高级编程技术第十二次作业

本次作业题目来源

 https://nbviewer.jupyter.org/github/schmit/cme193-ipython-notebooks-lecture/blob/master/Exercises.ipynb 

Anscombe's quartet

Anscombe's quartet comprises of four datasets, and is rather famous. Why? You'll find out in this exercise.

里面本来有的代码

%matplotlib inline

import random

import numpy as np
import scipy as sp
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

import statsmodels.api as sm
import statsmodels.formula.api as smf

sns.set_context("talk")
anascombe = pd.read_csv('data/anscombe.csv')
anascombe.head()

anascombe.head:

Part 1

For each of the four datasets...

• Compute the mean and variance of both x and y
• Compute the correlation coefficient between x and y
• Compute the linear regression line: y=β0+β1x+ϵy=β0+β1x+ϵ (hint: use statsmodels and look at the Statsmodels notebook)

代码

print('The mean of x and y:')    
print(anascombe.groupby('dataset')[['x', 'y']].mean())    
print('\nThe varience of x and y:')    
print(anascombe.groupby('dataset')[['x', 'y']].var())    
print('\nThe correlation coefficient between x and y:')    
print(anascombe.groupby('dataset')['x'].corr(anascombe['y']))
print('\nThe linear regression line:')  
for dataset in anascombe.dataset.unique():
    lin_model = smf.ols('y ~ x',anascombe.query("dataset == '{}'".format(dataset))).fit()    
    print(lin_model.summary())  

运行结果：

由于linear regression一个数据集的篇幅已经很多，这里只给出一个数据集的效果，其他数据集的效果在后面。

Part 2

Using Seaborn, visualize all four datasets.

hint: use sns.FacetGrid combined with plt.scatter

gra= sns.FacetGrid(anascombe, col='dataset')    
gra.map(plt.scatter, 'x', 'y') 

用提示给的facetgrid和plt.scatter完成画图的操作。效果：