"Reduce" function for Series
Question:
Is there an analog for reduce for a pandas Series?
For example, the analog for map is pd.Series.apply, but I can’t find any analog for reduce.
My application is, I have a pandas Series of lists:
>>> business["categories"].head()
0 ['Doctors', 'Health & Medical']
1 ['Nightlife']
2 ['Active Life', 'Mini Golf', 'Golf']
3 ['Shopping', 'Home Services', 'Internet Servic...
4 ['Bars', 'American (New)', 'Nightlife', 'Loung...
Name: categories, dtype: object
I’d like to merge the Series of lists together using reduce, like so:
categories = reduce(lambda l1, l2: l1 + l2, categories)
but this takes a horrific time because merging two lists together is O(n) time in Python. I’m hoping that pd.Series has a vectorized way to perform this faster.
Answers:
With itertools.chain() on the values
This could be faster:
from itertools import chain
categories = list(chain.from_iterable(categories.values))
Performance
from functools import reduce
from itertools import chain
categories = pd.Series([['a', 'b'], ['c', 'd', 'e']] * 1000)
%timeit list(chain.from_iterable(categories.values))
1000 loops, best of 3: 231 µs per loop
%timeit list(chain(*categories.values.flat))
1000 loops, best of 3: 237 µs per loop
%timeit reduce(lambda l1, l2: l1 + l2, categories)
100 loops, best of 3: 15.8 ms per loop
For this data set the chaining is about 68x faster.
Vectorization?
Vectorization works when you have native NumPy data types (pandas uses NumPy for its data after all). Since we have lists in the Series already and want a list as result, it is rather unlikely that vectorization will speed things up. The conversion between standard Python objects and pandas/NumPy data types will likely eat up all the performance you might get from the vectorization. I made one attempt to vectorize the algorithm in another answer.
Vectorized but slow
You can use NumPy’s concatenate:
import numpy as np
list(np.concatenate(categories.values))
Performance
But we have lists, i.e. Python objects already. So the vectorization has to switch back and forth between Python objects and NumPy data types. This make things slow:
categories = pd.Series([['a', 'b'], ['c', 'd', 'e']] * 1000)
%timeit list(np.concatenate(categories.values))
100 loops, best of 3: 7.66 ms per loop
%timeit np.concatenate(categories.values)
100 loops, best of 3: 5.33 ms per loop
%timeit list(chain.from_iterable(categories.values))
1000 loops, best of 3: 231 µs per loop
You can try your luck with business["categories"].str.join(''), but I am guessing that Pandas uses Pythons string functions. I doubt you can do better tha what Python already offers you.
I used "".join(business["categories"])
It is much faster than business["categories"].str.join('') but still 4 times slower than the itertools.chain method. I preferred it because it is more readable and no import is required.
Is there an analog for reduce for a pandas Series?
For example, the analog for map is pd.Series.apply, but I can’t find any analog for reduce.
My application is, I have a pandas Series of lists:
>>> business["categories"].head()
0 ['Doctors', 'Health & Medical']
1 ['Nightlife']
2 ['Active Life', 'Mini Golf', 'Golf']
3 ['Shopping', 'Home Services', 'Internet Servic...
4 ['Bars', 'American (New)', 'Nightlife', 'Loung...
Name: categories, dtype: object
I’d like to merge the Series of lists together using reduce, like so:
categories = reduce(lambda l1, l2: l1 + l2, categories)
but this takes a horrific time because merging two lists together is O(n) time in Python. I’m hoping that pd.Series has a vectorized way to perform this faster.
With itertools.chain() on the values
This could be faster:
from itertools import chain
categories = list(chain.from_iterable(categories.values))
Performance
from functools import reduce
from itertools import chain
categories = pd.Series([['a', 'b'], ['c', 'd', 'e']] * 1000)
%timeit list(chain.from_iterable(categories.values))
1000 loops, best of 3: 231 µs per loop
%timeit list(chain(*categories.values.flat))
1000 loops, best of 3: 237 µs per loop
%timeit reduce(lambda l1, l2: l1 + l2, categories)
100 loops, best of 3: 15.8 ms per loop
For this data set the chaining is about 68x faster.
Vectorization?
Vectorization works when you have native NumPy data types (pandas uses NumPy for its data after all). Since we have lists in the Series already and want a list as result, it is rather unlikely that vectorization will speed things up. The conversion between standard Python objects and pandas/NumPy data types will likely eat up all the performance you might get from the vectorization. I made one attempt to vectorize the algorithm in another answer.
Vectorized but slow
You can use NumPy’s concatenate:
import numpy as np
list(np.concatenate(categories.values))
Performance
But we have lists, i.e. Python objects already. So the vectorization has to switch back and forth between Python objects and NumPy data types. This make things slow:
categories = pd.Series([['a', 'b'], ['c', 'd', 'e']] * 1000)
%timeit list(np.concatenate(categories.values))
100 loops, best of 3: 7.66 ms per loop
%timeit np.concatenate(categories.values)
100 loops, best of 3: 5.33 ms per loop
%timeit list(chain.from_iterable(categories.values))
1000 loops, best of 3: 231 µs per loop
You can try your luck with business["categories"].str.join(''), but I am guessing that Pandas uses Pythons string functions. I doubt you can do better tha what Python already offers you.
I used "".join(business["categories"])
It is much faster than business["categories"].str.join('') but still 4 times slower than the itertools.chain method. I preferred it because it is more readable and no import is required.