Phyllis0314021805414 / 10_Python_Pandas_Module

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.

10_Python_Pandas_Module

Introduction 👋

What is Pandas in Python?

The most famous python library which is utilized for data analysis is called Pandas. Pandas render extremely optimized performance with back-end source code which is written totally in C or Python. Also, using pandas you can easily familiar with your data by cleaning, transforming, and analyzing it.

In Pandas, the data is usually utilized to support statistical analysis in SciPy, plotting functions from Matplotlib, and machine learning algorithms in Scikit-learn.

Core Components of Pandas Data Structure

Pandas 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:

• Series
• DataFrame

Table of contents 📋

001_Python_Pandas_DataFrame

002_Python_Pandas_Exercise_1

003_Python_Pandas_Exercise_2

automobile_data.csv

pokemon_data.csv

Python_Pandas_Methods:

001_Python_Pandas_DataFrame_from_Dictionary

002_Python_Pandas_DataFrame_from_List

003_Python_Pandas_DataFrame_head()_and_tail()

004_Python_Pandas_DataFrame_drop_columns

005_Python_Pandas_DataFrame_drop_duplicates

006_Python_Pandas_DataFrame_drop_columns_with_NA

007_Python_Pandas_DataFrame_rename_columns

008_Python_Pandas_DataFrame_to_Python_dictionary

009_Python_Pandas_DataFrame_set_index

010_Python_Pandas_DataFrame_reset_index

These are online read-only versions.

Frequently asked questions ❔

How can I thank you for writing and sharing this tutorial? 🌷

You can ⭐ star this tutorial. Starring 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 the "⭐ Star" button in the top right corner. You will be asked to create a GitHub account if you don't already have one.

How can I read this tutorial without an Internet connection? 🤔

1. Go here if you aren't here already.

2. Click the big green "Clone or download" button in the top right of the page, then click "Download ZIP".

   

3. 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.

4. Launch ipython notebook from the folder which contains the notebooks. Open each one of them

   Cell > All Output > Clear

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.

Authors ✍️

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 welcome🙏

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.

If you like this tutorial, please give it a ⭐ star.

Licence 📜

You may use this tutorial freely at your own risk. See LICENSE.