Python Packages for Data Analytics – What all you’ll need to learn?

Introduction
Python is now officially the most recommended programming language by various Data Science professionals from the industry. As a newbie to programming or python, you’d be figuring how and where to get started. 
 
Here are the four important packages you should master to practice Data Analytics using Python.
  1. Numpy
  2. Pandas
  3. Matplotlib
  4. Sea-born
Let’s now dive into each package to know what they are meant for!
1. Numpy 
Numpy is a library for adding support on large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. 
2. Pandas 
Pandas stands for “Python Data Analysis Library”. It’s one of those packages that makes importing and analysing data real easy. And is know for Data manipulation.
Pandas builds on packages like Numpy which will enable to you do most of your analysis and visualisation work together. 
3. Matplotlib 
Matplotlib is a 2D python plotting library. You can generate plots, histograms, power spectra, bar charts, errorcharts, scatterplots, etc., with just a few lines of code. 
4. Seaborn 
Seaborn is built on top of Matplotlib and introduces additional plot types. It’s focused to provide high-level interface to draw statistical checks. 
Seaborn compliments Matplotlib and if you know Matplotlib that means you already know 50% of Seaborn. 
It’s now your turn to get started with Python basics. 
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