pandas-multiprocess's Introduction

pandas-multiprocess

A Python package to process Pandas Dataframe using multi-processing.

Install

pip install pandas-multiprocess

Example

Import the package

from pandas_multiprocess import multi_process

Define a function which will process each row in a Pandas DataFrame

The func must take a pandas.Series as its first positional argument and returns either a pandas.Series or a list of pands.Series.

The function has one positional argument data_row, additional arguments can be defined and the values of the additional arguments will be passed through multi_process(). Here we use **args to stand for the additional arguments.

def func(data_row, **args):
    # data_row (pd.Series): a row of a panda Dataframe
    # args: a dict of additional arguments
    data_row['sum'] = data_row['col_1'] + data_row['col_2']
    return data_row

Initiate a DataFrame

import pandas as pd
import numpy as np
df_len = 1000
df = pd.DataFrame({'col_1': np.random.normal(size=df_len),
                   'col_2': np.random.cd normal(size=df_len)
                   })

Process it using multiprocess

# The `args` will be passed to the additional arguments of `func()`
args = {}
result = multi_process(func=func,
                       data=df,
                       num_process=8,
                       **args)

The above operation is equivalent as below, but much more efficient

result = df.apply(func, axis=1, **args)

The result of example demonstrate the efficiency of pandas-multiprocess in processing computational expensive operations for each row of a Datafram.

Running examples...
100%|████| 100/100 [00:01<00:00, 68.65it/s]8 processes run time 2.189883 seconds.
100%|████| 100/100 [00:00<00:00, 140.90it/s]16 processes run time 1.440812 seconds.
Pandas apply() run time 11.165841 seconds.

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