This tutorial will look at how to get the columns with missing values in a pandas dataframe.

## How to find columns with missing values in a pandas dataframe?

To get the columns containing missing values, you can use a combination of the pandas `isna()`

function and the `any()`

function in Python.

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The idea is to find the columns containing any missing values. The following is the syntax –

# get names of columns with missing values df.columns[df.isna().any()]

Here, we first, create a boolean index of columns with missing values and use it to filter `df.columns`

to get the columns that contain any missing values.

## Examples

Let’s now look at some examples of using the above syntax –

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First, we will create a pandas dataframe.

import numpy as np import pandas as pd # employee data data = { "Name": ["Jim", "Dwight", "Angela", "Tobi"], "Age": [26, np.nan, 27, 32], "Department": ["Sales", "Sales", None, np.nan] } # create pandas dataframe df = pd.DataFrame(data) # display the dataframe df

Output:

Here, we created a dataframe with information about some employees in an office. The dataframe has the columns – “Name”, “Age”, and “Department”. You can see that some of the values in the above dataframe are missing.

Let’s now get the columns in the above dataframe containing missing values.

We can use the pandas dataframe `isna()`

function to check whether a value in a dataframe is a missing value or not.

Let’s apply this function to the above dataframe.

df.isna()

Output:

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We get a dataframe with boolean values. Each cell here represents whether the value in this position (in the original dataframe) is missing (na) or not.

Now, we want columns that contain any missing values. You can apply the `any()`

function on top of the resulting dataframe from the `isna()`

function.

df.isna().any()

Output:

Name False Age True Department True dtype: bool

We get a boolean series with the columns containing any missing values marked at `True`

.

You can use the above result as a boolean index to filter the `df.columns`

and get the names of the columns with missing values.

df.columns[df.isna().any()]

Output:

Index(['Age', 'Department'], dtype='object')

We get the names of the columns with missing values (“Age” and “Department”) in the above dataframe.

You may also be interested in –

- Pandas – Count Missing Values in Each Column
- Pandas – fillna with values from another column
- Drop Rows with NaNs in Pandas DataFrame
- Pandas – Drop Duplicate Columns From Dataframe

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