loc and iloc (1)

With loc and ilocyou can do practically any data selection operation on DataFrames you can think of. loc is label-based, which means that you have to specify rows and columns based on their row and column labels. iloc is integer index based, so you have to specify rows and columns by their integer index like you did in the previous exercise.

Try out the following commands to experiment with loc and iloc to select observations. Each pair of commands here gives the same result.

cars.loc['RU']
cars.iloc[4]

cars.loc[['RU']]
cars.iloc[[4]]

cars.loc[['RU', 'AUS']]
cars.iloc[[4, 1]]

As before, code is included that imports the cars data as a Pandas DataFrame.

This is a part of the course

“Intermediate Python”

View Course

Exercise instructions

Use loc or iloc to select the observation corresponding to Japan as a Series. The label of this row is JPN, the index is 2. Make sure to print the resulting Series.
Use loc or iloc to select the observations for Australia and Egypt as a DataFrame. You can find out about the labels/indexes of these rows by inspecting cars. Make sure to print the resulting DataFrame.

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)

# Print out observation for Japan


# Print out observations for Australia and Egypt

Edit and Run Code