Question

Q.1:

Use the NumPy’s random number generation to create an array of five random integers that represent summertime temperatures in the range 60–100, then perform the following tasks:

a. Convert the array into the Series named temperatures and display it.

b. Determine the lowest, highest and average temperatures.

c. Produce descriptive statistics for the Series.

Q.2:

Given the following dictionary;

temps = {'Mon': [68, 89], 'Tue': [71, 93], 'Wed': [66, 82], 'Thu': [75, 97], 'Fri': [62, 79]}

perform the following tasks:

a. Convert the dictionary into the DataFrame named temperatures with Low and High as the indices, then display the DataFrame.

b. Use the column names to select only the columns for Mon through Wed.

c. Use the row index Low to select only the low temperatures for each day.

d. Set the floating-point precision to 2, then calculate the average temperature for each day.

e. Calculate the average low and high temperatures.

Q.3:

Given the following array:  

array([[ 1,  2,  3,  4,  5],

       [ 6,  7,  8,  9, 10],

       [11, 12, 13, 14, 15]])

write statements to perform the following tasks:

a. Select the second row.

b. Select the first and third rows.

c. Select the middle three columns.

Q.4:

Use NumPy function arange to create an array of 20 even integers from 2 through 40, then reshape the result into a 4-by-5 array.

Q.5:

Use NumPy random-number generation to create an array of twelve random grades in the range 60 through 100, then reshape the result into a 3-by-4 array. Calculate the average of all the grades, the averages of the grades in each column and the averages of the grades in each row.

Answer 1

Question 1

import numpy as np #import numpy package
import pandas as pd #import pandas package

arr = np.random.randint(60,100, 5) #generate 5 random values between 60 to 100

temperatures = pd.Series(arr) #convert array to series
print(temperatures) #display series

print("Minimum Temperature: ", min(temperatures)) #minimum temperature
print("Maximum Temperature: ", max(temperatures)) #maximum temperature
print("Average Temperature: ", temperatures.mean()) #average temperature

print(temperatures.describe()) #descriptive statistics for the Series

Question 2

temps = {'Mon': [68, 89], 'Tue': [71, 93], 'Wed': [66, 82], 'Thu': [75, 97], 'Fri': [62, 79]}

temperatures = pd.DataFrame(temps, index=['Low', 'High']) #create a dataframe with Low and High as Indices
print(temperatures) #display dataframe

print(temperatures.loc[:, 'Mon':'Wed'])# select columns from Monday to Wednesday

print(temperatures.loc['Low']) #select low temperatures for each day

print(round(temperatures.mean(axis=0),2)) #average temperature of each day

print(temperatures.mean(axis=1)) # average low and high temperatures

Question 3

arr = np.array([[ 1,  2,  3,  4,  5], [ 6,  7,  8,  9, 10], [11, 12, 13, 14, 15]])

print(arr[1]) #select second row
print(arr[[0,2]]) #select first and third row
print(arr[:, 1:4]) #select middle three columns

Question 4

a = np.arange(2, 41, 2) # array of 20 even integers from 2 to 40
print(a) #print the array before reshape

a = a.reshape(4,5) #reshape the array into 4x5
print(a) # print the array after reshape

Question 5

grade = np.random.randint(60, 100, 12) #create array of random number between 60 to 100

grade = grade.reshape(3,4) #reshape the array in 3x4

print(grade.mean()) #average of all grades

print(grade.mean(axis=0)) #column wise mean

print(grade.mean(axis=1)) #row wise mean

Screenshot with output

Question 1

Question 2

Question 3

Question 4

Question 5

Comments has been given for each line of code. Screenshot of the code as well as the output has been attached for the reference.