Line plot, bar plot, histogram, and box plot

Matplotlib: Python Programming Exercises

Ex 1.

Write a Python programming to display a bar chart of the popularity of mobile phone brands.

Sample data:

Mobile phone brands: Apple, OnePlus, Samsung, OPPO, VIVO, Xiaomi

Popularity: 22.2, 17.6, 8.8, 8, 7.7, 6.7

Solution :

import matplotlib.pyplot as plt

x = ['Apple', 'OnePlus', 'Samsung', 'OPPO', 'ViVo', 'Xiaomi']

popularity = [22.2, 17.6, 8.8, 8, 7.7, 6.7]

x_pos = [i for i, in enumerate(x)]

plt.bar(x_pos, popularity, color='blue')

plt.xlabel("Mobile Brands")

plt.ylabel("Popularity")

plt.title("Popularity of Mobile Phones \n")

plt.xticks(x_pos, x)

# Turn on the grid

plt.minorticks_on()

plt.grid(which='major', linestyle='‒', linewidth='0.5', color='red')

# Customize the minor grid

plt.grid(which='minor', linestyle=':, linewidth='0.5', color='black')

plt.show()

Output

Popularity of Mobile Phones

Ex 2.

Write a Python code snippet to create bar plot from a DataFrame.

Sample DataFrame:

a b c d e

2 ,4,8,5,7,6

4 2,3,4,2,6

6,4,7,4,7,8

8 2,6,4,8,6

10 2,4,3,3,2

Solution:

import pandas as pd

import matplotlib.pyplot as plt

import numpy as np

a=np.array([[4,8,5,7,6], [2,3,4,2,6],[4,7,4,7,8],[2,6,4,8,6], [2,4,3,3,2]])

df=pd.DataFrame(a, columns=['a', 'b','c','d', 'e'], index=[2,4,6,8,10])

df.plot(kind='bar')

plt.show()

Output

Ex 3.

Write a Python code snippet to plot two or more lines with different styles.

Solution :

import matplotlib.pyplot as plt

# line 1 points

x1 = [10,20,30]

y1 = [30,50,20]

# line 2 points

x2 = [10,20,30]

y2 = [30,10,20]

# Set the x axis label of the current axis.

plt.xlabel('x‒axis')

# Set the y axis label of the current axis.

plt.ylabel('y‒axis')

# Plot lines and/or markers to the Axes.

plt.plot(x1,y1, color='green', linewidth = 4, label= 'dotted_line',linestyle='dotted')

plt.plot(x2,y2, color='blue', linewidth 6, label = 'dashed_line', linestyle='dashed')

# Set a title

plt.title("Demonstrating Different Line Styles")

# show a legend on the plot

plt.legend()

# function to show the plot

plt.show()

Output

Demonstrating Different Line Styles

Ex 4.

Write a Python code snippet to create sine and cosine wave in the same plot.

Solution :

import matplotlib.pyplot as plt

import numpy as np

# Using Numpy to create an array X

np.arange(0, np.pi*3, 0.1)

# Assign variables to the y axis part of the curve

y = np.sin(x)

z = np.cos(x)

plt.plot(x, y, color='r', label='sin')

plt.plot(x,z, color='b', label='cos')

plt.xlabel("Angle")

plt.ylabel("Magnitude")

plt.title("Sine and Cosine Waves")

# Adding legend, to recognize the curve according to it's color

plt.legend()

plt.show()

Output

Sine and Cosine Waves

Ex 5.

Write a Python code snippet to plot a stacked bar chart.

Solution :

import matplotlib.pyplot as plt

labels = ['India', 'Japan', 'US', 'UK']

men = [25, 32, 30, 35]

women = [20, 35, 32, 38]

plt.bar(labels, men, color='b', label='Men')

plt.bar(labels, women, color='r', bottom=men, label='Women')

plt.legend()uster

plt.show()

Men

Output

Ex 6.

