Pandas row count given another column contains a certain value

Question

I'm brand new to pandas and I have a csv file which i have read in with csv_read with pandas eg. I have read it in as b = pandas.read_csv('BLA.csv')

|  Gender  |  ID   |
|     M    |   D   |
|     F    |   P   |
|     M    |   P   |
|     F    |   D   |
|     M    |   D   |
|     F    |   D   |

how do I go about counting the number of occurrences of the Genders given the ID status of the person is D?

Sorry are you asking for `df.loc[df['ID']=='D','Gender'].value_counts()`? — EdChum, Apr 22 '16 at 12:27
@ Kerjifire, welcome to stackoverflow. I think this is a duplicate question, see similar question [here](http://stackoverflow.com/questions/20995196/python-pandas-counting-and-summing-specific-conditions) — Manohar Swamynathan, Apr 22 '16 at 12:40
yeah I want the data to show that M has 2 occurrences and F has 2 in this example — Kerjifire, Apr 22 '16 at 12:41
This is really more a dupe of this: http://stackoverflow.com/questions/22391433/count-the-frequency-that-a-value-occurs-in-a-dataframe-column/22391554#22391554 but with a filter criteria — EdChum, Apr 22 '16 at 12:45

score 0 · Answer 1 · answered Apr 25 '16 at 08:22

0

Try this:

b['tmp'] = 1
b.groupby(['Gender', 'ID']).count().query('ID == "D"')

answered Apr 25 '16 at 08:22

Shovalt

1 Answers1