当处理大量特征之间的相关性时，我发现将相关特征聚类在一起很有用。这可以用seaborn clustermap图来完成。

import seaborn as sns
import matplotlib.pyplot as plt

g = sns.clustermap(df.corr(), 
                   method = 'complete', 
                   cmap   = 'RdBu', 
                   annot  = True, 
                   annot_kws = {'size': 8})
plt.setp(g.ax_heatmap.get_xticklabels(), rotation=60);

clustermap函数使用层次聚类将相关特征排列在一起并生成树状树状图。

在这个图中有两个值得注意的集群:

Y_des和dew.point_des Irradiance, y_seasonal和dew.point_seasonal

FWIW的气象数据，以产生这一数字可以访问与这木星笔记本。

试试这个函数，它也会显示相关矩阵的变量名:

def plot_corr(df,size=10):
    """Function plots a graphical correlation matrix for each pair of columns in the dataframe.

    Input:
        df: pandas DataFrame
        size: vertical and horizontal size of the plot
    """

    corr = df.corr()
    fig, ax = plt.subplots(figsize=(size, size))
    ax.matshow(corr)
    plt.xticks(range(len(corr.columns)), corr.columns)
    plt.yticks(range(len(corr.columns)), corr.columns)

请检查下面可读的代码

import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
plt.figure(figsize=(36, 26))
heatmap = sns.heatmap(df.corr(), vmin=-1, vmax=1, annot=True)
heatmap.set_title('Correlation Heatmap', fontdict={'fontsize':12}, pad=12)```

  [1]: https://i.stack.imgur.com/I5SeR.png

corrmatrix = df.corr()
corrmatrix *= np.tri(*corrmatrix.values.shape, k=-1).T
corrmatrix = corrmatrix.stack().sort_values(ascending = False).reset_index()
corrmatrix.columns = ['Признак 1', 'Признак 2', 'Корреляция']
corrmatrix[(corrmatrix['Корреляция'] >= 0.7) + (corrmatrix['Корреляция'] <= -0.7)]
drop_columns = corrmatrix[(corrmatrix['Корреляция'] >= 0.82) + (corrmatrix['Корреляция'] <= -0.7)]['Признак 2']
df.drop(drop_columns, axis=1, inplace=True)
corrmatrix[(corrmatrix['Корреляция'] >= 0.7) + (corrmatrix['Корреляция'] <= -0.7)]

Statmodels图形也提供了一个很好的相关矩阵视图

import statsmodels.api as sm
import matplotlib.pyplot as plt

corr = dataframe.corr()
sm.graphics.plot_corr(corr, xnames=list(corr.columns))
plt.show()

可以使用matplotlib中的imshow()方法

import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('ggplot')

plt.imshow(X.corr(), cmap=plt.cm.Reds, interpolation='nearest')
plt.colorbar()
tick_marks = [i for i in range(len(X.columns))]
plt.xticks(tick_marks, X.columns, rotation='vertical')
plt.yticks(tick_marks, X.columns)
plt.show()