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How to Create Pandas DataFrame from Online Data in Python

by ganesh Updated: Feb 3, 2023

A DataFrame is a two-dimensional, size-mutable, and heterogeneous tabular data structure with labeled axes (rows and columns) that can be used to store and manipulate data in a variety of formats, including numbers, strings, and dates. It is similar to a table in a relational database or a data frame in R or Python's pandas library. It can be thought of as a collection of Series (one-dimensional arrays) that share the same index. 


There are several techniques to extract web data into a pandas DataFrame. Here are several possibilities: 

  • To load data from an online CSV file, use the pandas library's read_csv() method. 
  • read_csv(): The read_csv() function is a function in the pandas’ library for reading data from a CSV (comma-separated values) file into a pandas DataFrame.  
  • To load data from an online Excel file, use the pandas library's read_excel() method. 
  • read_excel(): The pandas’ library includes the read_excel() method for reading data from an Excel file into a pandas DataFrame.  
  • To load data from an online HTML table, use the pandas library's read_html() method. 
  • read_html(): A pandas DataFrame may be created by reading data from an HTML table using the read_html() method in the pandas package. 


You can have a look at the code below to create Pandas DataFrame using online data. 

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 online 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 "download csv to pandas" in kandi. You can try any such use case!

Dependent Libraries

pandasby pandas-dev

Python star image 36783 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 36783 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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