Selecting Columns and Filtering Rows
~10 mincode completion
Write high_scorers(df, threshold) that returns the rows where score is strictly greater than threshold.
Examples
Only rows above the threshold survive
- Input
- high_scorers({"name": ["a", "b", "c"], "score": [90, 70, 85]}, 80)
- shape of result
- [2, 2]
A row exactly at the threshold is excluded, since the test is strict
- Input
- high_scorers({"name": ["a", "b"], "score": [80, 81]}, 80)
- shape of result
- [1, 2]
Nothing above the threshold gives an empty frame, not an error
- Input
- high_scorers({"name": ["a"], "score": [10]}, 50)
- shape of result
- [0, 2]
Hints
Hint 1
Index the array with a boolean mask to keep only the elements that match.
Hint 2
Reach for ampersand rather than and.
Requirements
: a DataFrame with a "score" column
threshold: a numberReturn the subset of rows where score > threshold.
Constraints
Standard library only, no imports required
Time limit: 200 ms, Memory: 64 MB
Where this shows up
~10 min
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Python
import pandas as pd
def high_scorers(df, threshold):
"""
Keep only the rows above a score threshold.
Args:
df: a DataFrame with a "score" column
threshold: a number
Returns:
The subset of rows where score > threshold.
"""
# YOUR CODE HERE
pass
def _run(df, threshold):
"""Builds a DataFrame from plain data, then calls high_scorers."""
import pandas as pd
return high_scorers(pd.DataFrame(df), threshold)
def _score_total(df, threshold):
"""Total of the surviving score column."""
import pandas as pd
return float(high_scorers(pd.DataFrame(df), threshold)["score"].sum())