Add column (2)

Using iterrows() to iterate over every observation of a Pandas DataFrame is easy to understand, but not very efficient. On every iteration, you're creating a new Pandas Series.

If you want to add a column to a DataFrame by calling a function on another column, the iterrows() method in combination with a for loop is not the preferred way to go. Instead, you'll want to use apply().

Compare the iterrows() version with the apply() version to get the same result in the brics DataFrame:

for lab, row in brics.iterrows() :
    brics.loc[lab, "name_length"] = len(row["country"])

brics["name_length"] = brics["country"].apply(len)

We can do a similar thing to call the upper() method on every name in the country column. However, upper() is a method, so we'll need a slightly different approach:

This is a part of the course

“Intermediate Python”

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Exercise instructions

Replace the for loop with a one-liner that uses .apply(str.upper). The call should give the same result: a column COUNTRY should be added to cars, containing an uppercase version of the country names.
As usual, print out cars to see the fruits of your hard labor

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)

# Use .apply(str.upper)
for lab, row in cars.iterrows() :
    cars.loc[lab, "COUNTRY"] = row["country"].upper()

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