如何在Python和Seaborn中使用`sns.heatmap`的`annot`方法給自定義標籤？

如何使用sns.heatmap的annot方法爲其提供自定義命名方案？如何在Python和Seaborn中使用`sns.heatmap`的`annot`方法給自定義標籤？

本質上，我想刪除所有低於我的閾值（在這種情況下爲0）的標籤。我嘗試了@ojy在Custom Annotation Seaborn Heatmap中所說的話，但是我收到以下錯誤消息。我看到一個例子，其中有人遍歷每個單元格，這是唯一的方法嗎？

Seaborn documentation: 
annot : bool or rectangular dataset, optional 
If True, write the data value in each cell. If an array-like with the same shape as data, then use this to annotate the heatmap instead of the raw data.

所以我試過如下：

# Load Datasets 
from sklearn.datasets import load_iris 
iris = load_iris() 
DF_X = pd.DataFrame(iris.data, index = ["%d_%d"%(i,c) for i,c in zip(range(X.shape[0]), iris.target)], columns=iris.feature_names) 

# Correlation 
DF_corr = DF_X.corr() 

# Figure 
fig, ax= plt.subplots(ncols=2, figsize=(16,6)) 
sns.heatmap(DF_corr, annot=True, ax=ax[0]) 

# Masked Figure 
threshold = 0 
DF_mask = DF_corr.copy() 
DF_mask[DF_mask < threshold] = 0 
sns.heatmap(DF_mask, annot=True, ax=ax[1]) 

# Annotating 
Ar_annotation = DF_mask.as_matrix() 
Ar_annotation[Ar_annotation == 0] = None 
Ar_annotation 
# array([[ 1.  ,   nan, 0.87175416, 0.81795363], 
#  [  nan, 1.  ,   nan,   nan], 
#  [ 0.87175416,   nan, 1.  , 0.9627571 ], 
#  [ 0.81795363,   nan, 0.9627571 , 1.  ]]) 
print(DF_mask.shape, Ar_annotation.shape) 
# (4, 4) (4, 4) 

sns.heatmap(DF_mask, annot=Ar_annotation, fmt="") 

# ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()