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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 becomingthe 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 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:

• Easy handling ofmissing data (represented asNaN) in floating point as well as non-floating point data
• Size mutability: columns can beinserted anddeleted from DataFrame and higher dimensionalobjects
• Automatic and explicitdata alignment: objects canbe explicitly aligned to a set of labels, or the user can simplyignore the labels and letSeries,DataFrame, etc. automaticallyalign the data for you in computations
• Powerful, flexiblegroup by functionality to performsplit-apply-combine operations on data sets, for both aggregatingand transforming data
• Make iteasy to convert ragged,differently-indexed data in other Python and NumPy data structuresinto DataFrame objects
• Intelligent label-basedslicing,fancyindexing, andsubsetting oflarge data sets
• Intuitivemerging andjoining datasets
• Flexiblereshaping andpivoting ofdata sets
• Hierarchical labeling of axes (possible to have multiplelabels per tick)
• Robust IO tools for loading data fromflat files(CSV and delimited),Excel files,databases,and saving/loading data from the ultrafastHDF5 format
• Time series-specific functionality: date rangegeneration and frequency conversion, moving window statistics,moving window linear regressions, date shifting and lagging, etc.

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

001_Python_Pandas_DataFrame

• 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

002_Python_Pandas_Exercise_1

003_Python_Pandas_Exercise_2

automobile_data.csv

pokemon_data.csv

Pandas Cheat Sheet Data Wrangling in Python.pdf

Pandas Cheat Sheet for Data Science in Python.pdf

These are online read-only versions.

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 Starring is free for you, but it tells me and other people that it was helpful and you like this tutorial.

Gohere 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.

1. Gohere 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.

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🙏

Seegithub's contributors page for details.

If you have trouble with this tutorial please tell me about it byCreate 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, pleasegive it a ⭐ star.

You may use this tutorial freely at your own risk. SeeLICENSE.