 Practice
Resources
Contests
Online IDE
New
Free Mock
Events New Scaler
Practice
Improve your coding skills with our resources
Contests
Compete in popular contests with top coders Events
Attend free live masterclass hosted by top tech professionals
New
Scaler
Explore Offerings by SCALER ## Data Analysis

Last Updated: Jan 10, 2022
Go to Problems
Contents # Matplotlib

One package that is familiar to almost all the data science and machine learning community would be matplotlib and the reason would be the simplicity with which it allows us to plot data in different forms of plots.

In a Python file, we can import the pyplot function that allows us to interface with a MATLAB-like plotting environment.

`import matplotlib.pyplot as plt%matplotlib inline`

The %matplotlib inline is a jupyter notebook specific command that let’s you see the plots in the notebook itself.

`# Plotplt.plot([1,2,3,4,10])#> [<matplotlib.lines.Line2D at 0x10edbab70>]-> its just the object matplotlib returned and we should use Plt.show() for matplotlib to show the plot not to return it.`

Just a list of numbers was given to plt.plot() and it drew a line chart automatically.

The plt.plot accepts 3 basic arguments in the following order: (x, y, format).

This format is a short hand combination of {color}{marker}{line}

In the above examples’ case, we have provided just one list which the matplotlib assumed as the frequency of values on the x-axis starting from 0.

`plt.plot([1,4,9,16,25], [1,2,3,4,10], 'gs--')plt.show()`

We can even have two sets of points in a single plot.

`# Draw two sets of pointsplt.plot([1,2,3,4,5], [1,2,3,10,15], 'gs')  # green squaresplt.plot([1,2,3,4,5], [2,3,4,15,20], 'k*')  # black starsplt.show()`

We can even add the basic plot features: Title, Legend, X and Y axis labels.

`plt.plot([1,2,3,4,5], [1,2,3,4,10], 'go', label='GreenDots')plt.plot([1,2,3,4,5], [2,3,4,5,11], 'b*', label='Bluestars')plt.title('A Simple Scatterplot')plt.xlabel('X')plt.ylabel('Y')plt.legend(loc='best')  # legend text comes from the plot's label parameter.plt.show()`

We can have the control of size of plots using plt.figure(figsize=(10,7))  #here 10 is the width and 7 is the height.

plt.subplots(x,y). This creates and returns two objects:

•  the figure
•  the axes (subplots) inside the figure

# Create Figure and Subplots

`fig, (ax1, ax2) = plt.subplots(1,2, figsize=(10,4), sharey=True, dpi=120)`

`# Plotax1.plot([1,2,3,4,5], [1,2,3,4,10], 'gs')  # greensquaresax2.plot([1,2,3,4,5], [2,3,4,5,11], 'b0')  # bluedots`

`# Title, X and Y labels, X and Y Limax1.set_title('Scatterplot Greensquares'); ax2.set_title('Scatterplot Bluedots')ax1.set_xlabel('X1');  ax2.set_xlabel('X2')  # x labelax1.set_ylabel('Y1');  ax2.set_ylabel('Y2')  # y labelax1.set_xlim(0, 6) ;  ax2.set_xlim(0, 6)   # setting x axis limitsax1.set_ylim(0, 12);  ax2.set_ylim(0, 12)  # setting y axis limitsax2.yaxis.set_ticks_position('none')plt.tight_layout()plt.show()`

Setting sharey=True in `plt.subplots()` divides the Y-axis between the two subplots.

The above setting of xlabel, ylabel, xlim, ylim can be done in the following format also:

`ax1.set(title='Scatterplot Greensquares', xlabel='X1', ylabel='Y1', xlim=(0,6), ylim=(0,12))ax2.set(title='Scatterplot Bluedots', xlabel='X2', ylabel='Y2', xlim=(0,6), ylim=(0,12))`

Matplotlib is also used in plotting and viewing images. After reading images we can plot them using plt.figure() and plt.imshow() functions.

After using each of these functions, we have to put plt.show(), which is used to display all the plots’ figures.

`import matplotlib.image as img# reading the imagetestImage = img.imread('pic.png') # here pic.png is the image address accessible by your editor# displaying the image as an arrayprint(testImage)    # it’ll print a matrix which actually represents the pixels of image.plt.imshow(testImage)  # this will plot the image`

0/7
Vector analysis (NumPy) 0/6
Data handling (pandas) 0/5
Basic Computer Vision (opencv) 0/5
Data Visualization (matplotlib) 0/5
Web scraping basics 0/2
EDA ## Video Courses By View All Courses Excel at your interview with Masterclasses Know More   Certificate included What will you Learn? Free Mock Assessment
Fill up the details for personalised experience.
Phone Number *
OTP will be sent to this number for verification
+91 *
+91
Change Number
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
*Enter the expected year of graduation if you're student
Current Employer
Company Name
College/University Name
Job Title
Job Title
Software Development Engineer (Backend)
Software Development Engineer (Frontend)
Software Development Engineer (Full Stack)
Data Scientist
Android Engineer
iOS Engineer
Devops Engineer
Support Engineer
Research Engineer
Engineering Intern
QA Engineer
Co-founder
SDET
Product Manager
Product Designer
Backend Architect
Program Manager
Release Engineer 