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使用python进行常见统计分析_python 统计分析

作者：喵喵爱编程 | 2024-07-23 05:20:30

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python 统计分析

文章目录


不可否认的是python的统计功能若于R和SAS，但对于常见的统计分析，python亦可以实现。本文介绍Python中的ggplot2绘图库：plotnine，使用python完成常见的统计描述、分布差异检验、相关分析和回归分析方法 

# plotnine：python中的ggplot2
import plotnine as pn 
from plotnine import data

import numpy as np
import pandas as pd

# 统计分析
from scipy import stats
import statsmodels.api as sm
from statsmodels.formula.api import ols, glm, poisson

import copy

# 阻止pandas产生warnings（提示DataFrame的相关操作生成引用or副本）
import warnings
warnings.filterwarnings("ignore")

# jupyter中同一个cell的多个结果均自动输出，不需挨个手工print
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"

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使用plotnine库自带的mtcars汽车数据集

选取mtcars的子集df，共32个记录，6个变量

df = data.mtcars[["wt", "mpg", "cyl", "vs", "am", "gear"]]

df.shape
df.dtypes
print(df.head())

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(32, 6)


wt      float64
mpg     float64
cyl       int64
vs        int64
am        int64
gear      int64
dtype: object

      wt   mpg  cyl  vs  am  gear
0  2.620  21.0    6   0   1     4
1  2.875  21.0    6   0   1     4
2  2.320  22.8    4   1   1     4
3  3.215  21.4    6   1   0     3
4  3.440  18.7    8   0   0     3

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将变量vs、am、gear由数值型连续变量转为字符型分类变量

df["vs"] = df["vs"].astype(str)
df["am"] = df["am"].astype(str)
df["gear"] = df["gear"].astype(str)

df.dtypes

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wt      float64
mpg     float64
cyl       int64
vs       object
am       object
gear     object
dtype: object

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变量分布

# 连续变量的常见分布统计量
print(df.describe())

# 分类变量的类别及频数
df["vs"].value_counts()
df["am"].value_counts()
df["gear"].value_counts()

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              wt        mpg        cyl
count  32.000000  32.000000  32.000000
mean    3.217250  20.090625   6.187500
std     0.978457   6.026948   1.785922
min     1.513000  10.400000   4.000000
25%     2.581250  15.425000   4.000000
50%     3.325000  19.200000   6.000000
75%     3.610000  22.800000   8.000000
max     5.424000  33.900000   8.000000

0    18
1    14
Name: vs, dtype: int64


0    19
1    13
Name: am, dtype: int64


3    15
4    12
5     5
Name: gear, dtype: int64

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plotnine绘图

plotnine是python的一个绘图库，模仿了ggplot2的语法和绘图样式，如果熟悉R的ggplot2，那么该库可快速上手。
官方文档

该库与R的ggplot2使用中主要有以下2点不同：

  • ggplot()的mapping参数，R中写为aes(x = mpg, y = wt)，plotnine写为aes(x="mpg", y="wt")
  • plotnine要求整个绘图语句为一个语句，若中间需要换行，需要使用\连接或者语句首尾加括号

以下是几个简单的例子

散点图+回归线

pn.ggplot(data = df, mapping=pn.aes(x="mpg", y="wt")) + \
    pn.geom_point() + \
    pn.geom_smooth(method="lm") + \
    pn.theme_classic()

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output_10_0__1

<ggplot: (-9223371901789411352)>

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分组

按照vs分组，分别绘制散点图、回归线及其95%置信区间

pn.ggplot(data = df, mapping=pn.aes(x="mpg", y="wt", color="vs")) + \
    pn.geom_point() + \
    pn.geom_smooth(method="lm") + \
    pn.theme_classic()

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output_13_0__2

<ggplot: (-9223371901789391480)>

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分面（xkcd主题）

按照变量vs和gear进行分面

pn.ggplot(data = df, mapping=pn.aes(x="mpg", y="wt")) + \
    pn.facet_grid("vs ~ gear") + \
    pn.geom_point() + \
    pn.geom_smooth(method="lm") + \
    pn.theme_xkcd()

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output_16_0__34

<ggplot: (-9223371901789166508)>

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描述性统计量

各变量的缺失值

df.apply(lambda x: sum(x.isnull())).sort_values()

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wt       0
mpg      0
cyl      0
vs       0
am       0
gear     0
count    0
dtype: int64

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连续变量和分类变量的分布信息

print(df.describe())

# count：分类变量非缺失值的数量
# unique：分类变量唯一值的数量
# top：分类变量中出现频次最高的数
# freq：分类变量中出现频次最高的数出现了几次
print(df.describe(include="object"))

