Skip to main content
TopMiniSite

Back to all posts

How to Divide Text After Symbol Into Rows In Pandas?

Published on
3 min read
How to Divide Text After Symbol Into Rows In Pandas? image

Best Text Processing Tools to Buy in October 2025

1 Text Processing with JavaScript: Regular Expressions, Tools, and Techniques for Optimal Performance

Text Processing with JavaScript: Regular Expressions, Tools, and Techniques for Optimal Performance

BUY & SAVE
$24.51 $51.95
Save 53%
Text Processing with JavaScript: Regular Expressions, Tools, and Techniques for Optimal Performance
2 flex & bison: Text Processing Tools

flex & bison: Text Processing Tools

BUY & SAVE
$16.05
flex & bison: Text Processing Tools
3 SEL From a Distance: Tools and Processes for Anytime, Anywhere

SEL From a Distance: Tools and Processes for Anytime, Anywhere

BUY & SAVE
$23.08 $27.95
Save 17%
SEL From a Distance: Tools and Processes for Anytime, Anywhere
4 Hands-On Python Natural Language Processing: Explore tools and techniques to analyze and process text with a view to building real-world NLP applications

Hands-On Python Natural Language Processing: Explore tools and techniques to analyze and process text with a view to building real-world NLP applications

BUY & SAVE
$41.99 $43.99
Save 5%
Hands-On Python Natural Language Processing: Explore tools and techniques to analyze and process text with a view to building real-world NLP applications
5 Gregg College Keyboarding & Document Processing (GDP); Lessons 1-120, main text

Gregg College Keyboarding & Document Processing (GDP); Lessons 1-120, main text

BUY & SAVE
$241.11
Gregg College Keyboarding & Document Processing (GDP); Lessons 1-120, main text
6 The Complete Guide to Mergers and Acquisitions: Process Tools to Support M&A Integration at Every Level (Jossey-Bass Professional Management)

The Complete Guide to Mergers and Acquisitions: Process Tools to Support M&A Integration at Every Level (Jossey-Bass Professional Management)

BUY & SAVE
$32.93 $69.00
Save 52%
The Complete Guide to Mergers and Acquisitions: Process Tools to Support M&A Integration at Every Level (Jossey-Bass Professional Management)
7 TPM in Process Industries (Step-By-Step Approach to TPM Implementation)

TPM in Process Industries (Step-By-Step Approach to TPM Implementation)

  • AFFORDABLE PRICES: SAVE MONEY WITH QUALITY PRE-OWNED BOOKS!
  • QUALITY GUARANTEE: EACH BOOK IS INSPECTED FOR GOOD CONDITION.
  • ECO-FRIENDLY CHOICE: PROMOTE SUSTAINABILITY BY BUYING USED!
BUY & SAVE
$83.07 $120.00
Save 31%
TPM in Process Industries (Step-By-Step Approach to TPM Implementation)
8 Taming Text: How to Find, Organize, and Manipulate It

Taming Text: How to Find, Organize, and Manipulate It

  • AFFORDABLE PRICING ON QUALITY PREOWNED READS.
  • ECO-FRIENDLY CHOICE: REDUCE WASTE WITH SECONDHAND BOOKS.
  • THOROUGHLY INSPECTED FOR GOOD QUALITY AND USABILITY.
BUY & SAVE
$26.77 $44.99
Save 40%
Taming Text: How to Find, Organize, and Manipulate It
9 Skills and Tools for Today's Counselors and Psychotherapists: From Natural Helping to Professional Counseling (with DVD) (Skills, Techniques, & Process)

Skills and Tools for Today's Counselors and Psychotherapists: From Natural Helping to Professional Counseling (with DVD) (Skills, Techniques, & Process)

  • CONVENIENT ACCESS: ENJOY INSTANT LISTENING WITH INCLUDED CD.
  • QUALITY SOUND: EXPERIENCE HIGH-FIDELITY AUDIO RIGHT OUT OF THE BOX.
  • BONUS CONTENT: EXCLUSIVE TRACKS AND MATERIALS INCLUDED ON THE CD.
BUY & SAVE
$97.81 $149.95
Save 35%
Skills and Tools for Today's Counselors and Psychotherapists: From Natural Helping to Professional Counseling (with DVD) (Skills, Techniques, & Process)
+
ONE MORE?

To divide text after a symbol into rows in pandas, you can use the str.split() function along with the expand=True parameter to create a new DataFrame with the split values in separate rows. For example, if you have a column 'text' in your DataFrame and you want to split the text after a comma ',', you can use the following code:

df['text_split'] = df['text'].str.split(',', expand=True)

This will create a new DataFrame column 'text_split' with the text split after the comma in separate rows. You can then further process or analyze the split text as needed.

How to split text into rows in pandas after a specific symbol?

To split text into rows in pandas after a specific symbol, you can use the str.split() method along with the expand=True parameter. Here is an example:

import pandas as pd

create a sample dataframe with a column containing text

df = pd.DataFrame({'text': ['apple:orange:banana', 'grape:kiwi', 'pear:melon:cherry']})

split the text into rows after the colon symbol

df = df['text'].str.split(':', expand=True).stack().reset_index(level=1, drop=True).rename('value').reset_index()

display the resulting dataframe

print(df)

In this example, we first create a sample dataframe with a column containing text. We then use the str.split() method to split the text into rows after the colon symbol. The stack() method is used to stack the resulting Series and reset_index() is used to reset the index of the dataframe. Finally, we display the resulting dataframe where each row contains a single value extracted from the original text after splitting it at the colon symbol.

What is the pandas function for splitting text into rows after a certain character?

The pandas function for splitting text into rows after a certain character is str.split. Here is an example of how to use this function:

import pandas as pd

data = {'text': ['apple,orange,banana', 'carrot,lettuce,tomato']} df = pd.DataFrame(data)

Split the text column into rows after the comma character

df['text_split'] = df['text'].str.split(',')

print(df)

This will output:

              text                     text\_split

0 apple,orange,banana [apple, orange, banana] 1 carrot,lettuce,tomato [carrot, lettuce, tomato]

How to split text into rows after a symbol occurrence in a pandas dataframe?

You can split text into rows after a symbol occurrence in a pandas dataframe by using the str.split method in combination with the explode method.

Here is an example code to split text into rows after a symbol occurrence:

import pandas as pd

create a sample dataframe

data = {'text': ['apple,banana,orange', 'grape,kiwi,melon']} df = pd.DataFrame(data)

split text into rows after comma occurrence

df['text'] = df['text'].str.split(',') df = df.explode('text')

print(df)

Output:

 text

0 apple 0 banana 0 orange 1 grape 1 kiwi 1 melon

In this code, we first split the text in the 'text' column by comma using the str.split method. Then, we use the explode method to split the list of values into separate rows. This will create a new row for each value separated by a comma in the original text.