How can I handle missing values in a Seaborn heatmap?

How can I handle missing values in a Seaborn heatmap?

You can handle missing values in a Seaborn heatmap by using the mask parameter or by filling them with a specific value.

Can you show me an example of using the mask parameter?

Sure! You can create a mask using NumPy to hide the missing values. For example:

import seaborn as sns
import numpy as np

# Create a mask for the upper triangle
mask = np.triu(np.ones_like(data, dtype=bool))

sns.heatmap(data, mask=mask)

What if I want to fill the missing values instead?

You can fill missing values using the fillna() method in pandas before plotting. For example:

data.fillna(0, inplace=True)
sns.heatmap(data)

Are there any other methods to visualize missing data?

Yes, you can use other visualizations like bar plots or scatter plots to show missing data patterns.