Matplotlib for Data Visualization python

Matplotlib for Data Visualization python

Introduction

Want to learn Data Visualization using Matplotlib?
 Hello friends, in the previous blog post we have to see basic pandas library Numpy and pandas so it’s time to see Data viasualization in python using Matplotlib. so first upon what is matplotlib ? let’s start. Matplotlib is the grandfathers library of data visualizations with Python. It was created by the John Hunter. He created it to try to replicate MatLab(another programming language) plotting capabilities in Pythons. So if you happen to be familiar with matlab, matplotlibs will feel natural to you.

It is an excellent 2D and 3D graphic library for generating scientific figures.
Matplotlib for Data Visualization
fig 01)Matplotlib for Data Visualization python
Some of the major Pros of Matplotlibs are:

  1. Generally easy to started for simple plot
  2. Support for custom labels and text
  3. Great control of every element in a figures
  4. High-quality output in many formats
  5. Very customizable in general
Matplotlib allows you to create reproducible figures programmatically. Let’s learn how to use it! Before continuing this lecture, I encourage you just to explore the official Matplotlib web page: http://matplotlib.org/

Installation

You’ll need to install matplotlib first with either:
conda install matplotlib
or pip install matplotlib



5.1 example:

Creating a subplot will delete any pre-existing subplots that overlaps with it beyond sharing a boundary.

import matplotlib.pyplot as plt


# plot a lines, implicitly creating a subplot(111)


plt.plot([1,2,3])


# now create a subplots which represents the top plot of a grid with 2 rows and 1 column.


#Since this subplots will overlap the first, the plot (and its axes) previously


created, will be removed


plt.subplot(211)


plt.plot(range(12))


plt.subplot(212, facecolor='y') # create 2nd subplots with yellow background


plt.plot(range(12))

The above line produce the following outputs −

Multiplots for data visualization
5.2 Example:

Following code plots a histogram of marks obtained by students in class. Four bins, 0-25, 26-50, 51-74 are defined. 

from matplotlib import pyplot as plt


import numpy as np


fig,ax = plt.subplots(1,1)


a = np.array([22,87,5,43,56,73,55,54,11,20,51,5,79,31,27])


ax.hist(a, bins = [0,25,50,75,100])


ax.set_title("histogram of result")


ax.set_xticks([0,25,50,75,100])


ax.set_xlabel('marks')


ax.set_ylabel('no. of students')


plt.show()

The plot appears as shown below −
Histogram for data visualization
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