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How to Concatenate and Reindex Dataframes in Pandas Python

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by Abdul Rawoof A R dot icon Updated: Feb 1, 2023

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Concatenation, in general terms, refers to the process of joining two or more things together. In the context of pandas, concatenation refers to joining two or more DataFrames along a particular axis, either rows or columns. 


Reindexing in pandas refers to creating a new DataFrame with a new set of indices or changing the order of the existing indices. This is useful when you want to align data from multiple sources or when you want to sort or rearrange the data in a DataFrame. 


It is possible to both concatenate and reindex DataFrames in pandas in one step by chaining the “pd.concat()” function and the “.reindex()” method. 

  • pd.concat(): pd.concat() is a pandas library function used to concatenate DataFrames along a particular axis. 
  • .reindex(): .reindex() is a method in the pandas library that is used to create a new DataFrame with a new set of indices, or to change the order of the existing indices. 


For more information about concatenate and reindex DataFrame in pandas, please look at the code below. 

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

Code

In this solution we're using Pandas and NumPy libraries.

Pandas: concatenate and reindex dataframes

PythondotLines of Code : 52dotLicense : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries : 
import numpy as np
import pandas as pd
# test data
df = pd.DataFrame(np.ones(25).reshape((5,5)),index = ['A','B','C','D','E'])
df1 = pd.DataFrame(np.ones(25).reshape((5,5))*2,index = ['A','B','C','D','E'])
df[2] = np.nan
df1[3] = np.nan
df[4] = np.nan
df1[4] = np.nan

# unstack tables and concat
newdf = pd.concat([df.unstack(),df1.unstack()], axis=1)
# reset multiindex for level 1
newdf.reset_index(1, inplace=True)
# rename columns
newdf.columns = ['OldIndex','df','df1']
# drop old index
newdf = newdf.reset_index().drop('index',1)
# set index from 1
newdf.index = np.arange(1, len(newdf) + 1)
# rename new index
newdf.index.name='NewIndex'
print(newdf)

         OldIndex   df  df1
NewIndex                   
1               A  1.0  2.0
2               B  1.0  2.0
3               C  1.0  2.0
4               D  1.0  2.0
5               E  1.0  2.0
6               A  1.0  2.0
7               B  1.0  2.0
8               C  1.0  2.0
9               D  1.0  2.0
10              E  1.0  2.0
11              A  NaN  2.0
12              B  NaN  2.0
13              C  NaN  2.0
14              D  NaN  2.0
15              E  NaN  2.0
16              A  1.0  NaN
17              B  1.0  NaN
18              C  1.0  NaN
19              D  1.0  NaN
20              E  1.0  NaN
21              A  NaN  NaN
22              B  NaN  NaN
23              C  NaN  NaN
24              D  NaN  NaN
25              E  NaN  NaN

Instructions

Follow the steps carefully to get the output easily.

  1. Install pandas and numpy on your IDE(Any of your favorite IDE).
  2. Copy the snippet using the 'copy' and paste it in your IDE.
  3. Add required dependencies and import them in 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 'concatenate and reindex dataframes in 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 PyCharm 2021.3.
  2. The solution is tested on Python 3.9.7.
  3. Pandas version-v1.5.2.
  4. NumPy version-v1.24.1.


Using this solution, we are able to implement concatenation and reindex dataframes in pandas with simple steps. This process also facilities an easy way to use, hassle-free method to create a hands-on working version of code which would help us to implement concatenation and reindex dataframes in 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

Support
    Quality
      Security
        License
          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

                                          You can also search for any dependent libraries on kandi like 'pandas' and 'numpy'.

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