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Building a DataFrame

~9 mincode completion

A DataFrame is a table: named columns, numbered rows. Where a NumPy array is one grid of one type, a DataFrame can hold a text column next to a number column, and you address columns by name rather than by position.

import pandas as pd

df = pd.DataFrame({
    "name": ["ada", "grace"],
    "score": [90, 85],
})

That dictionary is read as "column name -> the values down that column". Each list becomes one column and they must all be the same length.

The things you will do to every new frame, in this order:

df.shape        # (2, 2)     (rows, columns)
df.columns      # the column names
df.head()       # the first 5 rows
df.dtypes       # what type each column ended up as

df.dtypes is the one people skip and should not. A column that came out as object when you expected a number means something non-numeric got in, and every calculation on it will either fail or be quietly wrong.

Your task:

Write build_frame(names, scores) that returns a DataFrame with a "name" column and a "score" column, then returns its shape as a list.

The test checks the shape, so return the DataFrame and let the harness read .shape off it.

Example Tests

Two people gives a 2 by 2 table

Input: {"names":["ada","grace"],"scores":[90,85]}

Expected: [2,2]

Three people gives three rows and still two columns

Input: {"names":["a","b","c"],"scores":[1,2,3]}

Expected: [3,2]

The score column holds the values you passed in

Input: {"names":["a","b"],"scores":[7,8]}

Expected: 15

Python

import pandas as pd

def build_frame(names, scores):
    """
    Build a two-column DataFrame.

    Args:
        names: a list of strings
        scores: a list of numbers, the same length

    Returns:
        A DataFrame with columns "name" and "score".
    """
    # YOUR CODE HERE
    pass

def _score_total(names, scores):
    """Total of the score column of the frame that was built."""
    return float(build_frame(names, scores)["score"].sum())

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