Data Visualization using Matplotlib to understand Graphs

Intro. to Data Visualization
Simple Graphs in Python
using
matplotlib and pyplot

What is data visualization?
• Data visualization is the graphical representation of information
and data.
– Can be achieved using visual elements like figures, charts, graphs, maps,
and more.
• Data visualization tools provide a way to present these figures
and graphs.
• Often, it is essential to analyze massive amounts of information
and make data-driven decisions.
– converting complex data into an easy to understand representation.

Matplotlib
• Matplotlib is one of the most powerful tools for data
visualization in Python.
• Matplotlib is an incredibly powerful (and beautiful!) 2-D
plotting library.
– It is easy to use and provides a huge number of examples for tackling
unique problems
• In order to get matplotlib into your script,
– first you need to import it, for example:
import matplotlib.pyplot as plt
• However, if it is not installed, you may need to install it:
– Easiest way to install matplotlib is using pip.
– Type the following command in the command prompt (cmd) or your
Linux shell;
• pip install matplotlib
• Note that you may need to run the above cmd as an administrator

matplotlib
• Strives to emulate MATLAB
– matplotlib.pyplot is a collection of command style functions that make
matplotlib work like MATLAB.
• Each pyplot function makes some change to the figure:
– e.g.,
• creates a figure,
• creates a plotting area in the figure,
• plots some lines in the plotting area,
• decorates the plot with labels, etc.
• Note that various states are preserved across function calls
• Whenever you plot with matplotlib, the two main code lines should be
considered:
– Type of graph
• this is where you define a bar chart, line chart, etc.
– Show the graph
• this is to display the graph

E.g. Matplotlib
• Matplotlib allows you to make easy things
• You can generate plots, histograms, power
spectra, bar charts, errorcharts, scatterplots,
etc., with just a few lines of code.

Line Graphs
#create data for plotting
x_values = [0, 1, 2, 3, 4, 5 ]
y_values = [0, 1, 4, 9, 16,25]
#the default graph style for plot is a line
plt.plot(x_values, y_values)
#display the graph
plt.show()

More on Line Graph
• Note: if you provide a single list or
array to the plot() command,
– then matplotlib assumes it is a
sequence of y values, and
– automatically generates the
x values for you.
• Since python ranges start with 0,
the default x vector has the same
length as y but starts with 0.
– Hence the x data are[0, 1, 2, 3].
plt.plot([1, 2, 3, 4])
plt.ylabel('some numbers')
plt.show()

pyplot
• text() : adds text in an arbitrary location
• xlabel(): adds text to the x-axis
• ylabel(): adds text to the y-axis
• title() : adds title to the plot
• clear() : removes all plots from the axes.
• savefig(): saves your figure to a file
• legend() : shows a legend on the plot
All methods are available on pyplot and on the axes instance
generally.

y1 =[]
y2 =[]
x = range(-100,100,10)
for i in x: y1.append(i**2)
for i in x: y2.append(-i**2)
plt.plot(x, y1)
plt.plot(x, y2)
plt.xlabel("x")
plt.ylabel("y")
plt.ylim(-2000, 2000)
plt.axhline(0) # horizontal line
plt.axvline(0) # vertical line
plt.savefig("quad.png")
plt.show()
Incrementally
modify the figure.
Show it on the screen
Save your figure to a file

Plot
x = [1, 2, 3, 4]
y = [1, 4, 9, 16]
plt.plot(x, y)
no return value?
• We are operating on a “hidden” variable representing the figure.
• This is a terrible, terrible trick.
• Its only purpose is to pander to MATLAB users.
• I’ll show you how this works in the next lecture

Simple line
# importing the required module
# x axis values
x = [1,2,3]
# corresponding y axis values
y = [2,4,1]
# plotting the points
plt.plot(x, y)
# naming the x axis
plt.xlabel('x - axis')
# naming the y axis
plt.ylabel('y - axis')
# giving a title to my graph
plt.title('My first graph!')
# function to show the plot
plt.show()
• Define the x-axis and corresponding y-axis
values as lists.
• Plot them on canvas using .plot() function.
• Give a name to x-axis and y-axis using .xlabel()
and .ylabel() functions.
• Give a title to your plot using .title() function.
• Finally, to view your plot, we use .show()
function.

Simple 2 lines
# line 1 points
x1 = [1,2,3]
y1 = [2,4,1]
# plotting the line 1 points
plt.plot(x1, y1, label="line 1")
# line 2 points
x2 = [1,2,3]
y2 = [4,1,3]
# plotting the line 2 points
plt.plot(x2, y2, label = "line 2")
# naming the x axis
# naming the y axis
plt.title('Two lines on same graph!')
# show a legend on the plot
plt.legend()
plt.show()
• Here, we plot two lines on same graph. We
differentiate between them by giving them
a name(label) which is passed as an
argument of .plot() function.
• The small rectangular box giving
information about type of line and its color
is called legend. We can add a legend to
our plot using .legend() function.

