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Interestingly, although EWR has the highest overall rate of delays, it has the lowest rate of carrier-related delays compared to other airports. Weather-related delays are more common in northeastern airports (EWR and JFK) compared to ones located in the western part of the country. Labels=)įor all airports, late aircraft are the biggest contributor to flight delays, and security-related delays are the least common. + scale_fill_brewer(type='qual', palette='Pastel1', name='Cause of delay',

+ ggtitle('Proportion of flights delayed by cause in US airports, 2004-2015')

+ geom_bar(stat='identity', position='dodge') (ĭelays_by_airport_and_cause.isin(Īes(x='AirportCode', y='PropFlightsDelayed', fill='TypeOfDelay') As with the previous barplot, we include AirportCode as the x-axis variable and PropFlightsDelayed as the y-axis variable, but this time we include TypeOfDelay under the argument fill, which tells lets-plot that we want to show separate bars for each of the delay reasons. Since there won’t be room to fit every airport on the chart, we’ll pick five airports: Salt Lake City, Newark, Denver (DEN), New York (JFK), and San Francisco (SFO). assign(PropTypeOfDelay=lambda x: x / x.groupby('AirportCode').transform('sum')) assign(PropFlightsDelayed=lambda x: x / x)

assign(TypeOfDelay=lambda x: x.str.replace('NumDelays', '')) 'NumDelaysSecurity', 'NumDelaysCarrier'], Value_vars=['NumDelaysLateAircraft', 'NumDelaysWeather', delays_by_airport_and_cause = (Īirlines[['AirportCode', 'NumDelaysLateAircraft', To make this plot, we need to first create another summary DataFrame, this time finding the proportion of flights that were delayed by the airport and the cause of the delay. For instance, we might want to know why flights are getting delayed for each airport. This can allow you to get further insight into why groups differ from each other. If you want to explore your data a bit more deeply, you can also group your barplots by an additional categorical variable. Salt Lake City (SLC) has only 15% of flights delayed, while a whopping 29% of flights at Newark (EWR) were delayed. Most airports have less than 20% of their flights delayed over the whole data period, but there are some clear outliers. This plot allows us to get a really good sense of how airports compare in terms of how many of their flights are delayed.

Download the new JetBrains DataSpell 2023.1.3