7E7D/ZOuYWQSsBcaWuK2IiIiIiIisEksJofcA28xsq5lVyW80tPOYdXYC7yiWbwC+7vltd3cCN5pZzcy2AtuAH5yeoYuIiIiIiMhKc8rTcYtrPP8IuIv8ES23uvsDZvZh4IfuvhP4NPCvxY2HxsiDKsV6XyC/iVECvHsJd8b91PP/ceQMUl16j2rSm1SX3qOa9B7VpDepLr1HNelNqssyLek5oSIiIiIiIiKnw1JOxxURERERERE5LRRCRUREREREpGt6KoSa2XVm9jMze8TMPlD2eFYrM7vVzA6Z2e6OvmEz+6qZPVzMzylzjKuNmW02s2+Y2UNm9oCZvbfoV11KYmZ1M/uBme0qavKhon+rmd1d1OTfixu6SReZWWhm95nZHUVbNSmZme01s5+Y2f1zjzbQ51e5zGzIzG43s58W+5aXqyblMrOLivfI3DRhZu9TXcplZn9c7Od3m9nni/2/9ivL1DMh1MxC4BPAG4FLgJvM7JJyR7Vq/Qtw3TF9HwC+5u7bgK8VbemeBPgTd98OvAx4d/H+UF3K0wKudfcXA5cB15nZy4CPAn9X1OQIcHOJY1yt3gs81NFWTXrDq939so5nHurzq1wfB+5094uBF5O/Z1STErn7z4r3yGXAlcAM8BVUl9KY2UbgPcBV7r6D/CatN6L9yrL1TAgFXgo84u6PunsbuA24vuQxrUru/i3yuxx3uh74TLH8GeCtXR3UKufuB9z9R8XyJPkvCxtRXUrjuamiWSkmB64Fbi/6VZMuM7NNwK8DtxRtQzXpVfr8KomZrQFeRf50A9y97e5HUU16yWuAn7v746guZYuAhplFQB9wAO1Xlq2XQuhG4MmO9r6iT3rDue5+APJABKwveTyrlpmdD1wO3I3qUqritM/7gUPAV4GfA0fdPSlW0edY930MeD+QFe0RVJNe4MD/mNm9Zvauok+fX+W5ADgM/HNx6votZtaPatJLbgQ+XyyrLiVx96eAvwGeIA+f48C9aL+ybL0UQu0EfXp+jEgHMxsAvgS8z90nyh7PaufuaXHa1Cbyszm2n2i17o5q9TKzNwOH3P3ezu4TrKqadN/V7n4F+SU37zazV5U9oFUuAq4A/sndLwem0SmePaO4vvAtwBfLHstqV1x/ez2wFdgA9JN/jh1L+5XnqJdC6D5gc0d7E7C/pLHI8Z42s/MAivmhksez6phZhTyAfs7dv1x0qy49oDiN7Zvk1+sOFafsgD7Huu1q4C1mtpf8ko5ryY+MqiYlc/f9xfwQ+TVuL0WfX2XaB+xz97uL9u3koVQ16Q1vBH7k7k8XbdWlPK8FHnP3w+4eA18GXoH2K8vWSyH0HmBbcbepKvlpCDtLHpMs2Am8o1h+B/CfJY5l1Smua/s08JC7/23HS6pLScxsnZkNFcsN8h3VQ8A3gBuK1VSTLnL3D7r7Jnc/n3wf8nV3/11Uk1KZWb+ZDc4tA68HdqPPr9K4+0HgSTO7qOh6DfAgqkmvuImFU3FBdSnTE8DLzKyv+F1s7r2i/coymXvvHD02szeR/9U6BG5194+UPKRVycw+D1wDjAJPA38B/AfwBeCXyd+Qv+Xux968SM4QM3sl8G3gJyxc6/Zn5NeFqi4lMLNLyW9GEJL/Qe8L7v5hM7uA/CjcMHAf8Hvu3ipvpKuTmV0D/Km7v1k1KVfx7/+VohkB/+buHzGzEfT5VRozu4z8Bl5V4FHgDyg+y1BNSmNmfeT3SLnA3ceLPr1XSlQ8gu3t5E8quA94J/k1oNqvLENPhVARERERERE5u/XS6bgiIiIiIiJyllMIFRERERERka5RCBUREREREZGuUQgVERERERGRrlEIFRERERERka5RCBURkRXNzH7DzNzMLi57LCIiInJqCqEiIrLS3QR8B7ix7IGIiIjIqSmEiojIimVmA8DVwM0UIdTMAjP7RzN7wMzuMLP/MrMbiteuNLP/M7N7zewuMzuvxOGLiIisSgqhIiKykr0VuNPd9wBjZnYF8DbgfOBXgHcCLwcwswrw98AN7n4lcCvwkTIGLSIisppFZQ9ARERkGW4CPlYs31a0K8AX3T0DDprZN4rXLwJ2AF81M4AQONDd4YqIiIhCqIiIrEhmNgJcC+wwMycPlQ585WSbAA+4+8u7NEQRERE5AZ2OKyIiK9UNwGfdfYu7n+/um4HHgGeA3yyuDT0XuKZY/2fAOjObPz3XzF5UxsBFRERWM4VQERFZqW7i+KOeXwI2APuA3cAngbuBcXdvkwfXj5rZLuB+4BXdG66IiIgAmLuXPQYREZHTyswG3H2qOGX3B8DV7n6w7HGJiIiIrgkVEZGz0x1mNgRUgb9SABUREekdOhIqIiIiIiIiXaNrQkVERERERKRrFEJFRERERESkaxRCRUREREREpGsUQkVERERERKRrFEJFRERERESka/4fosUNp71HuwYAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 934.5x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# We could have done the same thing for the 'person' column to include children:\n",
+    "\n",
+    "fig = sns.FacetGrid(titanic_df, hue=\"person\",aspect=4)\n",
+    "fig.map(sns.kdeplot,'Age',shade= True)\n",
+    "oldest = titanic_df['Age'].max()\n",
+    "fig.set(xlim=(0,oldest))\n",
+    "fig.add_legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/lw/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x1a1e634390>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 906.375x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Let's do the same for class by changing the hue argument:\n",
+    "fig = sns.FacetGrid(titanic_df, hue=\"Pclass\",aspect=4)\n",
+    "fig.map(sns.kdeplot,'Age',shade= True)\n",
+    "oldest = titanic_df['Age'].max()\n",
+    "fig.set(xlim=(0,oldest))\n",
+    "fig.add_legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We've gotten a pretty good picture of who the passengers were based on Sex, Age, and Class. So let's move on to our 2nd question: What deck were the passengers on and how does that relate to their class?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "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>person</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>male</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>female</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>female</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>female</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>male</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  person  \n",
+       "0      0         A/5 21171   7.2500   NaN        S    male  \n",
+       "1      0          PC 17599  71.2833   C85        C  female  \n",
+       "2      0  STON/O2. 3101282   7.9250   NaN        S  female  \n",
+       "3      0            113803  53.1000  C123        S  female  \n",
+       "4      0            373450   8.0500   NaN        S    male  "