Write a Python code snippet to plot a horizontal bar chart.

Solution :

import matplotlib.pyplot as plt

labels = ['one', 'two', 'three', 'four']

values = [10, 20, 30, 40]

plt.barh(labels, values)

plt.show()

Ex 7.

How can you set a logarithmic scale for a plot in Matplotlib?

Solution:

In Matplotlib, you can set a logarithmic scale for either the x‒axis, y‒axis or both using the semilogx(), semilogy() and loglog() functions respectively. We can also set the scale of an axis to log scale by calling xscale and yscale. These functions plot data in logarithmic scales which are useful when dealing with exponential growth or decay. For example ‒

import matplotlib.pyplot as plt

# exponential function x = 10^y

x = [ 10**i for i in range(5)]

y = [i for i in range(5)]

#convert x‒axis to Logarithmic scale

plt.xscale("log")

plt.plot(x,y)

Output

Ex 8.

Write a Python program to draw a scatter plot comparing two subject marks of Mathematics and Science. Use marks of 10 students.

Test data:

math_marks = [50, 67, 90, 81, 98, 40, 60, 67, 100, 45]

science_marks = [35, 46, 72, 45, 95, 88, 32, 54, 18, 44]

marks_range=[10, 20, 30, 40, 50, 60, 70, 80, 90, 100]

Solution:

import matplotlib.pyplot as plt

import pandas as pd

math_marks = [50, 67, 90, 81, 98, 40, 60, 67, 100, 45]

science marks = [35, 46, 72, 45, 95, 88, 32, 54, 18, 44]

 marks_range = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]

plt.scatter(marks_range, math_marks, label='Math marks')

plt.scatter(marks_range, science_marks, label='Science marks')

plt.title('Mathematics and Science Marks Comparison')

plt.xlabel('Range of Marks')

plt.ylabel('Marks Obtained')

plt.legend()

plt.show()

Output

Ex 9.

How to overplot a line on scatter plot in Python. Illustrate with code.

Solution :

import matplotlib.pyplot as plt

import numpy as np

# Sample data for scatter plot

x = np.array([1, 2, 3, 4, 5])

y = np.array([2, 3, 4, 5, 3.5])

# Generate random values for line plot

np.random.seed(0) # Set seed for reproducibility

x line = np.random.rand(30) * 5 # Random values between 0 and 5

x_line = np.sort(x_line)

y_line = np.random.rand(30) * 10 # Random values between 0 and 10

plt.scatter(x, y, color="blue')

plt.plot(x_line, y_line, color='red')

plt.xlabel('X axis')

plt.ylabel('Y axis')

plt.title('Scatter Plot with Overplotted Line')

plt.show()

Output

Scatter Plot with Overplotted Line

Code explanation: In above code,

• The scatter plot is generated for the values ‒

o x = np.array([1, 2, 3, 4, 5])

o y = np.array([2, 3, 4, 5, 3.5])

•  The line plot is generated for the values ‒

o np.random.rand(30) * 5 generates 30 random values between 0 and 5 for x_line.

o np.random.rand(30) * 10 generates 30 random values between 0 and 10 for y_line.

o np.sort(x_line) sorts the x_line array to ensure a smooth line plot.

• The color of line plot is red and the scatter plot points are blue in color.

Ex 10. Plotting without a line

In [15]: from matplotlib import pyplot as plt

plt.plot([0,10], [10, 10], 'o')

plt.show()

Output

Ex 11. Plot using multiple points

Draw lines (1,7),(2,4), (3,15),(4,10)

Solution:

In [16]: from matplotlib import pyplot as plt

import numpy as np

x = np.array([1,2,3,4])

y = np.array([7,4,15,10])

plt.plot(x,y)

plt.show()

Output

Ex 12. Bar graphs

In [17]: from matplotlib import pyplot as plt

import numpy as np

x = np.array(["one", "two", "three", "four"])

y = np.array([7,4,15,10])

plt.bar(x,y)

plt.show()

