循环遍历列时如何在 pandas 中生成清晰的图? [英] How to generate legible plots in pandas when looping over columns?

问题描述

生成可复制的数据帧:

 <代码> DF = pd.DataFrame(np.random.randn(50，1000)，列=列表('ABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDED'))

检查每个变量分布的正态性(注意:这需要很长时间才能运行)

#设置列名列= df.columns# 遍历所有列图, axs = plt.subplots(len(df.columns), figsize=(5, 25))对于 n, col in enumerate(df.columns):df[col].hist(ax=axs[n])

结果生成难以辨认的直方图并且需要很长时间运行.

时间长度还可以，但我很好奇是否有人有生成清晰直方图的建议(不必花哨)，可以快速查看整个数据框以确保分布的正态性.

解决方案

我真的很喜欢

Generate the dataframe for replicability:

df = pd.DataFrame(np.random.randn(50, 1000), columns=list('ABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDED'))

Check for normalcy of distribution of each variable (note: this takes a long time to run)

# Set the column names

columns= df.columns

# Loop over all columns

fig, axs = plt.subplots(len(df.columns), figsize=(5, 25))
for n, col in enumerate(df.columns):
    df[col].hist(ax=axs[n])

Result generates illegible histograms and takes a very long time to run.

The length of time is okay, but I am curious if anyone has suggestions for generating legible histograms (do not have to be fancy), which can be quickly reviewed for the entire dataframe to ensure the normality of the distributions.

解决方案

I really like Nathaniel's answer but I will add my two cents.

I would go for seaborn and in particular seaborn.distplot. This will allow you to easily fit a normal distribution to each histogram plot and make the visualization easier.

import seaborn as sns
from scipy.stats import norm
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

cols = 1000
df = pd.DataFrame(np.random.normal(0, 1, [50, cols]))
from scipy.stats import norm
fig, ax = plt.subplots(figsize = (16, 10))
for i, col in enumerate(df.columns):
    ax=fig.add_subplot(25, 4, i+1)
    sns.distplot(df[col],fit=norm, kde=False,ax=ax)
plt.tight_layout()

Additionally, I am not sure if putting columns with the same name in your example was done on purpose. If that's the case the easiest solution to loop through the columns is to use .iloc and the code would look like this:

import seaborn as sns
from scipy.stats import norm
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

 df = pd.DataFrame(np.random.randn(50, 1000), columns=list('ABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDEDABCDABCDED'))

fig, ax = plt.subplots(figsize = (12, 10))
for i, col in enumerate(df.columns):
    plt.subplot(25, 40, i+1)
    sns.distplot(df.iloc[:,i],fit=norm, kde=False,ax=plt.gca())
    plt.axis('off')
plt.tight_layout()

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