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Count missing data in each column using Pandas in python

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by vsasikalabe dot icon Updated: Mar 2, 2023

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Counting missing data in each dataset column is a crucial step in data cleaning and preprocessing. It helps to identify which columns have missing data, how much is missing, and whether the missing data can be attributed or needs to be discarded. 


Pandas is a library for data manipulation and analysis in Python, and it provides a simple and efficient way to count missing data in each column of a DataFrame. The isnull() method can create a Boolean mask for the DataFrame, where True values represent missing values and False values represent non-missing values. The sum() method can then be used to count the number of True values in every column of the DataFrame, which represents the number of missing values in each column. 


In summary, counting missing data in each column using Pandas in Python is a useful technique for data cleaning and preprocessing that can help to identify and handle missing data in a dataset. 

Preview of the output that you will get on running this code from your IDE.

Code

In this solution we used pandas and numpy libraries of python.

How to count missing data in each column in python?

PythondotLines of Code : 16dotLicense : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries : 
In [3]: import numpy as np
In [4]: import pandas as pd
In [5]: df = pd.DataFrame({'a':[1,2,np.nan], 'b':[np.nan,1,np.nan]})
In [6]: df.isnull().sum()
Out[6]:
a    1
b    2
dtype: int64

In [1]: import numpy as np
In [2]: import pandas as pd
In [3]: s = pd.Series([1,2,3, np.nan, np.nan])

In [4]: s.isnull().sum()
Out[4]: 2

Instructions

Follow the steps carefully to get the output successfully:

  1. Install pandas and numpy on your IDE(Any of your favorite IDE)
  2. Create a new python file in your IDE.(Pycharm Preferable)
  3. Copy the code using the "Copy" button above, and paste it in a Python file.
  4. Run the file to generate the output.


I hope you found this useful. I have added the link to dependent libraries, version information in the following sections.


I found this code snippet by searching for "Count missing data in each column using Pandas in python"in kandi. You can try any such use case

Environment Tested

I tested this solution in the following versions. Be mindful of changes when working with other versions.

  1. The solution is created in Python 3.11.1 Version
  2. The solution is tested on pandas 1.5.2 Version
  3. numpy version 1.24.0.


Using this solution, we can Count missing data in each column using Pandas in python.This process also facilities an easy to use, hassle free method to create a hands-on working version of code in python. which would help us to Count missing data in each column using Pandas.

Dependent Libraries

pandasby pandas-dev

Python doticonstar image 38689 doticonVersion:v2.0.2doticon
License: Permissive (BSD-3-Clause)

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

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            pandasby pandas-dev

            Python doticon star image 38689 doticonVersion:v2.0.2doticon License: Permissive (BSD-3-Clause)

            Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
            Support
              Quality
                Security
                  License
                    Reuse

                      numpyby numpy

                      Python doticonstar image 23755 doticonVersion:v1.25.0rc1doticon
                      License: Permissive (BSD-3-Clause)

                      The fundamental package for scientific computing with Python.

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                                numpyby numpy

                                Python doticon star image 23755 doticonVersion:v1.25.0rc1doticon License: Permissive (BSD-3-Clause)

                                The fundamental package for scientific computing with Python.
                                Support
                                  Quality
                                    Security
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                                        Reuse

                                          If you do not have pandas library that is required to run this code, you can install it by clicking on the above link.

                                          You can search for any dependent library on kandi like pandas and numpy.

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