Lecture 34 & 35 -Data Visualizationand itd.pdf

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1. Data Visualization
Lecture 34 & 35
1
Computer Programming- CEN1008

2. RECAP Lecture 33
qData Visualization

3. Types of Data Visualization
•Line chart
•Bar chart
•Pie chart
•Histogram

4. Example-1
•Let’s say you want to compare sales of jackets to sales of socks over
the course of the previous year. There’s more than one way to present the data, and tables are one of the most common. Here’s what this
would look like:
Source:
https://www.microstrategy.com/us/resources/introductory-guides/data-visualization-
what-it-is-and-why-we-use-it

6. 0
0.5
1
1.5
2
2.5
3
3.5
024681012
Current (mA)
Voltage (V)
V-I relationship
Example-2 (V-I relationship)

7. Example-3 (Diode Forward Biased Curve)
Sr.N
o
Diode
Voltage
Diode
Current
10.00.0
20.290.0
30.540.44
40.570.85
50.601.50
60.611.87
70.622.76
80.643.57
90.654.48
100.665.69

8. Matplotlib
•Matplotlib is a plotting library for the Python programming language.
•Matplotlib is one of the most popular Python packages used for data visualization.
•Types of Plots
•Line graph
•Bar graph
•Scatter plot
•Pie chart
•Histogram
•Area plot

9. Simple line graph
import matplotlib.pyplot as plt
X = [1,2,3]
Y = [2,3,1]
plt.plot(X,Y)
Output
plt.show()

10. Graph Template
Source:
https://www.simplilearn.com/data-visualization-in-python-using-matplotlib-tutorial

11. Simple line graph (Adding labels)
import matplotlib.pyplot as plt
X = [1,2,3]
Y = [2,3,1]
plt.plot(X,Y)
Output
plt.show()
plt.xlabel(‘X-axis’)
plt.ylabel(‘Y-axis’)
plt.title(‘Graph’)

12. Simple line graph (Linewidth)
plt.plot(X,Y,linewidth=5)

13. Simple line graph (Plotting two or more lines)
import matplotlib.pyplot as plt
X1 = [1,2,3]
Y1 = [2,3,1]
plt.plot(X1,Y1,label=‘Line-1’,linewidth=4)
plt.show()
plt.xlabel(‘X-axis’)
plt.ylabel(‘Y-axis’)
plt.title(‘Two lines graph’)
X2 = [1,2,3]
Y2 = [3,2,3]
plt.plot(X2,Y2,label=‘Line-2’,linewidth=4)
plt.legend()

14. Simple line graph (Plotting two or more lines)

15. Simple line graph (Adding Grid)
plt.grid()

16. Simple line graph (Customized plot)
import matplotlib.pyplot as plt
X1 = [1,2,3,4]
Y1 = [2,1,3,1]
plt.plot(x, y, color='green', linestyle='dashed',
linewidth = 3,marker='o', markerfacecolor='blue',
markersize=12)
plt.show()
plt.xlabel(‘X-axis’)
plt.ylabel(‘Y-axis’)
plt.title(‘Graph’)

17. Simple line graph (Customized plot)

18. Simple line graph (Customized plot)
plt.ylim(1,4)
plt.xlim(1,4)

19. Scatter Plot (Example)
import matplotlib.pyplot as plt
x = [1,2,3,4,5,6]
y = [2,4,5,9,11,12]
plt.scatter(x, y, label= "stars", color= "green",
marker= "*", s=60)
plt.show()
plt.xlabel(‘X-axis’)
plt.ylabel(‘Y-axis’)
plt.title(‘Scatter plot’)
plt.legend()

20. Scatter Plot

21. Scatter Plot
import matplotlib.pyplot as plt
x1 = [1,1.5,2,2.5,3,3.5,3.6]
y1 = [6,7,8,8.2,8.5,9,9.5]
plt.scatter(x1,y1, label='High income',color='g')
marker= "*", s=60)
plt.show()
plt.xlabel(‘X-axis’)
plt.ylabel(‘Y-axis’)
plt.title(‘Scatter plot’)
plt.legend()
x2=[8,8.5,9,9.5,10,10.5,11]
y2=[2,2.5,2.7,3,3.5,4,5]
plt.scatter(x2,y2,label='Low income',color='r')

22. Scatter Plot (Example)

23. Bar Chart Example

24. Bar Chart
import matplotlib.pyplot as plt
X = [1, 2, 3, 4]
label = ['A', 'B', 'C', 'D']
plt.show()
plt.xlabel(‘X-axis’)
plt.ylabel(‘Y-axis’)
plt.title(‘Bar chart’)
plt.bar(X, Y, tick_label = label, width = 0.5)
Y = [10, 20, 25, 20]

25. Bar Chart
For horizontal graph use barh() function

26. Bar Chart
import matplotlib.pyplot as plt
X1 = [0.25,1.5,2.75,4.0,5.25]
Y1 = [50,40,70,80,20]
plt.bar(X1,Y1,label="Honda",width=.5)
plt.bar(X2,Y2,label="Toyota",
color='green',width=.5)
plt.show()
plt.xlabel(‘Days’)
plt.ylabel(‘Distance (Km)’)
plt.title(‘Comparison’)
Y2 = [80,20,20,50,60]
plt.legend()
X2 = [.75,2.0,3.25,4.5,5.75]

27. Bar Chart

28. Bar Chart
import matplotlib.pyplot as plt
Position = [1,2,3,4,5]
Y1 = [50,40,70,80,20]
plt.bar(Position,Y1,label="Honda",width=.5)
plt.bar(Position,Y2,label="Toyota",color='green',
width=.5,bottom = Y1)
plt.show()
plt.xlabel(‘X-axis’)
plt.ylabel(‘Y-axis’)
plt.title(‘Comparison’)
Y2 = [80,20,20,50,60]
plt.legend()

29. Bar Chart

30. Pie Chart
Source: https://www.originlab.com/doc/Origin-Help/2D-Pie-Chart

31. Pie Chart
import matplotlib.pyplot as plt
companies = ['A', 'B', 'C', 'D']
share = [30,20,25,25]
colors = ['r', 'y', 'g', 'b']
plt.show()
plt.pie(share, explode = explode, labels = companies,
colors=colors, startangle=90, radius = 1.2,
autopct = '%1.1f%%')
explode = [0.0,0.0,0.0,0.0]
plt.legend()

32. Pie Chart

33. Pie Chart
explode = [0.0,0.1,0.0,0.0]
explode = [0.0,0.1,0.1,0.0]

34. Histogram chart
Source: https://statistics.laerd.com/statistical-guides/understanding-histograms.php

35. Histogram
import matplotlib.pyplot as plt
Age = [2,5,7,12,15,25,31,35,43,40,44,56,66,76,85]
range = (0,100)
plt.hist(Age, Bins, range, color = 'purple',
histtype = 'bar', rwidth = 0.8)
plt.show()
Bins = 10
plt.ylabel('Number of People')
plt.xlabel('Age')
plt.title('Histogram')

36. Histogram

37. Bins = 4
Histogram