> ## Documentation Index
> Fetch the complete documentation index at: https://docs.sourcery.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Use-Isna

#### Sourcery rule id: `use-isna`

#### Description

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



#### Before

```python

df['column'] == np.nan
```

#### After

```python

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())
```

The output:

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

See also [Pandas Docs / Missing Data](https://pandas.pydata.org/pandas-docs/stable/user_guide/missing_data.html)