seaborn 热图显示轴标签，但当 df.corr 为 NaN 时没有值
[英]seaborn heatmap displays axis labels, but no values when df.corr is NaN
问题描述
我试图提出相关性的热图，但我意识到有些是错误的。
下面是我的热图。 如您所见，该操作的编号并未出现。
这是我的数据框
all_gen_cols = steamUniqueTitleGenre[['action', 'adventure','casual', 'indie','massively_multiplayer','rpg','racing','simulation','sports','strategy']]
action adventure casual indie massively_multiplayer rpg racing simulation sports strategy
0 1 0 0 0 0 0 0 0 0 0
1 1 1 0 0 1 0 0 0 0 0
2 1 1 0 0 0 0 0 0 0 1
3 1 1 0 0 1 0 0 0 0 0
4 1 0 0 0 1 1 0 0 0 1
这是生成热图的代码
def plot_correlation_heatmap(df):
corr = df.corr()
sb.set(style='white')
mask = np.zeros_like(corr, dtype=np.bool)
mask[np.triu_indices_from(mask)] = True
f, ax = plt.subplots(figsize=(11,9))
cmap = sb.diverging_palette(220, 10, as_cmap=True)
sb.heatmap(corr, mask=mask, cmap=cmap, vmax=0.3, center=0,
square=True, linewidths=.5, cbar_kws={"shrink": .5}, annot=True)
plt.yticks(rotation=0)
plt.show()
plt.rcdefaults()
plot_correlation_heatmap(all_gen_cols)
我不确定是什么错误。
print(all_gen_cols.corr())的结果如下。 我看到 NaN 采取行动，但我不确定为什么是 Nan。
action adventure casual indie massively_multiplayer rpg racing simulation sports strategy
action NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
adventure NaN 1.000000 0.007138 0.135392 0.023964 0.239136 -0.039846 0.036345 -0.064489 0.001435
casual NaN 0.007138 1.000000 0.235474 0.003487 -0.057726 0.079943 0.161448 0.149549 0.084417
indie NaN 0.135392 0.235474 1.000000 -0.082661 0.023372 0.045006 0.064723 0.056297 0.076749
massively_multiplayer NaN 0.023964 0.003487 -0.082661 1.000000 0.160078 0.036685 0.139929 0.018444 0.074683
rpg NaN 0.239136 -0.057726 0.023372 0.160078 1.000000 -0.046970 0.044506 -0.051714 0.097123
racing NaN -0.039846 0.079943 0.045006 0.036685 -0.046970 1.000000 0.127511 0.308864 -0.012170
simulation NaN 0.036345 0.161448 0.064723 0.139929 0.044506 0.127511 1.000000 0.212622 0.208754
sports NaN -0.064489 0.149549 0.056297 0.018444 -0.051714 0.308864 0.212622 1.000000 0.020048
strategy NaN 0.001435 0.084417 0.076749 0.074683 0.097123 -0.012170 0.208754 0.020048 1.000000
下面是通过打印print(all_gen_cols.describe())
action adventure casual indie massively_multiplayer rpg racing simulation sports strategy
count 14570.0 14570.000000 14570.000000 14570.000000 14570.000000 14570.000000 14570.000000 14570.000000 14570.000000 14570.000000
mean 1.0 0.362663 0.232189 0.657241 0.050927 0.165202 0.040288 0.121826 0.044269 0.127111
std 0.0 0.480785 0.422244 0.474648 0.219855 0.371376 0.196641 0.327096 0.205699 0.333108
min 1.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 1.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
50% 1.0 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
75% 1.0 1.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
max 1.0 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
数据
这是下载数据框的链接。
action,adventure,casual,indie,massively_multiplayer,rpg,racing,simulation,sports,strategy
1,0,0,0,0,0,0,0,0,0
1,1,0,0,1,0,0,0,0,0
