Pingouin: 基于pandas和numpy的统计包

Python网络爬虫与文本数据分析

pingouin是基于Pandas和numpy开发的Python3统计包。主要统计功能有

• 方差分析

• 多元线性回归

• 中介效应分析

• 卡方检验

• Q-Q图

• 贝叶斯因子

• 信效度检验

• 等等

我是统计小白，看不懂啊；还有很多功能没有列上，感兴趣的统计大神可以看看https://pingouin-stats.org/api.html

安装

pip3 install pingouin

快速上手

构造实验数据x，y

import numpy as np

#控制代码每次随机状态保持一致
np.random.seed(666)

n=30
mean= [4,5]
cov = [(1, 0.6), (0.6, 1)]

x, y = np.random.multivariate_normal(mean, cov, n).T

x

array([3.04817645, 2.54387965, 4.56033188, 4.40504338, 3.77876203,
       3.87177128, 3.4546112 , 4.47317551, 5.23133856, 5.40273745,
       5.19344217, 3.37061786, 3.23980982, 2.85574177, 4.67728276,
       4.31935242, 4.39440207, 3.87458876, 4.91426293, 3.13673286,
       3.73459839, 4.18708647, 5.48558345, 3.7066784 , 3.73400287,
       3.49664637, 3.95954844, 2.61545452, 5.11352964, 5.62666503])

y

array([4.47747109, 4.35695696, 5.46239455, 4.56091782, 4.07534588,
       4.03904897, 3.79549165, 5.06121364, 5.71635355, 6.60772697,
       6.94890455, 5.13347618, 5.41207983, 3.38254684, 5.49705058,
       5.93394729, 4.65224366, 4.59491971, 5.17926604, 4.25844527,
       5.72809738, 5.14997732, 5.27606588, 4.94570454, 6.02889647,
       5.85451666, 4.90231286, 4.69242625, 4.69367432, 6.71644528])

import matplotlib.pyplot as plt

plt.hist(x, bins=10)

plt.hist(y, bins=10)

1. T检验

import pingouin as pg

pg.ttest(x, y)

	T	dof	tail	p-val	CI95%	cohen-d	BF10	power
T-test	-4.597628	58	two-sided	0.000024	[-1.47, -0.58]	1.187102	786.346	0.994771

2. 皮尔森相关

pg.corr(x, y)

	n	r	CI95%	r2	adj_r2	p-val	BF10	power
pearson	30	0.60149	[0.31, 0.79]	0.36179	0.314515	0.000439	82.116	0.955747

3.鲁棒检验

#添加一个异常值
x[5] = 18
#使用Shepherd's pi correlation
pg.corr(x, y, method="shepherd")

	n	outliers	r	CI95%	r2	adj_r2	p-val	power
shepherd	30	1	0.569458	[0.26, 0.77]	0.324283	0.274229	0.001263	0.926066

4. 数据正态性检验

pg.normality(x)

	W	pval	normal
0	0.970533	0.553863	True

pg.normality(y)

	W	pval	normal
0	0.985161	0.939893	True

pg.multivariate_normality(np.column_stack((x, y)))

(True, 0.6257634649268228)

5. Q-Q plot

import numpy as np
import pingouin as pg

np.random.seed(666)

x = np.random.normal(size=50)
ax = pg.qqplot(x, dist='norm')

6. 单因素方差分析

# 读取数据
df = pg.read_dataset('mixed_anova')
df.sample(10)

	Scores	Time	Group	Subject
142	6.502562	January	Meditation	52
55	5.355380	January	Control	25
70	4.714565	June	Control	10
167	6.586494	June	Meditation	47
169	7.388138	June	Meditation	49
107	5.031982	August	Meditation	47
135	4.837971	January	Meditation	45
163	5.483801	June	Meditation	43
37	5.177205	January	Control	7
4	4.779411	August	Control	4

# Run the ANOVA
aov = pg.anova(data=df, 
               dv='Scores',  #因变量
               between='Group', 
               detailed=True)
aov

	Source	SS	DF	MS	F	p-unc	np2
0	Group	5.459963	1	5.459963	5.243656	0.0232	0.028616
1	Within	185.342729	178	1.041251	NaN	NaN	NaN

