%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()
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
print(anascombe.groupby('dataset' )['x' ].mean())
print(anascombe.groupby('dataset' )['x' ].var())dataset
I 9.0
II 9.0
III 9.0
IV 9.0
Name: x, dtype: float64
dataset
I 11.0
II 11.0
III 11.0
IV 11.0
Name: x, dtype: float64
print(anascombe.groupby('dataset' )['y' ].mean())
print(anascombe.groupby('dataset' )['y' ].var())dataset
I 7.500909
II 7.500909
III 7.500000
IV 7.500909
Name: y, dtype: float64
dataset
I 4.127269
II 4.127629
III 4.122620
IV 4.123249
Name: y, dtype: float64
df = anascombe.groupby('dataset' )['x' ]
X = []
for key, x in df:
X.append(x.values)
df = anascombe.groupby('dataset' )['y' ]
Y = []
for key, y in df:
Y.append(y.values)
for i in range(0 ,len(X)):
print(np.corrcoef(X[i],Y[i])[0 ][1 ])0.81642051634484
0.8162365060002427
0.8162867394895982
0.8165214368885031
df = dict(list(anascombe.groupby('dataset' )))
for key in df.keys():
g = df[key]
is_train = [True ] * len(g)
train = g[is_train].reset_index(drop=True )
lin_model = smf.ols('y ~ x' ,train).fit()
print('dataset : ' + key + ':' )
print(lin_model.summary())dataset : I:
OLS Regression Results
==============================================================================
Dep. Variable: y R-squared: 0.667
Model: OLS Adj. R-squared: 0.629
Method: Least Squares F-statistic: 17.99
Date: Mon, 11 Jun 2018 Prob (F-statistic): 0.00217
Time: 15:50:25 Log-Likelihood: -16.841
No. Observations: 11 AIC: 37.68
Df Residuals: 9 BIC: 38.48
Df Model: 1
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
Intercept 3.0001 1.125 2.667 0.026 0.456 5.544
x 0.5001 0.118 4.241 0.002 0.233 0.767
==============================================================================
Omnibus: 0.082 Durbin-Watson: 3.212
Prob(Omnibus): 0.960 Jarque-Bera (JB): 0.289
Skew: -0.122 Prob(JB): 0.865
Kurtosis: 2.244 Cond. No. 29.1
==============================================================================
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
dataset : II:
OLS Regression Results
==============================================================================
Dep. Variable: y R-squared: 0.666
Model: OLS Adj. R-squared: 0.629
Method: Least Squares F-statistic: 17.97
Date: Mon, 11 Jun 2018 Prob (F-statistic): 0.00218
Time: 15:50:25 Log-Likelihood: -16.846
No. Observations: 11 AIC: 37.69
Df Residuals: 9 BIC: 38.49
Df Model: 1
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
Intercept 3.0009 1.125 2.667 0.026 0.455 5.547
x 0.5000 0.118 4.239 0.002 0.233 0.767
==============================================================================
Omnibus: 1.594 Durbin-Watson: 2.188
Prob(Omnibus): 0.451 Jarque-Bera (JB): 1.108
Skew: -0.567 Prob(JB): 0.575
Kurtosis: 1.936 Cond. No. 29.1
==============================================================================
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
dataset : III:
OLS Regression Results
==============================================================================
Dep. Variable: y R-squared: 0.666
Model: OLS Adj. R-squared: 0.629
Method: Least Squares F-statistic: 17.97
Date: Mon, 11 Jun 2018 Prob (F-statistic): 0.00218
Time: 15:50:25 Log-Likelihood: -16.838
No. Observations: 11 AIC: 37.68
Df Residuals: 9 BIC: 38.47
Df Model: 1
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
Intercept 3.0025 1.124 2.670 0.026 0.459 5.546
x 0.4997 0.118 4.239 0.002 0.233 0.766
==============================================================================
Omnibus: 19.540 Durbin-Watson: 2.144
Prob(Omnibus): 0.000 Jarque-Bera (JB): 13.478
Skew: 2.041 Prob(JB): 0.00118
Kurtosis: 6.571 Cond. No. 29.1
==============================================================================
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
dataset : IV:
OLS Regression Results
==============================================================================
Dep. Variable: y R-squared: 0.667
Model: OLS Adj. R-squared: 0.630
Method: Least Squares F-statistic: 18.00
Date: Mon, 11 Jun 2018 Prob (F-statistic): 0.00216
Time: 15:50:25 Log-Likelihood: -16.833
No. Observations: 11 AIC: 37.67
Df Residuals: 9 BIC: 38.46
Df Model: 1
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
Intercept 3.0017 1.124 2.671 0.026 0.459 5.544
x 0.4999 0.118 4.243 0.002 0.233 0.766
==============================================================================
Omnibus: 0.555 Durbin-Watson: 1.662
Prob(Omnibus): 0.758 Jarque-Bera (JB): 0.524
Skew: 0.010 Prob(JB): 0.769
Kurtosis: 1.931 Cond. No. 29.1
==============================================================================
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
g = sns.FacetGrid(anascombe,col = 'dataset' )
g.map(plt.scatter,'x' ,'y' )<seaborn.axisgrid.FacetGrid at 0x161fd70>
本文详细记录了使用Jupyter Notebook进行数据分析和编程实践的过程，包括创建、运行代码单元格，以及Markdown笔记的编写技巧。通过实例展示了如何利用Jupyter进行交互式学习和项目开发。
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