在Python熊猫数据框中对唯一值进行分组和计数

Question

I have a dataframe of over 33,000 rows which I'd like to simplify:

                   Crime type
GeographyCode                              
E01006687          Burglary
E01007229          Anti-social behaviour
E01007229          Anti-social behaviour
E01007229          Anti-social behaviour
E01007229          Burglary
E01007229          Other theft
E01007229          Other theft
E01007229          Shoplifting
E01007229          Theft from the person
E01007230          Anti-social behaviour
E01007230          Anti-social behaviour
E01007230          Anti-social behaviour
E01007230          Anti-social behaviour
E01007230          Anti-social behaviour
E01007230          Anti-social behaviour
...

There are 207 unique values of 'GeographyCode' and 12 unique values of 'Crime type'.

I'd like to make a new dataframe which has 207 rows and 12 columns plus the 'GeographyCode' index column, with each column representing a crime type, and containing a count of all occurances of that crime type within the GeographyCode.

Something like this:

                Burglary   Anti-social    Theft   Shoplifting   etc...
GeographyCode
E01006687       1          3              9       5             ...
E01007229       1          3              2       1             ...
E01007230       0          6              12      5             ...
...

I've tried a few things, but because there are no numeric values I'm finding it really difficult to get what I need.

Answer 1

You could use crosstab to compute this:

>>> pd.crosstab(df.index, df['Crime type'])
Crime type      Anti-social behaviour  Burglary  Other theft  Shoplifting  ...

E01006687                           0         1            0            0
E01007229                           3         1            2            1
E01007230                           6         0            0            0