Easy

Have a go at it. The editor and the docs panel are open, and your code is saved as you type. Running it needs a free account — you’ll come back to exactly what you wrote.

Selecting Columns and Filtering Rows

~10 mincode completion

One column comes back as a Series, which is a single labelled column:

df["score"]              # a Series
df[["name", "score"]]    # a DataFrame, note the double brackets

Double brackets mean "a list of column names", which is why selecting several columns looks like that.

Filtering rows uses a boolean condition, exactly like NumPy's boolean indexing:

df["score"] > 80            # a Series of True/False, one per row
df[df["score"] > 80]        # only the rows where that was True

Combining conditions needs & and |, not and and or, and every condition needs its own parentheses:

df[(df["score"] > 80) & (df["score"] < 95)]     # correct
df[df["score"] > 80 and df["score"] < 95]       # ValueError

The parentheses are not optional. & binds more tightly than >, so without them Python tries to evaluate 80 & df["score"] first and the error message will not point at the real problem.

Your task:

Write high_scorers(df, threshold) that returns the rows where score is strictly greater than threshold.

Example Tests

Only rows above the threshold survive

Input: {"df":{"name":["a","b","c"],"score":[90,70,85]},"threshold":80}

Expected: [2,2]

A row exactly at the threshold is excluded, since the test is strict

Input: {"df":{"name":["a","b"],"score":[80,81]},"threshold":80}

Expected: [1,2]

Nothing above the threshold gives an empty frame, not an error

Input: {"df":{"name":["a"],"score":[10]},"threshold":50}

Expected: [0,2]

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())

Run your code to see results

⌘↵ runs against the visible tests

Loading docs…