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's get a quick look at our dataset again\n",
+    "titanic_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "统计不同船舱的人数分布\n",
+    "\n",
+    "So we can see that the Cabin column has information on the deck, but it has several NaN values, so we'll have to drop them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# First we'll drop the NaN values and create a new object, deck\n",
+    "deck = titanic_df['Cabin'].dropna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1      C85\n",
+       "3     C123\n",
+       "6      E46\n",
+       "10      G6\n",
+       "11    C103\n",
+       "Name: Cabin, dtype: object"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Quick preview of the decks\n",
+    "deck.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice we only need the first letter of the deck to classify its level (e.g. A,B,C,D,E,F,G)\n",
+    "\n",
+    "由上可发现船舱的类别由第一个字符可以加以区分可以得到各船舱人数的数量\n",
+    "\n",
+    "## 去cabin_df数据集的Cabin字段，颜色用winter_d，方法调用count\n",
+    "\n",
+    "palette的颜色有很多种，选择可以参考matplotlib 官方网站：http://matplotlib.org/users/colormaps.html   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/lw/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
+      "  warnings.warn(msg)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x1a1e9fc860>"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# So let's grab that letter for the deck level \n",
+    "# with a simple for loop\n",
+    "\n",
+    "# Set empty list\n",
+    "levels = []\n",
+    "\n",
+    "# Loop to grab first letter\n",
+    "for level in deck:\n",
+    "    levels.append(level[0])    \n",
+    "\n",
+    "# Reset DataFrame and use factor plot\n",
+    "cabin_df = DataFrame(levels)\n",
+    "cabin_df.columns = ['Cabin']\n",
+    "sns.factorplot('Cabin',data=cabin_df,palette='winter_d',kind='count')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Interesting to note we have a 'T' deck value there which doesn't make sense, we can drop it out with the following code:\n",
+    "\n",
+    "\n",
+    "因为上面T船舱的数量实在太小，酌情删除"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/lw/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
+      "  warnings.warn(msg)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x1a1f21d1d0>"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Redefine cabin_df as everything but where the row was equal to 'T'\n",
+    "cabin_df = cabin_df[cabin_df.Cabin != 'T']\n",
+    "#Replot\n",
+    "sns.factorplot('Cabin',data=cabin_df,palette='summer',kind='count')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Quick note: I used 'winter_d' and 'summer' as my palettes, but you can choose any palette you want. Check out this link for more palette names, you can add '_d' to the end of any palette name to make it darker.\n",
+    "\n",
+    "Link: http://matplotlib.org/users/colormaps.html\n",
+    "\n",
+    "Great now that we've analyzed the distribution by decks, let's go ahead and answer our third question:\n",
+    "\n",
+    "3.) Where did the passengers come from?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "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>person</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>male</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>female</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>female</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>female</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>male</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  person  \n",
+       "0      0         A/5 21171   7.2500   NaN        S    male  \n",
+       "1      0          PC 17599  71.2833   C85        C  female  \n",
+       "2      0  STON/O2. 3101282   7.9250   NaN        S  female  \n",
+       "3      0            113803  53.1000  C123        S  female  \n",
+       "4      0            373450   8.0500   NaN        S    male  "
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's take another look at our original data\n",
+    "titanic_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "统计进站港口的数量分布\n",
+    "\n",
+    "\n",
+    "Note here that the Embarked column has C,Q,and S values. Reading about the project on Kaggle you'll note that these stand for Cherbourg, Queenstown, Southhampton."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/lw/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
