Workbook¶

Use this notebook to complete the exercises throughout the workshop.

Table of Contents¶

• Section 1 – Getting Started with Pandas
• Section 2 – Data Wrangling
• Section 3 – Data Visualization

Section 1¶

Exercise 1.1¶

Create a DataFrame by reading in the 2019_Yellow_Taxi_Trip_Data.csv file. Examine the first 5 rows.¶

Exercise 1.2¶

Find the dimensions (number of rows and number of columns) in the data.¶

Exercise 1.3¶

Using the data in the 2019_Yellow_Taxi_Trip_Data.csv file, calculate summary statistics for the fare_amount, tip_amount, tolls_amount, and total_amount columns.¶

Exercise 1.4¶

Isolate the fare_amount, tip_amount, tolls_amount, and total_amount for the longest trip by distance (trip_distance).¶

Section 2¶

Exercise 2.1¶

Read in the meteorite data from the Meteorite_Landings.csv file, rename the mass (g) column to mass, and drop all the latitude and longitude columns. Sort the result by mass in descending order.¶

Exercise 2.2¶

Using the meteorite data from the Meteorite_Landings.csv file, update the year column to only contain the year, convert it to a numeric data type, and create a new column indicating whether the meteorite was observed falling before 1970. Set the index to the id column and extract all the rows with IDs between 10,036 and 10,040 (inclusive) with loc[].¶

Hint 1: Use year.str.slice() to grab a substring.¶

Hint 2: Make sure to sort the index before using loc[] to select the range.¶

Bonus: There's a data entry error in the year column. Can you find it? (Don't spend too much time on this.)¶

Exercise 2.3¶

Using the meteorite data from the Meteorite_Landings.csv file, create a pivot table that shows both the number of meteorites and the 95th percentile of meteorite mass for those that were found versus observed falling per year from 2005 through 2009 (inclusive). Hint: Be sure to convert the year column to a number as we did in the previous exercise.¶

Exercise 2.4¶

Using the meteorite data from the Meteorite_Landings.csv file, compare summary statistics of the mass column for the meteorites that were found versus observed falling.¶

Exercise 2.5¶

Using the taxi trip data in the 2019_Yellow_Taxi_Trip_Data.csv file, resample the data to an hourly frequency based on the dropoff time. Calculate the total trip_distance, fare_amount, tolls_amount, and tip_amount, then find the 5 hours with the most tips.¶

Section 3¶

Exercise 3.1¶

Using the TSA traveler throughput data in the tsa_melted_holiday_travel.csv file, create box plots for traveler throughput for each year in the data. Hint: Pass kind='box' into the plot() method to generate box plots.¶

Exercise 3.2¶

Using the TSA traveler throughput data in the tsa_melted_holiday_travel.csv file, create a heatmap that shows the 2019 TSA median traveler throughput by day of week and month.¶

Exercise 3.3¶

Annotate the medians in the box plot from Exercise 3.1. Hint: The x coordinates will be 1, 2, and 3 for 2019, 2020, and 2021, respectively. Alternatively, to avoid hardcoding values, you can use the Axes.get_xticklabels() method, in which case you should look at the documentation for the Text class.¶