pandarallel | efficient tool to parallelize Pandas operations | Genomics library

 by   nalepae Python Version: v1.6.5 License: BSD-3-Clause

kandi X-RAY | pandarallel Summary

kandi X-RAY | pandarallel Summary

pandarallel is a Python library typically used in Artificial Intelligence, Genomics, Numpy applications. pandarallel has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has high support. You can install using 'pip install pandarallel' or download it from GitHub, PyPI.

A simple and efficient tool to parallelize Pandas operations on all available CPUs

            kandi-support Support

              pandarallel has a highly active ecosystem.
              It has 3108 star(s) with 190 fork(s). There are 30 watchers for this library.
              It had no major release in the last 12 months.
              There are 70 open issues and 122 have been closed. On average issues are closed in 219 days. There are 7 open pull requests and 0 closed requests.
              It has a negative sentiment in the developer community.
              The latest version of pandarallel is v1.6.5

            kandi-Quality Quality

              pandarallel has 0 bugs and 0 code smells.

            kandi-Security Security

              pandarallel has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              pandarallel code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              pandarallel is licensed under the BSD-3-Clause License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              pandarallel releases are available to install and integrate.
              Deployable package is available in PyPI.
              Build file is available. You can build the component from source.
              Installation instructions, examples and code snippets are available.

            Top functions reviewed by kandi - BETA

            kandi has reviewed pandarallel and discovered the below as its top functions. This is intended to give you an instant insight into pandarallel implemented functionality, and help decide if they suit your requirements.
            • 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
            Get all kandi verified functions for this library.

            pandarallel Key Features

            No Key Features are available at this moment for pandarallel.

            pandarallel Examples and Code Snippets

            `Pandarallel `__
            Pythondot img1Lines of Code : 0dot img1License : Permissive (BSD-3-Clause)
            copy iconCopy
            from pandarallel import pandarallel
            # df.apply(func)
            How to multicore processing a for loop with iterrows in python
            Pythondot img2Lines of Code : 11dot img2License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            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
            Vectorization or efficient way to calculate Longest Increasing subsequence of tuples with Pandas
            Pythondot img3Lines of Code : 10dot img3License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            >>> 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
            # j
            Split text in dataframe column to multiple columns
            Pythondot img4Lines of Code : 6dot img4License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            df,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','
            Interpolate CubicSpline with Pandas
            Pythondot img5Lines of Code : 10dot img5License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            from pandarallel import pandarallel
            from math import sin
            def func(x):
                return sin(x**2)
            df.parallel_apply(func, axis=1)
            Parallelize for loop in pandas
            Pythondot img6Lines of Code : 15dot img6License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            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)
            pandarallel widgets don't work on Google Colab
            Pythondot img7Lines of Code : 4dot img7License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            from pandarallel.utils import progress_bars
            progress_bars.is_notebook_lab = lambda : True
            How to vectorize groupby and apply in pandas?
            Pythondot img8Lines of Code : 5dot img8License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            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)
            How Does Python Apply a Method from one Library to the Object of Another?
            Pythondot img9Lines of Code : 2dot img9License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            DataFrame.parallel_apply = parallelize(*args)
            How Does Python Apply a Method from one Library to the Object of Another?
            Pythondot img10Lines of Code : 25dot img10License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            def my_func(self):
                return 2*self
            pd.DataFrame.my_method = my_func
            a   b
            2   8
            4  10
            6  12
            def sum_x(self, x):
                return self+x
            pd.DataFrame.sum_x = sum_x
            a  b
            4  7
            5  8
            6  9

            Community Discussions


            Extracting latest values in a Dask dataframe with non-unique index column dates
            Asked 2022-Jan-24 at 23:36

            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.



            Answered 2021-Dec-16 at 07:42

            The snippet below shows that it's a very similar syntax:



            Vectorization or efficient way to calculate Longest Increasing subsequence of tuples with Pandas
            Asked 2021-May-30 at 03:13

            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 ...


            Answered 2021-May-27 at 13:27

            What 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)



            Split text in dataframe column to multiple columns
            Asked 2021-Apr-15 at 09:09

            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:



            Answered 2021-Apr-15 at 09:09

            Starting from the code, the critical point is:



            Optimize writing of each pandas row to a different .txt
            Asked 2021-Feb-15 at 14:48

            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!)



            Answered 2021-Feb-15 at 14:35

            This 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



            pandarallel widgets don't work on Google Colab
            Asked 2020-Nov-14 at 00:13

            Pandarallel supports nice progress widgets. However, I can't get them to appear when using Google Colab. I get output like this instead:

            This chunk of code, which is supposed to enable the widgets, runs successfully in my notebook (before I use any parallel calls):



            Answered 2020-Nov-14 at 00:13

            The is_notebook_lab check is too narrow, you can overwrite it and force to be true:



            Parallel processing of each row in Pandas iteration
            Asked 2020-Nov-10 at 06:57

            I have df_fruits, which is a dataframe of fruits.



            Answered 2020-Nov-10 at 06:37

            Yeah its possible, although not really provided in the pandas library straight out of the box.

            Maybe you can attempt something like this:



            How to vectorize groupby and apply in pandas?
            Asked 2020-Oct-28 at 09:22

            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:



            Answered 2020-Oct-28 at 09:22

            Not sure about performance, but here you can use GroupBy.transform:



            How Does Python Apply a Method from one Library to the Object of Another?
            Asked 2020-Aug-27 at 15:20

            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.



            Answered 2020-Aug-25 at 13:50


            Where does entry_point script is stored in custom Sagemaker Framework training job container?
            Asked 2020-May-25 at 20:07

            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 (, 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 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:


            Answered 2020-May-25 at 19:39

            SageMaker 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 :)



            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install pandarallel

            You can install using 'pip install pandarallel' or download it from GitHub, PyPI.
            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.


            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-core 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.
            Find more information at:

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