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Load Standard Iris data set in Pandas

by ganesh Updated: Nov 30, 2022

Python has many libraries which provide the ability to work with data. Pandas is a comprehensive and popular Python library which provides the ability to read, process and analyze data. In this solution kit, I am sharing the code snippet and library that I use to read standard Iris data available online and create a Pandas DataFrame which can be executed directly in the Jupyter Notebook.


Fig 1: Preview of the output that you will get on running this code from your Jupyter notebook


Code


In this solution, we use the read_csv function of the Pandas library

  1. Copy the code using the "Copy" button above, and paste it in a cell of Jupyter notebook.
  2. Run the cell to read Iris data and create a Pandas dataframe.


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 "read iris data pandas" in kandi. You can try any such use case!


Dependent Libraries


pandasby pandas-dev

Python star image 36769 Version:1.5.2

License: Permissive (BSD-3-Clause)

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

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pandasby pandas-dev

Python star image 36769 Version:1.5.2 License: Permissive (BSD-3-Clause)

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Support
Quality
Security
License
Reuse

If you do not have Pandas that is required to run this code, you can install it by clicking on the above link and following the installation instruction from either Github or Pypi links through the Pandas page in kandi.


You can search for any dependent library on kandi like Pandas.

Environment Tested


I tested this solution in the following versions. Be mindful of changes when working with other versions.

  1. The solution is created in Python3.7.
  2. The solution is tested on Pandas 1.3.1 version.


Using this solution, we are able to read online data and create a Dataframe using the 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 read data in Pandas.

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