Unification of data training methods

Following my recent posts, in which I showed basic functions and methods of working with datasets available in sklearn and seaborn, I was thinking about the summary in the form of a universal data handling program. Designed in a way, where user can choose the source to download the dataset, then indicate the data to work with on and finally, by selecting appropriate columns, perform basic calculations to finally present it in…

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My first dataset

Steps of creation List elements from catalog: If we want to list songs or videos we have to install mutagen lib: for images we will use Pillow lib: #Enter the music catalog to get songs names #Enter the music catalog and read: band names, album names and songs names Export as TXT and an Excel file

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Dive in Datasets

Exercises on ready-made datasets available in Python libraries such as scikit learn or seaborn are more enjoyable the better we understand the data we are working with. I made some examples with the dataset I have chosen for today’s lesson. dataset: taxis dataset: diamonds ops on diamonds: count, drop column, pivot table

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