[Course] Advanced Computer Programming, Homework, week 14, Jupyter Notebook_pandas.py:56: futurewarning: the pandas.core.datet-优快云博客

本文链接：https://blog.youkuaiyun.com/GeassTaiga/article/details/80670987

这是一篇关于高级计算机编程课程的作业总结，重点介绍了第14周的内容，主要使用了Jupyter Notebook作为工具进行编程和分析。文章分为Part 1和Part 2两个部分，详细探讨了相关编程概念和实践应用。

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# Jupyter Notebook Exercises

%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")

C:\ProgramData\Anaconda3\lib\site-packages\statsmodels\compat\pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead. from pandas.core import datetools

anascombe = pd.read_csv('anscombe.csv')
anascombe.head()

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	dataset	x	y
0	I	10.0	8.04
1	I	8.0	6.95
2	I	13.0	7.58
3	I	9.0	8.81
4	I	11.0	8.33

Part 1

anascombe.groupby(['dataset']).mean()

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	x	y
dataset
I	9.0	7.500909
II	9.0	7.500909
III	9.0	7.500000
IV	9.0	7.500909

anascombe.groupby(['dataset']).std()

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	x	y
dataset
I	3.316625	2.031568
II	3.316625	2.031657
III	3.316625	2.030424
IV	3.316625	2.030579

anascombe.groupby(['dataset']).corr()

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		x	y
dataset
I	x	1.000000	0.816421
I	y	0.816421	1.000000
II	x	1.000000	0.816237
II	y	0.816237	1.000000
III	x	1.000000	0.816287
III	y	0.816287	1.000000
IV	x	1.000000	0.816521
IV	y	0.816521	1.000000

for i in range(4):
    model = smf.ols('y ~ x', anascombe[11*i:11*i+11]).fit()
    print('----'+ str(i) +'----')
    print(model.params)

----0----
Intercept    3.000091
x            0.500091
dtype: float64
----1----
Intercept    3.000909
x            0.500000
dtype: float64
----2----
Intercept    3.002455
x            0.499727
dtype: float64
----3----
Intercept    3.001727
x            0.499909
dtype: float64

Part 2

fig = sns.FacetGrid(anascombe, row='dataset')
fig.map(plt.scatter, 'x', 'y')

<seaborn.axisgrid.FacetGrid at 0x1f1ba7b3d30>