A simple and efficient tool to parallelize Pandas operations on all available CPUs
See the code| Without parallelization | ![]() |
|---|---|
| With parallelization | ![]() |
Pandaral.lel provides a simple way to parallelize your pandas operations on all your CPUs by changing only one line of code. It also displays progress bars.
If you are interested, please contact me or open a GitHub issue.
pip install pandarallel [--upgrade] [--user]
from pandarallel import pandarallel
pandarallel.initialize(progress_bar=True)
# df.apply(func)
df.parallel_apply(func)
Be sure to check out the documentation.
An example of each available pandas API is available:
Overall
Maintained
Code review
PyPI
Latest release
Releases this year
326 followers · starred Mar 2022
37 followers · starred Nov 2022
376 followers · starred Jun 2024
42 followers · starred Mar 2022
A simple and efficient tool to parallelize Pandas operations on all available CPUs
See the code| Without parallelization | ![]() |
|---|---|
| With parallelization | ![]() |
Pandaral.lel provides a simple way to parallelize your pandas operations on all your CPUs by changing only one line of code. It also displays progress bars.
If you are interested, please contact me or open a GitHub issue.
pip install pandarallel [--upgrade] [--user]
from pandarallel import pandarallel
pandarallel.initialize(progress_bar=True)
# df.apply(func)
df.parallel_apply(func)
Be sure to check out the documentation.
An example of each available pandas API is available:
Overall
Maintained
Code review
PyPI
Latest release
Releases this year
326 followers · starred Mar 2022
37 followers · starred Nov 2022
376 followers · starred Jun 2024
42 followers · starred Mar 2022