Once you have tidy data, a common first step is to transform it. Tidy data is important because the consistent structure lets you focus your struggle on questions about the data, not fighting to get the data into the right form for different functions. In brief, when your data is tidy, each column is a variable, and each row is an observation. Tidying your data means storing it in a consistent form that matches the semantics of the dataset with the way it is stored. Once you’ve imported your data, it is a good idea to tidy it. If you can’t get your data into Python, you can’t do data science on it! This typically means that you take data stored in a file, database, or web API, and load it into a data frame in Python. Our model of the tools needed in a typical data science project looks something like this:įirst you must import your data into Python. The goal of this book is to give you a foundation in the essential tools.
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