How to evaluate Categories instead of Books in pandas (Python)?

Question

I've completely reformulated my question. I am pretty sure makes way more sense now.

I have the following condition: logic1 & (logic2 | logic3) & logic4. All books of a Category should be flagged as Evaluated, once at least 1 book satisfies the condition. How can I do this for every CategoryID?

Logic CategoryEvaluated

logic1 = (books['CategoryPopulair'] == True)
logic2 = (books['BookTitle'] == 'Hello1') & (books['BookRead'] == True) & (books['BookNice'] == True)
logic3 = (books['BookTitle'].isin(['Hello2', 'Hello3'])) & (books['BookNice'] == True) & (books['BookOld'] == True)
logic4 = (books['BookYear'].isin([393, 1962, 1964, 3295]))

Input

| CategoryID | BookID | BookStatus | BookTitle | BookRead | BookNice | BookOld | BookYear | CategoryPopulair |
|------------|--------|------------|-----------|----------|----------|---------|----------|------------------|
| 337830     | 1      | 4          | Hello1    | TRUE     | TRUE     | TRUE    | 1964     | TRUE             |
| 337830     | 2      | 4          | Hello2    |          | TRUE     | TRUE    | 1964     | TRUE             |
| 337830     | 3      | 1          | Hello3    |          | TRUE     | TRUE    | 1964     | TRUE             |
| 337830     | 4      | 1          | Hello4    |          | TRUE     | TRUE    | 1964     | TRUE             |
| 337830     | 5      | 1          | Hello5    |          | TRUE     | FALSE   | 1964     | TRUE             |
| 337830     | 6      | 1          | Hello6    |          | TRUE     | FALSE   | 1964     | TRUE             |
| 337830     | 7      | 1          | Hello7    |          | TRUE     | TRUE    | 1964     | TRUE             |
| 337830     | 8      | 1          | Hello8    |          | TRUE     | FALSE   | 1964     | TRUE             |
| 337830     | 9      | 1          | Hello9    |          | TRUE     | TRUE    | 1964     | TRUE             |
| 337830     | 10     | 1          | Hello10   |          | TRUE     | FALSE   | 1964     | TRUE             |
| 337830     | 11     | 1          | Hello11   |          | TRUE     | TRUE    | 1964     | TRUE             |
| 337830     | 12     | 1          | Hello12   |          | TRUE     | TRUE    | 1964     | TRUE             |

| 331232     | 1      | 4          | Hello1    | TRUE     | TRUE     | TRUE    | 1964     | TRUE             |
| 331232     | 2      | 4          | Hello2    | TRUE     | TRUE     | TRUE    | 1964     | TRUE             |
| 331232     | 13     | 1          | Hello13   |          | TRUE     | TRUE    | 1964     | TRUE             |
| 331232     | 14     | 1          | Hello14   |          | TRUE     | TRUE    | 1964     | TRUE             |
| 331232     | 5      | 1          | Hello5    |          | TRUE     | FALSE   | 1964     | TRUE             |
| 331232     | 6      | 1          | Hello6    |          | TRUE     | FALSE   | 1964     | TRUE             |
| 331232     | 15     | 1          | Hello15   |          | TRUE     | TRUE    | 1964     | TRUE             |
| 331232     | 8      | 1          | Hello8    |          | TRUE     | FALSE   | 1964     | TRUE             |
| 331232     | 9      | 1          | Hello9    |  TRUE    | TRUE     | TRUE    | 1964     | TRUE             |
| 331232     | 10     | 1          | Hello10   |          | TRUE     | FALSE   | 1964     | TRUE             |
| 331232     | 17     | 1          | Hello17   |  TRUE    | TRUE     | TRUE    | 1964     | TRUE             |
| 331232     | 16     | 1          | Hello16   |          | TRUE     | TRUE    | 1964     | TRUE             |

Output I need (see column CategoryEvaluated)

FALSE in column BookOld of BookID 2 with CategoryID 331232 causes CategoryEvaluated to be FALSE.

