﻿ Beginners' guide to Python Matplotlib | Data Visualization

Python Matplotlib Tutorial | Data Visualization in Python - Part 1¶

Import the library matplotlib.pyplot¶

This is a library of Python which allows the user to create different types of data visualization¶

In [1]:
import matplotlib.pyplot


We will learn how to create Scatter plot¶

Weight of the car (wt) & Miles per Gallon (mpg); milage of a car¶

In [2]:
#creating lists
mpg = [21 ,	21 ,	22.8 ,	21.4 ,	18.7 ,	18.1 ,	14.3 ,	24.4 ,	22.8 ,	19.2 ,	17.8 ,	16.4 ,	17.3 ,	15.2 ,	10.4 ,	10.4 ,	14.7 ,	32.4 ,	30.4 ,	33.9 ,	21.5 ,	15.5 ,	15.2 ,	13.3 ,	19.2 ,	27.3 ,	26 ,	30.4 ,	15.8 ,	19.7 ,	15 ,	21.4]
wt = [2.62 ,	2.875 ,	2.32 ,	3.215 ,	3.44 ,	3.46 ,	3.57 ,	3.19 ,	3.15 ,	3.44 ,	3.44 ,	4.07 ,	3.73 ,	3.78 ,	5.25 ,	5.424 ,	5.345 ,	2.2 ,	1.615 ,	1.835 ,	2.465 ,	3.52 ,	3.435 ,	3.84 ,	3.845 ,	1.935 ,	2.14 ,	1.513 ,	3.17 ,	2.77 ,	3.57 ,	2.78]

In [3]:
# Create the scatter plot
matplotlib.pyplot.scatter(mpg,wt)
# creating the scatter plot with mpg & wt variable

Out[3]:
<matplotlib.collections.PathCollection at 0x18ec74a8>

We may also use a shorter name for matplotlib.pyplot by providing a shorter name. We can give this name while importing the library¶

In [9]:
import matplotlib.pyplot as plt
#(you may provide any name instead of plt but usually data scientists use plt as a standard name)

In [5]:
# Create the scatter plot once again with the shorter name
plt.scatter(mpg,wt)
# We will get the same result as above

Out[5]:
<matplotlib.collections.PathCollection at 0x6888358>
In [6]:
# You may Change colour with "c" function
plt.scatter(mpg,wt, c= "red")

Out[6]:
<matplotlib.collections.PathCollection at 0x68edcf8>
In [10]:
# You may Change colour with colour code as well
plt.scatter(mpg,wt, c= "#ed7c31")
#ed7c31    orange
#0273bf    blue

Out[10]:
<matplotlib.collections.PathCollection at 0x5536128>
In [15]:
# change size of the dots with "s" function
plt.scatter(mpg,wt, c= "purple",s=50)

Out[15]:
<matplotlib.collections.PathCollection at 0x5670d30>
In [16]:
# change axis limit of x axis with plt.xlim or y axis with plt.ylim

plt.scatter(mpg,wt, c= "red",s=50)
plt.xlim(10,40)
plt.ylim(0,6)

Out[16]:
(0, 6)

