Skip to content
  • YouTube
  • FaceBook
  • Twitter
  • Instagram

Data Analytics Ireland

Data Analytics and Video Tutorials

  • Home
  • Contact
  • About Us
    • Latest
    • Write for us
    • Learn more information about our website
  • Useful Links
  • Glossary
  • All Categories
  • Faq
  • Livestream
  • Toggle search form
  • create read update delete using Tkinter class
  • Python tutorial: Pandas groupby ( Video 1) Python
  • How to change the headers on a CSV file CSV
  • How To Add Values to a Python Dictionary Python
  • Free ways to Extract Data from Files Livestream
  • How to Group By in a Select Statement SQL
  • Python Tutorial: Pandas groupby columns ( video 2) Python
  • How To Join Tables In SQL SQL

ValueError: pattern contains no capture groups

Posted on March 7, 2021May 14, 2021 By admin No Comments on ValueError: pattern contains no capture groups

Estimated reading time: 2 minutes

In Python, there are a number of re-occurring value errors that you will come across.

In this particular error it is usually related to when you are running regular expressions as part of a pattern search.

So how does the problem occur?

In the below, the aim of the code is to purely create a data frame, that can then be searchable.

To search the data frame we will use str.extract

import pandas as pd
rawdata = [['Joe', 'Jim'],
           ['Jane', 'Jennifer'],
           ['Ann','Alison']]
datavalue = pd.DataFrame(data=rawdata, columns=['A', 'B'])

We then add the below code to complete the extract of the string “Joe”.

a = datavalue['A'].str.extract('Joe')
print(a)

But it gives the below error, what we are trying to solve for:

ValueError: pattern contains no capture groups
Process finished with exit code 1

But why did the error occur , and how can we fix it?

In essence when you try to complete a str.extract, the value you are looking for should be enclosed in brackets i.e ()

In the above, it views ‘Joe’ as an incorrect value to be passed into the str.extract function, and returns the error.

So to fix this problem, we would change this line to:

a = datavalue['A'].str.extract('(Joe)')

As a result the program runs without error, and returns the below result:

     0
0  Joe
1  NaN
2  NaN

The full corrected code to be used is then:

import pandas as pd
rawdata = [['Joe', 'Jim'],
           ['Jane', 'Jennifer'],
           ['Ann','Alison']]
datavalue = pd.DataFrame(data=rawdata, columns=['A', 'B'])
a = datavalue['A'].str.extract('(Joe)')
print(a)
Value Error Tags:Data Frame, pattern matching, Python, python regular expressions, regular expression

Post navigation

Previous Post: What does a data analyst do?
Next Post: how to insert data into a table in SQL

Related Posts

  • ValueError: invalid literal for int() with base 10 Value Error
  • ValueError: Columns must be same length as key exception handling
  • ValueError: cannot convert float NaN to integer Null values

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Select your language!

  • हिंदी
  • Español
  • Português
  • Français
  • Italiano
  • TypeError: ‘list’ object is not an iterator Python
  • R – How to open a file R Programming
  • What are the reserved keywords in Python Definition
  • How do I fix TypeError: unhashable type: ‘list’ Error? Articles
  • How to Pass Python Variables to Javascript Javascript
  • hide a column from a data frame Python Dataframe
  • planning your machine learning model machine learning
  • How to create a class in Python class

Copyright © 2023 Data Analytics Ireland.

Powered by PressBook Premium theme

This website uses cookies to improve your experience. We'll assume you're ok with this, but you can opt-out if you wish. Cookie settingsACCEPT
Privacy & Cookies Policy

Privacy Overview

This website uses cookies to improve your experience while you navigate through the website. Out of these cookies, the cookies that are categorized as necessary are stored on your browser as they are essential for the working of basic functionalities of the website. We also use third-party cookies that help us analyze and understand how you use this website. These cookies will be stored in your browser only with your consent. You also have the option to opt-out of these cookies. But opting out of some of these cookies may have an effect on your browsing experience.
Necessary
Always Enabled
Necessary cookies are absolutely essential for the website to function properly. This category only includes cookies that ensures basic functionalities and security features of the website. These cookies do not store any personal information.
Non-necessary
Any cookies that may not be particularly necessary for the website to function and is used specifically to collect user personal data via analytics, ads, other embedded contents are termed as non-necessary cookies. It is mandatory to procure user consent prior to running these cookies on your website.
SAVE & ACCEPT