pandas 使用 Seaborn FacetGrid 绘制相关热图
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Plotting correlation heatmaps with Seaborn FacetGrid
提问by fakedrake
I am trying to create a single image with heatmaps representing the correlation of features of data points for each label separately. With seaborn I can create a heatmap for a single class like so
我正在尝试创建一个带有热图的单个图像，分别表示每个标签的数据点特征的相关性。使用 seaborn，我可以像这样为单个类创建热图
grouped = df.groupby('target')
sns.heatmap(grouped.get_group('Class_1').corr())
An I get this which makes sense:
我明白这是有道理的：
But then I try to make a list of all the labels like so:
但后来我尝试列出所有标签，如下所示：
g = sns.FacetGrid(df, col='target')
g.map(lambda grp: sns.heatmap(grp.corr()))
And sadly I get this which makes no sense to me:
可悲的是，我明白了这对我来说毫无意义：
回答by fakedrake
Turns out you can do it pretty conciesely with just seaborn if you use map_dataframeinstead of map.
事实证明，如果您使用map_dataframe而不是map.
g = sns.FacetGrid(df, col='target')
g.map_dataframe(lambda data, color: sns.heatmap(data.corr(), linewidths=0))
@mwaskom points out in his comment that it might be a good idea to explicitly set the limits of the colormap so that the different facets can be more directly compared. The documentationdescribes relevant heatmapparameters:
@mwaskom 在他的评论中指出，明确设置颜色图的限制可能是一个好主意，以便可以更直接地比较不同的方面。该文档描述的相关heatmap参数：
vmin, vmax: floats, optional
Values to anchor the colormap, otherwise they are inferred from the data and other keyword arguments. When a diverging dataset is inferred, one of these values may be ignored.
vmin，vmax：浮点数，可选
锚定颜色图的值，否则它们是从数据和其他关键字参数推断出来的。当推断出发散数据集时，可能会忽略这些值之一。
回答by cphlewis
Without FacetGrid, but making a corr heatmap for each group in a column:
没有 FacetGrid，但为列中的每个组制作 corr 热图：
import pandas as pd
import seaborn as sns
from numpy.random import randint
import matplotlib.pyplot as plt
df = pd.DataFrame(randint(0,10,(200,12)),columns=list('abcdefghijkl'))
grouped = df.groupby('a')
rowlength = grouped.ngroups/2 # fix up if odd number of groups
fig, axs = plt.subplots(figsize=(9,4), nrows=2, ncols=rowlength)
targets = zip(grouped.groups.keys(), axs.flatten())
for i, (key, ax) in enumerate(targets):
sns.heatmap(grouped.get_group(key).corr(), ax=ax,
xticklabels=(i >= rowlength),
yticklabels=(i%rowlength==0),
cbar=False) # Use cbar_ax into single side axis
ax.set_title('a=%d'%key)
plt.show()
Maybe there's a way to set up a lambda to correctly pass the data from the g.facet_data()generator through corrbefore going to heatmap.
也许有一个办法λ设定了正确传递从数据 g.facet_data()通过发电机corr才去heatmap。