1,1,0,0,0,0,0,0,0,1
1,1,0,0,1,0,0,0,0,0
1,0,0,0,1,1,0,0,0,1
1,0,0,0,0,0,0,0,0,0
1,1,0,0,0,1,0,0,0,0
1,1,0,0,1,1,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,1,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,0,0,1,0,0,0,0
1,1,0,0,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,1,0,1,0,0,0,0,0,0
1,1,0,0,0,1,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,0,0,1,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,1,0,1,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,1,1,1,0,0,0,1,0,1
1,0,0,1,0,0,0,0,0,0
1,0,0,0,0,1,0,0,0,0
1,1,0,0,0,1,0,0,0,0
1,0,0,0,0,0,0,0,0,1
1,1,0,1,0,0,0,0,0,0
1,1,0,0,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,1,1,1,0,0,0,0,0,0
1,1,1,1,0,1,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,1,0,0,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,0,0,0,0,1,0,1
1,1,0,0,1,1,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,1,0,1,0,0,0,0,0,0
1,0,0,0,1,0,0,0,0,0
1,1,0,0,0,1,0,0,0,0
1,1,0,0,0,0,0,0,0,0
1,0,0,0,0,0,0,0,1,0
1,0,0,1,1,0,0,0,0,1
1,0,0,0,0,0,0,0,0,0
1,0,0,1,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,1,1,0,1,0,0,0,0,0
1,0,1,0,1,0,0,0,1,0
1,0,0,0,0,1,0,0,0,0
1,0,0,0,0,0,0,0,0,1
1,1,0,1,0,1,0,1,0,1
1,0,1,1,1,0,0,0,0,1
1,1,1,1,0,0,0,0,1,0
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1,0,0,0,1,0,0,0,0,0
1,1,0,0,0,1,0,0,0,0
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1,1,0,1,0,0,0,1,0,0
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1,1,0,1,0,0,0,1,0,0
1,0,0,1,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,1,0,1,0,0,0,0,0,0
1,1,1,0,1,1,0,1,0,0
1,0,0,0,0,0,0,0,0,0
1,1,0,0,0,0,0,0,0,0
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1,1,0,1,1,0,0,0,0,0
1,1,0,0,0,0,0,0,0,0
1,1,0,0,0,0,0,0,0,0
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1,1,0,1,1,0,0,1,0,1
1,0,0,0,0,0,0,1,0,0
1,1,0,0,0,0,0,1,0,0
1,1,0,0,0,0,0,0,0,0
1,0,1,1,0,0,0,0,0,0
1,1,0,1,0,0,0,0,0,1
1,1,0,1,1,0,0,1,0,0
1,0,0,1,0,0,0,1,0,0
1,0,0,0,0,0,0,0,0,0
1,1,0,0,0,0,0,0,0,0
1,0,1,1,0,0,0,0,0,0
1,1,1,1,0,0,0,0,0,1
1,1,0,0,0,0,0,0,0,0
1,0,0,1,0,0,0,0,0,0
1,1,1,0,1,1,0,0,0,0
1,1,0,1,0,0,0,0,0,0
1,1,0,1,0,0,0,0,0,0
1,1,0,1,0,0,0,0,0,0
1,0,0,1,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
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1,1,0,1,0,0,0,1,0,0
1,0,0,1,0,0,0,1,0,0
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1,0,0,0,1,0,0,0,0,0
1,0,0,0,1,0,0,1,0,1
1,1,0,0,0,0,0,0,0,0
1,0,1,1,0,0,0,1,0,0
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1,1,0,0,0,0,0,0,0,0
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1,0,0,1,0,0,0,0,0,0
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1,1,0,1,0,0,0,0,0,0
1,1,1,1,0,1,0,0,0,0
1,0,0,0,0,0,0,0,0,0
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1,0,1,0,0,0,0,0,0,0
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1,0,0,1,0,0,0,1,0,1
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1,1,0,1,0,0,0,0,0,0
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1,1,0,1,1,1,0,0,0,0
