• Series vs Dataframes
• Data exploration
• Data Cleaning (null values, outliers, data format conversion, duplicate rows)
• Data Manipulation (slicing, reindexing, groupby)
• Data Analysis (cross tabulation,
• Exercises with Solutions at the end

First refer to the misc notebook to get a general grasp on the pandas concepts. Then download the 2018 arrivals csv & 2019 arrivals csv files to use for practice our pandas commands in our airline arrivals data analysis notebook.

This workshop uses a dataset from the Bureau of Transportation Statistics containing 41,177 records of airline arrival delays for 18 airlines from 2018 and 2019. Each row represents a summary of delay data for a carrier-airport pair for the specified month.

Towards the end of the airline arrivals data analysis notebook, there are a list of exercises to complete, with solutions provided.