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Reshape a repeating wide pandas DataFrame

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by vsasikalabe dot icon Updated: Feb 27, 2023

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Reshaping a pandas data frame is one of the most common data-wrangling tasks in data analysis. It is also called transposing or pivoting/unpivoting a table from wide to long format or from long to wide. Data is declared in lots of shapes and sizes. For tabular data, there are two common formats used.  

Wide format: lots of columns. 

Long format: few columns, more rows. 


To convert a pandas Data Frame from a wide format to a long format, You can use the following basic syntax: 

df = (pd.wide_to_long(df.rename_axis('Rep').reset_index(). 

The drop() method removes the specified row or column in the Data frame. The drop() method removes the specified column by specifying the axis ( axis='columns' ). A MultiIndex Data Frame allows you to have multiple columns. It acts as a row identifier. Multiple rows act as a header identifier. Using MultiIndex, we can analyze data, especially for working with higher dimensional data. 

Different Types of Methods: 

  • pivot 
  • pivot_table 
  • unstack 
  • crosstab 
  • Unstack 

The unstack function is a fast and convenient way to cast a MultiIndex Data Frame from wide to long format. It will change the values of the index with the highest level. You’ll end up with a Data Frame with MultiIndex columns.  


Here is an example of how to reshape a repeating wide Pandas Data frame:

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.

How can I reshape a repeating wide pandas DataFrame to be stacked?

PythondotLines of Code : 30dotLicense : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries : 
df.columns = df.columns.str.lower()

df = (pd.wide_to_long(df.rename_axis('Rep').reset_index(),    # Use Rep as index
                      i='Rep',                                # index of output
                      j='will_drop',                          # Suffix labels  
                      stubnames=['sales', 'acc_number', 'time'],
                      sep='_', 
                      suffix='.*') 
        .reset_index()
        .drop(columns='will_drop'))

#    Rep  sales  acc_number  time
#0  John    100        1001     2
#1  Dave    300        1005     5
#2  John    200        1009     6
#3  Dave    500        1010     9

df.columns = pd.MultiIndex.from_arrays(zip(*df.columns.str.lower().str.rsplit('_', n=1)))
#     sales      time      acc_number      
#      acc1 acc2 acc1 acc2       acc1  acc2
#John   100  200    2    6       1001  1009
#Dave   300  500    5    9       1005  1010

df.stack(-1).reset_index(-1, drop=True)
#      acc_number  sales  time
#John        1001    100     2
#John        1009    200     6
#Dave        1005    300     5
#Dave        1010    500     9

Instructions

Follow the steps carefully to get the output easily.

  1. Download and Install PyCharm on your Computer.
  2. Open a new Python file and Install pandas on your IDE
  3. Copy the snippet using the 'copy' and paste it into your IDE.
  4. Run the file to generate the output.


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


I found this code snippet by searching for ' Reshape a repeating wide pandas DataFrame' 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 2022.3.
  2. The solution is tested on Python 3.11.1
  3. Pandas version-1.5.2.


Using this solution, we are able to Reshape a repeating wide pandas DataFrame 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 Reshape a repeating wide pandas DataFrame.

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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