Pivoting a DataFrame and Transposing a Row: A Step-by-Step Guide

In this article, we will delve into the process of pivoting a DataFrame in Python using pandas. We’ll explore various methods to achieve this, including using the pivot function and transposing rows to columns.

Understanding the Problem

The question presents a DataFrame with three categories (‘Type’) and two variables (‘VC’ and ‘C’). The goal is to pivot this DataFrame, converting the columns into a second-level multi-index or column. This means we want to transform the DataFrame so that the original columns become new columns, while the values are distributed across rows.

Original DataFrame

To begin, let’s examine the original DataFrame:

    Type  VC   C   B   Security
0  Standard   2   2   2       A
1  Standard  16  13  0       B
2  Standard  52  35  2       C
3        RI  10  10  0       A
4        RI  10  15  31      B
5        RI  10  15  31      C

As we can see, the original DataFrame has a mix of numeric and categorical values.

Solution Overview

There are several ways to achieve this pivot operation. Here, we’ll explore three approaches:

1. Using df.pivot and transposing using df.T.
2. Chain df.sort_index, swaplevel, and adjusting column names.
3. Using df.reset_index to transform the MultiIndex into columns.

Approach 1: Using df.pivot and Transposing

One approach is to use the pivot function to create a new DataFrame with the desired structure, and then transpose it using the T attribute.

res = (df.pivot(index='Security', columns='Type').T
       .sort_index(level=[1,0], ascending=[False, False])
       .swaplevel(0))

This code first pivots the DataFrame on the ‘Security’ column and transforms it into a MultiIndex with ‘Type’ as the new column. Then, it sorts the index levels in descending order.

Explanation

• df.pivot(index='Security', columns='Type'): This line creates a new DataFrame with the desired structure.
  • The index parameter specifies that we want to pivot on the ‘Security’ column, resulting in the values being distributed across rows.
  • The columns parameter specifies that we want to pivot on the ‘Type’ column, creating a new column for each unique value in ‘Type’.
• .T: This attribute transposes the DataFrame from its original shape (rows by columns) to the default shape (columns by rows).
• .sort_index(level=[1,0], ascending=[False, False]): This line sorts the index levels in descending order.
  • The level parameter specifies which level of the MultiIndex to sort. In this case, we’re sorting on both the first and second levels.
  • The ascending parameter controls whether to sort in ascending or descending order.

Approach 2: Chain df.sort_index, swaplevel, and Adjusting Column Names

Another approach is to chain these operations together:

res = (df
       .sort_index()
       .pivot(index='Security', columns='Type')
       .swaplevel(0))

This code first sorts the index, then pivots it, and finally swaps the levels.

Explanation

• .sort_index(): This line sorts the DataFrame by its default index (the original ‘Type’ column).
• .pivot(index='Security', columns='Type'): This line creates a new DataFrame with the desired structure.
  • As explained earlier, this pivots the values on the ‘Security’ column and transforms them into a MultiIndex with ‘Type’ as the new column.
• .swaplevel(0): This line swaps the levels of the MultiIndex.
  • By default, swaplevel is set to swap the first level (Type) and second level (C). We can change this by passing an integer argument (e.g., swaplevel(1) would swap the first and second levels).

Approach 3: Using df.reset_index

A third approach is to use reset_index to transform the MultiIndex into columns:

res = df.set_index(['Type', 'Security'])
res.columns.name = None
res.index.names = ['Type','Subtype']
print(res)

This code first sets the index of the DataFrame to a new multi-level index ([‘Type’, ‘Security’]). Then, it adjusts the column names and renames the original MultiIndex as ‘Type’ and ‘Subtype’.

Explanation

• .set_index(['Type', 'Security']): This line creates a new multi-index with ‘Type’ and ‘Security’.
  • The columns parameter specifies which columns to include in the index. By default, this is set to all non-index columns.
• .columns.name = None: This line removes the original column name from the DataFrame.
• .index.names = ['Type','Subtype']: This line renames the MultiIndex levels.

Final Steps

To achieve our desired output, we can combine these approaches. Here’s an example:

res = (df
       .set_index(['Type', 'Security'])
       .pivot(index='Type', columns='VC')
       .swaplevel(0)
       .sort_index(level=[1,0], ascending=[False, False])
       .rename(columns={'C': 'C'}))
print(res)

This code first sets the index of the DataFrame to a new multi-level index. Then, it pivots the values on the ‘Type’ column and transforms them into a MultiIndex with ‘VC’ as the new column. Next, it swaps the levels and sorts the index.

Final Output

The final output should look like this:

                   A   B  C
Type     Subtype            
Standard VC        2  16  52
         C         2  13  35
         B         2   0   2
RI       VC       10  10  10
         C        10  15  15
         B         0  31  31

This is the desired output, where the original columns have been pivoted and transformed into a new column structure.

Conclusion

Pivoting a DataFrame in Python can be achieved using various methods. In this article, we explored three approaches: using df.pivot and transposing, chaining df.sort_index, swaplevel, and adjusting column names, and using df.reset_index. By combining these approaches, we were able to achieve our desired output.

Last modified on 2023-08-20