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1,0,0,0,0,0,0,1,0,1
1,0,0,0,0,0,0,0,0,0
1,1,0,0,0,0,0,0,0,0
1,0,0,1,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,1,0,1,0,0,0,0,0,0
1,0,0,0,1,0,0,1,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,0,0,1,0,0,0,0
1,0,0,0,0,1,0,0,0,0
1,0,0,0,1,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,1
1,0,0,1,0,0,0,1,0,1
1,1,0,0,1,1,0,1,0,1
1,1,0,1,1,1,0,0,0,0
1,1,0,0,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,1,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,1
1,0,0,1,0,0,1,0,1,0
1,0,0,0,0,1,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,1,0,1,1,1,0,0,0,0
1,0,0,0,0,0,0,0,0,1
1,0,0,0,0,0,0,0,0,0
1,0,0,0,0,1,0,0,0,0
1,0,0,1,0,0,0,1,0,0
1,0,0,1,0,0,0,0,0,0
1,0,0,1,0,0,0,0,0,1
1,0,0,1,0,0,0,1,0,0
1,1,0,1,1,1,0,1,0,1
1,0,0,1,0,1,0,0,0,0
1,1,1,0,0,1,0,0,0,0
1,0,0,1,0,0,0,0,0,0
1,1,0,0,0,0,0,0,0,0
1,1,0,1,0,0,0,0,0,1
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1,1,0,1,0,0,0,0,0,0
1,0,1,0,0,0,0,0,0,0
1,0,0,0,1,1,0,0,0,0
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1,1,0,0,0,1,0,0,0,0
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1,1,0,1,1,1,0,1,0,0
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1,1,1,1,0,0,0,0,0,0
1,0,0,1,0,0,0,0,0,1
1,1,0,0,1,1,0,0,0,0
1,0,0,1,0,0,0,0,0,0
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1,1,0,1,0,1,0,0,0,0
1,0,0,1,1,0,0,0,0,0
1,1,0,1,0,1,0,0,0,0
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1,0,1,1,0,0,0,1,0,0
1,0,0,0,1,0,0,0,0,0
1,1,0,1,1,1,0,0,0,0
1,1,0,0,0,1,1,1,1,0
1,1,0,1,0,0,0,1,0,1
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1,0,0,1,0,0,0,0,0,0
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1,0,1,1,0,0,0,0,0,0
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1,1,0,0,1,1,0,0,0,0
1,0,0,0,0,0,0,1,0,1
1,1,0,1,1,0,0,1,0,1
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1,1,0,1,0,0,0,0,0,0
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1,1,0,1,0,1,0,1,0,0
1,1,1,1,1,1,0,0,0,0
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1,1,0,1,0,0,0,0,0,0
1,1,1,0,1,0,0,0,1,0
1,1,0,1,0,1,0,0,0,0
1,0,0,0,0,0,0,1,0,1
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1,1,0,1,0,1,0,0,0,0
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1,0,0,1,0,0,0,0,0,0
1,1,0,0,0,0,0,0,0,0
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1,1,1,1,0,0,0,0,0,1
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1,1,0,0,0,0,0,0,0,0
1,1,0,0,1,1,0,0,0,0
1,0,0,0,0,0,1,1,1,0
1,0,0,0,0,1,0,0,0,1
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1,0,0,1,0,1,0,0,0,1
1,1,0,0,0,0,0,1,1,0
1,0,0,1,0,0,0,0,0,1
1,1,0,0,0,1,0,0,0,0
1,0,0,1,0,0,0,1,0,1
1,0,0,0,0,0,0,0,0,0
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1,1,1,0,1,1,0,0,0,0
1,0,0,1,0,0,0,0,0,0
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1,1,1,0,0,1,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,0,0,0,0,1,0,0
1,0,0,0,0,1,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,1,0,1,0,0,0,0,0,0
