FD tool is written in python. It is clear that the library is not supporte by its creators. Whats frustrating I decide to write my own version of the bike which would make architectural analysis more convenient. The code is poste on github . Below in the case I will show the use of the tool on a real table. git clone from Flat able Analysis. Flat able Analysis import Case study of table analysis from huggingface As a table I took a dataset of music tracks from huggingface link . Its already in a nice flat form so we can start analyzing right away bypassing the boring ETL stage. Colab with code here. It contains table analysis using my Python class Flat Table Analysis.
Keyboards are the main working
Below I will describe the main findings. As an experiment you can look at the table header yourself and think about how you would do a review analysis. Table header Primary key what does the tool give us The first question is what is the primary key. No single column Israel Telegram Number Data ensures that all rows are unique rows. And the combination of two columns trackeid trackegenre ensures uniqueness. In general this is strange one would think that trackeid would be the primary key. But about thousand tracks have or more genres. Functional dependencies what does the tool give us.
A platform for implementing
This is actually interesting. From the graphs you can see that trackeid functionally defines almost all columns except trackegenre of course. The only exception is the populatiry column . tracks have two popularity the remaining thousand have exactly one. This nees to be Poland Whatsapp Number List explore further. Looks like theres a data error. It would be logical to assume that a track always has the same popularity. Another interesting situation with the explicit column . This is a binary flag indicating that the track contains info offensive or inappropriate for children at least thats what perplexity.ai says . If we lower the threshold for displaying functional dependencies to.
