Flights

2023 U.S. Flights Dataset
Analyzed with a Tableau Dashboard

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Flights

2023 U.S. Flights dataset contains information about all the flights that happened in the U.S. territory from January to December 2023.

The main objective of this project is to create a Tableau dashboard to analyze the information retrieved.

Dataset information

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The dataset is composed by 4 different subsets (CSV formatted files):

• airlines: Contains all the information about the airlines that flew during the 2023 between two airports in the U.S. territory.
• airports: All the U.S. commercial airports during the 2023
• delay_reasons: Specifies the different types of delay that the flights suffered during the January-December 2023 period
• flights: The information about each flight. For example: flight time, expected departure and arrive time vs real departure and arrive time, delay information, cancelled or diverted flight, etc.

Where was the dataset information obtained from?

The Bureau of Transportation Statistics of the government of the United States, has a lot of information that could be used to create our own dataset.

I retrieved the information of the subsets flights, airports and airlines from that page. You can find them inside the raw folder of this repository.

The delay_reasons subset was created from the flights subset, after analyzing the 3 subsets.

Analysis

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Step 1:

• Run a dataset profiling. ydata_profiling was used to achieve this task and obtain valuable information about the different subsets.
  • The script to run that process is create_profiling.py

Step 2:

With the information retrieved and with the objective of "creating a Tableau Dashboard" in mind, the next step was to remove the unnecessary data.

• This part is the most important because that data will be then converted into information with the visualization of the Dashboard, and there was a lot of things that were not necessary or the format was not the one I needed.
• Imported the airlines and airports datasets into a Google Sheets document, and using the script inspect_flights_subset.py, I completed the subsets, adding the missing airlines and airports.

Step 3:

Lastly but not less important, the flights subset was cleaned up.

• Imported a sample of the January CSV, analyzed which columns added value to the future Tableau Dashboard, and which of them should be renamed, deleted or transformed.
• Expanded the FL_DATE column into 3 columns DAY, MONTH and YEAR (I could have download that data directly from the bts.gov page but as each download took a lot of time and I had that information in another column, I decided not to do that, and use the one I had to obtain the same result), and removed the hour because it was always 12:00:00
• Removed the unnecessary columns
• Created the delay_reasons subset to reduce the information of delays to 2 columns instead of 5 (in the flights subset).
• Created the script clean_flights_subset.py to do this whole process

After all this analysis, the new subsets were created in the 'processed' folder.

This process helped me to reduce the flights subset size in MB, to the half.

Relational model diagram

---
title: Entity Relationship Diagram
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erDiagram
    FLIGHTS ||--|{ AIRLINES : ""
    FLIGHTS ||--|{ AIPORTS : ""
    FLIGHTS ||--|{ DELAY_REASON : ""
    FLIGHTS {
        INTEGER DAY "CK"  
        INTEGER MONTH "CK"       
        INTEGER YEAR "CK" 
        INTEGER DAY_OF_WEEK "Day of the week, being Sunday = 0"
        VARCHAR(2) CARRIER FK "CK"  
        VARCHAR(3) ORIGIN FK "CK"  
        VARCHAR(3) DEST FK "CK"     
        INTEGER CRS_DEP_TIME "Computerized reservation system (CRS) Departure time (local time: hhmm)"
        INTEGER DEP_TIME "Actual Departure Time (local time: hhmm)"
        INTEGER DEP_DELAY "Difference in minutes between scheduled and actual departure time. Early departures show negative numbers"
        INTEGER TAXI_OUT "Taxi Out Time, in Minutes"   
        INTEGER TAXI_IN "Taxi In Time, in Minutes"
        INTEGER CRS_ARR_TIME "Computerized reservation system (CRS) Arrival Time (local time: hhmm)"
        INTEGER ARR_TIME "Actual Arrival Time (local time: hhmm)"
        INTEGER ARR_DELAY "Difference in minutes between scheduled and actual arrival time. Early arrivals show negative numbers" 
        INTEGER AIR_TIME "Time lenght between TAXI_IN and TAXI_OUT"  
        BOOLEAN CANCELLED "0 if the flight wasn't cancelled, 1 if the flight was cancelled"  
        BOOLEAN DIVERTED "0 if the flight wasn't diverted, 1 if the flight was diverted"    
        INTEGER DELAY "Delay time in minutes. Empty if the flight was on time"       
        INTEGER DELAY_REASON FK "Delay reason ID. Empty if the flight was on time"
    }
    AIRLINES {
        VARCHAR(2) AIR_CARRIER_IATA_CODE PK "IATA airline code (2 characters)"
        VARCHAR(30) AIR_CARRIER_NAME "Airline name"
    }
    AIPORTS {
        VARCHAR(3) AIRPORT_IATA_CODE PK "IATA airport code (3 characters)"
        VARCHAR(150) AIRPORT_NAME "Airport name"
        VARCHAR(50) AIRPORT_CITY "City where the airport is located"
        VARCHAR(2) AIRPORT_STATE "State where the airport is located (2 characters)"
        FLOAT(7) AIRPORT_LATITUDE "Airport latitude"
        FLOAT(7) AIRPORT_LONGITUDE "Airport longitude"
        VARCHAR(100) AIRPORT_COUNTRY "Country where the airport is located"
    }
    DELAY_REASON {
        INTEGER DELAY_ID PK "Delay ID"
        VARCHAR(30) DELAY_CAUSE "Delay cause"
    }

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Tableau Dashboard

You can find the Tableau Workbook inside the tableau folder.

That workbook contains two dashboards that can be used a storytelling of the analysis of the 2023 U.S. Flights dataset.

It is also online, check it up here.

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License

MIT

Logo License

Author credit: 58pic from PNGTree