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Adding a Computed Column

~10 mincode completion

Arithmetic on a column applies to every row at once, with no loop:

df["total"] = df["price"] * df["quantity"]
df["price_with_tax"] = df["price"] * 1.2

This is vectorised. Writing a Python loop over rows to do the same thing is typically fifty times slower and much longer, and df.iterrows() in particular is a smell: almost every use of it can be replaced by column arithmetic.

Two columns line up by their index, not by position. That matters as soon as you have filtered or sorted one of them: pandas aligns on the labels and fills anything unmatched with NaN, which is usually a helpful surprise and occasionally a baffling one.

Assigning to a filtered frame is where the SettingWithCopyWarning comes from:

subset = df[df["price"] > 10]
subset["flag"] = True          # warns: subset may be a view or a copy
subset = df[df["price"] > 10].copy()    # be explicit, no warning

Your task:

Write add_total(df) that adds a "total" column equal to price * quantity, and returns the DataFrame.

Example Tests

The new column holds price times quantity for each row

Input: {"df":{"price":[2,3],"quantity":[4,5]}}

Expected: [8,15]

The frame gains exactly one column

Input: {"df":{"price":[1],"quantity":[1]}}

Expected: [1,3]

A quantity of zero gives a total of zero, not a dropped row

Input: {"df":{"price":[5,5],"quantity":[0,2]}}

Expected: [0,10]

Python

import pandas as pd

def add_total(df):
    """
    Add a "total" column of price * quantity.

    Args:
        df: a DataFrame with "price" and "quantity" columns

    Returns:
        The DataFrame with the extra column.
    """
    # YOUR CODE HERE
    pass

def _run(df):
    import pandas as pd
    return add_total(pd.DataFrame(df))


def _totals(df):
    import pandas as pd
    return [float(v) for v in add_total(pd.DataFrame(df))["total"]]

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