modin | Pandas workflows by changing a single line | SQL Database library

 by   modin-project Python Version: 0.25.1 License: Apache-2.0

kandi X-RAY | modin Summary

kandi X-RAY | modin Summary

modin is a Python library typically used in Database, SQL Database, Pandas applications. modin 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 modin' or download it from GitHub, PyPI.

For the complete documentation on Modin, visit our ReadTheDocs page.

            kandi-support Support

              modin has a highly active ecosystem.
              It has 8711 star(s) with 613 fork(s). There are 114 watchers for this library.
              There were 10 major release(s) in the last 6 months.
              There are 837 open issues and 2777 have been closed. On average issues are closed in 80 days. There are 74 open pull requests and 0 closed requests.
              It has a negative sentiment in the developer community.
              The latest version of modin is 0.25.1

            kandi-Quality Quality

              modin has 0 bugs and 0 code smells.

            kandi-Security Security

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

            kandi-License License

              modin is licensed under the Apache-2.0 License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              modin 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.
              modin saves you 23990 person hours of effort in developing the same functionality from scratch.
              It has 59408 lines of code, 4243 functions and 337 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed modin and discovered the below as its top functions. This is intended to give you an instant insight into modin implemented functionality, and help decide if they suit your requirements.
            • Make a proxy class .
            • Return a dict of command - line arguments for distutils .
            • Create a grid from a dataframe .
            • Merge two DataFrames .
            • Handle aggregation results .
            • Return an OrderedDict of block indices .
            • Check to see if read_csv is supported .
            • Initializes the ray .
            • Assign row partitions to each actor .
            • Read partitioned file .
            Get all kandi verified functions for this library.

            modin Key Features

            No Key Features are available at this moment for modin.

            modin Examples and Code Snippets

            Public API
            Pythondot img1Lines of Code : 70dot img1License : Non-SPDX (NOASSERTION)
            copy iconCopy
            import modin.config
            # This explicitly sets the number of partitions
            import modin.pandas as pd
            import pandas
            # Create Modin DataFrame from the external file
            pd_dataframe = pd.read_csv("test_data.csv")
            # Create Modin   
            Pythondot img2Lines of Code : 39dot img2License : Non-SPDX (NOASSERTION)
            copy iconCopy
            from modin.core.storage_formats import BaseQueryCompiler
            class DefaultToPandasQueryCompiler(BaseQueryCompiler):
                def __init__(self, pandas_df):
                    self._pandas_df = pandas_df
                def from_pandas(cls, df, *args, **kwargs):
            Pythondot img3Lines of Code : 20dot img3License : Non-SPDX (NOASSERTION)
            copy iconCopy
            import os
            # Setting `MODIN_STORAGE_FORMAT` environment variable.
            # Also can be set outside the script.
            os.environ["MODIN_STORAGE_FORMAT"] = "Hdk"
            import modin.config
            import modin.pandas as pd
            # Checking initially set `StorageFormat` config,
            # whic  
            modin - census hdk
            Pythondot img4Lines of Code : 224dot img4License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            # Licensed to Modin Development Team under one or more contributor license agreements.
            # See the NOTICE file distributed with this work for additional information regarding
            # copyright ownership.  The Modin Development Team licenses this file to you   
            modin - census
            Pythondot img5Lines of Code : 209dot img5License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            # Licensed to Modin Development Team under one or more contributor license agreements.
            # See the NOTICE file distributed with this work for additional information regarding
            # copyright ownership.  The Modin Development Team licenses this file to you   
            modin - nyc taxi hdk
            Pythondot img6Lines of Code : 205dot img6License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            # Licensed to Modin Development Team under one or more contributor license agreements.
            # See the NOTICE file distributed with this work for additional information regarding
            # copyright ownership.  The Modin Development Team licenses this file to you   
            Optimizing an Excel to Pandas import and transformation from wide to long data
            Pythondot img7Lines of Code : 39dot img7License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            df = pd.DataFrame({'ID' : [1, 2],
                               'Property' : ['A', 'B'],
                               'Info1' : ['x', 'a'],
                               'Info2' : ['y', 'b'],
                               'Info3' : ['z', 'c'],
            How to find all combinations of 3 dataframes and return them as list
            Pythondot img8Lines of Code : 50dot img8License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            >>> from datar.all import f, tibble, bind_cols, expand, nesting
            >>> df1 = tibble(
            ...     name=["John", "Nick", "Eric"], job=["engineer", "architect", "deisgner"]
            ... )
            >>> df2 = tibble(
            ...     cit
            How to replace type: pandas.core.frame.DataFrame with type: modin.pandas.dataframe.DataFrame
            Pythondot img9Lines of Code : 7dot img9License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            import modin.pandas as m_pd
            if not isinstance(X, m_pd.DataFrame):
                raise TypeError(
                    "X is not a pandas dataframe. The dataset should be a pandas dataframe.")
            Pandas Modin ray library fails to startup
            Pythondot img10Lines of Code : 6dot img10License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            import os
            os.environ["MODIN_ENGINE"] = "ray"
            import ray
            import modin.pandas as pd

