Pandaral·lel

Without parallelisation
With parallelisation

Latest Release
License

Installation

$ pip install pandarallel [--user]

Requirements

Warnings

Parallelization has a cost (instanciating new processes, sending data via shared memory, etc ...), so parallelization is efficiant only if the amount of calculation to parallelize is high enough. For very little amount of data, using parallezation not always worth it.
Functions applied should NOT be lambda functions.

from pandarallel import pandarallel
from math import sin

pandarallel.initialize()

# FORBIDDEN
df.parallel_apply(lambda x: sin(x**2), axis=1)

# ALLOWED
def func(x):
    return sin(x**2)

df.parallel_apply(func, axis=1)

Examples

An example of each API is available here.

Benchmark

For some examples, here is the comparative benchmark with and without using Pandaral·lel.

Computer used for this benchmark:

OS: Linux Ubuntu 16.04
Hardware: Intel Core i7 @ 3.40 GHz - 4 cores

For those given examples, parallel operations run approximatively 4x faster than the standard operations (except for series.map which runs only 3.2x faster).

API

First, you have to import pandarallel:

from pandarallel import pandarallel

Then, you have to initialize it.

pandarallel.initialize()

This method takes 3 optional parameters:

shm_size_mb: The size of the Pandarallel shared memory in MB. If the default one is too small, it is possible to set a larger one. By default, it is set to 2 GB. (int)
nb_workers: The number of workers. By default, it is set to the number of cores your operating system sees. (int)
progress_bar: Put it to True to display a progress bar.

WARNING: Progress bar is an experimental feature. This can lead to a considerable performance loss. Not available for DataFrameGroupy.parallel_apply.

With df a pandas DataFrame, series a pandas Series, func a function to apply/map, args1, args2 some arguments & col_name a column name:

Without parallelisation	With parallelisation
`df.apply(func)`	`df.parallel_apply(func)`
`df.applymap(func)`	`df.parallel_applymap(func)`
`df.groupby(args).apply(func)`	`df.groupby(args).parallel_apply(func)`
`df.groupby(args1).col_name.rolling(args2).apply(func)`	`df.groupby(args1).col_name.rolling(args2).parallel_apply(func)`
`series.map(func)`	`series.parallel_map(func)`
`series.apply(func)`	`series.parallel_apply(func)`
`series.rolling(args).apply(func)`	`series.rolling(args).parallel_apply(func)`

You will find a complete example here for each line of this table.

Troubleshooting

I have 8 CPUs but parallel_apply speeds up computation only about x4. Why ?

Actually Pandarallel can only speed up computation until about the number of cores your computer has. The majority of recent CPUs (like Intel core-i7) uses hyperthreading. For example, a 4 cores hyperthreaded CPU will show 8 CPUs to the Operating System, but will really have only 4 physical computation units.

On Ubuntu, you can get the number of cores with $ grep -m 1 'cpu cores' /proc/cpuinfo.

anuragreddygv323 / pandarallel