How do I count the NaN values in a column in pandas DataFrame?

You can use the isna() function to create a boolean mask of the NaN values in a column, and then use the sum() function to count the number of True values in the mask. Here's an example:

count the NaN values in a column in pandas DataFrame

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3, 4, float('nan')],
                   'B': [float('nan'), float('nan'), 5, 6, 7]})

# Count the number of NaN values in column 'A'
nan_count_a = df['A'].isna().sum()

# Count the number of NaN values in column 'B'
nan_count_b = df['B'].isna().sum()

print(nan_count_a) #1
print(nan_count_b) #2

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You can also use df.isna().sum() to get the number of NaN values of all columns.