For our example, we’ll use a CSV and read in our data that way. Pandas is able to read several different types of stored data, including CSVs (comma separated values), TSVs (tab separated values), JSONs (JavaScript Object Notation, HTML (Hypertext Markup Language), among others. We’ll use a dataset from the online version of “An Introduction to Statistical Learning with Applications in R”, which datasets can be found here. Once you’ve imported the library, let’s import a dataset so we can begin to look at pandas and its functionality. While you don’t need to import pandas using an alias, it helps to use the alias so you can use pd.command rather than typing out mand every time you need to call a method or property. Below is the commonly used shortcut for pandas. You can find more information on installing pandas here. Once you’ve installed pandas, the first step is importing it.
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