            Community Discussions


            Optimizing an Excel to Pandas import and transformation from wide to long data
            Asked 2022-Mar-24 at 15:06

            I need to import and transform xlsx files. They are written in a wide format and I need to reproduce some of the cell information from each row and pair it up with information from all the other rows:

            [Edit: changed format to represent the more complex requirements]

            Source format

            ID Property Activity1name Activity1timestamp Activity2name Activity2timestamp 1 A a 1.1.22 00:00 b 2.1.22 10:05 2 B a 1.1.22 03:00 b 5.1.22 20:16

            Target format

            ID Property Activity Timestamp 1 A a 1.1.22 00:00 1 A b 2.1.22 10:05 2 B a 1.1.22 03:00 2 B b 5.1.22 20:16

            The following code works fine to transform the data, but the process is really, really slow:



            Answered 2022-Mar-24 at 15:06

            The df.melt function should be able to do this type of operation much faster.



            How to replace type: pandas.core.frame.DataFrame with type: modin.pandas.dataframe.DataFrame
            Asked 2022-Feb-20 at 22:39

            I try to replace pandas with modin pandas in the code:



            Answered 2022-Feb-20 at 22:39

            As mentioned by devin-petersohn on Github related to this issue you can simply import modin.pandas as such:



            Pandas Modin ray library fails to startup
            Asked 2022-Feb-09 at 23:35

            I am trying to accelerate my pandas data processing using modin



            Answered 2022-Feb-09 at 23:35

            Try initing ray before you import modin:



            Modin AttributeError when importing from sparse matrix
            Asked 2021-Dec-21 at 14:43

            I am trying to use Modin package to import a sparse matrix created with scipy (specifically, a scipy.sparse.csr_matrix).

            Invoking the method:



            Answered 2021-Dec-21 at 14:43

            This is a bug. The code in this package uses a classmethod to call an instance method, and as a result the self reference is not bound to the inference, but is instead a reference to the first argument (which here is a function).

            This is the code that fails:



            How do I validate a value in a dataframe which is dependent on other value in that specific row?
            Asked 2021-Dec-21 at 01:54

            Suppose I have a .csv which follows this format:

            Name, Salary, Department, Mandatory

            Rob, 5500, Aviation, Yes

            Bob, 1000, Facilities, No

            Tom, 6000, IT, Yes

            After exporting this to pandas/modin, I'd like to perform row-differentiated checks, where:

            1. People named Rob working in aviation cannot earn less than 5000

            2. People named Bob working in facilities cannot earn less than 1000

            3. Whoever works in facilities has to report their salary, while people working in aviation or IT can choose to leave their salary unreported.

            4. If any check is violated, we store this in a dataframe and pass forward this case to the human resources department for further investigation.

            How would you validate this .csv using Pandera?

            Sorry if that is a noobish question but I've read the entire Pandera documentation from A to Z and found no straightforward answer to the task at hand.



            Answered 2021-Dec-21 at 01:54

            Depending on which API you're using, you can check out the wide checks for the object-based API or dataframe checks for the class-based API.

            Note: the code snippets below aren't tested, but should be going in the right direction

            Class-based API:



            Using Prophet or Auto ARIMA with Ray
            Asked 2021-Nov-22 at 16:19

            There is something about Ray that I could not find a clear answer. Ray is a distributed framework for dataprocessing and training. In order to make it work in a distributed fashion Modin or some other distributed data analysis tool supported by Ray must be used so the data can flow on the whole cluster, but what if I want to use a model like Facebook's Prophet or ARIMA that takes pandas dataframe as input? When I use pandas dataframe as the arguments of the model functions will it work on just a single node or is there a possible workaround for it to work on the cluster?



            Answered 2021-Nov-22 at 16:19

            Ray is able to train models with pandas dataframes as inputs!