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              wt        mpg        cyl
count  32.000000  32.000000  32.000000
mean    3.217250  20.090625   6.187500
std     0.978457   6.026948   1.785922
min     1.513000  10.400000   4.000000
25%     2.581250  15.425000   4.000000
50%     3.325000  19.200000   6.000000
75%     3.610000  22.800000   8.000000
max     5.424000  33.900000   8.000000
        vs  am gear
count   32  32   32
unique   2   2    3
top      0   0    3
freq    18  19   15

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# 频次
df["vs"].value_counts()
# 构成比
df["vs"].value_counts(normalize=True)

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0    18
1    14
Name: vs, dtype: int64


0    0.5625
1    0.4375
Name: vs, dtype: float64

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其他描述性统计量

# 方差
np.var(df["wt"])
# 标准差
np.std(df["wt"])

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0.927460875


0.9630477013107918

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# 众数
stats.mode(df["wt"])

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ModeResult(mode=array([3.44]), count=array([3]))

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# 偏度
stats.skew(df["wt"])

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0.44378553550607736

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# 峰度
stats.kurtosis(df["wt"])

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0.1724705401587343

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正态分布样本均值的标准差（即标准误）及其95%置信区间

# 标准误
se = np.std(df["wt"]) / np.sqrt(len(df["wt"]))
se
# 区间上下限
np.mean(df["wt"]) - 1.96 * se
np.mean(df["wt"]) + 1.96 * se

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0.17024439005074438


2.883570995500541


3.550929004499459

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统计学检验

正态性检验

p = 0.09，在0.05的显著性水平下接受原假设，即未发现变量wt不符合正态分布

stats.shapiro(df["wt"])

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ShapiroResult(statistic=0.9432578682899475, pvalue=0.09265592694282532)

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两独立样本均值t检验

p = 0.38，未发现方差不齐

stats.bartlett(df.loc[df["vs"] == "0", "wt"], df.loc[df["vs"] == "1", "wt"])

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BartlettResult(statistic=0.7611752294629192, pvalue=0.38296098768025166)

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p = 0.001，两总体均值存在显著性差异

# 方差齐性检验显示方差齐，故equal_var=True；若不齐，需要使用校正后的t检验（指定equal_var=False）或使用秩和检验
stats.ttest_ind(df.loc[df["vs"] == "0", "wt"], df.loc[df["vs"] == "1", "wt"], equal_var=True)

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Ttest_indResult(statistic=3.653533152238974, pvalue=0.0009798492309250216)

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两相关样本均值t检验

df_copy = copy.deepcopy(df)
df_copy.loc[:16, "group"] = "0"
df_copy.loc[16:, "group"] = "1"
df_copy["group"].value_counts()

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0    16
1    16
Name: group, dtype: int64

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stats.ttest_rel(df_copy.loc[df_copy["group"] == "0", "wt"], 
                df_copy.loc[df_copy["group"] == "1", "wt"])

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Ttest_relResult(statistic=2.0782957471723527, pvalue=0.05526298801644364)

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关于独立样本还是相关样本，举个例子：
一项RCT（随机对照实验）想研究药物A和药物B对老年人的降压效果：研究分A组和B组两组，两组各用药14天，分别在基线（入组时）和14天时测血压值。对于A组或B组内部来说，想研究用药前后的血压值是否存在显著性差异，可使用两相关样本t检验（相当于比较前后差值的均值是否显著异于0）；如果想比较A组和B组的降压效果是否存在显著性差异，可先分别求得A组和B组血压前后的变化值，然后对二组的变化值进行独立样本t检验【其他方法如协方差分析，基线血压作为协变量】。

两独立样本分布的秩和检验

stats.ranksums(df.loc[df["vs"] == "0", "wt"], df.loc[df["vs"] == "1", "wt"])

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RanksumsResult(statistic=3.2668698585096214, pvalue=0.0010874365767340446)

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两相关样本分布的秩和检验

stats.wilcoxon(df_copy.loc[df_copy["group"] == "0", "wt"], 
               df_copy.loc[df_copy["group"] == "1", "wt"])

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WilcoxonResult(statistic=27.0, pvalue=0.033538818359375)

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方差分析

先拟合回归模型，再对回归模型进行方差分析得到方差分析表
p = 0.0003，说明三组间均值存在显著性差异

print(sm.stats.anova_lm(ols("wt ~ C(gear)", data = df).fit()))

