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

Question

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。

打印(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 link下载数据框。

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

Answer 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。

相关矩阵将一个常数值dataframe列对应的行列设置为NaN。

一种解决方法可能是删除 NaN 列和行，如 @TrentonMcKinney 建议的那样，例如 corr = corr.dropna(how='all', axis=1).dropna(how='all '，轴=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)