HANDOUT 2
Machine Learning-Matplotlib and Pandas
Q1. Write a Python program to draw a line using given axis values with suitable label in the x
axis, y axis and a title.
Solution: import matplotlib.pyplot as plt
# x axis values
x = [1,2,3]
# y axis values
y = [2,4,1]
# Plot lines and/or markers to the Axes.
plt.plot(x, y)
# 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')
# Set a title
plt.title('Sample graph!')
# Display a figure.
plt.show()
( A line plot can be created by calling the plot() function and passing the x-axis data for
the regular interval, and y-axis for the observations.)
Q2. Write a Python program to draw a line with suitable label in the x axis, y axis(3 times of
x) and a title.
Solution:
import matplotlib.pyplot as plt
X = range(1, 50)
Y = [value * 3 for value in X]
print("Values of X:")
print(*range(1,50))
print("Values of Y (thrice of X):")
print(Y)
# Plot lines and/or markers to the Axes.
plt.plot(X, Y)
# 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')
# Set a title
plt.title('Draw a line.')
# Display the figure.
plt.show()
Q3. Write a Python program to plot quantities which have an x and y position making use
of pylab
Solution:
import numpy as np
import pylab as pl
# Make an array of x values
x1 = [2, 3, 5, 6, 8]
# Make an array of y values for each x value
y1 = [1, 5, 10, 18, 20]
# Make an array of x values
x2 = [3, 4, 6, 7, 9]
# Make an array of y values for each x value
y2 = [2, 6, 11, 20, 22]
# set new axes limits
pl.axis([0, 10, 0, 30])
# use pylab to plot x and y as red circles
pl.plot(x1, y1,'b*', x2, y2, 'ro')
# show the plot on the screen
pl.show()
Q4. Write a Python program to plot several lines with different format styles in one
command using arrays
Solution: import numpy as np
import matplotlib.pyplot as plt
# Sampled time at 200ms intervals
t = np.arange(0., 5., 0.2)
# green dashes, blue squares and red triangles
plt.plot(t, t, 'g--', t, t**2, 'bs', t, t**3, 'r^')
plt.show()
Q5. Write a Python program to create multiple plots
Solution:
import matplotlib.pyplot as plt
fig = plt.figure()
fig.subplots_adjust(bottom=0.020, left=0.020, top = 0.400,right=0.500)
plt.subplot(2, 1, 1)
plt.xticks(()), plt.yticks(())
plt.subplot(2, 3, 4)
plt.xticks(())
plt.yticks(())
plt.subplot(2, 3, 5)
plt.xticks(())
plt.yticks(())
plt.subplot(2, 3, 6)
plt.xticks(())
plt.yticks(())
plt.show()
Q6. Write a Python program to display a DataFrame having values for Name, Exam,
Subject and Result.
Solution: import pandas as pd
import numpy as np
d = { 'Name':['Alisa','Bobby','Cathrine','Alisa','Bobby','Cathrine'],
'Exam':['Semester 1','Semester 1','Semester 1', Semester2', 'Semester
2','Semester 2'], 'Subject':
['Mathematics','Mathematics','Mathematics','Science','Science',
'Mathematics'],
'Result':['Pass','Pass','Fail','Pass','Fail','Pass']}
df = pd.DataFrame(d,columns=['Name','Exam','Subject','Result'])
df
Q7. Write a Python program to create a two-way cross tab for above DataFrame having
for Subject vs Results summary.
Solution: pd.crosstab(df.Subject, df.Result,margins=True)
Q8. Write a Python program to create a three-way cross tab for above DataFrame having
for Subject,Exams vs Results summary.
Solution:
pd.crosstab([df.Subject, df.Exam],df.Result, margins=True)
Q9. Write a Python program to create a scatter plot for given x and y values.
Solution:
import matplotlib.pyplot as plt
x = [5,7,8,7,2,17,2,9,4,11,12,9,6]
y = [99,86,87,88,111,86,103,87,94,78,77,85,86]
plt.scatter(x,y)
plt.show()
Q10 . Write a python program to read data from a csv file.(diabetes.csv)
Solution:
a) # Load libraries
import pandas as pd
# load dataset
pima = pd.read_csv('D://diabetes.csv')
X = list(pima.columns)
print("List of Attributes:", X)
X.remove('Outcome') #Remove the class attribute
print("Predicting Attributes:", X)
X=pima[X]
Y=pima.Outcome
pima.head()
b) import numpy as np
import pandas as pd
from pandas import DataFrame
df= pd.read_csv('D://weather.csv')
cols = list(df.columns)
print("List of Attributes:",cols)