+      "  warnings.warn(msg)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x1a1f7544a8>"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 402.375x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Now we can make a quick factorplot to check out the results, note the x_order argument, used to deal with NaN values\n",
+    "# sns.factorplot('Embarked',data=titanic_df,hue='Pclass',x_order=['C','Q','S'],kind='count')\n",
+    "\n",
+    "sns.factorplot('Embarked',data=titanic_df,hue='Pclass',kind='count')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "An interesting find here is that in Queenstown, almost all the passengers that boarded there were 3rd class. It would be intersting to look at the economics of that town in that time period for further investigation.\n",
+    "\n",
+    "Now let's take a look at the 4th question:\n",
+    "\n",
+    "4.) Who was alone and who was with family?\n",
+    "\n",
+    "\n",
+    "统计单身及有家庭的人数分布"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       1\n",
+       "1       1\n",
+       "2       0\n",
+       "3       1\n",
+       "4       0\n",
+       "5       0\n",
+       "6       0\n",
+       "7       4\n",
+       "8       2\n",
+       "9       1\n",
+       "10      2\n",
+       "11      0\n",
+       "12      0\n",
+       "13      6\n",
+       "14      0\n",
+       "15      0\n",
+       "16      5\n",
+       "17      0\n",
+       "18      1\n",
+       "19      0\n",
+       "20      0\n",
+       "21      0\n",
+       "22      0\n",
+       "23      0\n",
+       "24      4\n",
+       "25      6\n",
+       "26      0\n",
+       "27      5\n",
+       "28      0\n",
+       "29      0\n",
+       "       ..\n",
+       "861     1\n",
+       "862     0\n",
+       "863    10\n",
+       "864     0\n",
+       "865     0\n",
+       "866     1\n",
+       "867     0\n",
+       "868     0\n",
+       "869     2\n",
+       "870     0\n",
+       "871     2\n",
+       "872     0\n",
+       "873     0\n",
+       "874     1\n",
+       "875     0\n",
+       "876     0\n",
+       "877     0\n",
+       "878     0\n",
+       "879     1\n",
+       "880     1\n",
+       "881     0\n",
+       "882     0\n",
+       "883     0\n",
+       "884     0\n",
+       "885     5\n",
+       "886     0\n",
+       "887     0\n",
+       "888     3\n",
+       "889     0\n",
+       "890     0\n",
+       "Name: Alone, Length: 891, dtype: int64"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's start by adding a new column to define alone\n",
+    "\n",
+    "# We'll add the parent/child column with the sibsp column\n",
+    "titanic_df['Alone'] =  titanic_df.Parch + titanic_df.SibSp\n",
+    "titanic_df['Alone']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "### 由上可知，大于1的都是有兄弟姐妹或者父母孩子的\n",
+    "\n",
+    "Now we know that if the Alone column is anything but 0, then the passenger had family aboard and wasn't alone. So let's change the column now so that if the value is greater than 0, we know the passenger was with his/her family, otherwise they were alone."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "cannot convert the series to <class 'int'>",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-57-495e516e56b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Look for >0 or ==0 to set alone status\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mtitanic_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Alone'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtitanic_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Alone'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'With Family'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mtitanic_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Alone'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtitanic_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Alone'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Alone'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    116\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mconverter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m         raise TypeError(\"cannot convert the series to \"\n\u001b[0;32m--> 118\u001b[0;31m                         \"{0}\".format(str(converter)))\n\u001b[0m\u001b[1;32m    119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    120\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: cannot convert the series to <class 'int'>"
+     ]
+    }
+   ],
+   "source": [
+    "# Look for >0 or ==0 to set alone status\n",
+    "\n",
