seaborn 热图显示轴标签，但当 df.corr 为 NaN 时没有值答案

【问题标题】：seaborn heatmap displays axis labels, but no values when df.corr is NaNseaborn 热图显示轴标签，但当 df.corr 为 NaN 时没有值
【发布时间】：2021-12-20 06:27:41
【问题描述】：

我正在尝试为相关性提供热图，但我意识到有些是错误的。

下面是我的热图。如您所见，该操作的编号没有出现。

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()) 协同作用的结果如下。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

下面是通过打印输出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

这是下载数据帧的link。

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
1,0,0,0,0,0,0,0,0,0
1,0,0,0,1,0,0,0,0,0
1,1,0,0,0,1,0,0,0,0
1,1,0,0,0,1,0,0,0,0
1,1,0,1,0,0,0,1,0,0
1,1,0,0,0,0,0,0,0,0
1,1,0,1,0,0,0,1,0,0
1,0,0,1,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
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
1,0,0,0,0,0,0,0,0,0
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
1,0,0,0,1,0,0,0,0,0
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
1,0,0,0,0,0,0,0,0,0
1,1,0,1,0,0,0,1,0,0
1,0,0,1,0,0,0,1,0,0
1,0,0,0,0,1,0,0,0,0
1,0,0,0,1,0,0,0,0,0
1,0,0,0,1,0,0,1,0,1
1,1,0,0,0,0,0,0,0,0
1,0,1,1,0,0,0,1,0,0
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
1,1,0,1,0,0,0,0,0,0
1,1,0,0,1,1,0,1,0,1
1,0,0,1,1,1,0,0,0,0
1,1,0,0,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,1,0,1,0,0,0,1,0,0
1,0,0,0,0,1,0,0,0,0
1,0,0,0,0,1,0,0,0,0
1,0,0,1,0,0,0,0,0,0
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
1,0,0,1,0,0,1,1,1,0
1,0,0,1,0,0,0,0,0,0
1,0,0,0,1,0,0,0,0,1
1,0,0,1,0,1,0,1,0,1
1,0,0,0,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,1,0,0,0,0,0,1
1,0,0,0,1,0,1,0,0,0
1,1,0,1,0,1,0,0,0,0
1,1,0,1,0,1,0,1,0,1
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
1,1,0,0,0,0,0,0,0,0
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
1,1,0,1,0,0,0,0,0,0
1,1,1,1,0,1,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,0,0,1,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,1,0,0,0,0,0,0
1,1,0,0,0,0,0,0,0,0
1,1,0,0,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,1,0,0,0,0,0,0,0
1,0,0,1,0,0,0,0,0,0
1,0,0,1,0,0,0,1,0,1
1,1,0,1,0,1,0,1,0,0
1,1,0,0,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,1
1,0,0,1,0,1,0,0,0,0
1,0,0,1,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,1
1,0,0,0,1,0,0,1,0,0
1,1,0,0,0,0,0,0,0,0
1,1,0,0,1,1,0,1,0,1
1,0,0,1,0,0,0,1,0,1
1,1,0,0,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,1,0,0,0,1,0,0,0,0
1,1,0,0,0,1,0,0,0,0
1,1,0,1,0,0,0,1,0,0
1,0,1,1,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,1,0,0,0,0,0,1
1,1,0,1,0,1,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,1,0,0,0,1,0,0,0,0
1,1,0,1,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,1,0,1,1,1,0,0,0,0
1,0,0,0,0,1,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,0,0,0,0,1,0,1
1,0,0,0,0,0,0,0,0,0
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
1,0,0,0,0,0,0,0,0,0
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
1,0,1,0,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,1,1,1,0,0,0,1
1,1,0,0,0,0,0,0,0,0
1,0,0,1,0,0,1,0,0,0
1,1,0,0,0,1,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,1,0,0,0,0,0,0,0,0
1,1,0,1,1,1,0,1,0,0
1,1,0,0,0,0,0,0,0,0
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
1,0,0,1,0,0,0,0,0,0
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
1,1,0,0,0,0,0,0,0,0
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
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
1,0,1,1,0,0,0,0,0,0
1,0,0,0,0,0,0,1,0,0
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
1,0,0,0,0,1,0,0,0,0
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
1,1,0,0,0,1,0,0,0,0
1,0,0,1,0,0,0,1,0,0
1,0,0,0,0,0,0,0,0,0
1,1,0,1,0,1,0,1,0,0
1,1,1,1,1,1,0,0,0,0
1,1,0,0,0,1,0,0,0,0
1,0,0,1,0,1,0,0,0,0
1,0,0,1,0,0,0,1,0,0
1,0,0,1,0,0,0,1,0,0
1,0,1,1,0,0,0,0,0,0
1,1,0,1,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
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
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
1,1,0,0,0,0,0,0,0,0
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
1,0,0,1,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,1,0,0,0,1,0,0
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
1,0,0,0,0,0,0,0,0,0
1,1,0,1,0,1,0,0,0,0
1,0,0,0,0,0,0,1,0,0
1,0,0,1,0,0,0,0,0,0
1,1,0,0,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,1,1,1,0,0,0,0,0,1
1,0,0,0,0,0,0,0,0,0
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
1,0,0,0,1,0,1,0,0,0
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
1,1,0,0,0,0,0,0,0,0
1,1,1,0,1,1,0,0,0,0
1,0,0,1,0,0,0,0,0,0
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1,1,0,1,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,1,0,0,1,1,0,0,0,0
1,1,0,1,0,0,0,1,0,1
1,1,0,1,0,0,1,1,0,0
1,0,0,0,0,1,0,0,0,1
1,1,0,1,0,0,0,0,0,0
1,1,0,1,0,0,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,0,0,0,0,0,0,1,0,1
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
1,1,1,1,0,0,0,1,0,0
1,0,0,0,0,0,0,0,0,0
1,1,0,0,1,1,0,1,0,0
1,1,1,0,1,1,0,0,0,0
1,0,0,0,0,0,0,0,0,0
1,1,0,0,0,0,0,0,0,0
1,0,0,1,0,0,1,0,0,0
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