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            df     sum_sq   mean_sq          F    PR(>F)
C(gear)    2.0  12.878947  6.439473  11.115889  0.000261
Residual  29.0  16.799801  0.579303        NaN       NaN

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进一步进行多重比较（参考文章），发现3组和4组、3组和5组间存在显著性差异

mc = sm.stats.multicomp.MultiComparison(df["wt"], df["gear"])
print(mc.tukeyhsd())

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Multiple Comparison of Means - Tukey HSD, FWER=0.05 
====================================================
group1 group2 meandiff p-adj   lower   upper  reject
----------------------------------------------------
     3      4  -1.2759  0.001 -2.0038 -0.5481   True
     3      5    -1.26 0.0089 -2.2305 -0.2895   True
     4      5   0.0159    0.9 -0.9844  1.0163  False
----------------------------------------------------

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卡方检验

方法使用选择：（n为总样本数，E为每个单元格的理论频数）

  • 当n >= 40，且E >= 5：使用普通卡方检验；当 p ≈ α p\approx \alpha p≈α（ α \alpha α为显著性水平）使用Fisher确切概率法
  • 当n >= 40，但1 < E < 5：使用校正卡方检验或者Fisher确切概率法
  • 当n < 40或者E <= 1：使用Fisher确切概率法
print(pd.crosstab(df["vs"], df["am"]))

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am   0  1
vs       
0   12  6
1    7  7

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由于n = 32 < 40，应直接使用Fisher确切概率法；这里使用校正卡方检验（correction = True）仅为显示其用法

stats.chi2_contingency(pd.crosstab(df["vs"], df["am"]), correction = True)

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(0.34753550543024225,
 0.5555115470131495,
 1,
 array([[10.6875,  7.3125],
        [ 8.3125,  5.6875]]))

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Fisher确切概率法：p = 0.47，未发现两变量分布存在显著性关联

stats.fisher_exact(pd.crosstab(df["vs"], df["am"]))

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(2.0, 0.4726974416017807)

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相关分析

pearson相关

stats.pearsonr(df["wt"], df["mpg"])

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(-0.8676593765172278, 1.2939587013505124e-10)

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spearman相关

stats.spearmanr(df["wt"], df["mpg"])

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SpearmanrResult(correlation=-0.8864220332702978, pvalue=1.487594858127497e-11)

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回归分析

多重线性回归

lm.summary()返回大量信息，各变量回归系数为coef。如mpg的回归系数为-0.1191且p < 0.05，意味着在保持cyl不变的情况下，mpg每减少1个单位，wt相应减少0.12 (95%CI, -0.18 ~ -0.06)个单位。

lm = ols("wt ~ mpg + cyl", df).fit()
print(lm.summary())

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                            OLS Regression Results                            
==============================================================================
Dep. Variable:                     wt   R-squared:                       0.760
Model:                            OLS   Adj. R-squared:                  0.743
Method:                 Least Squares   F-statistic:                     45.82
Date:                Sun, 19 Jul 2020   Prob (F-statistic):           1.05e-09
Time:                        17:06:33   Log-Likelihood:                -21.393
No. Observations:                  32   AIC:                             48.79
Df Residuals:                      29   BIC:                             53.18
Df Model:                           2                                         
Covariance Type:            nonrobust                                         
==============================================================================
                 coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------
Intercept      5.0760      1.117      4.544      0.000       2.791       7.361
mpg           -0.1191      0.028     -4.216      0.000      -0.177      -0.061
cyl            0.0863      0.095      0.905      0.373      -0.109       0.281
==============================================================================
Omnibus:                        5.603   Durbin-Watson:                   0.987
Prob(Omnibus):                  0.061   Jarque-Bera (JB):                4.488
Skew:                           0.910   Prob(JB):                        0.106
Kurtosis:                       3.234   Cond. No.                         277.
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

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wt的回归拟合值

lm.predict(df)

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0     3.092788
1     3.092788
2     2.705930
3     3.045155
4     3.539185
5     3.438123
6     4.063142
7     2.515401
8     2.705930
9     3.307134
10    3.473847
11    3.813072
12    3.705899
13    3.955969
14    4.527559
15    4.527559
16    4.015510
17    1.562751
18    1.800914
19    1.384130
20    2.860736
21    3.920245
22    3.955969
23    4.182224
24    3.479645
25    2.170065
26    2.324871
27    1.800914
28    3.884521
29    3.247593
30    3.979786
31    2.872644
dtype: float64

