4. Data wrangling – Football Analytics with Python & R [Book]

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Notice that schedule is on the left. For example, often you want to adjust the results of a play – say the number of passing yards – by the weather in which the game was played. When working with the numpy arrays, we need to use the where function, but this logic will be the same and use similar notation with Pandas later in this chapter. Whereas an outer join may possibly grow data frames, an inner join shrinks data frames. We needed to rename columns to avoid duplicate names later and also keep column names clear.


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The left dataset is the one on the left or the first datasets and the right dataset is the one on the por or the second dataset.


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Italic type indicates book titles, emphasis, or placeholder variables Download the Oracle Linux ISO from one of the following websites. replace.me, sony vegas pro 9 download free full version free free, adobe illustrator cc 5 無料ダウンгѓгѓјгѓ‰book collector pro 3. Selection from Football Analytics with Python & R [Book] But, professional data analysis requires the us of code to wrangle data.

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