Splitting a Column into Multiple Lists While Keeping the Delimiter

Introduction

In this article, we will explore how to split a column in a pandas DataFrame into multiple lists while keeping the delimiter. We’ll use Python and its popular library, pandas, to achieve this.

Background

Pandas is a powerful library for data manipulation and analysis in Python. It provides data structures such as Series (1-dimensional labeled array) and DataFrames (2-dimensional labeled data structure with columns of potentially different types).

We will start by importing the necessary libraries and defining our DataFrame.

import pandas as pd

d1 = pd.DataFrame({'user': [1,2,3],'action': ['YNY','NN','NYYN']})

Splitting a Column into Multiple Lists

Our goal is to split the ‘action’ column into multiple lists based on the character ‘Y’. We will use the str.split() method along with a regular expression to achieve this.

The str.split() method splits a string into a list where each word is a list item. However, it does not keep the original delimiter in the resulting list items.

We can modify the delimiter by adding a non-capturing group ( [...] ) around the delimiter Y, which will prevent the delimiter from being included in the resulting list items.

d1.action.str.split('([^Y]*Y)').map(lambda x : [z for z in x  if z!= ''])

Explanation of Code

Here’s a breakdown of how this code works:

• str.split(): This method splits a string into substrings based on the delimiter specified.
  • In our case, the delimiter is '([^Y]*Y)'.
    • The first part [^Y] matches any character that is not ‘Y’.
    • The second part * matches 0 or more occurrences of the preceding element (i.e., [^Y]).
    • So '([^Y]*Y)' will match any sequence of characters that do not include a ‘Y’ followed by one or more ‘Y’s.
  • By using str.split() with this regular expression, we effectively split on the occurrence of ‘Y’ that is followed by zero or more other non-‘Y’ characters.

We then use the map() function to apply a lambda function to each list item in the resulting list.

.map(lambda x : [z for z in x  if z!= ''])

This lambda function iterates through each item x in the list and creates a new list containing only those items that are not equal to an empty string (""). This is done using another list comprehension, where we iterate through each character z in the original list item x, and create a new list containing only the characters that do not match an empty string.

The result is a DataFrame with the ‘action’ column split into multiple lists based on the occurrence of ‘Y’, while keeping the delimiter intact.

Example Usage

Here’s how we can use this code to achieve our desired output:

print(d1.action.str.split('([^Y]*Y)').map(lambda x : [z for z in x  if z!= '']))

Output:

0    [Y, NY]
1       [NN]
2   [NY, Y, N]
Name: action, dtype: object

Conclusion

In this article, we learned how to split a column in a pandas DataFrame into multiple lists while keeping the delimiter. We used the str.split() method with a regular expression to achieve this.

The resulting list will have each item from the original list as a separate sublist, while preserving the original delimiter. This technique can be applied to any string-based data manipulation tasks that require splitting or processing strings.

By using pandas and its powerful string methods, you can efficiently handle various text-related data manipulation tasks in your Python applications.

Future Articles

In our next article, we will explore more advanced techniques for working with DataFrames in pandas.

Last modified on 2024-04-13