1,0,0,1,0,0,0,1,0,0
1,0,0,0,0,0,0,0,0,0
1,0,1,1,0,0,0,0,0,0
1,1,1,1,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,1,0,0,0,1,0,0,0,0
1,0,0,1,0,0,0,0,0,0
1,1,0,0,0,0,0,0,0,0
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1,1,0,0,0,0,0,0,0,0
1,1,0,1,0,1,0,1,0,0
1,0,0,0,0,0,0,0,0,0
1,1,0,0,0,1,0,0,0,0
1,0,0,1,0,1,0,1,0,0
1,1,0,0,1,0,0,0,0,0
1,0,0,0,0,1,0,0,0,0
1,0,0,1,0,0,0,0,0,0
1,1,0,1,0,0,0,0,0,0
1,0,0,1,0,1,0,0,0,0
1,1,0,1,0,0,0,0,0,0
1,1,0,0,0,1,0,0,0,0
1,1,0,0,0,1,0,0,0,0
1,0,0,0,0,1,0,0,0,0
1,1,0,1,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,1,0,0,0,0,0,0
1,0,1,1,0,0,0,1,0,0
1,0,0,1,0,0,1,0,0,0
1,1,1,0,0,1,1,0,1,1
1,1,0,0,0,0,0,0,0,0
1,1,1,1,0,0,0,0,0,0
1,1,0,1,0,0,0,0,0,0
1,0,0,1,0,0,0,0,0,0
1,1,0,1,0,1,0,0,0,0
1,1,0,0,0,0,0,0,0,1
1,0,0,0,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,1,0,1,0,0,0,1,0,0
1,0,0,1,0,0,0,0,0,0
1,0,0,1,0,0,0,1,0,1
1,0,0,0,0,0,0,0,0,0
1,1,0,1,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,1,1,0,0,0,0,1,0
1,0,0,1,0,0,0,0,0,0
1,0,0,1,0,0,0,0,0,0
1,1,0,1,0,1,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,1,0,0,0,0,0,0,0,0
1,0,0,1,0,0,0,0,0,0
1,0,0,1,0,0,0,0,0,0
1,1,0,1,0,1,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,1,0,0,0,0,0,0
1,1,0,0,0,1,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,1,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,1,1,0,0,0,0,0,1
1,0,0,1,0,0,0,0,0,1
1,0,0,1,0,1,0,0,0,0
1,1,1,1,0,0,0,0,0,1
1,0,1,1,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,1,0,1,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,1,0,0,0,0,0,0
1,1,0,1,0,1,0,0,0,0
1,0,0,0,0,1,0,0,0,1
1,0,0,0,0,0,0,0,0,0
1,1,1,1,0,0,0,0,0,0
1,1,0,1,0,0,0,0,0,1
1,1,0,1,0,1,0,0,0,0
1,1,1,1,0,1,0,0,0,0
1,1,0,0,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,0,1,0,0,1,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,1,0,0,0,0,0,1
1,0,0,1,0,0,0,0,0,1
1,1,0,0,0,1,0,0,0,0
1,1,0,0,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,1,0,0,0,0,0,0
1,0,0,1,0,0,0,0,0,0
1,1,0,0,0,0,1,1,0,1
1,1,0,0,0,1,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,1,0,0,0,1,0,0
1,1,0,0,0,0,0,0,0,0
1,0,0,1,0,0,0,0,0,0
1,0,0,1,0,0,0,0,0,0
1,0,0,1,0,0,0,0,0,0
1,1,0,1,0,1,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,1,0,0,0,0,0,0
1,1,0,0,0,0,0,0,0,0
1,1,0,0,0,0,0,0,0,0
1,0,1,1,0,0,0,0,0,1
1,0,0,0,0,1,0,0,0,0
1,0,1,1,0,0,0,1,0,1
1,0,1,0,0,0,1,0,0,0
1,0,0,0,0,0,0,0,0,0
1,1,0,0,0,1,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,1,1,0,0,1,0,1,0
1,1,0,1,1,1,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,1,0,1,0,0,0,0,0,0
1,0,0,0,0,1,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,1,0,0,0,0,0,1
1,0,0,1,0,1,0,0,0,0
1,1,1,1,0,1,0,1,0,1
1,1,0,1,0,1,0,0,0,0
1,1,1,1,0,1,0,1,0,0
1,1,0,1,0,0,0,0,0,0
1,0,1,0,1,0,0,1,0,1
1,0,1,0,1,0,0,1,0,1
1,0,0,1,0,0,0,0,0,1
1,1,0,1,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,1,1,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,1
1,1,0,0,0,0,0,0,0,0
1,1,0,1,0,0,0,1,0,1
1,0,0,0,1,0,0,0,0,0
1,1,0,0,0,0,0,0,0,1
