Splitting a Given Field in a Pandas DataFrame

As data analysts, we often encounter datasets with comma-separated values (CSVs) or dot-delimited values that need to be split into separate rows. In this article, we will explore how to achieve this using the pandas library in Python.

Introduction to Pandas DataFrames

A Pandas DataFrame is a two-dimensional labeled data structure with columns of potentially different types. It is similar to an Excel spreadsheet or a table in a relational database. DataFrames are the most powerful and flexible data structures in pandas, making them ideal for data analysis and manipulation.

The Problem: Splitting Comma-Separated Values

Suppose we have a DataFrame sbj with a single row containing comma-separated values:

import pandas as pd

sbj = pd.DataFrame(["Africa, Business", "Oceania", 
                   "Business.Biology.Pharmacology.Therapeutics", 
                   "French Litterature, Philosophy, Arts", "Biology,Business", ""
                  ])

We want to split these comma-separated values into separate rows. However, some fields contain dot-delimited values that need to be split as well.

The Challenge: Handling Dot-Delimited Values

When splitting the dot-delimited values, we encounter an AttributeError exception because the apply() function does not know how to handle the dot notation. This is where we need to get creative and use the chain() function from the itertools module to split both comma- and dot-separated values.

Solution: Using Chain from itertools

Here’s a step-by-step solution using Counter together with chain from itertools:

from collections import Counter
import pandas as pd
import itertools

# Replace periods with commas before parsing
trimmed_list = [i.replace('.', ',').split(',') for i in sbj[0].tolist() if i != ""]

# Strip whitespace and chain the lists together
item_list = [item.strip() for item in itertools.chain(*trimmed_list)]

# Count the frequency of each item using Counter
item_count = Counter(item_list)

# Get the top 10 most common items
top_10_items = item_count.most_common(10)

In this code:

1. We replace periods with commas before parsing to ensure that both types are treated equally.
2. We strip whitespace from each item in the list using strip().
3. We chain the lists together using itertools.chain() to create a single list of items without comma separators.
4. We count the frequency of each item using Counter().
5. Finally, we retrieve the top 10 most common items using most_common(10).

Output: A DataFrame with Split Values

To display the output in a more readable format, we can create a new DataFrame:

# Create a new DataFrame from the top 10 items
df = pd.DataFrame(item_list, columns=['subject'])

print(df)

This will print a DataFrame with the split values, sorted by frequency.

Example: Plotting the Top 10 Items

To visualize the distribution of these items, we can create a bar chart:

import matplotlib.pyplot as plt

# Sort the top 10 items in descending order of frequency
df = df.sort_values(by='subject', ascending=False)

# Plot the top 10 items as a bar chart
plt.title("Distribution of the Top 10 Subjects")
plt.ylabel("Frequency")
df.head(10).plot(kind='bar', color="#348ABD")

plt.show()

This will display a bar chart showing the frequency of each item in the top 10 list.

Conclusion

In this article, we explored how to split comma-separated values and dot-delimited values using pandas. We used Counter together with chain from itertools to achieve this goal. This solution is versatile and can be applied to various datasets containing comma- or dot-separated values. By understanding these concepts and techniques, you’ll be better equipped to handle complex data analysis tasks in your own projects.

Additional Tips and Variations

• To handle missing values more robustly, consider using the na parameter when creating the Counter.

item_count = Counter(item_list)

• If you want to preserve the original column names from the DataFrame, consider assigning them as separate variables before splitting the values.

subject_list = sbj[0].tolist()
subject_counts = Counter(subject_list)

# Create a new DataFrame with the split values
df = pd.DataFrame(subject_counts.items(), columns=['Subject', 'Frequency'])

These variations will help you refine your approach to suit specific use cases.

Last modified on 2025-04-05