From the course: Data Analysis with Python and Pandas

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Pro tip: Transforming DataFrames

Pro tip: Transforming DataFrames

From the course: Data Analysis with Python and Pandas

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Pro tip: Transforming DataFrames

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- [Instructor] Alright, so, so far when we've been doing aggregation, part of that aggregation has included reducing the number of rows in our dataframe. And a lot of the time that's exactly what we want. We want a summary table. So when we think about grouping by the store numbers in our retail dataframe, we go from several hundred thousand rows of data down to 54, one row for each of the stores in our dataframe. But occasionally, we might want to generate aggregate statistics. So I might want to generate the mean sales of my store, but I want to compare that to the data in each row. So maybe I want to say, okay, how well did this store perform on this day versus that store's average? And with our current aggregation tools, we're unable to do that very easily. With the transform method, this is actually quite easy to do and it's super useful when we need to calculate group level statistics to perform a row level analysis, let's take a look. So here we're calling the assigned method…

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