Use-Isna¶

Sourcery rule id: `use-isna`¶

Description¶

Use .isna() or .isnull() instead of == np.nan for detecting missing values.

Before¶

import numpy as np

df['column'] == np.nan

After¶

import numpy as np

df['column'].isna()

Explanation¶

Use .isna() or .isnull() for detecting missing values.

A comparison like df['column'] == np.nan doesn't produce the expected results when checking for missing or NaN (Not a Number) values. This is due to the peculiar nature of NaN: It is not considered equal to any value, even itself.

data = {
    'A': [1, 2, np.nan, 4],
    'B': [9, 10, 11, 12]
}
df = pd.DataFrame(data)

print(df['A'] == np.nan)

The output:

0 False 1 False 2 False 3 False Name: A, dtype: bool

print(df['A'].isna())