+    "titanic_df['Alone'].loc[titanic_df['Alone'] > 0] = 'With Family'\n",
+    "titanic_df['Alone'].loc[titanic_df['Alone'] == 0] = 'Alone'\n",
+    "\n",
+    "# Note it's okay to ignore an  error that sometimes pops up here. For more info check out this link\n",
+    "# url_info = 'http://stackoverflow.com/questions/20625582/how-to-deal-with-this-pandas-warning'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "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>person</th>\n",
+       "      <th>Alone</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>male</td>\n",
+       "      <td>With Family</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>female</td>\n",
+       "      <td>With Family</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>female</td>\n",
+       "      <td>Alone</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>female</td>\n",
+       "      <td>With Family</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>male</td>\n",
+       "      <td>Alone</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  person        Alone  \n",
+       "0      0         A/5 21171   7.2500   NaN        S    male  With Family  \n",
+       "1      0          PC 17599  71.2833   C85        C  female  With Family  \n",
+       "2      0  STON/O2. 3101282   7.9250   NaN        S  female        Alone  \n",
+       "3      0            113803  53.1000  C123        S  female  With Family  \n",
+       "4      0            373450   8.0500   NaN        S    male        Alone  "
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's check to make sure it worked\n",
+    "titanic_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/lw/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
+      "  warnings.warn(msg)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x1a1f8c1e48>"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "# 统计Alone的发布人数\n",
+    "# Let us visualise the Alone column\n",
+    "\n",
+    "sns.factorplot('Alone',kind='count',data=titanic_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/lw/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
+      "  warnings.warn(msg)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x1a1f976710>"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 统计存活的以及没存活的分布\n",
+    "\n",
+    "# Now let's get a simple visualization!\n",
+    "sns.factorplot('Alone',data=titanic_df,palette='Blues',kind=\"count\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/lw/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
+      "  warnings.warn(msg)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x1a1f99f400>"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 402.375x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# let us see who are alone according to class\n",
+    "sns.factorplot('Alone',kind='count',data=titanic_df,hue='Pclass')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Great work! Now that we've throughly analyzed the data let's go ahead and take a look at the most interesting (and open-ended) question: What factors helped someone survive the sinking?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/lw/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
+      "  warnings.warn(msg)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x1a1fc68f28>"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Let's start by creating a new column for legibility purposes through mapping (Lec 36)\n",
+    "titanic_df[\"Survivor\"] = titanic_df.Survived.map({0: \"no\", 1: \"yes\"})\n",
+    "\n",
+    "# Let's just get a quick overall view of survied vs died. \n",
+    "sns.factorplot('Survivor',data=titanic_df,kind='count')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So quite a few more people died than those who survived. Let's see if the class of the passengers had an effect on their survival rate, since the movie Titanic popularized the notion that the 3rd class passengers did not do as well as their 1st and 2nd class counterparts."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/lw/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
+      "  warnings.warn(msg)\n",
+      "/Users/lw/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x1a1eacb358>"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Let's use a factor plot again, but now considering class\n",
