Question

use numpy

B. (3 points) Using Python, parse the provided CSV file and do the following: 1. Calculate the mean, median, and mode of each dataset (column). 2. Calculate the variance and standard deviation of each dataset. 3. Calculate the number of data points in each dataset. 4. Calculate the minimum, maximum, and range of each dataset. 5. Plot each dataset using matplotlib. The charts should have a title, x- and y-axis labels, and grid lines (horizontal / vertical).

% growth of Weight Watchers Stock	Daily # of Walmart Toy Units Sold
1.937	1
0.334	4
0.913	4
0.771	4
0.346	7
0.913	2
4.242	3
2.595	6
0.526	5
0.807	3
0.076	3
2.355	4
0.98	4
0.605	5
2.192	4
0.1	3
0.457	2
0.099	2
0.461	3
4.253	1
1.151	5
2.19	5
0.463	7
2.802	2
0.395	3
0.739	6
1.186	3
0.661	4
0.347	6
0.206	3
1.153	3
1.167	5
1.064	9
0.406	4
0.15	2
0.272	3
0.133	3
0.138	0
1.756	5
0.162	5
0.041	5
0.261	3
0.134	8
2.697	3
0.436	3

Answer 1

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy import stats
dataset=pd.read_csv(r"C:\Users\MAHE\Desktop\data.csv")
##part 1
col1=dataset['% growth of Weight Watchers Stock']
col2=dataset['Daily # of Walmart Toy Units Sold']
print('mean of % growth of Weight Watchers Stock: ',np.mean(col1))
print('mean of Daily # of Walmart Toy Units Sold: ',np.mean(col2))
print('median of % growth of Weight Watchers Stock: ',np.median(col1))
print('median of Daily # of Walmart Toy Units Sold: ',np.median(col2))
print('mode of % growth of Weight Watchers Stock: ',stats.mode(col1)[0])
print('mode of Daily # of Walmart Toy Units Sold: ',stats.mode(col2)[0])

##part 2
print('variance of % growth of Weight Watchers Stock: ',np.var(col1))
print('variance of Daily # of Walmart Toy Units Sold: ',np.var(col2))
print('standard deviation of % growth of Weight Watchers Stock: ',np.std(col1))
print('standard deviation of Daily # of Walmart Toy Units Sold: ',np.std(col2))

###part 3
print('no of data points:', len(col1))

##part4
print('min of % growth of Weight Watchers Stock: ',np.min(col1))
print('min of Daily # of Walmart Toy Units Sold: ',np.min(col2))
print('max of % growth of Weight Watchers Stock: ',np.max(col1))
print('max of Daily # of Walmart Toy Units Sold: ',np.max(col2))
print('range of % growth of Weight Watchers Stock: ',[np.min(col1),np.max(col1)])
print('range of Daily # of Walmart Toy Units Sold: ',[np.min(col2),np.max(col2)])

plt.scatter(col1,col2)
plt.xlabel(' % growth of Weight Watchers Stock')
plt.ylabel('Daily # of Walmart Toy Units Sold')
plt.grid()
plt.show()

mean of Daily # of Walmart Toy Units Sold: 3.888888888888889 median of % growth of Weight Watchers Stock: 0.605 median of Daily # of Walmart Toy Units Sold: 4.0 mode of % growth of Weight Watchers Stock: [0.913] mode of Daily # of Walmart Toy Units Sold: [3] variance of % growth of Weight Watchers Stock: 1.0810524622222222 variance of Daily # of Walmart Toy Units Sold: 3.2543209876543213 standard deviation of % growth of Weight Watchers Stock: 1.0397367273604516 standard deviation of Daily # of Walmart Toy Units Sold: 1.8039736660091026 no of data points: 45 min of % growth of Weight Watchers Stock: 0.040999999999999995 min of Daily # of Walmart Toy Units Sold: 0 max of % growth of Weight Watchers Stock: 4.253 max of Daily # of Walmart Toy Units sold: 9 range of % growth of Weight Watchers Stock: [0.040999999999999995, 4.253] range of Daily # of Walmart Toy Units Sold: [0, 9] Daily # of Walmart Toy Units Sold % growth of Weight Watchers Stock Activat Go to PC