Feature Engineer

Changing data types of DataFrame columns to smaller data types can significantly reduce the memory usage of the DataFrame.
Instead of manually choosing smaller data types, is there a way that you can automatically change data types in one line of code?
That is when the df_shrink method of fastai comes in handy. In the code above, the memory usage of the DataFrame decreases from 200 bytes to 146 bytes,
Learn more about df_shrink here.
Link to the source code.
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When working with time series, other features such as year, month, week, day of the week, day of the year, whether it is the end of the year or not, can be really helpful to predict future events. Is there a way that you can get all of those features in one line of code?
Fastai’s add_datepart method allows you to do exactly that. Find an example of how to use this method above.
Link to Fastai.
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If you work with location data, you might want to visualize them on the map. Geopy makes it easy to locate the coordinates of addresses across the globe based on a Python string.
After defining the app name and insert location, all you need to exact information about the location is to use location.address.
To extract the latitude and longitude or the use location.latitide, location.longitude.
Above is an example. Try it with any address that you can think of. Geopy will figure it out.
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If you want to decompose high dimensional data into two or three dimensions to visualize it, what should you do? A common technique is PCA. Even though PCA is useful, I always find it complicated to create a PCA plot until I found this doc in Yellowbrick.
I really recommend using this tool if you want to visualize PCA in a few lines of code
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