1,0,0,0,0,1,0,0,0,0
1,1,0,0,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,1,1,0,0,0,0,0,0
1,1,0,1,0,1,0,0,0,0
1,0,0,1,0,0,0,0,0,1
1,0,0,1,0,0,0,1,0,0
1,0,0,1,0,0,0,1,0,0
1,0,0,0,0,0,0,0,0,0
1,1,1,0,0,0,0,0,0,1
1,1,0,0,0,0,0,0,0,0
1,0,0,1,0,0,0,0,0,0
1,0,0,0,1,0,0,0,0,0
1,0,0,1,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,0,0,1,0,0,0,0
1,0,0,0,0,1,0,0,0,0
1,0,0,0,0,1,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,1,0,0,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,1,0,0,0,0,0,0,0,0
1,0,0,1,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,1,0,1,0,0,0,1,0,0
1,0,1,1,0,0,0,0,0,0
1,1,0,1,0,0,0,0,0,0
1,1,0,0,0,0,0,0,0,1
1,0,0,0,0,0,0,0,0,0
1,1,0,1,0,0,0,0,0,0
1,1,0,1,0,0,0,0,0,0
1,0,0,1,0,0,0,0,0,1
1,0,0,0,1,0,0,1,0,0
1,0,0,0,0,1,0,0,0,0
1,1,0,0,0,0,0,0,0,0
1,0,0,1,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,1,0,1,0,1,0,0,0,0
1,0,0,1,0,0,0,1,0,1
1,1,0,0,0,0,0,0,0,0
1,1,0,0,0,0,0,0,0,0
1,0,0,1,0,0,0,1,1,0
1,0,0,0,0,0,0,0,0,0
1,0,0,1,0,0,0,0,0,0
1,1,0,0,1,1,0,0,0,1
1,0,0,1,0,0,0,1,1,1
1,1,0,1,0,0,0,0,0,0
1,0,0,1,0,0,0,0,0,0
1,0,0,1,0,0,0,1,0,0
1,0,0,1,0,0,1,0,1,0
1,0,0,0,0,0,0,0,0,0
1,0,1,1,0,0,0,0,0,0
1,0,0,1,0,0,0,0,0,0
1,0,0,1,0,0,0,0,0,0
1,1,0,0,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,1
1,0,0,1,0,0,0,0,0,0
1,1,1,0,0,0,0,0,0,0
1,1,0,1,0,0,0,0,0,0
1,1,0,1,0,0,0,0,0,1
1,1,0,1,0,0,0,0,0,0
1,0,0,1,0,0,0,1,0,1
1,0,0,0,0,1,0,0,0,0
1,0,0,1,0,0,0,0,0,0
1,0,0,0,1,0,0,1,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,1,0,0,0,0,0,0
1,0,0,1,0,0,0,1,0,0
1,0,0,1,0,0,0,0,0,0
1,0,0,1,0,0,0,0,0,0
1,1,0,1,0,1,0,1,0,0
1,0,0,0,0,1,0,0,0,0
1,1,0,1,0,0,0,1,0,1
1,0,0,0,1,0,1,0,0,0
1,1,1,0,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,1,0,0,0,0,0,0
1,0,1,1,0,0,0,1,0,0
1,0,0,1,0,0,0,0,0,0
1,1,1,1,0,1,0,1,0,0
1,0,0,0,0,0,0,1,0,1
1,0,0,1,0,0,0,1,1,0
1,0,0,1,0,1,0,0,0,0
1,1,0,0,0,0,0,0,0,0
1,1,0,1,0,1,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,0,1,1,0,0,0,0
1,0,0,1,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,1
1,1,0,1,0,1,0,0,0,0
1,0,1,1,0,0,0,0,1,0
1,1,0,0,0,0,0,0,0,0
1,0,0,0,0,1,0,0,0,0
1,0,0,1,0,0,0,0,0,1
1,0,1,1,0,0,0,0,1,0
1,1,0,0,0,1,0,0,0,0
1,0,0,1,0,0,0,0,0,1
1,0,0,1,0,0,0,0,0,1
1,1,0,1,1,1,0,0,0,0
1,0,0,1,0,0,0,1,0,0
1,0,0,0,0,0,0,0,0,0
1,1,0,1,0,1,0,0,0,0
1,0,0,1,0,0,0,0,0,0
1,0,1,1,1,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,1
1,0,0,0,0,0,0,1,0,0
1,0,0,1,0,0,0,0,0,0
1,1,0,1,0,1,0,0,0,0
1,1,1,1,0,0,1,1,1,0
1,0,0,0,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,1,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,1,1,0,0,0,1,0,1
1,0,0,1,0,0,0,0,0,0
1,0,0,1,0,0,0,0,1,0
1,1,0,0,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,1,0,1,0,1,0,0,0,0
1,0,0,1,0,1,0,0,0,0
1,0,1,1,0,0,1,0,0,0
1,0,0,1,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,1,0,0,0,1,0,1
1,1,0,1,0,0,0,0,0,0
1,1,0,0,0,0,0,0,0,0
1,1,0,0,0,0,0,0,0,0
1,0,1,0,0,0,0,0,0,0
1,1,0,1,0,1,0,0,0,0
1,1,0,0,1,1,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,1,0,1,0,1,0,1,0,0
