Reshaping a Pandas DataFrame: A Step-by-Step Guide

Pandas is a powerful library in Python for data manipulation and analysis. One of its most useful features is the ability to reshape dataframes, which can be particularly useful when working with data that needs to be transformed from one format to another.

In this article, we will explore how to reshape a Pandas dataframe from shape (2,3) to shape (4,2) by stacking the first two columns and repeating the third. We will use both the pandas library as well as the numpy library to achieve this transformation.

Setting Up Our Example

Before we dive into the reshaping process, let’s set up our example dataframe using Pandas:

df1 = pd.DataFrame({0: ['r1c1', 'r2c1'], 1: ['r1c2', 'r2c2'], 2: ['r1c3', 'r2c3']})

This creates a dataframe with three columns (index 0, 1, and 2) and two rows. The values in each column are represented as strings.

Reshaping Using Pandas

One way to reshape our dataframe is by using the set_index, unstack, reset_index, and iloc methods:

df1.set_index(2).unstack().reset_index(1).iloc[:, ::-1]

Here’s what each part of this line does:

• df1.set_index(2): Sets the value at index 2 (the third column) as the new index.
• .unstack(): Transposes the dataframe, effectively stacking the values in the first two columns on top of the index values. This is done by setting these columns as the new rows and keeping the original index as the new column headers.
• .reset_index(1): Replaces the old index (now the second column) with a new integer index starting from 0.
• .iloc[:, ::-1]: Swaps the order of the first two columns.

The result is a dataframe reshaped to have four rows and two columns, where each row corresponds to one of the values in the original third column:

      0     2
0  r1c1  r1c3
0  r2c1  r2c3
1  r1c2  r1c3
1  r2c2  r2c3

Reshaping Using Numpy

Another way to reshape our dataframe is by using the numpy library. Here’s how:

v = df1.values
np.hstack([v[:, :2].reshape(-1, 1), v[:, 2].repeat(2)[:, None]])

Here’s what each part of this line does:

• df1.values: Converts the dataframe into a numpy array, where each row corresponds to one row in the dataframe and each column corresponds to one element.
• [v[:, :2].reshape(-1, 1), v[:, 2].repeat(2)[:, None]]: Creates an array of two rows. The first row consists of the values from columns 0 and 1 (i.e., r1c1 and r1c2) repeated in a column format, while the second row consists of the value from column 3 (r1c3) repeated twice.
• np.hstack: Concatenates these two arrays horizontally to form our desired output.

The result is also an array with four elements, where each element corresponds to one of the values in the original dataframe:

array([['r1c1', 'r1c3'],
       ['r2c1', 'r2c3'],
       ['r1c2', 'r1c3'],
       ['r2c2', 'r2c3']], dtype=object)

Choosing the Right Method

Both methods achieve the desired transformation, but they approach it from different angles. The Pandas method is generally more intuitive and easier to read for data scientists familiar with Pandas operations. On the other hand, the Numpy method can be more efficient if you’re working with large datasets.

Conclusion

Reshaping a dataframe from one format to another is an essential skill when working with data in Python. By understanding how to use both Pandas and Numpy, you’ll have access to a range of powerful tools for transforming your data into the desired shape.

Last modified on 2025-03-29