Yingying_Exercise 7

Exercise_7.py

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+# Question 1
+# I am not a big fan of plotnine, so I used the matplotlib and seaborn, which I think are a little bit more popular. 
+import pandas as pd
+import matplotlib.pyplot as plt
+data=pd.read_table('/Users/chenyingying/Documents/Intro_Biocom_ND_319_Tutorial7/Lecture11.fasta',header=None)
+df=pd.DataFrame(columns=['ID','Length','%GC'])
+df['ID']=range(1,101,1)
+for i in range(0,100,1):
+    temp=data.iloc[i*2+1,0]
+    df.iloc[i,1]=len(temp)
+    df.iloc[i,2]=100*(temp.count('C')+temp.count('G'))/len(temp)
+fig=plt.figure(figsize=(8,8))
+fig.suptitle('Histogram')
+ax1 = fig.add_subplot(121)
+ax1=plt.hist(df['Length'],edgecolor="black")
+plt.title('Sequence Length')
+ax2 = fig.add_subplot(122)
+ax2=plt.hist(df['%GC'],edgecolor="black")
+plt.title('%GC')
+# Question 2
+import pandas as pd
+import seaborn as sns
+data_2=pd.read_table('/Users/chenyingying/Documents/Intro_Biocom_ND_319_Tutorial7/LakeGPP.txt',sep='\t')
+sns.lmplot(x="TP_mgm3", y="GPP_mmolm3d", data=data_2,size=8)
+#Question 3
+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn.preprocessing import LabelEncoder
+import seaborn as sns
+data_3=pd.read_table('/Users/chenyingying/Documents/Intro_Biocom_ND_319_Tutorial7/data.txt',sep=',')
+le=LabelEncoder()
+le.fit(data_3['region'])
+data_3['Index']=le.transform(data_3['region'])
+# Barplot of mean 
+fig=plt.figure(figsize=(8,8))
+data_3.groupby('region')['observations'].mean().plot.bar()
+plt.title('Barplot of mean')
+# Scatter plot
+f2=sns.pairplot(x_vars=["Index"], y_vars=["observations"], data=data_3, hue="region",size=8)
+f2.set(xticks=[0,1,2,3])
+f2.set(xticklabels=['east','north','south','west'])
+f2.set(xlabel='region')
+f2.set(title='Scatter plot')

LakeGPP.txt

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+"TP_mgm3"	"GPP_mmolm3d"
+39.8	34.2
+9.4	11.8
+41.5	74.9
+55.9	36.9
+13.2	3.8
+17.9	34.4
+4.4	6.1
+7.1	10
+23.2	25.5
+19.9	7.8
+20.2	13.3
+34.3	15
+34.9	77.9
+105.3	144.3
+18.6	35
+78.3	47.9
+15.3	7.1
+21.3	60.8
+28	28.3
+34	13.3
+42.5	30.5
+29.3	49.1
+25.2	7.3
+28.1	32.1
+13.8	29.7