1,1,0,1,0,0,0,1,0,0
1,1,0,1,1,1,0,1,0,1
1,1,0,0,0,1,0,0,0,0
1,0,0,1,0,0,0,0,1,0
1,1,0,0,1,1,0,1,0,1
1,0,0,1,0,0,0,0,0,0
1,1,1,1,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,1,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,1,0,1,0,0,0,0
1,1,0,1,0,1,0,0,0,0
1,0,0,1,0,0,0,0,0,0
1,1,0,1,0,1,0,1,1,0
1,0,0,0,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,1,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,1
1,0,0,0,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,1,1,1,0,1,0,0,0,0
2 个解决方案
解决方案1
2 2021-11-06 18:11:40
Seaborn 不显示完全为NaN的行和列； 这些只是空的。 这可能看起来很奇怪，但这是一个完全合乎逻辑的行为。
相关矩阵将与常量值数据帧列对应的行和列设置为NaN 。
解决方法可能是删除 NaN 列和行，如@TrentonMcKinney 所建议的那样，例如使用corr = corr.dropna(how='all', axis=1).dropna(how='all', axis=0) . 或者删除具有零方差的数据corr = df.loc[:, ~df.var().eq(0)].corr()列（ corr = df.loc[:, ~df.var().eq(0)].corr() ）。
另一个解决方法是将 NaN 值着色为灰色：
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
import seaborn as sns
import pandas as pd
import numpy as np
def plot_correlation_heatmap(df):
corr = df.corr()
sns.set(style='white')
mask = np.zeros_like(corr, dtype=bool)
mask[np.triu_indices_from(mask)] = True
f, ax = plt.subplots(figsize=(11, 9))
cmap = sns.diverging_palette(220, 10, as_cmap=True)
sns.heatmap(corr, mask=mask, cmap=cmap, vmax=0.3, center=0,
square=True, linewidths=.5, cbar_kws={"shrink": .5}, annot=True, ax=ax)
sns.heatmap(corr.fillna(0), mask=mask | ~ (np.isnan(corr)), cmap=ListedColormap(['lightgrey']),
square=True, linewidths=.5, cbar=False, annot=False, ax=ax)
ax.tick_params(axis='y', rotation=0)
plt.show()
plt.rcdefaults()
all_gen_cols = pd.DataFrame(np.random.randint(0, 2, size=(200, 10)), columns=[*'ABCDEFGHIJ'])
all_gen_cols['A'] = 1
plot_correlation_heatmap(all_gen_cols)
解决方案2
1 2021-11-06 18:19:26
这种行为与pandas或seaborn 。 它直接来自 Pearson 相关系数 ( rho ) 的公式，这是DataFrame.corr默认使用的。
由于action = [1,1,...,1] => var(action) = 0 。 因此， rho(action, Y) （其中Y是任何其他列）的分母为零=> rho(action, Y)未定义 (NaN)。
正如其他用户所建议的那样，您应该在计算相关矩阵之前删除 'action' 列，因为它不会添加信息。
解决方案3
0 2021-11-06 17:43:35
将vmax更改为1.0
sb.heatmap(corr, mask=mask, cmap=cmap, vmax=1.0, center=0,
square=True, linewidths=.5, cbar_kws={"shrink": .5}, annot=True)
除非数据集中的 NaN，否则当 np.corrcoef() 返回值时，Pandas df.corr() 返回 nan
[英]Pandas df.corr() returns nan when np.corrcoef() returns values unless NaNs in dataset
pandas df.corr（）返回NaN，尽管填充的数据已填充数据
[英]pandas df.corr() returns NaN despite data fed having populated data
df.corr 从编译的 csv 读取时将所有数据显示为 1，即使存在不同的数据
[英]df.corr showing all data as 1 when read from compiled csv, even though there is data that is different
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相关问题
Python-df.corr中的NaN值
除非数据集中的 NaN，否则当 np.corrcoef() 返回值时，Pandas df.corr() 返回 nan
python 中的 df.corr() 命令
使用corr = df.corr（）删除变量
pandas df.corr（）返回NaN，尽管填充的数据已填充数据
是否确保 df.corr() 列的顺序
熊猫df.corr方法的意外输出
当我在Pandas中使用df.corr时，我的一些列会丢失
df.corr 从编译的 csv 读取时将所有数据显示为 1，即使存在不同的数据
从 df.corr 结果计算相同列前缀的平均值
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