Creating a new column in a dataframe based on a condition can be useful in several ways:
- Data analysis: The new column can provide additional information not present in the original columns. For instance, if you have a column of temperatures in Celsius, you can create a new column with the temperatures converted to Fahrenheit.
- Data visualization: The new column can create visualizations that reveal patterns or relationships in the data that were not apparent. For example, you could create a new column that categorizes data points based on their values and then create a chart or graph that shows how the data is distributed across the categories.
- Machine learning: The new column can be used as a target variable in a machine learning model or as a feature in a predictive model. For example, you could create a new column indicating whether a customer is likely to purchase based on their demographic data and past behavior. Then, use that column as the target variable in a machine-learning model that predicts customer behavior.
str is a built-in Python class that represents a string object. It provides a wide range of methods that can be used to manipulate strings, such as split(), join(), replace(), find(), lower(), upper(), and many more. These methods allow you to perform various operations on strings, such as splitting them into substrings, replacing parts of the string, searching for specific substrings, and changing the case of the characters. The str class is used extensively in Python programming for processing text data, parsing files, and building user interfaces, among other tasks.
Creating a new column in a dataframe based on a condition can enrich and enhance the data, making it more useful for analysis, visualization, and machine learning.
Here is an example of how to create a column in a dataframe based on condition:
Preview of the output that you will get running on this code
In this code we have used Pandas Library
check_both = ['Yes' if str(row['col2']) in sublist else 'No' for sublist in row['col1']]
check_any = 'Yes' if 'Yes' in check_both else 'No'
test['col3'] = test.apply(sublist_checker, axis=1)
col1 col2 col3
0 [(330420, 0.932249605656), (76546, 0.932200312614)] 76546 Yes
1 [(330420, 0.932249605656), (500826, 0.932200312614)] 876546 No
- Copy this code using "Copy" button above and paste in your python ide.
- Import pandas library in your code.
- Create your Dataframe(df)
- Enter the Data
- Run this code to create new Column.
I hope you have found this useful. I have added the dependent Library and version information in following sections.
I found this code snippet by searching "Create column in pandas dataframe based on Condition" in Kandi .you can try any use case.
Python 38689 Version:v2.0.2 License: Permissive (BSD-3-Clause)
If you do not have Pandas that is required to run this code you can install it by clicking on th above link and copying the pip install command from the pandas page in Kandi. You can search for any dependent library in Kandi like Pandas.
In this solution we have used the following versions. Be mindful to change when working with other versions.
- This solution is created and tested using Vscode version 1.75.1
- This solution is created using Python version 3.7.15
- This solution is Tested using Pandas 1.5.2
Using this solution we can able to Create a column in dataframe with a specific condition with Pandas library in python with simple Steps. This process also facilities an easy to use, hassle free method to create a hands-on working version of code which would help us Create new column in Python.