【问题讨论】：

没有更多信息，很难猜测发生了什么。你能显示print(all_gen_cols.corr()) 的结果吗？最好是文本，而不是图像。你能把all_gen_cols.describe()的结果加起来吗？
@JohanC 我添加了 .corr() 和 .describe() 的结果。是的，它会产生 NaN 以供行动。我不知道它被认为是一个错误。
这不会解决结果，但seaborn 的习惯别名是sns，而不是sb。请参阅doc。使用 dtype='bool' 或 dtype=np.bool_ 而不是 dtype=np.bool（已弃用）
.corr 的结果没有错误。动作值是不变的（无差异），这会正确地产生nanDataFrame correlation produces NaN although its values are all integers 的列。正如@JohanC 已经说过的那样，只需绘制没有操作列的热图。 plot_correlation_heatmap(df.iloc[:, 1:]) 是最简单的方法，否则在函数中添加corr = corr.dropna(how='all', axis=1) 和corr = corr.dropna(how='all', axis=0)。
@TrentonMcKinney 是的，你是对的。没有错误，在 pandas 和 seaborn 中都没有，尽管结果可能看起来很奇怪。

标签： python pandas seaborn heatmap correlation

【解决方案1】：

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。

相关矩阵将一个常量值dataframe列对应的行列设置为NaN。

如@TrentonMcKinney 建议的那样，解决方法可能是删除NaN 列和行，例如corr = corr.dropna(how='all', axis=1).dropna(how='all', axis=0)。或者删除方差为零的数据框列 (corr = df.loc[:, df.var().ne(0)].corr())。

另一个解决方法是将 NaN 值涂成灰色：

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)

【讨论】：

也许这是最好的选项df.loc[:, ~df.var().eq(0)]，它会删除所有方差为 0 的列，从而产生NaN 列和行。随意将我的 cmets 中的任何代码合并到答案中。

【解决方案2】：

该行为与pandas 或seaborn 无关。直接来源于皮尔逊相关系数公式（rho），DataFrame.corr默认使用。

自从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)。

根据其他用户的建议，您应该在计算相关矩阵之前删除“操作”列，因为它不会添加信息。

【讨论】：