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: