def func(df):
return df.shape
cores = mp.cpu_count()
df_split = np.array_split(
df, cores,
axis=0) # split an array into sub-arrays (array, num of sub arrary)
# creative the multiprocessing pool
pool = Pool(cores)
# process the DataFrame by mapping unction to each df2 across the pool
df_out = np.vstack(pool.map(
func, df_split)) # stack arrays in sequence verticlally (row1 row2...)
print("dataframe iteration:", df_out)
# close down the pool and join
pool.close()
pool.join()
pool.clear()
# [25, 16, 9, 4, 1]
#