We will learn how to create Line Chart¶

Goals scored by 2 football teams - East Bengal (EB) & Mohun Bagan (MB)¶

In [7]:
# Creating the lists

EB = [6 ,	0 ,	2 ,	1 ,	2 ,	1 ,	2 ,	2 ,	2 ,	0 ,	1 ,	3 ,	0 ,	3 ,	1 ,	0 ,	4 ,	1 ,	3 ,	3 ,	6 ,	2 ,	0 ,	0 ,	4 ,	1 ,	2 ,	1 ,	1 ,	0 ,	1 ,	3 ,	0 ,	1 ,	1 ,	1 ,	3 ,	1 ,	3 ,	0 ,	2 ,	2 ,	2 ,	1 ,	2 ,	2 ,	1 ,	2 ,	1 ,	2 ,	1 ,	2 ,	4 ,	0 ,	1 ,	1 ,	2 ,	2 ,	2 ,	0 ,	2 ,	2 ,	0 ,	0 ,	2 ,	3 ,	2 ,	2 ,	3 ,	0 ,	3 ,	3 ,	0 ,	1 ,	0 ,	0 ,	2 ,	1 ,	1 ,	3 ,	1 ,	3 ,	1 ,	1 ,	0 ,	2 ,	0 ,	3 ,	3 ,	3 ,	2 ,	0 ,	0 ,	1 ,	4 ,	1 ,	0 ,	2 ,	1 ,	1 ,	2 ,	2 ,	0 ,	2 ,	1 ,	1 ,	2 ,	3 ,	1 ,	4 ,	4 ,	1 ,	2 ,	2 ,	2 ,	1 ,	0 ,	0 ,	5 ,	1 ,	1 ,	1 ,	0 ,	1 ,	1 ,	0 ,	2 ,	1 ,	3 ,	2 ,	1 ,	1 ,	1 ,	0 ,	1 ,	1 ,	3 ,	2 ,	1 ,	3 ,	1 ,	2 ,	1 ,	1 ]
MB = [0 ,	2 ,	1 ,	0 ,	0 ,	1 ,	1 ,	1 ,	2 ,	2 ,	0 ,	1 ,	2 ,	1 ,	0 ,	0 ,	4 ,	2 ,	0 ,	1 ,	0 ,	1 ,	4 ,	0 ,	1 ,	3 ,	0 ,	1 ,	0 ,	2 ,	5 ,	0 ,	0 ,	1 ,	3 ,	2 ,	1 ,	0 ,	3 ,	1 ,	3 ,	0 ,	1 ,	1 ,	1 ,	1 ,	1 ,	2 ,	0 ,	0 ,	2 ,	0 ,	0 ,	0 ,	1 ,	1 ,	2 ,	1 ,	1 ,	0 ,	1 ,	1 ,	1 ,	3 ,	0 ,	1 ,	1 ,	1 ,	3 ,	3 ,	1 ,	0 ,	0 ,	3 ,	2 ,	0 ,	0 ,	3 ,	2 ,	0 ,	0 ,	0 ,	1 ,	0 ,	3 ,	5 ,	2 ,	2 ,	1 ,	1 ,	3 ,	0 ,	1 ,	0 ,	2 ,	2 ,	1 ,	2 ,	2 ,	4 ,	1 ,	1 ,	0 ,	2 ,	1 ,	1 ,	1 ,	1 ,	3 ,	1 ,	0 ,	1 ,	1 ,	1 ,	3 ,	1 ,	2 ,	0 ,	0 ,	1 ,	0 ,	0 ,	1 ,	0 ,	0 ,	1 ,	2 ,	0 ,	1 ,	0 ,	1 ,	1 ,	0 ,	1 ,	2 ,	1 ,	0 ,	1 ,	2 ,	0 ,	1 ,	0 ,	1 ,	2 ]

In [10]:
# create line chart for EB (use keyword plot for line chart)
plt.plot(EB)

Out[10]:
[<matplotlib.lines.Line2D at 0x18fb6da0>]
In [20]:
# create 2 line charts in same graph and decrease the limit of x axis; 0 to 145 to remove white space at the end
plt.plot(EB)
plt.plot(MB)
plt.xlim(0,145)

Out[20]:
(0, 145)
In [21]:
# change the colour to brown & show seperately by putting plt.show() in between plot statement of EB & MB
# New lines of codes written in this cell, which were not there in the previous cell, are marked as #new code
plt.plot(EB, c="brown")#new code
plt.show()#new code
plt.plot(MB)
plt.xlim(0,145)

Out[21]:
(0, 145)
In [22]:
# Put Title with title keyword (given some title to both the charts)

plt.plot(EB, c="brown")
plt.title("Plot no. 1")#new code
plt.show()
plt.plot(MB)
plt.title("Plot no. 2")#new code
plt.xlim(0,145)

