5 Practical Methods to Reshape DataFrame in Pandas

Reshaping pandas DataFrame is to transform the structure of the DataFrame to better suit the analysis or visualization needs

Reshaping a pandas DataFrame is a common task when working with data analysis and manipulation. It involves transforming the structure of the DataFrame to better suit the analysis or visualization needs. Fortunately, pandas provides several methods that allow us to reshape the data effortlessly. In this tutorial, we will explore different techniques to reshape a pandas DataFrame using functions like transpose() or T, pivot(), melt(), stack(), unstack(), and combining groupby() and agg().

Methods for Reshaping a Pandas DataFrame:

‘transpose()’ or T: This function interchanges the rows and columns of a DataFrame, effectively reshaping it.

‘pivot()’: This function converts unique values from one column into multiple columns, resulting in a wider DataFrame. It allows us to create a new column for each unique value, making it useful for summarizing and comparing data across categories.

‘melt()’: The melt() function is used to transform a DataFrame from wide format to long format. It gathers columns into rows, creating a "variable" column and a "value"…