Reshaping DataFrames With Pandas

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Introduction

In order to conduct different deep-dive analyses, transforming data table is an unavoidable step. In this article, I will show you some Pandas DataFrame Basic of Reshaping Data. I hope this would be some of help for your analytical tasks in the future.

Reshaping with Hierarchical Indexing

Stack() and Unstack() are 2 ways to rearrange data in a data frame with multi-index rows and columns.

• Stack(): Rotating the innermost column index to become the innermost row index.
• Unstack(): The inverse action of Stack(). This function pivots the rows into the columns.

Single Column Level

I will depict how these 2 functions generate in the example below. But first, let’s create a simple data frame.

df = pd.DataFrame([[7, 9], [11, 13]], index=['Alex', 'Jack'], columns=['Books', 'Pens'])

By applying the stack method on this data, the columns are changed into the rows, creating a series.

From df1, we can get back to the original data with unstack().

Multi-level Column Level

Now, let’s create a new data frame.

#Create multi-level…