+    "sns.factorplot('Pclass','Survived',data=titanic_df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Look like survival rates for the 3rd class are substantially lower! But maybe this effect is being caused by the large amount of men in the 3rd class in combination with the women and children first policy. Let's use 'hue' to get a clearer picture on this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/lw/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
+      "  warnings.warn(msg)\n",
+      "/Users/lw/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x1a1ea19c18>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 430.5x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Let's use a factor plot again, but now considering class and gender\n",
+    "sns.factorplot('Pclass','Survived',hue='person',data=titanic_df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From this data it looks like being a male or being in 3rd class were both not favourable for survival. Even regardless of class the result of being a male in any class dramatically decreases your chances of survival.\n",
+    "\n",
+    "But what about age? Did being younger or older have an effect on survival rate?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/lw/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x1a1dcaadd8>"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Let's use a linear plot on age versus survival\n",
+    "sns.lmplot('Age','Survived',data=titanic_df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Looks like there is a general trend that the older the passenger was, the less likely they survived. Let's go ahead and use hue to take a look at the effect of class and age."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/lw/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x1a1dfef080>"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 402.375x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Let's use a linear plot on age versus survival using hue for class seperation\n",
+    "sns.lmplot('Age','Survived',hue='Pclass',data=titanic_df,palette='winter')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also use the x_bin argument to clean up this figure and grab the data and bin it by age with a std attached!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/lw/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x1a1eca93c8>"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 402.375x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Let's use a linear plot on age versus survival using hue for class seperation\n",
+    "generations=[10,20,40,60,80]\n",
+    "sns.lmplot('Age','Survived',hue='Pclass',data=titanic_df,palette='winter',x_bins=generations)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Interesting find on the older 1st class passengers! What about if we relate gender and age with the survival set?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/lw/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x1a1ef15b38>"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 430.5x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.lmplot('Age','Survived',hue='Sex',data=titanic_df,palette='winter',x_bins=generations)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Fantastic work on your first go at a Data Analysis Project! Go ahead and keep playing with the data or try following along with Kaggle's sci-kit learn tutorial for this data (we'll look at it through a machine learning perspective later in the course)[¶](http://nbviewer.jupyter.org/github/jmportilla/Udemy-notes/blob/master/Intro%20to%20Data%20Projects%20-%20Titanic.ipynb#Fantastic-work-on-your-first-go-at-a-Data-Analysis-Project!-Go-ahead-and-keep-playing-with-the-data-or-try-following-along-with-Kaggle's-sci-kit-learn-tutorial-for-this-data-(we'll-look-at-it-through-a-machine-learning-perspective-later-in-the-course))\n",
+    "\n",
+    "#### Finally, I'll leave you with a gif of my favorite scene from the movie Titanic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<img src=\"http://i.imgur.com/DGNjT.gif\"/>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import Image\n",
+    "Image(url='http://i.imgur.com/DGNjT.gif')"
+   ]
+  },
+  {
+   "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": "3.6.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git "a/\347\233\230\345\217\244\345\274\200\345\244\251\345\234\260" "b/\347\233\230\345\217\244\345\274\200\345\244\251\345\234\260"
new file mode 100644
index 0000000..eb4bca4
--- /dev/null
+++ "b/\347\233\230\345\217\244\345\274\200\345\244\251\345\234\260"
@@ -0,0 +1 @@
+HaHaha