Slicing the Dataframe using Index Values in Pandas
by Abdul Rawoof A R Updated: Feb 10, 2023
Slicing a DataFrame refers to selecting specific rows and columns from the DataFrame and creating a new DataFrame with those selected rows and columns. This can be done using the .loc or .iloc attributes in pandas, where .loc uses the labels of the rows and columns and .iloc uses the integer index of the rows and columns. Slicing a DataFrame can be useful for various purposes. Some common uses of slicing a DataFrame include:
- Data Exploration.
- Data Cleaning.
- Data Visualization.
- Data Preparation.
In pandas, you can slice a DataFrame using the following:
- “.iloc” attribute allows you to select rows and columns by their integer index.
- You can select multiple rows by passing in a list of index values.
- You can also select specific columns by passing in the integer index of the columns after the row index value(s).
- You can also slice the dataframe with a range of rows and columns.
For more information about slicing the DataFrame using index value in Pandas, please look at the code below.
Fig : Preview of the output that you will get on running this code from your IDE.
In this solution we're using Pandas library.
Follow the steps carefully to get the output easily.
- Install pandas on your IDE(Any of your favorite IDE).
- Copy the snippet using the 'copy' and paste it in your IDE.
- Add required dependencies and import them in Python file(import pandas).
- Run the file to generate the output.
I hope you found this useful. I have added the link to dependent libraries, version information in the following sections.
I found this code snippet by searching for 'slicing the dataframe using index values in pandas' in kandi. You can try any such use case!
I tested this solution in the following versions. Be mindful of changes when working with other versions.
- The solution is created in PyCharm 2021.3.
- The solution is tested on Python 3.9.7.
- Pandas version-v1.5.2.
Using this solution, we are able to slice dataframe using index values in pandas with simple steps. This process also facilities an easy way to use, hassle-free method to create a hands-on working version of code which would help us to slice dataframe using index values in pandas.
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Python 37415 Version:v2.0.0rc1 License: Permissive (BSD-3-Clause)
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