![]() Now we are interested only in dates and numbers. What happened in 2008? Did the number of flights decrease only in EWR? Let's find out by analyzing the whole dataset. The overall number of flights decreased significantly in the middle of 2008, which naturally resulted in fewer delays. # Decrease the density of ticks on x-axisĪx2.plot(date, total, '-', color='g', label='Total flights') Total = ewr_dataĪx.plot(date, cancelled, label='Cancellations')Īx.set_ylabel('Flights (delayed/cancelled)')Īx.set_title('Cancellations and delays in EWR 2003-2016') # Select rows with 'EWR' in the first columnĮwr_data = data='EWR']ĭelayed = ewr_dataĬancelled = ewr_data Let's start with selecting the necessary data from the dataset: It would be interesting to find out how the number of cancelled and delayed flights changed over time. Let's continue researching the data for this particular airport. You can see that the highest ratio of delayed flights was in the Newark Liberty International airport (EWR). # Create a figure and set its size to 15x5 in.Īx.bar(airport_code, delayed, bottom=cancelled, label='Delayed')Īx.bar(airport_code, cancelled, label='cancelled')Īx.set_title('Ratio of delayed and cancelled flights to total flights') You should have a notebook with the following cell:Īirport_data = airport_data.sort_values(by='Ratio Delayed.Total', ascending=False)ĭelayed = airport_dataĬancelled = airport_data This dataset contains the information on flight delays and cancellations in the US airports for the period of 2003-2016. In this tutorial, we will use the "Airline Delays from 2003-2016" dataset by Priank Ravichandar licensed under CC0 1.0. Otherwise, download the data set and add it to the DataSpell workspace as described in the section Add data to the workspace. If you have completed the previous tutorial, just proceed to Transform data. If you're using macOS or Linux, your computer already has Python installed. You have Python 3.6 or newer on your computer. Also, consider taking part in the DataSpell Early Access Program. You can download DataSpell and use all its features for free during a 30-day trial period. This tutorial was created in DataSpell 2022.2.1. While learning, you will perform the following tasks:įind out which US airport had the highest ratios of delayed and cancelled flights in 2003-2016. ![]() In this tutorial, you will learn to visualize data by using the Matplotlib library.
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