Matplotlib in Python: Data Visualization Plots & how to use it

 Data Visualisation is one among the critical skills expected from data scientists. Most of the business problems might be understood and addressed using visualisation techniques. Visualisation basically involves Exploratory Data Analysis (EDA) and Graphical Plots. Effective visualisation helps the users to know the patterns from the info and solve the business problem more effectively. Another advantage of visualisation is to simplify the complex data into a clear format.

People find it very easy to read a picture much easier than text. Visualisation is the best communication platform to analyse and interpret the info . It helps the users to know vast amounts of data easily. Data visualisation helps in understanding the trends, correlation, patterns, distributions etc.,

There are multiple tools and technologies available within the industry for data visualisation, python being the foremost used. Python Training offers multiple libraries for data visualisation, few of the favored graphic libraries are:

Matplotlib

Seaborn

Pandas visualisation

Plotly

This document helps in understanding the matplotlib library which is widely utilized in the industry. Matplotlib features a sort of graphical features and is extremely easy to know . this text focuses on different graphical features including syntax.

Installing Matplotlib

There are multiple ways to put in the matplotlib library. the simplest thanks to install matplotlib is to download the Anaconda package. Matplotlib is default installed with Anaconda package and doesn't require any additional steps.

Download anaconda package from the official site of Anaconda

To install matplotlib, attend anaconda prompt and run the subsequent command

pip install matplotlib

or

conda install matplotlib

Verify whether the matplotlib is correctly installed using the subsequent command in Jupyter notebook

import matplotlib

matplotlib.__version__

How to use Matplotlib

Before using matplotlib, we'd like to import the package. this will be done using the ‘import’ method in Jupyter notebook. PyPlot is that the graphical module in matplotlib which is usually used for data visualisation, importing PyPlot is sufficient to figure around data visualisation.

# import matplotlib library as mpl

import matplotlib as mpl

#import the pyplot module from matplotlib as plt (short name used for referring the object)

import matplotlib.pyplot as plt

The relation between – Matplotlib, Pyplot and Python

Python Training In Jodhpur may be a very fashionable programing language , used for web development, mathematics and statistical analysis. Python works on most of the platforms and is additionally simple to use.

Python has multiple libraries used for specific purposes, below libraries are mostly used for visualization and data analysis.

NumPy

Pandas

Matplotlib

Seaborn

Plotly

SciKit-Learn

As you observe one among the packages is matplotlib which is developed using python. This library is extremely widely used for data visualisations.

PyPlot may be a module in matplotlib which provides MATLAB like interface. MATLAB is heavily used for statistical analysis within the manufacturing industry. MATLAB may be a licensed software and requires a big amount of cash to shop for and use, whereas PyPlot is an open-source module and provides similar functionality as MATLAB using python. Just to conclude PyPlot has been seen as a replacement of MATLAB within the context of open source.

Create an easy Plot

Here we'll be depicting a basic plot using some random numbers generated using NumPy. the only thanks to create a graph is using the ‘plot()’ method. to get a basic plot, we'd like two axes (X) and (Y), and that we will generate two random numbers using the ‘linspace()’ method from Numpy.