Plot categorical data #100
Conversation
manycoding
requested review from
raphapassini,
ejulio,
victor-torres and
ivankivanov
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@victor-torres @ejulio @raphapassini please check the layout and how do you like it. |
Codecov Report
@@ Coverage Diff @@
## master #100 +/- ##
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+ Coverage 78.45% 78.98% +0.53%
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Files 23 23
Lines 1564 1599 +35
Branches 273 280 +7
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+ Hits 1227 1263 +36
Misses 290 290
+ Partials 47 46 -1
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How will it look per column? There's
What is the use case here? This can be achieved with pandas. |
the use case would be: If you have products nested in different categories - to check distribution for the other fields. Let say category food. Check what is the categorical data among other fields. This will give you the ability to dive deeper in the data. For the first point - I didn't know for this option. Thanks |
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I just made an update introducing
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Percentages (distribution) or numbers as shown above? |
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That's it, please review. |
src/arche/rules/result.py
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| """ | ||
| data = [] | ||
| for vc in values_counts: | ||
| data.extend( |
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Maybe a nested for loop would read better
| """ | ||
| result = Result("Categories") | ||
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| result.stats = [ |
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If there's not a cache, then you're calculating value_counts twice here.
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Possible solution:
result.stats = map(lambda c: df[c].value_counts(dropna=False), df)
result.stats = [vc for vc in result.stats if len(vc) <= max_uniques]
This one implements categorial and enum (almost) task and doesn't require any schema.
Basically I plot any categorical data on one graph. 1 bar per field, and each bar shows the distribution of unique values. A categorical data is any column with < 11 (default for
max_uniques) unique values.This rule can be used separatedly, where one can specify
max_uniques.I replaced the colours, initially it looked like:
