Dictionary to DataFrame (1)

Pandas is an open source library, providing high-performance, easy-to-use data structures and data analysis tools for Python. Sounds promising!

The DataFrame is one of Pandas' most important data structures. It's basically a way to store tabular data, where you can label the rows and the columns. One way to build a DataFrame is from a dictionary.

In the exercises that follow you will be working with vehicle data in different countries. Each observation corresponds to a country and the columns give information about the number of vehicles per capita, whether people drive left or right, and so on.

Three lists are defined in the script: - names, containing the country names for which data is available. - dr, a list with booleans that tells whether people drive left or right in the corresponding country. - cpc, the number of motor vehicles per 1000 people in the corresponding country.

Each dictionary key is a column label and each value is a list which contains the column elements.

This exercise is part of the course

Intermediate Python for Data Science

View Course

Exercise instructions

Import pandas as pd.
Use the pre-defined lists to create dictionary, called my_dict. There should be three key value pairs:
- key 'country' and value names.
- key 'drives_right' and value dr.
- key 'cars_per_cap' and value cpc.
Use pd.DataFrame() to turn your dict into a DataFrame called cars.
Print out cars and see how beautiful it is.

Hands-on interactive exercise

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

# Pre-defined lists
names = ['United States', 'Australia', 'Japan', 'India', 'Russia', 'Morocco', 'Egypt']
dr =  [True, False, False, False, True, True, True]
cpc = [809, 731, 588, 18, 200, 70, 45]

# Import pandas as pd


# Create dictionary my_dict with three key:value pairs: my_dict


# Build a DataFrame cars from my_dict: cars


# Print cars

Edit and Run Code