相关热图 [英] Correlation heatmap

问题描述

我想用热图表示相关矩阵。 R中有一个叫做

以下是代码：

  plt.imshow（correlation_matrix，cmap ='hot '，interpolation ='nearest'）
  

解决方案

在seaborn中使用热图函数绘制协方差。此示例使用R中ISLR包中的Auto数据集（与您显示的示例相同）。

  import pandas .rpy.com通常是com 
，进口是seasn的sns 
％matplotlib内联
 
＃加载R包ISLR 
 infert = com.importr（ ISLR）
 
＃加载自动数据集
 auto_df = com.load_data（'Auto'）
 
＃计算相关矩阵
 corr = auto_df.corr（） 
 
＃绘制热图
 sns.heatmap（corr，
 xticklabels = corr.columns，
 yticklabels = corr.columns）
  

如果需要为了更加精美，您可以使用

I want to represent correlation matrix using a heatmap. There is something called correlogram in R, but I don't think there's such a thing in Python.

How can I do this? The values go from -1 to 1, for example:

[[ 1.          0.00279981  0.95173379  0.02486161 -0.00324926 -0.00432099]
 [ 0.00279981  1.          0.17728303  0.64425774  0.30735071  0.37379443]
 [ 0.95173379  0.17728303  1.          0.27072266  0.02549031  0.03324756]
 [ 0.02486161  0.64425774  0.27072266  1.          0.18336236  0.18913512]
 [-0.00324926  0.30735071  0.02549031  0.18336236  1.          0.77678274]
 [-0.00432099  0.37379443  0.03324756  0.18913512  0.77678274  1.        ]]

I was able to produce the following heatmap based on another question, but the problem is that my values get 'cut' at 0, so I would like to have a map which goes from blue(-1) to red(1), or something like that, but here values below 0 are not presented in an adequate way.

Here's the code for that:

plt.imshow(correlation_matrix,cmap='hot',interpolation='nearest')

解决方案

Another alternative is to use the heatmap function in seaborn to plot the covariance. This example uses the Auto data set from the ISLR package in R (the same as in the example you showed).

import pandas.rpy.common as com
import seaborn as sns
%matplotlib inline

# load the R package ISLR
infert = com.importr("ISLR")

# load the Auto dataset
auto_df = com.load_data('Auto')

# calculate the correlation matrix
corr = auto_df.corr()

# plot the heatmap
sns.heatmap(corr, 
        xticklabels=corr.columns,
        yticklabels=corr.columns)

If you wanted to be even more fancy, you can use Pandas Style, for example:

cmap = cmap=sns.diverging_palette(5, 250, as_cmap=True)

def magnify():
    return [dict(selector="th",
                 props=[("font-size", "7pt")]),
            dict(selector="td",
                 props=[('padding', "0em 0em")]),
            dict(selector="th:hover",
                 props=[("font-size", "12pt")]),
            dict(selector="tr:hover td:hover",
                 props=[('max-width', '200px'),
                        ('font-size', '12pt')])
]

corr.style.background_gradient(cmap, axis=1)\
    .set_properties(**{'max-width': '80px', 'font-size': '10pt'})\
    .set_caption("Hover to magify")\
    .set_precision(2)\
    .set_table_styles(magnify())

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