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How to Find and Remove Duplicate Rows in Pandas DataFrame

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by vsasikalabe dot icon Updated: Jan 30, 2023

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Removing duplicate rows from a Pandas DataFrame can be useful in a variety of situations, such as:  

  • Data cleaning: Due to mistakes in data collection or storage, duplicate rows may appear in a dataset. The analysis can be built on correct and trustworthy data if these duplicates are eliminated.  
  • Data analysis: Excessive duplicate rows might influence the outcomes of statistical tests and machine learning models. The accuracy and interpretability of the results can both be enhanced by removing them.  
  • Data Visualization: Duplicate rows might make it challenging to produce accurate and instructive data representations. It may be simpler to spot patterns and trends in the data if they are eliminated.  
  • Data Storage: Eliminating duplicate rows can also help reduce storage requirements and enhance database query performance.  
  • Machine Learning: Having duplicate data can result in overfitting and subpar generalization when working with a machine learning model. Duplicate data removal can help with this issue.  


Here is how you can remove duplicate rows in a DataFrame using pandas:  

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

Code

In this solution we used pandas library of python.

Removing duplicate rows in a dataframe with some conditions on data in a particular column

PythondotLines of Code : 20dotLicense : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries : 
import pandas as pd

df = pd.DataFrame({'time':[9,9,33,47,47,100,120,120],'block':[25,25,35,4,17,21,21,36],'cell':'c1;c1;c2;c1;c2;c1;c1;c2'.split(';')})

categories = df['cell'].astype('category').unique()
df2 = pd.DataFrame()
for category in categories:
    df2 = pd.concat([df2, df[df['cell'] == category].drop_duplicates(keep='first')])

df2 = df2.sort_index()

    time  block cell
0     9     25   c1
2    33     35   c2
3    47      4   c1
4    47     17   c2
5   100     21   c1
6   120     21   c1
7   120     36   c2

Instructions

Follow the steps carefully to get the output successfully:

  1. Download and install PyCharm on your Computer.
  2. Create new python file in your IDE.
  3. Copy the code using the "Copy" button above, and paste it in a Python file.
  4. Install Pandas from settings (python interpreter).
  5. 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 "Removing duplicate rows in a dataframe using pandas"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


Using this solution, we can Remove duplicate rows in a dataframe using pandas.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 remove duplicate rows in a dataframe using pandas.

Dependent Library

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

Support
    Quality
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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

                      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.

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