            Currently, there is a slight work-around needed for ARIMA, since it typically uses the statsmodels library behind the scenes. In order to ensure the models are serialized correctly, an extra pickle step is needed. Ray might eliminate the need for the pickle work-around in the future.

            See explanation of pickle work-around:

            Here is an excerpt of code for python 3.8 and ray 1.8. Notice that the inputs to train_model() and inference_model() functions are pandas dataframes. The extra pickle step is embedded within those functions.



            Writing a dataset to multiple directories with modin and Ray pauses unexplainably
            Asked 2021-Sep-27 at 13:27

            I am trying to perform IO operations with multiple directories using ray, modin(with ray backend) and python. The file writes pause and the memory and disk usages do not change at all and the program is blocked.


            I have a ray actor set up as this



            Answered 2021-Sep-27 at 13:27

            For any future readers,

            modin.DataFrame.to_csv() pauses unexplainably for unknown reasons, but pickle() doesnt with the same logic.

            There is also a significant performance increase in terms of read/write times, when data is stored as .pkl files.



            Why does matplotlib.pyplot.savefig() mess up image outputs for very large pandas.plotting.scatter_matrix()?
            Asked 2021-Jul-30 at 15:08

            I was trying to compute the pandas.plotting.scatter_matrix() values for very large pandas.DataFrame() (relatively speaking for this specific operation, most libraries either run OOM most of the time or implement a row count check of 50000, see vaex-scatter).

            The 'Time series' DataFrame shape I have is (10000000, 41). Every value is either a float or an integer.

            Q1: So the first thing I would already like to ask is how do I do that memory and space efficiently.

            What I tried for Q1
            • I tried to do it typically (like in the examples in the documentation) using matplotlib and modin.pandas.DataFrames looping over each pair, so the indexing and operations/calculations I want to do are relatively fast including the to_numpy() method. How ever as you might have already seen from the image 1 pair takes 18.1 secs at least and 41x41 pairs are too difficult to handle in my task and I feel there is a relatively faster way of doing things. :)

            • I tried using the pandas scatter plot function which is also too slow and crashes my memory. This is done using the native pandas package and not the modin.pandas. This was done by first converting the modin.pandas.DataFrame to pandas.DataFrame via the private modin.pandas.DataFrame._to_pandas() accessor. This approach is too slow too. I stopped waiting after I ran out of memory 1 hour later.

            • I tried plotting with vaex. This was the fastest but I ran into other errors which arent related to the question.

            • please do not suggest seaborn's pair plot. Tried and it takes around 5 mins to generate a pairplot() for a pandas.DataFrame of shape (1000x8), also is cantered around pandas.

            Current workaround for Q1 and new Q2
            • I am plotting a scatter matrix of all the features sampled 10000 times. so modin.DataFrame.sample(10000) since it kind of is okay to view at the general trend but i do not wish to do this if there is a better option.
            • Converting it to pandas.DataFrame and using pandas.plotting.scatter_matrix like this, so that i dont have to wait for it to be rendered onto the jupyter notebook.


            Answered 2021-Jul-30 at 15:08

            For future readers, the process I opted was to use as @JodyKlymak suggested in his comment(Thanks) with pandas.DataFrame.

            please bear in mind that this approach answers both the questions.

            1. Convert your modin.pandas.DataFrame to pandas.DataFrame with the private modin.pandas.DataFrame._to_pandas()
            2. plot the graphs first to an xarray image like so xarray-imshow.



            modin shown a warning message "Perhaps you already have a cluster running?"
            Asked 2021-Apr-13 at 06:20

            I am using modin to read an sql table, however I am getting this warning



            Answered 2021-Apr-13 at 06:20

            It seems you are using Modin in which engine initialization is being occurred while importing, i.e. at this moment import modin.pandas as pd. You don't need to create dask client yourself after that because dask environment has already been initialized. But if you want to create dask client yourself, you just need to move some lines:



            How to solve type object 'Series' has no attribute '_get_dtypes' error using modin.pandas?
            Asked 2021-Mar-09 at 06:36

            I am using modin.pandas to remove the duplicates from dataframe.



            Answered 2021-Mar-09 at 06:36

            It looks like that Modin version, which you are using, is old enough. I don't have the issue on the latest master. Please, try install Modin from sources:


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


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            Install modin

            Modin can be installed with pip on Linux, Windows and MacOS:.


            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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