7. 重复测量方差分析

pg.rm_anova(data=df, 
            dv='Scores', 
            within='Time', 
            subject='Subject', 
            detailed=True)

	Source	SS	DF	MS	F	p-unc	np2	eps
0	Time	7.628428	2	3.814214	3.912796	0.022629	0.062194	0.998751
1	Error	115.027023	118	0.974805	NaN	NaN	NaN	NaN

8. 有交互作用的双因素方差分析

# Compute the two-way mixed ANOVA and export to a .csv file
aov = pg.mixed_anova(data=df, 
                     dv='Scores', 
                     between='Group', 
                     within='Time',
                     subject='Subject', 
                     correction=False, 
                     effsize="np2")
pg.print_table(aov)

=============
ANOVA SUMMARY
=============

Source          SS    DF1    DF2     MS      F    p-unc    np2      eps
-----------  -----  -----  -----  -----  -----  -------  -----  -------
Group        5.460      1     58  5.460  5.052    0.028  0.080  nan
Time         7.628      2    116  3.814  4.027    0.020  0.065    0.999
Interaction  5.167      2    116  2.584  2.728    0.070  0.045  nan

9. 多元线性回归

pg.linear_regression(data[['X', 'Z']], data['Y'])

	names	coef	se	T	pval	r2	adj_r2	CI[2.5%]	CI[97.5%]
0	Intercept	2.916901	1.444715	2.019015	0.053516	0.26855	0.214368	-0.047409	5.881210
1	X	0.610580	0.202261	3.018775	0.005487	0.26855	0.214368	0.195575	1.025584
2	Z	-0.007227	0.192089	-0.037624	0.970264	0.26855	0.214368	-0.401361	0.386907

10. 中介效应分析

pg.mediation_analysis(data=data, x='X', m='Z', y='Y', seed=42, n_boot=1000)

	path	coef	se	pval	CI[2.5%]	CI[97.5%]	sig
0	Z ~ X	-0.287032	0.191454	0.145006	-0.679207	0.105142	No
1	Y ~ Z	-0.165299	0.209888	0.437572	-0.595235	0.264637	No
2	Total	0.612654	0.191099	0.003354	0.221205	1.004103	Yes
3	Direct	0.610580	0.202261	0.005487	0.195575	1.025584	Yes
4	Indirect	0.002074	0.042262	0.976000	-0.088619	0.092009	No

Pingouin与Pandas

pandas.DataFrame可直接使用Pingouin的很多统计方法，例如

import pingouin as pg

# Example 1 | ANOVA
df = pg.read_dataset('mixed_anova')
df.anova(dv='Scores', between='Group', detailed=True)

# Example 2 | Pairwise correlations
data = pg.read_dataset('mediation')
data.pairwise_corr(columns=['X', 'M', 'Y'], covar=['Mbin'])

# Example 3 | Partial correlation matrix
data.pcorr()

pandas.DataFrame支持的pingouin统计方法有:

• pingouin.anova()

• pingouin.ancova()

• pingouin.rm_anova()

• pingouin.mixed_anova()

• pingouin.welch_anova()

• pingouin.pairwise_ttests()

• pingouin.pairwise_tukey()

• pingouin.pairwise_corr()

• pingouin.partial_corr()

• pingouin.pcorr()

• pingouin.rcorr()

• pingouin.mediation_analysis()

R语言相关

R语言 | 读写txt、csv、excel文件 
R语言 | 数据操作tidyr包

R语言 | 数据操作dplyr包  
R语言 | jiebaR中文分词包


Python相关[更新] Python网络爬虫与文本数据分析 
读完本文你就了解什么是文本分析

文本分析在经管领域中的应用概述  
综述:文本分析在市场营销研究中的应用

plotnine: Python版的ggplot2作图库
小案例: Pandas的apply方法  
stylecloud:简洁易用的词云库 
用Python绘制近20年地方财政收入变迁史视频  
Wow~70G上市公司定期报告数据集

漂亮~pandas可以无缝衔接Bokeh  
YelpDaset: 酒店管理类数据集10+G  



后台回复关键词【pingouin】获取本文代码和数据

“分享”和“在看”是更好的支持！