![]() To make our plots, we need to create a summary DataFrame that contains the number of delays by cause (late aircraft, weather, security, or carrier issues) over time. We’ve also created an explicit datetime variable from the TimeLabel variable, which contains both the month and year. First, we’ll read our raw data: import pandas as pdĪirlines = pd.read_csv("data/airlines.csv")Īirlines = pd.to_datetime(airlines, infer_datetime_format=True)Īirlines = airlines.isin())]Īs 20 only have partial data for the year, we’ve removed them from the dataset. Let’s use a line plot to take a look at the total number of flight delays due to late aircraft across the whole period of the dataset. In order to show a trend, the y-axis then needs to contain a continuous variable, like the number of goods in stock, the price of an item, or a volume of water. This means that on the x-axis, you’ll use some sort of datetime variable – anything from milliseconds to years. Line plots are designed to demonstrate a trend over time. The code for this blog post can be found in this repo. This dataset contains information on flight delays and cancellations in US airports from 2003 to 2016. In this blog post, we’ll use the “Airline Delays from 2003–2016” dataset by Priank Ravichandar, licensed under CC0 1.0. In this series of blog posts, we’ll go over five of the most commonly used visualizations, and how they can help you tell your data’s story. ![]() However, when you first start using visualizations, it’s easy to get overwhelmed by the huge number of plots you can make. Data visualizations are one of the most powerful tools when exploring and presenting data.
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