Lecture 37 – Conclusion, Next Steps¶

Data 94, Spring 2021¶

In [1]:
from datascience import *
import numpy as np
Table.interactive_plots()

In [2]:
surveys = Table.read_table('data/hw_times.csv')

In [3]:
surveys

Out[3]:
How much time did you spend on Homework 1? How much time did you spend on Homework 2? How much time did you spend on Homework 3? How much time did you spend on Homework 4? How much time did you spend on Homework 5? How much time did you spend on Homework 6? How much time did you spend on Homework 7? How much time did you spend on Homework 8?
7 7 7 7 5 8 6 4
4 5 5 3 3 5 3 3
4 6 6 6 4 8 3 5
4 5 4 4 3 5 3 3
3 5 5 6 5 6 4 4
5 6 5 5 5 5 5 5
5 6 7 6 6 7 7 7
4 5 4 4 4 5 3 4
6 6 6 4 4 6 3 2
3 3 3 4 4 6 3 3

... (5 rows omitted)

In [5]:
np.mean(surveys)

Out[5]:
How much time did you spend on Homework 1? How much time did you spend on Homework 2? How much time did you spend on Homework 3? How much time did you spend on Homework 4? How much time did you spend on Homework 5? How much time did you spend on Homework 6? How much time did you spend on Homework 7? How much time did you spend on Homework 8?
4.6 5.13333 4.93333 4.73333 4.33333 6.53333 3.86667 4.13333
In [6]:
np.array(np.mean(surveys).row(0))

Out[6]:
array([4.6       , 5.13333333, 4.93333333, 4.73333333, 4.33333333,
6.53333333, 3.86666667, 4.13333333])
In [7]:
mean_time = Table().with_columns(
'Homework', np.arange(1, 9),
'Mean Time Spent', np.array(np.mean(surveys).row(0))
)

mean_time

Out[7]:
Homework Mean Time Spent
1 4.6
2 5.13333
3 4.93333
4 4.73333
5 4.33333
6 6.53333
7 3.86667
8 4.13333
In [9]:
mean_time.plot('Homework')