PANDAS INTRODUCTION, HISTORY, ANALYZING

In this tutorial, we are going to learn about what is Pandas Library, its introduction, history and how to analyse data through it. We will also look at what makes pandas so popular and what are the major areas where we can use Pandas.

What is Pandas

Pandas or Python Data Analysis Library is the most frequently used, open-source and popular library in python that is mainly used for in depth data analysis. Many people jump onto machine learning without having to understand Pandas thoroughly as it provides the ability to process, munge and classify your data. In order to understand ML (Machine Learning), you have to have a good grip on pandas. In simple words, pandas work exactly in python how excel works in microsoft office.

Pandas Introduction





History of Pandas

So far, we have covered about pandas introduction, now in order to understand what pandas is, we must look at the history of it. Pandas was developed by Wes McKinney in 2008 because of the need for an excellent, robust and super fast data analysis tool for data. Years later, python was sponsored by NUMFOCUS in 2015 which helped pandas to gain a wider and more connected community. Pandas is declared an open source library for performing data analysis in Python.

Wes McKinney

Wes McKinney (Creator of Pandas Library)

Analysing Data with Pandas

There are two main ways to analyse data in Pandas through which we will discuss more in the upcoming tutorials:

  • Series
  • DataFrame

Usage

Pandas can be used in different areas and fields like:

  • Statistics
  • Space Centers (NASA, ISRO etc)
  • Data Centers (for analyzing data)
  • Social Media Websites
  • FinTech Companies

Popularity

Pandas is largely popular because of the following reasons:

  • It is easy to learn and interpret (code is quite understandable for everyone)
  • Pandas has a good speed on execution
  • It is super friendly with other libraries in Python, you can correlate with them along with Pandas. For example, you can work with matplotlib, numpy libraries at the same time working with Pandas.