# x axis values
x = [1,2,3,4,5,6]
# corresponding y axis values
y = [2,4,1,5,2,6]
# plotting the points
plt.plot(x, y, color='green', linestyle='dashed', linewidth = 3,
marker='o', markerfacecolor='blue', markersize=12)
# setting x and y axis range
plt.ylim(1,8)
plt.xlim(1,8)
# naming the x axis
# naming the y axis
plt.title('Some cool customizations!')
plt.show()
Customization of Plots

Bar graphs
• When using a bar graph, the change in code will be from
plt.plot() to plt.bar() changes it into a bar chart.
#Create data for plotting
values = [5, 6, 3, 7, 2]
names = ["A", "B", "C", "D", "E"]
plt.bar(names, values, color="green")
plt.show()

Bar graphs
We can also flip the bar graph horizontally with the following
#Create data for plotting
values = [5,6,3,7,2]
names = ["A", "B", "C", "D", "E"]
# Adding an "h" after bar will flip the graph
plt.barh(names, values, color="yellowgreen")
plt.show()

Bar Chart
# heights of bars
height = [10, 24, 36, 40, 5]
# labels for bars
names = ['one','two','three','four','five']
# plotting a bar chart
c1 =['red', 'green']
c2 =['b', 'g'] # we can use this for color
plt.bar(left, height, width=0.8, color=c1)
# naming the x-axis
# naming the y-axis
# plot title
plt.title('My bar chart!')
plt.show()
• Here, we use plt.bar() function
to plot a bar chart.
• you can also give some name to x-axis
coordinates by defining tick_labels

Histogram
# frequencies
ages=[2,5,70,40,30,45,50,45,43,40,44,60,7,13,57,18,90,77,32,21,20,40]
# setting the ranges and no. of intervals
range = (0, 100)
bins = 10
# plotting a histogram
plt.hist(ages, bins, range, color='green',histtype='bar',rwidth=0.8)
# x-axis label
plt.xlabel('age')
# frequency label
plt.ylabel('No. of people')
# plot title
plt.title('My histogram')
plt.show()

Histograms
• Looking at the code snippet, I added
two new arguments:
– Bins — is an argument specific to a
histogram and allows the user to customize
how many bins they want.
– Alpha — is an argument that displays the
level of transparency of the data points.
#generate fake data
x = [2,1,6,4,2,4,8,9,4,2,4,10,6,4,5,7,7,3,2,7,5,3,5,9,2,1]
#plot for a histogram
plt.hist(x, bins = 10, color='blue', alpha=0.5)
plt.show()

Scatter Plots
• Can you see the pattern? Now
the code changed from
plt.bar() to
plt.scatter().
#create data for plotting
x_values = [0,1,2,3,4,5]
y_values = [0,1,4,9,16,25]
plt.scatter(x_values, y_values, s=30, color=“blue")
plt.show()

Scatter plot
# x-axis values
x = [1,2,3,4,5,6,7,8,9,10]
# y-axis values
y = [2,4,5,7,6,8,9,11,12,12]
# plotting points as a scatter plot
plt.scatter(x, y, label= "stars", color="green", marker="*", s=30)
# x-axis label
# frequency label
# plot title
plt.title('My scatter plot!')
# showing legend
plt.legend()
plt.show()

Pie-chart
# defining labels
activities = ['eat', 'sleep', 'work', 'play']
# portion covered by each label
slices = [3, 7, 8, 6]
# color for each label
colors = ['r', 'y', 'g', 'b']
# plotting the pie chart
plt.pie(slices, labels = activities, colors=colors,
startangle=90, shadow = True, explode = (0, 0, 0.1, 0),
radius = 1.2, autopct = '%1.1f%%')
# plotting legend
plt.legend()
# showing the plot
plt.show()

Plotting curves of given equation
# importing the required modules
import numpy as np
# setting the x - coordinates
x = np.arange(0, 2*(np.pi), 0.1)
# setting the corresponding y - coordinates
y = np.sin(x)
# potting the points
plt.plot(x, y)
plt.show()
Examples taken from:
Graph Plotting in Python | Set 1

Summary
• We just scratched the surface of the power of matplotlib.
• You can read more and find how you can create more colorful,
detailed, and vibrant graphs.
• There are a lot more graphs available in the matplotlib library as
well as other popular libraries available in python, including:
– seaborn
• https://seaborn.pydata.org/
– pandas plot (pandas.DataFrame.plot)
• https://
pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.html
– plotly (Plotly Python Open Source Graphing Library)
• https://plotly.com/python/

References
• Matplotlib: Visualization with Python
– https://matplotlib.org/index.html
• matplotlib.pyplot
– https://matplotlib.org/3.2.1/api/pyplot_summary.html
• Tutorials
– https://matplotlib.org/tutorials/index.html
• Gallery & Examples
– https://matplotlib.org/gallery/index.html
• Videos
– https://www.youtube.com/watch?v=3Fp1zn5ao2M&feature=plcp
• Book: Mastering matplotlib
– https://
www.packtpub.com/big-data-and-business-intelligence/mastering-matplotlib

Data Visualization using Matplotlib to understand Graphs

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