Pandas is a high-level data manipulation tool developed by Wes McKinney. It is built on the Numpy package and its key data structure is called the DataFrame. DataFrames allow you to store and manipulate tabular data in rows of observations and columns of variables.
is the most famous python library providing fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way towards this goal.
In Pandas, the data is usually utilized to support the statistical analysis in SciPy, plotting functions from Matplotlib, and machine learning algorithms in Scikit-learn.
Here are just a few of the things that pandas does well:
NaN
) in floating point as well as non-floating point dataSeries
, DataFrame
, etc. automatically align the data for you in computationsPandas have two core data structure components, and all operations are based on those two objects. Organizing data in a particular way is known as a data structure. Here are the two pandas data structures:
These are online read-only versions. However you can Run βΆ
all the codes online by clicking here β
Open your Prompt and type and run the following command (individually):
pip install pandas
Once Installed now we can import it inside our python code.
You can and Starring and Forking is free for you, but it tells me and other people that it was helpful and you like this tutorial.
Go here
if you aren't here already and click β β° Star
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Go here
and click the big green β Code
button in the top right of the page, then click β Download ZIP
.
Extract the ZIP and open it. Unfortunately I don't have any more specific instructions because how exactly this is done depends on which operating system you run.
Launch ipython notebook from the folder which contains the notebooks. Open each one of them
Kernel > Restart & Clear Output
This will clear all the outputs and now you can understand each statement and learn interactively.
If you have git and you know how to use it, you can also clone the repository instead of downloading a zip and extracting it. An advantage with doing it this way is that you don't need to download the whole tutorial again to get the latest version of it, all you need to do is to pull with git and run ipython notebook again.
I'm Dr. Milaan Parmar and I have written this tutorial. If you think you can add/correct/edit and enhance this tutorial you are most welcomeπ
See github's contributors page for details.
If you have trouble with this tutorial please tell me about it by Create an issue on GitHub. and I'll make this tutorial better. This is probably the best choice if you had trouble following the tutorial, and something in it should be explained better. You will be asked to create a GitHub account if you don't already have one.
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You may use this tutorial freely at your own risk. See LICENSE.