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:


@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
   Col1  Col2  Col3
1     0   NaN   0.0

In [29]: df[np.isnan(df.Col2)]
   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:


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:


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:


If you want to select rows with no NaN values, you could notna + all on 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