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You are here: Home / Python / Get the Count of NaN in pandas Using Python

Get the Count of NaN in pandas Using Python

December 16, 2020 Leave a Comment

To get the count of NaN in a pandas dataframe, the simplest way is to use the pandas isnull() function and pandas sum() function.

df["variable"].isnull().sum()

When working with data as a data science or data analyst, it’s important to be able to find the basic descriptive statistics of a set of data.

One basic descriptive statistic which is important is the number of missing or NaN values in a dataset.

The pandas describe() function can get us a number of great descriptive statistics, but it cannot return the number of missing values of a series.

To get the number of missing values of a series in Python, we use the isnull() and sum() functions.

The following code will get you the count of missing values of a series in Python:

df["variable"].isnull().sum()

Getting the Count of NaN of a Column Using Pandas

Let’s say I have the following pandas dataframe:

   animal_type  gender         type variable level  count    sum   mean        std   min    25%   50%    75%    max
0          cat  female      numeric      age   N/A    5.0   18.0   3.60   1.516575   2.0   3.00   3.0   4.00    6.0
1          cat    male      numeric      age   N/A    2.0    3.0   1.50   0.707107   1.0   1.25   1.5   1.75    2.0
2          dog  female      numeric      age   N/A    2.0    8.0   4.00   0.000000   4.0   4.00   4.0   4.00    4.0
3          dog    male      numeric      age   N/A    4.0   15.0   3.75   1.892969   1.0   3.25   4.5   5.00    5.0
4          cat  female      numeric   weight   N/A    5.0  270.0  54.00  32.093613  10.0  40.00  50.0  80.00   90.0
5          cat    male      numeric   weight   N/A    2.0  110.0  55.00  63.639610  10.0  32.50  55.0  77.50  100.0
6          dog  female      numeric   weight   N/A    2.0  100.0  50.00  42.426407  20.0  35.00  50.0  65.00   80.0
7          dog    male      numeric   weight   N/A    4.0  180.0  45.00  23.804761  20.0  27.50  45.0  62.50   70.0
8          cat  female  categorical    state    FL    2.0    NaN    NaN        NaN   NaN    NaN   NaN    NaN    NaN
9          cat  female  categorical    state    NY    1.0    NaN    NaN        NaN   NaN    NaN   NaN    NaN    NaN
10         cat  female  categorical    state    TX    2.0    NaN    NaN        NaN   NaN    NaN   NaN    NaN    NaN
11         cat    male  categorical    state    CA    1.0    NaN    NaN        NaN   NaN    NaN   NaN    NaN    NaN
12         cat    male  categorical    state    TX    1.0    NaN    NaN        NaN   NaN    NaN   NaN    NaN    NaN
13         dog  female  categorical    state    FL    1.0    NaN    NaN        NaN   NaN    NaN   NaN    NaN    NaN
14         dog  female  categorical    state    TX    1.0    NaN    NaN        NaN   NaN    NaN   NaN    NaN    NaN
15         dog    male  categorical    state    CA    1.0    NaN    NaN        NaN   NaN    NaN   NaN    NaN    NaN
16         dog    male  categorical    state    FL    1.0    NaN    NaN        NaN   NaN    NaN   NaN    NaN    NaN
17         dog    male  categorical    state    NY    2.0    NaN    NaN        NaN   NaN    NaN   NaN    NaN    NaN
18         cat  female  categorical  trained   yes    5.0    NaN    NaN        NaN   NaN    NaN   NaN    NaN    NaN
19         cat    male  categorical  trained    no    2.0    NaN    NaN        NaN   NaN    NaN   NaN    NaN    NaN
20         dog  female  categorical  trained    no    1.0    NaN    NaN        NaN   NaN    NaN   NaN    NaN    NaN
21         dog  female  categorical  trained   yes    1.0    NaN    NaN        NaN   NaN    NaN   NaN    NaN    NaN
22         dog    male  categorical  trained    no    4.0    NaN    NaN        NaN   NaN    NaN   NaN    NaN    NaN

In this dataframe, we have a lot of NaN values.

To get the count of NaN values for a specific column, I can do the following in my python code:

df["type"].isnull().sum()

#output: 15 

Hopefully this article has been useful for you to find the count of NaN values in a pandas dataframe using Python.

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