Given this dataframe, how to select only those rows that have “Col2” equal to `NaN`?

``````df = pd.DataFrame([range(3), [0, np.NaN, 0], [0, 0, np.NaN], range(3), range(3)], columns=["Col1", "Col2", "Col3"])
``````

which looks like:

``````   0   1   2
0  0   1   2
1  0 NaN   0
2  0   0 NaN
3  0   1   2
4  0   1   2
``````

The result should be this one:

``````   0   1   2
1  0 NaN   0
``````

Try the following:

``````df[df['Col2'].isnull()]
``````

@qbzenker provided the most idiomatic method IMO

Here are a few alternatives:

``````In [28]: df.query('Col2 != Col2') # Using the fact that: np.nan != np.nan
Out[28]:
Col1  Col2  Col3
1     0   NaN   0.0

In [29]: df[np.isnan(df.Col2)]
Out[29]:
Col1  Col2  Col3
1     0   NaN   0.0
``````

If you want to select rows with at least one NaN value, then you could use `isna` + `any` on `axis=1`:

``````df[df.isna().any(axis=1)]
``````

If you want to select rows with a certain number of NaN values, then you could use `isna` + `sum` on `axis=1` + `gt`. For example, the following will fetch rows with at least 2 NaN values:

``````df[df.isna().sum(axis=1)>1]
``````

If you want to limit the check to specific columns, you could select them first, then check:

``````df[df[['Col1', 'Col2']].isna().any(axis=1)]
``````

If you want to select rows with all NaN values, you could use `isna` + `all` on `axis=1`:

``````df[df.isna().all(axis=1)]
``````

If you want to select rows with no NaN values, you could `notna` + `all` on `axis=1`:

``````df[df.notna().all(axis=1)]
``````

This is equivalent to:

``````df[df['Col1'].notna() & df['Col2'].notna() & df['Col3'].notna()]
``````

which could become tedious if there are many columns. Instead, you could use `functools.reduce` to chain `&` operators:

``````import functools, operator
df[functools.reduce(operator.and_, (df[i].notna() for i in df.columns))]
``````

or `numpy.logical_and.reduce`:

``````import numpy as np
df[np.logical_and.reduce([df[i].notna() for i in df.columns])]
``````

If you’re looking for filter the rows where there is no NaN in some column using `query`, you could do so by using `engine="python"` parameter:

``````df.query('Col2.notna()', engine="python")
``````

or use the fact that `NaN!=NaN` like @MaxU – stop WAR against UA

``````df.query('Col2==Col2')
``````