Data Science Matplotlib Library Data Visualization Question Exercise Practice Solutions

Question

This assignment is for visualization using Python Matplotlib Library

1. Load the necessary package for plotting using pyplot from matplotlib. Example - Days(x-axis) represents 8 days and Speed represents a car’s speed. Plot a Basic line plot between days and car speed, put x axis label as days and y axis label as car speed and put title Car Speed Measurement.

         Days=[1,2,3,4,5,6,7,8]

         Speed=[60,62,61,58,56,57,46,63]​​​​​

     2. Now to above car data apply some string formats  like line style example green dotted line, marker shape like +, change markersize, markerface color etc.

   3. Plot Axes Labels, Chart title, Legend, Grid in Car minimum, Maximum and average speed in 8 days.

days=[1,2,3,4,5,6,7,8]

max_speed=[80,91,92,88,77,79,76,75]

min_speed=[42,43,40,42,33,36,34,35]

avg_speed=[46,58,57,56,40,42,41,36]

4. Plotting a basic sine graph by adding more features. Adding Multiple plots by Superimposition like cosine wave.

5. Plot Simple bar chart showing popularity of Programming Languages.

Languages =['Python', 'SQL', 'Java', 'C++', 'JavaScript']

Popularity = [56, 39, 34, 34, 29]

Security = [44 ,36 ,55, 50, 42]

Plot Multiple Bars showing Popularity and Security of major Programming Languages. Also Create Horizontal bar chart using barh function.

6.  Plot Histogram, We have a sample data of Students marks of various Students, we will try to plot number of Students by marks range and try to figure out how many Students are average, below-average and Excellent.

Marks = [ 61,86,42,46,73,95,65,78,53,92,55,69,70,49,72,86,64]

Histogram showing Below Average, Average and Execellent distribution

40-60: Below Average

60-80: Average

80-100: Excellent

7.  Titanic Data Set Download Data

 Load the data file

 (i) Create a pie chart presenting the male/female proportion 

 (ii) Create a scatterplot with the Fare paid and the Age, differ the plot color by gender

Answer 1

1. Question

import matplotlib.pyplot as plt # Import Required Package

Days = [1,2,3,4,5,6,7,8]

Speed = [60,62,61,58,56,57,46,63]

plt.plot(Days, Speed, color = 'blue') # For Create a Line Plot

plt.xlabel('Days')

plt.ylabel('Car Speed')

plt.title('Car Speed Measurement')

plt.show()

2. Question

Days = [1,2,3,4,5,6,7,8]

Speed = [60,62,61,58,56,57,46,63]

plt.plot(Days, Speed, color = 'blue', marker = 'o', ms = 20, mec = 'red', mfc = 'green', linestyle = 'dotted', linewidth = 3.5)

plt.xlabel('Days') # ms for marker size, mfc for marker face color, mec for border color

plt.ylabel('Car Speed')

plt.title('Car Speed Measurement')

plt.show()

3. Question

days = [1,2,3,4,5,6,7,8]

max_speed = [80,91,92,88,77,79,76,75]

min_speed = [42,43,40,42,33,36,34,35]

avg_speed = [46,58,57,56,40,42,41,36]

plt.figure(figsize=(8,6))

plt.subplot(2,1,1)

plt.plot(Days, max_speed, color = 'red', marker = '*', ms = 10, mec = 'red', mfc = 'orange', label = 'Maximum Speed')

plt.xlabel('Days')

plt.ylabel('Maximum Speed')

plt.title('Car Speed Measurement')

plt.legend()

plt.grid()

plt.show()

plt.figure(figsize=(8,6))

plt.subplot(2,1,2)

plt.plot(Days, min_speed, color = 'green', marker = 'o', ms = 10, mec = 'black', mfc = 'blue', label = 'Minimum Speed')

plt.xlabel('Days')

plt.ylabel('Minimum Speed')

plt.title('Car Speed Measurement')

plt.legend()

plt.grid()

plt.show()

plt.figure(figsize=(8,6))

plt.subplot(2,1,1)

plt.plot(Days, avg_speed, color = 'yellow', marker = '^', ms = 10, mec = 'red', mfc = 'orange', label = 'Average Speed')

plt.xlabel('Days')

plt.ylabel('Maximum Speed')

plt.title('Car Speed Measurement')

plt.legend()

plt.grid()

plt.show()

4. Question

x = np.arange(0,4*np.pi,0.1)  

y = np.sin(x)

z = np.cos(x)

plt.plot(x,y, color = 'green', marker='o')

plt.plot(x,z, color = 'blue', marker = '*')

plt.xlabel('Sin and Cos Value From 0 to 4pi')

plt.ylabel('Sin(x) and Cos(x)')

plt.title('Sin And Cosine Wave')

plt.legend(['sin(X)','Cos(x)'])

plt.show()

5. Question

Languages =['Python', 'SQL', 'Java', 'C++', 'JavaScript']

Popularity = [56, 39, 34, 34, 29]

Security = [44 ,36 ,55, 50, 42]

plt.subplot(2,1,1)

plt.bar(Languages, Popularity, width=0.5, color = 'orange', align='center')

plt.xlabel('Languages')

plt.ylabel('Popularity')

plt.legend(['Popularity'])

plt.show()

plt.subplot(2,1,2)

plt.bar(Languages, Security, width=0.5, color = 'red', align='center')

plt.xlabel('Languages')

plt.ylabel('Security')

plt.legend(['Security'])

plt.show()

Horizontal Bar

Languages =['Python', 'SQL', 'Java', 'C++', 'JavaScript']

Popularity = [56, 39, 34, 34, 29]

Security = [44 ,36 ,55, 50, 42]

plt.subplot(2,1,1)

plt.barh(Languages, Popularity, color = 'g', align='center')

plt.xlabel('Languages')

plt.ylabel('Popularity')

plt.legend(['Popularity'])

plt.show()

plt.subplot(2,1,2)

plt.barh(Languages, Security, color = 'red', align='center')

plt.xlabel('Languages')

plt.ylabel('Security')

plt.legend(['Security'])

plt.show()

Answer 2

GO_STP_6266

https://www.linkedin.com/posts/sahil-parmar-4099391bb_google-colaboratory-activity-6805368766604701696-cnMg

Answer 3

6. Question

Marks = np.array([61,86,42,46,73,95,65,78,53,92,55,69,70,49,72,86,64])

below_avg = Marks[np.logical_and(Marks >= 40, Marks < 60)]

avg_marks = Marks[np.logical_and(Marks >= 60, Marks < 80)]

exe_marks = Marks[np.logical_and(Marks >= 80, Marks <= 100)]

plt.hist(Marks)

plt.show()

print('Below Average Students Are : ',below_avg.size)

print('Average Students Are : ',avg_marks.size)

print('Execelent Students Are : ',exe_marks.size)

7. Question

import pandas as pd

df = pd.read_csv('/content/train.csv')

df.head()

df['Sex']

x = df.Sex.value_counts()

x

plt.pie(x, autopct='%.0f%%')

plt.legend(['Male','Female'])

plt.title('Male And Female Proportion')

plt.show()

male = df[df['Sex']=='male']

male.Sex.value_counts()

female = df[df['Sex']=='female']

female.Sex.value_counts()

plt.scatter(male.Fare, male.Age, label = 'Male', marker = '*')

plt.scatter(female.Fare, female.Age, label = 'Female', marker = '^')

plt.legend()

plt.xlabel('Fare')

plt.ylabel('Age')

plt.grid()

plt.show()