Abysmal Grades¶
You work at Goober Elementary 
, where students recently took their initial exams. The grades are in, and you want to
analyze them. In their current form they look like this
|student_id |gender | math_score| reading_score| writing_score|
|:----------|:------|----------:|-------------:|-------------:|
|AyL1u |male | 92.12| 68.87| 100.00|
|Tp312 |female | 71.40| 44.97| 76.11|
|rq5zh |female | 78.38| 47.51| 82.11|
...
|JWRWU |male | 71.26| 84.21| 57.17|
|3Vku1 |male | 77.79| 54.90| 100.00|
|5en5d |male | 82.53| 65.67| 59.59|
You decided to categorize scores into the following tiers
- stellar: score >= 90
- passing: score in the range [60, 90)
- failing: score in the range [10, 60)
- abysmal: score < 10. Just for kicks. No one would score this low.....
Pop the grades.csv file into a BigQuery table named grades. Then measure the number of students who fall into each
(gender, tier) pair, for each subject, producing an output like this.
|gender |tier | math| reading| writing|
|:------|:-------|----:|-------:|-------:|
|female |stellar | 12| 11| 10|
|female |passing | 24| 15| 25|
|female |failing | 7| 17| 8|
|male |stellar | 7| 17| 11|
|male |passing | 39| 28| 30|
|male |failing | 11| 12| 15|
|male |abysmal | 0| 0| 1|
Make sure the output is ordered by (gender, tier), where the tiers are in order from best to worst.