Out[22]:
(0, 145)
In [23]:
# Put some name to x axis and y axis with xlabel keyword
# Here we have removed the plt.show(), hence both the lines have been plotted on the same graph
plt.plot(EB, c="brown")
plt.title("Plot no. 1")
plt.plot(MB)
plt.xlim(0,145)
plt.xlabel("East Bengal")#new code
plt.ylabel("Mohun Bagan")#new code

Out[23]:
Text(0,0.5,'Mohun Bagan')

X axis & Y axis are marked with their values like 0, 20, 40 & 0, 1, 2 etc respectively. If we wish then we may change this check points and put custom made check points like 0 = Bad, 60 = Average & 100 = Good etc¶

In [24]:
# Custom made check points of x axis or y axis can be changed with plt.xtricks or plt.ytricks keyword

plt.plot(EB, c="brown")
plt.title("Plot no. 1")
plt.plot(MB)
plt.xlim(0,145)
plt.xlabel("East Bengal")
plt.ylabel("Mohun Bagan")


Out[24]:
([<matplotlib.axis.XTick at 0x1a549518>,
<matplotlib.axis.XTick at 0x1a542e10>,
<matplotlib.axis.XTick at 0x1a542a58>,
<matplotlib.axis.XTick at 0x1a565d68>],
<a list of 4 Text xticklabel objects>)
In [27]:
# May change the angle of the text Bad, Average etc with rotation function
plt.plot(EB, c="brown")
plt.title("Plot no. 1")
plt.plot(MB)
plt.xlim(0,145)
plt.xlabel("East Bengal")
plt.ylabel("Mohun Bagan")
rotation = 45)#new code; we may also make it as 90 degree

Out[27]:
([<matplotlib.axis.XTick at 0x1a463f98>,
<matplotlib.axis.XTick at 0x1a4549b0>,
<matplotlib.axis.XTick at 0x1a4545f8>,
<matplotlib.axis.XTick at 0x1a40ceb8>],
<a list of 4 Text xticklabel objects>)
In [28]:
## We may also replace the line style with hyphen or dot etc
# Change the line style of EB with ls function

plt.plot(EB, c="brown", ls="--")#new code
plt.title("Plot no. 1")
plt.plot(MB)
plt.xlim(0,145)
plt.xlabel("East Bengal")
plt.ylabel("Mohun Bagan")

Out[28]:
([<matplotlib.axis.XTick at 0x1a3bcf98>,
<matplotlib.axis.XTick at 0x1a3b1c88>,
<matplotlib.axis.XTick at 0x1a3b18d0>,
<matplotlib.axis.XTick at 0x6befc18>],
<a list of 4 Text xticklabel objects>)

You may also add an extra text into the chart to point out something; it's called annotate. e.g. we may want to point out the highest spike of the graph.¶

In [29]:
# Annotate
# Use plt.annotate keyword to annotate
# xy shows the point which we want to highlight (x and y value shows the coordinates which we want to highlight).
# Here, the maximum spike is at the point where x axis =20 & y axis = 6
# xytext shows the coordinate where we want to put the remark or text which is to be displayed in the graph
# arrowprops means it is to be pointed with an arrow sign
# dict means dictionary of arrow properties; here we have mentioned only the colour

plt.plot(EB, c="brown")
plt.title("Plot no. 1")
plt.plot(MB)
plt.xlim(0,145)
plt.xlabel("East Bengal")
plt.ylabel("Mohun Bagan")
plt.annotate("Max value", xy=(20,6), xytext=(70,6),
arrowprops=dict(facecolor="magenta"))#new code

Out[29]:
Text(70,6,'Max value')
In [30]:
# we may incrase the figure size with plt.figure keyword
# here we mentioned 10 as width and 5 as length

plt.figure(figsize=(10,5))#new code
plt.plot(EB, c="brown")
plt.title("Plot no. 1")
plt.plot(MB)
plt.xlim(0,145)
plt.xlabel("East Bengal")
plt.ylabel("Mohun Bagan")
plt.annotate("Max value", xy=(20,6), xytext=(70,6),
arrowprops=dict(facecolor="magenta"))

