How do I capture all complying values using a mask in Pandas?

Question

Value_counts performed in one specific data frame column shows visually that there are 441 values lower than 10. When I run a mask (boolean indexing) in order to access those values it only gets 12 of the 441.

I thought it was a datatype issue. However, right before the operations above I changed the column data type to float using the astype function. There should not be an issue with data type.

Can you please provide us with a sample data similar to what you're dealing with? — ashkangh, Mar 12 '21 at 15:31
https://drive.google.com/file/d/1fuFEIKSohXGKQWLLq9zeXRtVXVlzwlDG/view?usp=sharing — Manuel A. Albornoz, Mar 12 '21 at 18:02
https://drive.google.com/file/d/1ptdiYzF6W5I1S3e1FFSlCGxNfjT-t9Jk/view?usp=sharing — Manuel A. Albornoz, Mar 12 '21 at 18:02
I don't quite understand what you're trying to do. A little explanation or creating a small sample data, and also your expected output would help a lot! — ashkangh, Mar 12 '21 at 18:11
Thanks!. Here is the data https://drive.google.com/open?id=1cgo9Y9FPUGAhUpMzat-inH_zP54PFpQ7 — Manuel A. Albornoz, Mar 12 '21 at 18:16
I am trying to capture all rating_numerator values below 10...It shows 441 (visually) in the value_counts but using the mask <'10' it only captures 12 corresponding to 1, 0 and 007. — Manuel A. Albornoz, Mar 12 '21 at 18:28

score 0 · Answer 1 · answered Mar 12 '21 at 21:33

0

I used the function astype(float) all along for masking and value_counts operations. This catches all the numeric values complying the filter and not only a few str values present in the data.

answered Mar 12 '21 at 21:33

Manuel A. Albornoz

1

How do I capture all complying values using a mask in Pandas?

1 Answers1