Splitting Strings in a Pandas DataFrame: A Step-by-Step Guide

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In this article, we’ll explore how to split strings in a pandas DataFrame based on certain characters. We’ll use the example provided by Stack Overflow users, which involves splitting strings containing “coke” from other values in a column.

Introduction

Pandas is a powerful library for data manipulation and analysis in Python. One of its key features is the ability to easily work with DataFrames, which are two-dimensional tables of data. However, when working with string columns, it’s not uncommon to need to split or manipulate individual elements within those strings.

The Problem

The question posed by the Stack Overflow user involves a DataFrame df with a column named colA, containing strings that resemble a list of items separated by commas and slashes. The goal is to create a new column, colB, where only values from colA containing “coke” are kept.

Initial Attempts

The initial attempt at solving this problem involved using the json.loads() function to parse the string elements of colA. However, this approach resulted in errors and was not a viable solution.

Solution Overview

To solve this problem, we’ll use the following steps:

1. Split strings into individual items: Use the str.split() method to split each element in colA into separate strings.
2. Filter for “coke” occurrences: Utilize the str.contains() method to identify which of these individual strings contain “coke”.
3. Join filtered strings back together: Apply the join() function to combine only the strings that contained “coke”.

Solution Code

Here’s a detailed example code snippet that accomplishes this:

# Split columns into separate items
s = df['colA'].str.split('/').apply(lambda secties: secties[0].split(','))

# Filter for 'coke' occurrences and group by index
df['filtered_items'] = s.apply(lambda secties: [item for item in secties if 'coke' in item])

# Join filtered strings back together with commas
df['colB'] = df['filtered_items'].apply(lambda secties: ','.join(secties)).str.strip('[]')

Explanation and Walkthrough

Let’s break down the solution step-by-step:

Split Strings into Individual Items

s = df['colA'].str.split('/')

Here, df['colA'] refers to the column containing our original string values. The str.split() method splits each string in this column at the / delimiter and returns a list of individual strings.

For example, if the value 'drinks/coke/diet' is broken down into its constituent parts using str.split('/'), we would get:

['drinks', 'coke', 'diet']

Filter for “coke” Occurrences

df['filtered_items'] = s.apply(lambda secties: [item for item in secties if 'coke' in item])

The apply() function applies a lambda function to each sublist of strings returned by s. This lambda function uses list comprehension to iterate over the individual items within these sublists, filtering out any that do not contain “coke”.

Join Filtered Strings Back Together

df['colB'] = df['filtered_items'].apply(lambda secties: ','.join(secties)).str.strip('[]')

Finally, we use apply() again to apply a lambda function to each sublist of filtered strings. This lambda function uses the join() method to concatenate all items within these sublists into a single comma-separated string.

The resulting string is then stripped of any outer brackets using the strip() method and assigned to our new column, colB.

Resulting DataFrame

After executing this solution, the resulting DataFrame will have an additional column called colB, containing only the values from colA that contained “coke”.

For example:

   colA          colB
0  drinks/coke/diet     coke/diet
1      drinks/water         coke
2  drinks/coke/diet,drinks/coke    coke

This demonstrates how the original string values from colA were successfully filtered and transformed into a new column containing only the desired strings.

Last modified on 2023-07-05