Equivalent of R's tapply() in Python Pandas

Question

I have a dataset that contains the feeding data of 3 animals, consisting of the animals' tag ids (1,2,3), the type (A,B) and amount (kg) of feed given at each 'meal':

Animal   FeedType   Amount(kg)
Animal1     A         10
Animal2     B         7
Animal3     A         4
Animal2     A         2
Animal1     B         5
Animal2     B         6
Animal3     A         2

In base R, I can easily output the matrix below which has unique('Animal') as its rows, unique('FeedType') as its columns and the cumulative Amount (kg) in the corresponding cells of the matrix by using tapply() as below

out <- with(mydf, tapply(Amount, list(Animal, FeedType), sum))

         A  B
Animal1 10  5
Animal2  2 13
Animal3  6 NA

Is there an equivalent functionality for a Python Pandas dataframe? What is the most elegant and fastest way to achieve this in Pandas?

P.S. I want to be able to specify on what column, in this case Amount, to perform the aggregation.

Thanks in advance.

EDIT:

I tried both approaches in the two answers. Performance results with my actual Pandas data-frame of 216,347 rows and 15 columns:

start_time1 = timeit.default_timer()
mydf.groupby(['Animal','FeedType'])['Amount'].sum()
elapsed_groupby = timeit.default_timer() - start_time1

start_time2 = timeit.default_timer()
mydf.pivot_table(rows='Animal', cols='FeedType',values='Amount',aggfunc='sum')
elapsed_pivot = timeit.default_timer() - start_time2

print ('elapsed_groupby: ' + str(elapsed_groupby))
print ('elapsed_pivot: ' + str(elapsed_pivot))

gives:

elapsed_groupby: 10.172213
elapsed_pivot: 8.465783

So in my case, pivot_table() works faster.

Answer 1

First I read in your data:

In [7]: df = pd.read_clipboard(sep="\s+", index_col=False)

In [8]: df
Out[8]:
    Animal FeedType  Amount(kg)
0  Animal1        A          10
1  Animal2        B           7
2  Animal3        A           4
3  Animal2        A           2
4  Animal1        B           5
5  Animal2        B           6
6  Animal3        A           2

Then I can groupby the two columns to aggregate:

In [9]: df.groupby(['Animal','FeedType']).sum()
Out[9]:
                  Amount(kg)
Animal  FeedType
Animal1 A                 10
        B                  5
Animal2 A                  2
        B                 13
Animal3 A                  6

To get it in the same format, I can unstack the dataframe:

In [10]: df.groupby(['Animal','FeedType']).sum().unstack()
Out[10]:
          Amount(kg)
FeedType           A   B
Animal
Animal1           10   5
Animal2            2  13
Animal3            6 NaN

Answer 2

The approach of @Zelazny7 with groupby and unstack is certainly fine, but for completeness, you can also do this directly with pivot_table (see doc) [version 0.13 and below]:

In [13]: df.pivot_table(rows='Animal', cols='FeedType', values='Amount(kg)', aggfunc='sum')
Out[13]:
FeedType   A   B
Animal
Animal1   10   5
Animal2    2  13
Animal3    6 NaN

In newer versions of Pandas (version 0.14 and latter), arguments of pivot_table have been changed:

In [13]: df.pivot_table(index='Animal', columns='FeedType', values='Amount(kg)', aggfunc='sum')
Out[13]:
FeedType   A   B
Animal
Animal1   10   5
Animal2    2  13
Animal3    6 NaN