This article provides a step-by-step guide on how to analyze flight delays using PySpark. It covers concepts such as reading air traffic data, filtering delayed flights, getting delayed counts, and calculating the total number of flights and delayed flights.
Reading air traffic data
To read air traffic data, we use SparkSession to load the data from a specific path.
airtraffic_path = "/public/airtraffic_all/airtraffic-part/flightmonth=200801"
airtraffic = spark.read.parquet(airtraffic_path)
airtraffic.printSchema()
Get flights with delayed arrival
We can filter flights that have delayed arrival using SQL style or DataFrame style queries.
airtraffic.filter("IsArrDelayed = 'YES' AND Cancelled = 0").show()
Get delayed counts
To get the counts of delayed departures and arrivals, we use filter operations and count the instances.
Both Departure Delayed and Arrival Delayed
To calculate the total number of flights, departure delays, and arrival delays, we aggregate the data based on certain conditions.
Hands-On Tasks
- Read air traffic data from a specific path.
- Filter and display flights with delayed arrival.
- Calculate the delayed counts for departures and arrivals.
- Compute the total number of flights, departure delays, and arrival delays.
Conclusion
In this article, we have explored how to analyze flight delays using PySpark. By following the step-by-step instructions and practicing the hands-on tasks, readers can gain a better understanding of working with flight data. I encourage you to engage with the community for further learning and practice these concepts thoroughly.
Solution - Problem 1
Get the total number of flights as well as the number of flights delayed in departure and the number of flights delayed in arrival.
- Output contains 3 columns - FlightCount, DepDelayedCount, ArrDelayedCount.
You can utilize the provided code snippet in a PySpark environment to execute the solution.