Creating Multiple DataFrames from a Single Row: A Pandas Tutorial

Creating DataFrames for Multiple Rows from a Single Row

When working with data that consists of multiple rows in a single cell, it can be challenging to create separate DataFrames for each row. In this article, we will explore how to achieve this using Python and the popular Pandas library.

Problem Statement

Suppose we have a Google search result that provides us with the top 5 links for five animals. We want to create a DataFrame for each animal, where each animal has its own separate DataFrame with five rows, one row for each link. The existing code is producing a single row per animal instead of multiple rows.

Solution Overview

To solve this problem, we will use the explode method provided by Pandas. This method allows us to split a Series or DataFrame into multiple rows based on a specific delimiter.

We will also utilize the groupby function, which enables us to group data by one or more columns and perform operations on each group separately.

Step 1: Importing Libraries and Creating the Data

First, we need to import the necessary libraries and create our sample DataFrame.

import pandas as pd

# Create a DataFrame with animal names and their corresponding links
df = pd.DataFrame({'Animal':['Panda', 'Tiger','Monkey'],
                   'Link':['abcde.com, fghijk.com, lmnopq.com, rstuvw.com, xyz.com',
                           'adobe.com, facebook.com, linkedin.com, google.com, citi.com',
                           'amazon.com, bbc.com, cnn.com, fox.com, abc.com']})

Step 2: Exploding the Links

Next, we will use the explode method to split each link into multiple rows.

# Explode the links
df = df.explode('Link')

This step is equivalent to running the following code manually:

import pandas as pd

# Create a DataFrame with animal names and their corresponding links
df = pd.DataFrame({'Animal':['Panda', 'Tiger','Monkey'],
                   'Link':['abcde.com, fghijk.com, lmnopq.com, rstuvw.com, xyz.com',
                           'adobe.com, facebook.com, linkedin.com, google.com, citi.com',
                           'amazon.com, bbc.com, cnn.com, fox.com, abc.com']})

# Split each link into multiple rows
df = df.set_index('Animal')
df['Link'] = df['Link'].str.split(',')
df = df.explode('Link')

Step 3: Grouping the Data

After exploding the links, we will group the data by animal names and perform any necessary operations on each group.

# Print the resulting DataFrame
print(df)

This step is equivalent to running the following code:

import pandas as pd

# Create a DataFrame with animal names and their corresponding links
df = pd.DataFrame({'Animal':['Panda', 'Tiger','Monkey'],
                   'Link':['abcde.com, fghijk.com, lmnopq.com, rstuvw.com, xyz.com',
                           'adobe.com, facebook.com, linkedin.com, google.com, citi.com',
                           'amazon.com, bbc.com, cnn.com, fox.com, abc.com']})

# Split each link into multiple rows
df = df.set_index('Animal')
df['Link'] = df['Link'].str.split(',')
df = df.explode('Link')

# Group the data by animal names and print the resulting DataFrame
print(df.groupby('Animal'))

Conclusion

In this article, we demonstrated how to create separate DataFrames for each row in a single cell using Python and Pandas. We utilized the explode method to split links into multiple rows and the groupby function to group data by animal names.

By following these steps, you can easily achieve your goal of creating multiple DataFrames from a single DataFrame with multiple rows in a single cell.

Last modified on 2025-01-13