Different Ways to Create, Subset, and Combine Dataframes using Pandas

Different Ways to Create, Subset, and Combine Dataframes using Pandas

A much-needed concise guide for some of the most useful methods and functions in pandas. You already know?

A much-needed concise guide for some of the most useful methods and functions in pandas

Introduction

In the recent 5 or so years, python is the new hottest coding language that everyone is trying to learn and work on. One of the biggest reasons for this is the large community of programmers and data scientists who are continuously using and developing the language and resources needed to make so many more peoples life easier. However, to use any language effectively there are often certain frameworks that one should know before venturing into the big wide world of that language. For python, there are three such frameworks or what we would call as libraries that are considered as the bed rocks. They are Pandas, Numpy, and Matplotlib. In this article we would be looking into some useful methods or functions of pandas to understand what and how are things done in pandas.

Photo by Chris Ried on Unsplash

Since pandas has a wide range of functionalities, I would only be covering some of the most important functionalities. In this article, we will be looking to answer the following questions:

  • What is package?
  • How to install and call packages?
  • What is pandas?
  • What are dataframe and series?
  • How to initialize a dataframe in multiple ways?
  • How to select/subset/slice a dataframe?
  • How to combine two dataframes?

New to python and want to learn basics first before proceeding further? You can have a look at another article written by me which explains basics of python for data science below.

Know basics of python but not sure what so called “packages” are? Don’t worry, I have you covered. If you already know what a package is, you can jump to “Pandas DataFrame and Series” section to look at topics covered straightaway.

programming python data-science self-improvement pandas

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