How to use `annot` method of `sns.heatmap` to give custom labels, in Python and Seaborn?

Question

How can I use the annot method of sns.heatmap to give it a custom naming scheme?

Essentially, I want to drop all the labels that are lower than my threshold (0 in this case). I tried doing what @ojy said in Custom Annotation Seaborn Heatmap but I'm getting the following error. I saw an example where somebody iterated through every cell, is that the only way to do it?

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.

So I tried the following:

# 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()

Before mask (left), after mask (right)

Answer 1

That's easy!

Update to 0.7.1 and restart Jupyter kernel.

https://github.com/mwaskom/seaborn/issues/981