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含有定性变量

定性变量写入C()内，reference指定参照水平
如回归系数0.0738意味着，保持mpg不变的情况下，vs = 1组的wt相比于vs = 0组的wt的均值高出0.07个单位，但p值不显著（一般情况下先看p值，若p值显著大于设定的显著性水平如p = 0.65，则不必再看回归系数）

lm = ols("wt ~ mpg + C(vs, Treatment(reference=0))", df).fit()
print(lm.summary())

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                            OLS Regression Results                            
==============================================================================
Dep. Variable:                     wt   R-squared:                       0.754
Model:                            OLS   Adj. R-squared:                  0.737
Method:                 Least Squares   F-statistic:                     44.36
Date:                Sun, 19 Jul 2020   Prob (F-statistic):           1.51e-09
Time:                        17:06:33   Log-Likelihood:                -21.786
No. Observations:                  32   AIC:                             49.57
Df Residuals:                      29   BIC:                             53.97
Df Model:                           2                                         
Covariance Type:            nonrobust                                         
======================================================================================================
                                         coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------------------------------
Intercept                              6.0973      0.353     17.274      0.000       5.375       6.819
C(vs, Treatment(reference=0))[T.1]     0.0738      0.239      0.308      0.760      -0.416       0.563
mpg                                   -0.1450      0.020     -7.243      0.000      -0.186      -0.104
==============================================================================
Omnibus:                        5.895   Durbin-Watson:                   1.108
Prob(Omnibus):                  0.052   Jarque-Bera (JB):                4.733
Skew:                           0.932   Prob(JB):                       0.0938
Kurtosis:                       3.279   Cond. No.                         89.3
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

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加入非线性变换

对因变量进行变换，此处相当于先对wt取对数获得变量log_wt，再利用log_wt对其它变量回归
mpg的回归系数-0.0495可解释为：mpg每增加1个单位，wt减少 [ e − 0.05 − 1 ] × 100 % [e^{-0.05} -1]\times 100\% [e−0.05−1]×100%，p值显著

lm = ols("np.log(wt) ~ mpg + C(vs, Treatment(reference=0))", df).fit()
print(lm.summary())

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                            OLS Regression Results                            
==============================================================================
Dep. Variable:             np.log(wt)   R-squared:                       0.812
Model:                            OLS   Adj. R-squared:                  0.799
Method:                 Least Squares   F-statistic:                     62.66
Date:                Sun, 19 Jul 2020   Prob (F-statistic):           2.97e-11
Time:                        17:06:33   Log-Likelihood:                 18.563
No. Observations:                  32   AIC:                            -31.13
Df Residuals:                      29   BIC:                            -26.73
Df Model:                           2                                         
Covariance Type:            nonrobust                                         
======================================================================================================
                                         coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------------------------------
Intercept                              2.0995      0.100     20.988      0.000       1.895       2.304
C(vs, Treatment(reference=0))[T.1]     0.0371      0.068      0.547      0.588      -0.102       0.176
mpg                                   -0.0495      0.006     -8.724      0.000      -0.061      -0.038
==============================================================================
Omnibus:                        2.197   Durbin-Watson:                   1.607
Prob(Omnibus):                  0.333   Jarque-Bera (JB):                1.872
Skew:                           0.471   Prob(JB):                        0.392
Kurtosis:                       2.282   Cond. No.                         89.3
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

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对自变量进行变换，此处相当于对mpg取平方后获得变量mpg2，再使用mpg2参与回归
回归方程可写为 w t ^ = 4.53 − 0.003 × m p g 2 − 0.10 × v s \hat {wt} = 4.53 - 0.003\times mpg^{2} - 0.10\times vs wt^=4.53−0.003×mpg2−0.10×vs

lm = ols("wt ~ np.square(mpg) + C(vs, Treatment(reference=0))", df).fit()
print(lm.summary())

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                            OLS Regression Results                            
==============================================================================
Dep. Variable:                     wt   R-squared:                       0.680
Model:                            OLS   Adj. R-squared:                  0.658
Method:                 Least Squares   F-statistic:                     30.78
Date:                Sun, 19 Jul 2020   Prob (F-statistic):           6.75e-08
Time:                        17:06:33   Log-Likelihood:                -25.982
No. Observations:                  32   AIC:                             57.96
Df Residuals:                      29   BIC:                             62.36
Df Model:                           2                                         
Covariance Type:            nonrobust                                         
======================================================================================================
                                         coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------------------------------
Intercept                              4.5263      0.198     22.906      0.000       4.122       4.930
C(vs, Treatment(reference=0))[T.1]    -0.0965      0.265     -0.364      0.718      -0.638       0.445
np.square(mpg)                        -0.0029      0.000     -5.803      0.000      -0.004      -0.002
==============================================================================
Omnibus:                        6.860   Durbin-Watson:                   0.883
Prob(Omnibus):                  0.032   Jarque-Bera (JB):                5.754
Skew:                           1.027   Prob(JB):                       0.0563
Kurtosis:                       3.307   Cond. No.                     1.35e+03
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.35e+03. This might indicate that there are
strong multicollinearity or other numerical problems.