Output

Ex 13. Bar graphs

import numpy as np

x = np.array(["one", "two","three", "four"])

y = np.array([7,4,15,10])

plt.barh(x,y)

plt.show()

Output

Ex 14. Histogram

In [9]: from matplotlib import pyplot as plt

import numpy as np

data = np.random.randn(1000)

plt.hist(data, bins=20)

plt.show()

Output

Ex 15. Scatter plots

In [12]: from matplotlib import pyplot as plt

import numpy as np

age = np.array([10,22,35,50,40,3,65,54,29,70])

weight = np.array([20,60,75,90,55,8,65,100,64,46])

plt.scatter(age, weight)

plt.show()

Output

Ex 16. Pie chart

In [8]: from matplotlib import pyplot as plt

import numpy

data = [20,30,35,5,10]

plt.pie(data)

plt.show()

Output

Ex 17. Pie chart

from matplotlib import pyplot as plt

import numpy

data = [20,30,35,5,10]

1 = ["OPPO","OnePlus","Samsung", "Apple", "Vivo"]

plt.pie(data,labels = 1)

plt.show()

Output

Ex 18. Pie chart

from matplotlib import pyplot as plt

import numpy

data = [20,30,35,5,10]

1 =["OPPO","OnePlus", "Samsung", "Apple", "Vivo"]

plt.pie(data,labels = 1,autopct=%1.1f%%')

plt.show()

Output

Ex 19. Setting Axis Limits

Python program

import matplotlib.pyplot as plt

x = [1,2,3,4,5]

y = [2,4,6,8,10]

plt.plot(x,y)

plt.xlim(0,6)

plt.ylim(0,12)

plt.show()

Output

Ex 20. Titles and Labels

In [11]: from matplotlib import pyplot as plt

import numpy as np

age = np.array([10,22,35,50,40,3,65,54,29,70])

weight = np.array([20,60,75,90,55,8,65,100,64,46])

plt.scatter(age, weight)

plt.title("Age‒Weight Analysis in India")

plt.xlabel("Age")

plt.ylabel("weight")

plt.show()

Output

Ex 21. Ticks

Syntax

plt.xticks([list of tick values])

plt.yticks([list of tick values])

Python program

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]

y = [10, 20,30, 40,50]

plt.plot(x, y, label='Sample Line')

plt.xticks([1, 2, 3, 4, 5], ['A', 'B', 'C', 'D', 'E'],color='red')

plt.yticks([10, 20, 30, 40, 50], ['Ten', 'Twenty', 'Thirty', 'Forty', 'fifty'])

plt.xlabel('X‒axis Label')

plt.ylabel('Y‒axis Label')

plt.title('Customized Ticks Demo')

plt.show()

Output

Ex 22. Colors

Python code

from matplotlib import pyplot as plt

import numpy as np

y = np.array([5,2,10,8])

plt.plot(y,color = 'b')

plt.show()

Output

Ex 23. Adding Text

Demo example

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]

y = [10, 15, 20, 25, 30]

plt.plot(x, y,marker='0')

# Add text

plt.text(3, 20, "Mid Point", fontsize=14, color='black')

plt.show()

Output

Ex 24. Adding annotations

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]

y = [10, 15, 20, 25, 30]

# Scatter plot

plt.scatter(x, y, color='green')

# Annotate the highest point

plt.annotate("Peak Point", xy=(5, 30), xytext=(6, 30),

     arrowprops=dict(arrowstyle="‒>"), fontsize=12)

# Annotate the lowest point

plt.annotate("Lowest Point", xy=(1, 10), xytext=(1.5, 4),

       arrowprops=dict(facecolor='blue', shrink=0.02))

plt.show()

Output

Code explanation: In above program,

1) plt.annotate("Peak Point", xy=(5, 30), xytext=(6, 30),

       arrowprops=dict(arrowstyle="‒>"), fontsize=12)

• This line annotates the highest point (5, 30) with the label "Peak Point".

