Creating a Daraframe from Dictionary of List

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by vigneshchennai74 dot icon Updated: Feb 24, 2023

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Creating a Pandas DataFrame from a Dictionary of Lists is common in data analysis and manipulation. Pandas is a famous Python library for data analysis that provides powerful tools for working with tabular data, and the DataFrame is the primary data structure in Pandas. 

A dictionary of lists is a common data structure that contains data in key-value pairs, where each key corresponds to a column in the DataFrame, and each value corresponds to the values in that column. Creating a DataFrame from a dictionary of lists involves converting the dictionary into a DataFrame object with the columns and values properly aligned. The dictionary's keys become the column names of the DataFrame, while the dictionary's values become the data in the columns. By default, each list in the dictionary is assumed to have the same length, representing the number of rows in the resulting DataFrame. 

Here is an example of how to create a dataframe from the dictionary of the list: 

Preview of the output that you will get running on this code


In this code we have used Pandas Library

  1. Copy this code using "Copy" button above and paste in your python ide.
  2. Run this code to create Dataframe.

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 "Creating a pandas dataframe from dictionary of lists"in Kandi .you can try any use case.

Environment Tested

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

Using this solution we can able to Create Dataframe using Dictionary of List 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 Dataframe in Python.


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