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:
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
In this solution, we use the read_csv function of the Pandas library
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!
Python 36783 Version:1.5.2 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 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.
I tested this solution in the following versions. Be mindful of changes when working with other versions.
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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