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Logistic回归

Binomial指定因变量为二项分布，logit指定连接函数为logit: l o g i t ( p ) = l n ( p 1 − p ) logit(p) = ln(\frac{p}{1 - p}) logit(p)=ln(1−pp​)

logit = glm("vs ~ mpg + C(am, Treatment(reference=0))", df, 
            family=sm.families.Binomial(sm.families.links.logit)).fit()
print(logit.summary())

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                 Generalized Linear Model Regression Results                  
==============================================================================
Dep. Variable:     ['vs[0]', 'vs[1]']   No. Observations:                   32
Model:                            GLM   Df Residuals:                       29
Model Family:                Binomial   Df Model:                            2
Link Function:                  logit   Scale:                          1.0000
Method:                          IRLS   Log-Likelihood:                -10.323
Date:                Sun, 19 Jul 2020   Deviance:                       20.646
Time:                        17:06:33   Pearson chi2:                     20.2
No. Iterations:                     6                                         
Covariance Type:            nonrobust                                         
======================================================================================================
                                         coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------------------------------
Intercept                             12.7051      4.625      2.747      0.006       3.640      21.770
C(am, Treatment(reference=0))[T.1]     3.0073      1.599      1.880      0.060      -0.128       6.142
mpg                                   -0.6809      0.252     -2.698      0.007      -1.176      -0.186
======================================================================================================

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OR值

Logistic回归中各变量回归系数经自然指数转化后可解释为OR（Odds Ratio，比值比或优势比），当结局发生率较低时（<10%）可用作RR的估计值反映暴露变量效应大小：如am的回归系数为3.0073，意味着保持mpg不变时，am = 1组的结局发生风险是am = 0组的 e 3.01 = 20.23 e^{3.01} = 20.23 e3.01=20.23倍，p值边缘不显著（p = 0.06）。【由于vs的结局发生率40%多，此处使用Logistic回归估计OR值反应暴露变量效应是不合适的，此处可使用Log-binomial模型估计现患比PR】

np.exp(logit.params[1:])

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C(am, Treatment(reference=0))[T.1]    20.232169
mpg                                    0.506151
dtype: float64

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泊松回归

泊松回归的因变量为单位时间或单位空间事件数的发生数，为计数变量（正整数）

# 生成符合泊松分布的因变量count，均值为5
df["count"] = np.random.poisson(lam=5, size=32)
df["count"].value_counts()

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5     11
4      5
3      4
6      3
2      3
10     2
8      2
13     1
7      1
Name: count, dtype: int64

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poi = poisson("count ~ mpg + C(vs, Treatment(reference=0))", df).fit()
print(poi.summary())

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Optimization terminated successfully.
         Current function value: 2.174190
         Iterations 5
                          Poisson Regression Results                          
==============================================================================
Dep. Variable:                  count   No. Observations:                   32
Model:                        Poisson   Df Residuals:                       29
Method:                           MLE   Df Model:                            2
Date:                Sun, 19 Jul 2020   Pseudo R-squ.:                 0.02153
Time:                        17:06:33   Log-Likelihood:                -69.574
converged:                       True   LL-Null:                       -71.105
Covariance Type:            nonrobust   LLR p-value:                    0.2163
======================================================================================================
                                         coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------------------------------
Intercept                              2.0939      0.313      6.691      0.000       1.481       2.707
C(vs, Treatment(reference=0))[T.1]    -0.0290      0.211     -0.137      0.891      -0.443       0.385
mpg                                   -0.0218      0.018     -1.199      0.230      -0.057       0.014
======================================================================================================

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RR值

泊松回归中各变量回归系数经自然指数转化后可解释为RR（Relative Risk，相对危险度），关联性研究中反映了暴露变量效应大小
如vs的回归系数为-0.0290，意味着保持mpg不变时，vs = 1组的结局发生风险是vs = 0组的 e − 0.03 = 0.98 e^{-0.03} = 0.98 e−0.03=0.98倍，但p值不显著（p = 0.89）

np.exp(poi.params[1:])

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C(vs, Treatment(reference=0))[T.1]    0.971449
mpg                                   0.978414
dtype: float64

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