How to Concatenate and Reindex Dataframes in Pandas Python
by Abdul Rawoof A R Updated: Feb 1, 2023
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.
In this solution we're using Pandas and NumPy 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 = np.nan df1 = np.nan df = np.nan df1 = 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
Follow the steps carefully to get the output easily.
- Install pandas and numpy on your IDE(Any of your favorite IDE).
- Copy the snippet using the 'copy' and paste it in your IDE.
- Add required dependencies and import them in Python file.
- 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!
I tested this solution in the following versions. Be mindful of changes when working with other versions.
- The solution is created in PyCharm 2021.3.
- The solution is tested on Python 3.9.7.
- Pandas version-v1.5.2.
- 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.
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