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How to Split cell into multiple rows in pandas Dataframe

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by vigneshchennai74 dot icon Updated: Feb 20, 2023

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In a Pandas DataFrame, a column may contain multiple values separated by commas in a single cell. This can be problematic when trying to perform calculations or analysis on the data, as the data needs to be in a format that is easy to work with. To work with such data, you can split the cell into multiple rows containing a single value. 


This task involves taking a Pandas DataFrame column containing comma-separated values and creating a new DataFrame where each value is on a separate row. This can be accomplished by creating a function that will split each cell in the column into multiple values. Then, a new DataFrame repeats the other columns as necessary while appending each value in a separate row. This can be helpful when performing further analysis or calculations on the data and can make the data easier to work with.

 

itertools is a Python module that provides a collection of tools for handling iterators. Chain is one of the functions available in the itertools module. Chain takes one or more iterables and returns a single iterable that produces the elements of the first iterable, followed by the elements of the second iterable until all the iterables have been exhausted. The chain function is used to combine multiple iterables into a single iterable.

 

Here is an example of how to split a cell into multiple rows in pandas 

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

Code

In this solution we have used Split function.

Split cell into multiple rows in pandas dataframe

PythondotLines of Code : 26dotLicense : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries : 
import numpy as np
from itertools import chain

# return list from series of comma-separated strings
def chainer(s):
    return list(chain.from_iterable(s.str.split(',')))

# calculate lengths of splits
lens = df['package'].str.split(',').map(len)

# create new dataframe, repeating or chaining as appropriate
res = pd.DataFrame({'order_id': np.repeat(df['order_id'], lens),
                    'order_date': np.repeat(df['order_date'], lens),
                    'package': chainer(df['package']),
                    'package_code': chainer(df['package_code'])})

print(res)

   order_id order_date package package_code
0         1  20/5/2018      p1         #111
0         1  20/5/2018      p2         #222
0         1  20/5/2018      p3         #333
1         3  22/5/2018      p4         #444
2         7  23/5/2018      p5         #555
2         7  23/5/2018      p6         #666
  1. Copy this code using "copy " button above and paste it in your Python IDE.
  2. Import Pandas Library into the code.
  3. Enter the Data Frame (df) which users need to split
  4. Run the code to Split the cell into multiple rows


I hope you found this useful. I have added the dependent Library, versions information in following Sections.


I found this code snippet by searching for "Split cell into multiple rows in pandas Dataframe" in kandi. you can try any use case.

Environment Tested

In this solution we have used the following versions. Be mindful to change when working with other versions.


  1. This solution is created using Python version 3.7.15
  2. This solution is Tested using Pandas 1.5.2


Using this solution we can able to Split the cells into Multiple rows in pandas as data frame using Pandas library in python with simple Steps. This process also facilities an easy to use, hassle free method to create a hands-on working version of code which would help us Split the cell in Python.

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
      Security
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          Reuse

            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.

                      Support
                        Quality
                          Security
                            License
                              Reuse

                                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
                                      License
                                        Reuse

                                          If you do not have Pandas and numpy that is required to run this code, you can install it by clicking on the above link and copying the pip Install command from the Pandas page in kandi.

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

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