• The annotate() function is used for this purpose.

Ex 25. Legends

import matplotlib.pyplot as plt

x = [1, 2, 3, 4]

y1 = [1, 4, 9, 16]

y2 = [1, 2, 3, 4]

plt.plot(x,y1,label='Quadratic')

plt.plot(x,y2, label='Linear")

plt.legend()

plt.title('Demo for Legend')

plt.xlabel('X‒axis')

plt.ylabel('Y‒axis')

plt.show()

Output

Code explanation: In above program,

1.We have created three different data sets namely x,y1 and y2.

2. Using plt.plot we draw two lines ‒ Using x,y1 the quadratic line is drawn and using x,y2 the linear line is drawn. When we plot data, you can add a label parameter to each plot element. These labels will be used in the legend.

3.To add a legend to the plot, we use the plt.legend() function.

4. Finally display the plot with title, xlabel and ylabel.

Ex :26

In the competitive business world, tracking sales trends over time is crucial for making informed decisions. Companies analyze sales data to identify which products perform well and to strategize future marketing efforts.

Write a Python code to plot the sales of three products in the four months. Make use of following data. And to distinguish each product sale make use of legend in your program.

months = ['Jan', 'Feb', 'Mar', 'Apr']

sales_a = [10, 15, 20, 25]

sales_b = [5, 10, 15, 20]

sales c= [7, 14, 21, 28]

Solution:

import matplotlib.pyplot as plt

months = ['Jan', 'Feb', 'Mar', 'Apr']

sales a = [10, 15, 20, 25]

sales_b = [5, 10, 15, 20]

sales_c = [7, 14, 21, 28]

plt.plot(months, sales_a, label='Product A')

plt.plot(months, sales_b, label='Product B')

plt.plot(months, sales_c, label='Product C')

plt.legend(title='Products', loc='upper left')

plt.title('Monthly Sales Data')

plt.xlabel('Month')

plt.ylabel('Sales')

plt.show()

Output

Ex 27.

Educational institutions often analyze student performance to identify trends and improve teaching strategies. One important aspect of this analysis is understanding how students of different genders perform in academics across different age groups. Take hypothetical sample data of students' ages and marks for both males and females. Write a Python program Represent the data using a scatter plot where:

(a) Male students are marked with blue circles (0).

(b) Female students are marked with red triangles (^).

(c) Add a legend to indicate gender categories.

Solution :

import matplotlib.pyplot as plt

# Sample data (Age vs. Marks)

male_ages = [18, 19, 20, 21, 22]

male_marks = [85, 78, 90, 88, 76]

female_ages = [18, 19, 20, 21, 22]

female_marks = [92, 80, 89, 95, 84]

# Scatter plot

plt.scatter(male_ages, male_marks, color='blue', marker='o', label="Male") plt.scatter(female_ages, female_marks, color='red', marker='^', label="Female")

plt.xlabel("Age")

plt.ylabel("Marks")

plt.title("Student Sample Data")

plt.legend()

plt.show()

Output

Ex 28. Customization: Line style and Line width

import matplotlib.pyplot as plt

import numpy as np

x = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])

y1 = np.array([2, 3, 5, 7, 6, 8, 10, 9, 11, 13, 12])

y2 = np.array([1, 2, 4, 6, 5, 7, 9, 8, 10, 12, 11])

#Thick solid line

plt.plot(x, y1, linestyle='', linewidth=5, color='blue', label="Thick Line')

#Thin dashed line

plt.plot(x, y2, linestyle='‒‒', linewidth=1, color='black', label="Thin Line')

# Customizations

plt.xlabel("X‒axis")

plt.ylabel("Y‒axis")

plt.title("Line Width and Style Customization")

plt.legend()

plt.grid(True)

plt.show()

Output

Line Width and Style Customization

Ex 29. Customization: Marker

Python code

import numpy as np

y = np.array([5,2,10,8])

plt.plot(y,marker = 'D')

plt.show()