| CategoryID | BookID | BookStatus | BookTitle | BookRead | BookNice | BookOld | BookYear | CategoryPopulair | CategoryEvaluated |
|------------|--------|------------|-----------|----------|----------|---------|----------|------------------|-------------------|
| 337830     | 1      | 4          | Hello1    | TRUE     | TRUE     | TRUE    | 1964     | TRUE             | TRUE              |
| 337830     | 2      | 4          | Hello2    |          | TRUE     | TRUE    | 1964     | TRUE             | TRUE              |
| 337830     | 3      | 1          | Hello3    |          | TRUE     | TRUE    | 1964     | TRUE             | TRUE              |
| 337830     | 4      | 1          | Hello4    |          | TRUE     | TRUE    | 1964     | TRUE             | TRUE              |
| 337830     | 5      | 1          | Hello5    |          | TRUE     | FALSE   | 1964     | TRUE             | TRUE              |
| 337830     | 6      | 1          | Hello6    |          | TRUE     | FALSE   | 1964     | TRUE             | TRUE              |
| 337830     | 7      | 1          | Hello7    |          | TRUE     | TRUE    | 1964     | TRUE             | TRUE              |
| 337830     | 8      | 1          | Hello8    |          | TRUE     | FALSE   | 1964     | TRUE             | TRUE              |
| 337830     | 9      | 1          | Hello9    |          | TRUE     | TRUE    | 1964     | TRUE             | TRUE              |
| 337830     | 10     | 1          | Hello10   |          | TRUE     | FALSE   | 1964     | TRUE             | TRUE              |
| 337830     | 11     | 1          | Hello11   |          | TRUE     | TRUE    | 1964     | TRUE             | TRUE              |
| 337830     | 12     | 1          | Hello12   |          | TRUE     | TRUE    | 1964     | TRUE             | TRUE              |

| 331232     | 1      | 4          | Hello1    | TRUE     | TRUE     | TRUE    | 1964     | TRUE             | FALSE             |
| 331232     | 2      | 4          | Hello2    | TRUE     | TRUE     |->FALSE<-| 1964     | TRUE             | FALSE             |
| 331232     | 13     | 1          | Hello13   |          | TRUE     | TRUE    | 1964     | TRUE             | FALSE             |
| 331232     | 14     | 1          | Hello14   |          | TRUE     | TRUE    | 1964     | TRUE             | FALSE             |
| 331232     | 5      | 1          | Hello5    |          | TRUE     | FALSE   | 1964     | TRUE             | FALSE             |
| 331232     | 6      | 1          | Hello6    |          | TRUE     | FALSE   | 1964     | TRUE             | FALSE             |
| 331232     | 15     | 1          | Hello15   |          | TRUE     | TRUE    | 1964     | TRUE             | FALSE             |
| 331232     | 8      | 1          | Hello8    |          | TRUE     | FALSE   | 1964     | TRUE             | FALSE             |
| 331232     | 9      | 1          | Hello9    |  TRUE    | TRUE     | TRUE    | 1964     | TRUE             | FALSE             |
| 331232     | 10     | 1          | Hello10   |          | TRUE     | FALSE   | 1964     | TRUE             | FALSE             |
| 331232     | 17     | 1          | Hello17   |  TRUE    | TRUE     | TRUE    | 1964     | TRUE             | FALSE             |
| 331232     | 16     | 1          | Hello16   |          | TRUE     | TRUE    | 1964     | TRUE             | FALSE             |

You should really make this example [reproducible](https://stackoverflow.com/questions/20109391/how-to-make-good-reproducible-pandas-examples). — cmaher, Apr 04 '18 at 15:10
I suspect you wouldn't like to get the answer as an image so don't post images yourself. Please add example of input and desired output. — zipa, Apr 04 '18 at 15:11
I basically want to do `worlds['BlaIsC'] = logic1 & (logic2 | logic3) & logic4` per `BlaID` instead of every row. — konichiwa, Apr 04 '18 at 15:12
I wish the first paragraph of the question was bit more clear. — Shridhar R Kulkarni, Apr 04 '18 at 15:12
I will try to improve my question. Sorry for confusing you guys... — konichiwa, Apr 04 '18 at 15:12
have you looked at the [`any()`](https://docs.python.org/2/library/functions.html#any)? — MattR, Apr 04 '18 at 15:26
May be you could implement this using Pandas apply function. Here is an [example](https://stackoverflow.com/questions/16353729/pandas-how-to-use-apply-function-to-multiple-columns#16354730) — Avikd, Apr 04 '18 at 15:52

RCA · Accepted Answer · 2018-04-05T20:06:08.890

1

Edited to answer your question better.

Because any() can be kind of tricky, I tend to create a boolean column and then evaluate that per group. You'll do all your logic per book, then check if any book satisfied the criteria.

books['FitsLogic'] = logic1 & (logic2 | logic3) & logic4
books['CategoryEvaluted'] = books['FitsLogic'].groupby(books['CategoryID']).transform(
                               lambda x: True if any(x) else False)

edited Apr 05 '18 at 20:06

answered Apr 04 '18 at 17:32

RCA

508
4
12

Where do I put my logic? – konichiwa Apr 05 '18 at 17:57
This helped me out. Thank you so much! – konichiwa Apr 06 '18 at 14:54
No problem! This is an issue I have had to deal with many a time. – RCA Apr 06 '18 at 15:03

How to evaluate Categories instead of Books in pandas (Python)?

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