You can call the histograms function to draw the

You can call the histograms function to draw the histograms!
Note: More charts will be displayed when running the test cell. Please feel free to ignore the charts.
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[38]: grader . check( "q3_1" )
[38]: q3_1 passed!
1.3.1 Convenience sampling
One sampling methodology, which is generally a bad idea , is to choose players who are somehow
convenient to sample. For example, you might choose players from one team who are near your
house, since it’s easier to survey them. This is called, somewhat pejoratively, convenience sampling .
Suppose you survey only relatively new players with ages less than 22. (The more experienced
players didn’t bother to answer your surveys about their salaries.)
Question 2. Assign convenience_sample to a subset of full_data that contains only the rows
for players under the age of 22.
[43]: convenience_sample = full_data . where( 'Age' , are . below( 22 ) )
convenience_sample
[43]: PlayerName | Salary | Age | Team | Games | Rebounds | Assists | Steals |
Blocks | Turnovers | Points
Aaron Gordon | 3992040 | 19 | ORL | 47 | 169 | 33 | 21 |
22 | 38 | 243
Alex Len | 3649920 | 21 | PHO | 69 | 454 | 32 | 34 |
105 | 74 | 432
Andre Drummond | 2568360 | 21 | DET | 82 | 1104 | 55 | 73 |
153 | 120 | 1130
Andrew Wiggins | 5510640 | 19 | MIN | 82 | 374 | 170 | 86 |
50 | 177 | 1387
Anthony Bennett | 5563920 | 21 | MIN | 57 | 216 | 48 | 27 |
16 | 36 | 298
Anthony Davis | 5607240 | 21 | NOP | 68 | 696 | 149 | 100 |
200 | 95 | 1656
Archie Goodwin | 1112280 | 20 | PHO | 41 | 74 | 44 | 18 |
9 | 48 | 231
Ben McLemore | 3026280 | 21 | SAC | 82 | 241 | 140 | 77 |
19 | 138 | 996
Bradley Beal | 4505280 | 21 | WAS | 63 | 241 | 194 | 76 |
18 | 123 | 962
Bruno Caboclo | 1458360 | 19 | TOR | 8 | 2 | 0 | 0 |
1 | 4 | 10
… (34 rows omitted)
[44]: grader . check( "q3_2" )
[44]: q3_2 passed!
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Question 3. Assign convenience_stats to an array of the average age and average salary of
your convenience sample, using the compute_statistics function. Since they’re computed on a
sample, these are called sample averages .
[47]: convenience_stats = compute_statistics(convenience_sample)
convenience_stats
[47]: array([2.03636364e+01, 2.38353382e+06])
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[48]: grader . check( "q3_3" )
[48]: q3_3 passed!
Next, we’ll compare the convenience sample salaries with the full data salaries in a single histogram.
To do that, we’ll need to use the bin_column option of the hist method, which indicates that all
columns are counts of the bins in a particular column. The following cell does not require any
changes; just run it .
[49]: def compare_salaries (first, second, first_title, second_title):
"""Compare the salaries in two tables."""
first_salary_in_millions = first . column( 'Salary' ) /1000000
second_salary_in_millions = second . column( 'Salary' ) /1000000
first_tbl_millions = first . drop( 'Salary' ) . with_column( 'Salary' , ␣
, →
first_salary_in_millions)
second_tbl_millions = second . drop( 'Salary' ) . with_column( 'Salary' , ␣
, →
second_salary_in_millions)
max_salary = max (np . append(first_tbl_millions . column( 'Salary' ), ␣
, →
second_tbl_millions . column( 'Salary' )))
bins = np . arange( 0 , max_salary +1 , 1 )
first_binned = first_tbl_millions . bin( 'Salary' , bins = bins) . relabeled( 1 , ␣
, →
first_title)
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