Apply function to each cell in DataFrame multithreadedly in pandas

Question

Is it possible to apply function to each cell in a DataFrame multithreadedly in pandas?

I'm aware of pandas.DataFrame.applymap but it doesn't seem to allow multithreading natively:

import numpy as np
import pandas as pd
np.random.seed(1)
frame = pd.DataFrame(np.random.randn(4, 3), columns=list('bde'), 
                     index=['Utah', 'Ohio', 'Texas', 'Oregon'])
print(frame)
format = lambda x: '%.2f' % x
frame = frame.applymap(format)
print(frame)

returns:

               b         d         e
Utah    1.624345 -0.611756 -0.528172
Ohio   -1.072969  0.865408 -2.301539
Texas   1.744812 -0.761207  0.319039
Oregon -0.249370  1.462108 -2.060141

            b      d      e
Utah     1.62  -0.61  -0.53
Ohio    -1.07   0.87  -2.30
Texas    1.74  -0.76   0.32
Oregon  -0.25   1.46  -2.06

Instead, I would like to use more than one core to perform the operation, since the applied function may be complex.

Answer 1

Split by columns:

from multiprocessing import Pool

def format(col):
    return col.apply(lambda x: '%.2f' % x)

cores = 5
pool = Pool(cores)
for out_col in pool.imap(format, [frame[i] for i in frame]):
    frame[out_col.name] = out_col
pool.close()
pool.join()

Or split by partitions size as mentioned in comments:

size = 10
frame_split = np.array_split(frame, size)
frame = pd.concat(pool.imap(func, frame_split))

Answer 2

Note on Microsoft Windows to avoid the issue Attempt to start a new process before the current process has finished its bootstrapping phase , one has to place the code inside a main function, eg:

import numpy as np
import pandas as pd
from multiprocessing import Pool

def format(col):
    return col.apply(lambda x: '%.2f' % x)

if __name__ == "__main__":
    np.random.seed(1)
    frame = pd.DataFrame(np.random.randn(4, 3), columns=list('bde'), 
                         index=['Utah', 'Ohio', 'Texas', 'Oregon'])
    print(frame)
    cores = 2
    pool = Pool(cores)
    for out_col in pool.imap(format, [frame[i] for i in frame]):
        frame[out_col.name] = out_col
    pool.close()
    pool.join()
    print(frame)

---

Regarding the use of np.array_split , since the dataframe is cast into a numpy array, it only works for numbers. Example:

import numpy as np
import pandas as pd

from multiprocessing import Pool

def myfunc(a, b):
    '''
    Return a-b if a>b, otherwise return a+b
    Taken from https://docs.scipy.org/doc/numpy/reference/generated/numpy.vectorize.html
    '''
    if a > b:
        return a - b
    else:
        return a + b

def format(col):
    vfunc = np.vectorize(myfunc)
    return pd.DataFrame(vfunc(col,2))

if __name__ == "__main__":
    np.random.seed(1)
    frame = pd.DataFrame(np.random.randn(4, 3), columns=list('bde'), 
                         index=['Utah', 'Ohio', 'Texas', 'Oregon'])
    print(frame)
    cores = 2
    size = 2
    pool = Pool(cores)
    frame_split = np.array_split(frame.as_matrix(), size)
    print (frame_split)

    columns = frame.columns
    frame = pd.concat(pool.imap(format, frame_split)).set_index(frame.index)    
    frame.columns = columns
    pool.close()
    pool.join()
    print(frame)

returns:

               b         d         e
Utah    1.624345 -0.611756 -0.528172
Ohio   -1.072969  0.865408 -2.301539
Texas   1.744812 -0.761207  0.319039
Oregon -0.249370  1.462108 -2.060141
[array([[ 1.62434536, -0.61175641, -0.52817175],
       [-1.07296862,  0.86540763, -2.3015387 ]]), array([[ 1.74481176, -0.7612069 ,  0.3190391 ],
       [-0.24937038,  1.46210794, -2.06014071]])]
               b         d         e
Utah    3.624345  1.388244  1.471828
Ohio    0.927031  2.865408 -0.301539
Texas   3.744812  1.238793  2.319039
Oregon  1.750630  3.462108 -0.060141