pandarallel | efficient tool to parallelize Pandas operations | Genomics library
kandi X-RAY | pandarallel Summary
kandi X-RAY | pandarallel Summary
A simple and efficient tool to parallelize Pandas operations on all available CPUs
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Top functions reviewed by kandi - BETA
- R Parallelize data with memory system
- Wrap a reduce function for saving data to disk
- Check if notebook is a notebook lab
- Returns a progress bar for the given maxs
- Prepare extra data for reduction
- Return an iterator over nb_workers
- Extract extra information from the data
- Updates the bars
- Removes the displayed lines
- Returns the progress bar
- Returns a list of all lines
- R Parallelize data using a pipe
- Reduce datas
pandarallel Key Features
pandarallel Examples and Code Snippets
Lines of Code : 0License : Permissive (BSD-3-Clause)
Copyfrom pandarallel import pandarallel
pandarallel.initialize(progress_bar=True)
# df.apply(func)
df.parallel_apply(func) from pandarallel import pandarallel
pandarallel.initialize(progress_bar=True, nb_workers = n)
# nb_workers = n ; I set the nb_workers fo CPU core - 1 so the system is more stable
def something(x):
#do stuff
return result
df['result>>> seq=[(10, 11), (16, 17), (17, 18), (11, 12), (12, 13), (13, 14)]
>>> subsequence(seq)
[(10, 11), (11, 12), (12, 13), (13, 14)]
from pandarallel import pandarallel
pandarallel.initialize()
# jdf,df.attrs = df.join(df['binaryString'].str.split('',expand=True).iloc[:, 1:-1].add_prefix('Bit').astype(np.uint8)), df.attrs
df.join(pd.DataFrame(df['binaryString'].map(list).to_list(), columns=['a','b','c','d','from pandarallel import pandarallel
from math import sin
pandarallel.initialize()
def func(x):
return sin(x**2)
df.parallel_apply(func, axis=1)
cols = self.variables
contains_address = [y + '_CONTAINS_ADDRESS' for y in cols]
X[cols] = X[cols].applymap(self.text_cleanup)
X[contains_address] = X[cols].applymap(lambda y:
y*1 if '|'.join(self.address_list) in y else y)
....
<from pandarallel.utils import progress_bars
progress_bars.is_notebook_lab = lambda : True
val_cols = ['value_1_diff', 'value_2_diff', 'value_3_diff']
g = df.groupby(['user_id', 'category', 'date'])[val_cols]
df[val_cols] = df[val_cols].sub(g.transform('min')).div(g.transform('std') + 0.01)
DataFrame.parallel_apply = parallelize(*args)
def my_func(self):
return 2*self
pd.DataFrame.my_method = my_func
df.my_method()
a b
2 8
4 10
6 12
def sum_x(self, x):
return self+x
pd.DataFrame.sum_x = sum_x
df.sum_x(3)
a b
4 7
5 8
6 9
<Community Discussions
Trending Discussions on pandarallel
QUESTION
I'm quite familiar with pandas dataframes but I'm very new to Dask so I'm still trying to wrap my head around parallelizing my code. I've obtained my desired results using pandas and pandarallel already so what I'm trying to figure out is if I can scale up the task or speed it up somehow using Dask.
Let's say my dataframe has datetimes as non-unique indices, a values column and an id column.
...ANSWER
Answered 2021-Dec-16 at 07:42The snippet below shows that it's a very similar syntax:
QUESTION
Using pandas/python, I want to calculate the longest increasing subsequence of tuples for each DTE group, but efficiently with 13M rows. Right now, using apply/iteration, takes about 10 hours.
Here's roughly my problem:
DTE Strike Bid Ask 1 100 10 11 1 200 16 17 1 300 17 18 1 400 11 12 1 500 12 13 1 600 13 14 2 100 10 30 2 200 15 20 2 300 16 21 ...ANSWER
Answered 2021-May-27 at 13:27What is the complexity of your algorithm of finding the longest increasing subsequence?
This article provides an algorithm with the complexity of O(n log n).
Upd: doesn't work.
You don't even need to modify the code, because in python comparison works for tuples: assert (1, 2) < (3, 4)
QUESTION
I have a very large dataframe, and one column has strings with a fixed-length binary number.
I want to split every binary digit into his own column, and I have a working code, but is ultra slow. My code is:
...ANSWER
Answered 2021-Apr-15 at 09:09Starting from the code, the critical point is:
QUESTION
I am dealing with a df with two columns: 'body' and 'label'. I need to write each row's 'body' to a different .txt. Currently I am doing that by iterating over the rows and writing them with python's file IO manager, but it's becoming too slow as the number of rows I'm dealing with is increasing..
Below, how the actual code is: (The number of the row MUST be the filename!)
...ANSWER
Answered 2021-Feb-15 at 14:35This won't get faster in a meaningful way. Even if parallel_apply does operate in parallel, you're not gaining much because the slowness comes from the File I/O, not the iteration.
If you were writing all the rows to the same file (and not a new file for each row), then there could be some speedup through buffering but that's still much slower than pure iteration.
If parallel_apply works the same way as df.apply (but in parallel), that last line
QUESTION
ANSWER
Answered 2020-Nov-14 at 00:13The is_notebook_lab check is too narrow, you can overwrite it and force to be true:
QUESTION
I have df_fruits, which is a dataframe of fruits.
ANSWER
Answered 2020-Nov-10 at 06:37Yeah its possible, although not really provided in the pandas library straight out of the box.
Maybe you can attempt something like this:
QUESTION
I'm trying to calculate (x-x.mean()) / (x.std +0.01) on several columns of a dataframe based on groups. My original dataframe is very large. Although I've splitted the original file into several chunks and I'm using multiprocessing to run the script on each chunk of the file, but still every chunk of the dataframe is very large and this process never finishes.
I used the following code:
...ANSWER
Answered 2020-Oct-28 at 09:22Not sure about performance, but here you can use GroupBy.transform:
QUESTION
When using pandarallel to use all cores when running .apply methods on my dataframes, I came across a syntax which I never saw before. Rather, it's a way of using dot syntax that I don't understand.
ANSWER
Answered 2020-Aug-25 at 13:50It appears to happen in initialize:
QUESTION
I am trying to create my own custom Sagemaker Framework that runs a custom python script to train a ML model using the entry_point parameter.
Following the Python SDK documentation (https://sagemaker.readthedocs.io/en/stable/estimators.html), I wrote the simplest code to run a training job just to see how it behaves and how Sagemaker Framework works.
My problem is that I don't know how to properly build my Docker container in order to run the entry_point script.
I added the train.py script into the container that only logs the folders and files paths as well as the variables in the containers environment.
I was able to run the training job, but I couldn't find any reference of the entry_point script neither in environment variable nor the files in the container.
Here is the code I used:
- Custom Sagemaker Framework Class:
ANSWER
Answered 2020-May-25 at 19:39SageMaker team created a python package sagemaker-training to install in your docker so that your customer container will be able to handle external entry_point scripts.
See here for an example using Catboost that does what you want to do :)
https://github.com/aws-samples/sagemaker-byo-catboost-container-demo
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install pandarallel
You can use pandarallel like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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