Output

Ex 30. Customization: Grid and Background

from matplotlib import pyplot as plt

import numpy as np

age = np.array([10,22,35,50,40,3,65,54,29,70])

weight = np.array([20,60,75,90,55,8,65,100,64,46])

plt.scatter(age, weight)

plt.title("Age‒Weight Analysis in India")

plt.xlabel("Age")

plt.ylabel("Weight")

plt.grid()

plt.show()

Ex 31. The subplot() function

Syntax

plt.subplot(nrows,ncols,index)

Python program

import matplotlib.pyplot as plt

x=(1,2,3,4,5]

y1=[2,4,6,8,10]

y2=[1,4,9,16,25]

plt.subplot(1,2,1)

plt.plot(x,y1,'r‒‒')

plt.title("plot#1")

plt.subplot(1,2,2)

plt.plot(x,y2,'b‒.')

plt.title("plot #2")

plt.suptitle("Main Plot")

plt.show()

Output

Code explanation: In above code,

1) We have imported the library file Matplotlib.

2) We have with us common x axis co‒ordinates and y1 and y2 arrays for y axis co‒ordinates.

3) plt.subplot(1, 2, 1): Creates a subplot grid with row 1 and two columns and activates first subplot.

4) plt.plot(x, y1, 'r‒‒'): It plots yl Vs x in the first subplot. The 'r‒‒' means red dashed line.

5) plt.title("plot#1"): Sets the title of the first subplot.

6) plt.subplot(1, 2, 2): Activates the second subplot in the same 1*2 grid.

7) plt.plot(x, y2, 'b‒.'): It plots y2 vs x in the second subplot. The 'b‒.' means blue dash (lol reM)eluque lig dot line.

8) plt.title("plot#2"): Sets the title of the second subplot.

9) plt.suptitle("Main Plot"): This adds the title(super title) for entire plot, above both the subplots.

10) Finally using plt.show() the plot with both the subplots is displayed.

Ex 32. The subplots() function

syntax

fig, axes = plt.subplots(nrows, ncols)

Python program

import matplotlib.pyplot as plt

x=(1,2,3,4,5]

y1=[2,4,6,8,10]

y2=[1,4,9,16,25]

fig,axes = plt.subplots(1,2)

axes[0].plot(x,y1,color = 'red')

axes[0].set_title("plot#1")

axes[1].plot(x,y2,color = 'blue')

axes[1].set_title("plot#2")

plt.suptitle("Main Plot")

plt.show()

Output

Ex 33. Drawing lines and shapes

import matplotlib.pyplot as plt

fig, axes = plt.subplots(1,2, figsize=(8,3))

x = [1,2,3,4,5]

y1 = [2,4,6,8,10]

y2 = [1,3,2,5,4]

axes[0].plot(x,y1)

axes[0].set_title("Plot#1")

axes[0].axhline(y=sum(y1)/len(y1), linestyle="‒‒") # average line

axes[0].annotate("Peak", xy=(5,10), xytext=(4,9), arrowprops= dict(arrowstyle="‒>"))

axes[1].plot(x,y2)

axes[1].set_title("Plot#2")

axes[1].axvspan(3,5, alpha=0.12)                 # highlight x range

axes[1].text(3.2, 2.5, "busy period")

fig.suptitle("Subplots with Annotations & Drawings")

plt.tight_layout()

plt.show()

Output

Ex 34. Saving Plots to Files

import matplotlib.pyplot as plt

x = [1,2,3,4,5]

y = [10,20,25,30,40]

plt.plot(x,y)

plt.title("Simple Line Plot")

plt.xlabel("X‒axis")

plt.ylabel("Y‒axis")

plt.savefig("my_plot.pdf")

plt.show()

Step 2:

Open my_plot.pdf which gets created at the current working directory. This pdf file stored the plot created by above Python program.