Python: Counting Cumulative Occurrences Of Values In A Pandas Series

I have a DataFrame that looks like this: fruit 0 orange 1 orange 2 orange 3 pear 4 orange 5 apple 6 apple 7 pear 8 pear 9 orange I want to add a column that

Solution 1:

You could use groupby and cumcount:

df['cum_count'] = df.groupby('fruit').cumcount() + 1

In [16]: df
Out[16]:
    fruit  cum_count
0  orange          1
1  orange          2
2  orange          3
3    pear          1
4  orange          4
5   apple          1
6   apple          2
7    pear          2
8    pear          3
9  orange          5

Timing

In [8]: %timeit [(df.fruit[0:i+1] == x).sum() for i, x in df.fruit.iteritems()]
100 loops, best of 3: 3.76 ms per loop

In [9]: %timeit df.groupby('fruit').cumcount() + 11000 loops, best of 3: 926 µs per loop

So it's faster in 4 times.

Solution 2:

Maybe better is use groupby with cumcount with specify column, because it is more efficient way:

df['cum_count'] = df.groupby('fruit' )['fruit'].cumcount() + 1
printdf

    fruit  cum_count
0  orange          1
1  orange          2
2  orange          3
3    pear          1
4  orange          4
5   apple          1
6   apple          2
7    pear          2
8    pear          3
9  orange          5

Comparing len(df) = 10, my solution is the fastest:

In [3]: %timeit df.groupby('fruit')['fruit'].cumcount() + 1
The slowest run took 11.67 times longer than the fastest. This could mean that an intermediate result is being cached 
1000 loops, best of 3: 299 µs per loop

In [4]: %timeit df.groupby('fruit').cumcount() + 1
The slowest run took 12.78 times longer than the fastest. This could mean that an intermediate result is being cached 
1000 loops, best of 3: 921 µs per loop

In [5]: %timeit [(df.fruit[0:i+1] == x).sum() for i, x in df.fruit.iteritems()]
The slowest run took 4.47 times longer than the fastest. This could mean that an intermediate result is being cached 
100 loops, best of 3: 2.72 ms per loop

Comparing len(df) = 10k:

In [7]: %timeit df.groupby('fruit')['fruit'].cumcount() + 1
The slowest run took 4.65 times longer than the fastest. This could mean that an intermediate result is being cached 
1000 loops, best of 3: 845 µs per loop

In [8]: %timeit df.groupby('fruit').cumcount() + 1
The slowest run took 5.59 times longer than the fastest. This could mean that an intermediate result is being cached 
100 loops, best of 3: 1.59 ms per loop

In [9]: %timeit [(df.fruit[0:i+1] == x).sum() for i, x in df.fruit.iteritems()]
1 loops, best of 3: 5.12 s per loop