Square Brackets (1)

In the video, you saw that you can index and select Pandas DataFrames in many different ways. The simplest, but not the most powerful way, is to use square brackets.

In the sample code, the same cars data is imported from a CSV files as a Pandas DataFrame. To select only the cars_per_cap column from cars, you can use:

cars['cars_per_cap']
cars[['cars_per_cap']]

The single bracket version gives a Pandas Series, the double bracket version gives a Pandas DataFrame.

This is a part of the course

“Intermediate Python”

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Exercise instructions

Use single square brackets to print out the country column of cars as a Pandas Series.
Use double square brackets to print out the country column of cars as a Pandas DataFrame.
Use double square brackets to print out a DataFrame with both the country and drives_right columns of cars, in this order.

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 country column as Pandas Series


# Print out country column as Pandas DataFrame


# Print out DataFrame with country and drives_right columns

Edit and Run Code

This exercise is part of the course

Intermediate Python

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Level up your data science skills by creating visualizations using Matplotlib and manipulating DataFrames with pandas.

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Chapter 1: Matplotlib

Data visualization is a key skill for aspiring data scientists. Matplotlib makes it easy to create meaningful and insightful plots. In this chapter, you’ll learn how to build various types of plots, and customize them to be more visually appealing and interpretable.

Exercise 1: Basic plots with Matplotlib Exercise 2: Line plot (1)Exercise 3: Line Plot (2): Interpretation Exercise 4: Line plot (3)Exercise 5: Scatter Plot (1)Exercise 6: Scatter plot (2)Exercise 7: Histogram Exercise 8: Build a histogram (1)Exercise 9: Build a histogram (2): bins Exercise 10: Build a histogram (3): compare Exercise 11: Choose the right plot (1)Exercise 12: Choose the right plot (2)Exercise 13: Customization Exercise 14: Labels Exercise 15: Ticks Exercise 16: Sizes Exercise 17: Colors Exercise 18: Additional Customizations Exercise 19: Interpretation

Chapter 2: Dictionaries & Pandas

Learn about the dictionary, an alternative to the Python list, and the pandas DataFrame, the de facto standard to work with tabular data in Python. You will get hands-on practice with creating and manipulating datasets, and you’ll learn how to access the information you need from these data structures.

Exercise 1: Dictionaries, Part 1 Exercise 2: Motivation for dictionaries Exercise 3: Create dictionary Exercise 4: Access dictionary Exercise 5: Dictionaries, Part 2 Exercise 6: Dictionary Manipulation (1)Exercise 7: Dictionary Manipulation (2)Exercise 8: Dictionariception Exercise 9: Pandas, Part 1 Exercise 10: Dictionary to DataFrame (1)Exercise 11: Dictionary to DataFrame (2)Exercise 12: CSV to DataFrame (1)Exercise 13: CSV to DataFrame (2)Exercise 14: Pandas, Part 2 Exercise 15: Square Brackets (1)

Exercise 16: Square Brackets (2)Exercise 17: loc and iloc (1)Exercise 18: loc and iloc (2)Exercise 19: loc and iloc (3)

Chapter 3: Logic, Control Flow and Filtering

Boolean logic is the foundation of decision-making in Python programs. Learn about different comparison operators, how to combine them with Boolean operators, and how to use the Boolean outcomes in control structures. You'll also learn to filter data in pandas DataFrames using logic.

Exercise 1: Comparison Operators Exercise 2: Equality Exercise 3: Greater and less than Exercise 4: Compare arrays Exercise 5: Boolean Operators Exercise 6: and, or, not (1)Exercise 7: and, or, not (2)Exercise 8: Boolean operators with NumPy Exercise 9: if, elif, else Exercise 10: Warmup Exercise 11: if Exercise 12: Add else Exercise 13: Customize further: elif Exercise 14: Filtering pandas DataFrames Exercise 15: Driving right (1)Exercise 16: Driving right (2)Exercise 17: Cars per capita (1)Exercise 18: Cars per capita (2)

Chapter 4: Loops

There are several techniques you can use to repeatedly execute Python code. While loops are like repeated if statements, the for loop iterates over all kinds of data structures. Learn all about them in this chapter.

Exercise 1: while loop Exercise 2: while: warming up Exercise 3: Basic while loop Exercise 4: Add conditionals Exercise 5: for loop Exercise 6: Loop over a list Exercise 7: Indexes and values (1)Exercise 8: Indexes and values (2)Exercise 9: Loop over list of lists Exercise 10: Loop Data Structures Part 1 Exercise 11: Loop over dictionary Exercise 12: Loop over NumPy array Exercise 13: Loop Data Structures Part 2 Exercise 14: Loop over DataFrame (1)Exercise 15: Loop over DataFrame (2)Exercise 16: Add column (1)Exercise 17: Add column (2)

Chapter 5: Case Study: Hacker Statistics

This chapter will allow you to apply all the concepts you've learned in this course. You will use hacker statistics to calculate your chances of winning a bet. Use random number generators, loops, and Matplotlib to gain a competitive edge!

Exercise 1: Random Numbers Exercise 2: Random float Exercise 3: Roll the dice Exercise 4: Determine your next move Exercise 5: Random Walk Exercise 6: The next step Exercise 7: How low can you go?Exercise 8: Visualize the walk Exercise 9: Distribution Exercise 10: Simulate multiple walks Exercise 11: Visualize all walks Exercise 12: Implement clumsiness Exercise 13: Plot the distribution Exercise 14: Calculate the odds

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