Pandas DataFrame can be created from lists, dictionaries, and a list of dictionaries, etc. In this solution, we will index a Dataframe using the Pandas library. Python consists of Numerous numbers of Library that help the developer to develop the code. In that, there is a specific library that helps to machine learning tasks, Data analysis. In this solution kit, I am sharing the code snippet and Library that Indexes a Dataframe in Python, which can be executed directly in the IDE.
Preview of the output that you will get on running this code.
In this code we have used loc function in Pandas Library
a = df.loc[[date]] print (df) Symbol Order Shares Date 2011-01-09 AAPL BUY 1500 2011-01-10 AAPL BUY 1500 2011-01-10 AAPL SELL 1500 date = '2011-01-09' a = df.loc[[date]] print (a) Symbol Order Shares Date 2011-01-09 AAPL BUY 1500 date = '2011-01-10' a = df.loc[[date]] print (a) Symbol Order Shares Date 2011-01-10 AAPL BUY 1500 2011-01-10 AAPL SELL 1500
- Copy this code using "Copy" button above and paste it in your Python ide
- Enter the Data that and create a Dataframe that need to be indexed
- Import Pandas library of python.
- Run the code to get a Index as Date.
I hope you have found this useful. I have added the dependent library and version information in the following section.
I found this code snippet by searching "Indexing while using pandas loc" 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 using Python version 3.7.15
- This solution is Tested using Pandas 1.5.2
Using this solution we can able to Indexing a Dataframe with Boolean using 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 to index a Dataframe in Python.