Out[30]:
Text(70,6,'Max value')
In [31]:
# legend can be added with label & legend keyword

plt.figure(figsize=(10,5))
plt.plot(EB, c="brown", label="East Bengal")#new code
plt.title("Plot no. 1")
plt.plot(MB, label="mohun Bagan")#new code
plt.legend()#new code
plt.xlim(0,145)
plt.xlabel("East Bengal")
plt.ylabel("Mohun Bagan")
plt.annotate("Max value", xy=(20,6), xytext=(70,6),
arrowprops=dict(facecolor="magenta"))

Out[31]:
Text(70,6,'Max value')
In [11]:
# legend location can be altered as upper right (default location), lower right, lower left etc
# usually data scientists prefer upper right position - the default one

plt.figure(figsize=(10,5))
plt.plot(EB, c="brown", label="East Bengal")
plt.title("Plot no. 1")
plt.plot(MB, label="mohun Bagan")
plt.legend(loc = "upper left")#upper left/lower right #new code
plt.xlim(0,145)
plt.xlabel("East Bengal")
plt.ylabel("Mohun Bagan")
plt.annotate("Max value", xy=(20,6), xytext=(70,6),
arrowprops=dict(facecolor="magenta"))

Out[11]:
Text(70,6,'Max value')
In [ ]:
# we may save the figure as pdf or png or jpeg etc
# We may also put a location to save it into that very folder

plt.figure(figsize=(10,5))
plt.plot(EB, c="brown", label="East Bengal")
plt.title("Plot no. 1")
plt.plot(MB, label="mohun Bagan")
plt.legend(loc = "upper right")#upper left/lower right #new code
plt.xlim(0,145)
plt.xlabel("East Bengal")
plt.ylabel("Mohun Bagan")
plt.annotate("Max value", xy=(20,6), xytext=(70,6),
arrowprops=dict(facecolor="magenta"))
plt.savefig("C:/Users/Desktop/python plot/Line chart.pdf")#new code;
#it will be saved in python plot folder placed in desktop as line chart.pdf;
#change the extension to save it in other format of .jpeg or .png


We will learn how to create Histogram¶

We will be using the same lists - EB & MB¶

In [12]:
# we can create histogram with hist keyword
plt.hist(EB)

Out[12]:
(array([28., 49.,  0., 38.,  0., 20.,  6.,  0.,  1.,  2.]),
array([0. , 0.6, 1.2, 1.8, 2.4, 3. , 3.6, 4.2, 4.8, 5.4, 6. ]),
<a list of 10 Patch objects>)
In [13]:
# The above graph is pretty ugly looking, we need to make it look better.
# We will add bins - different discrete values
plt.hist(EB, bins=range(8))

Out[13]:
(array([28., 49., 38., 20.,  6.,  1.,  2.]),
array([0, 1, 2, 3, 4, 5, 6, 7]),
<a list of 7 Patch objects>)
In [14]:
# We will add figsize and legends

plt.figure(figsize=(8,5))#figure size
plt.hist(EB, bins=range(8), label="East Bengal")
plt.legend()#legends

Out[14]:
<matplotlib.legend.Legend at 0x19117400>
In [15]:
# We will remove the extra space from both sides by using xlim

plt.figure(figsize=(8,5))
plt.hist(EB, bins=range(8), label="East Bengal")
plt.legend()
plt.xlim(0,7)#removes the extra white space
plt.xlabel("Goals scored")#x axis name
plt.ylabel("No of Goals")#y axis name

Out[15]:
Text(0,0.5,'No of Goals')

We will now plot 2 variables - EB & MB simulteniously in the same chart to compare¶

In [18]:
# We may plot goals of EB & MB in same chart

plt.figure(figsize=(8,5))
plt.hist((EB,MB), bins=range(8),

Text(0,0.5,'No of Goals')