msgpack-python | MessagePack serializer implementation for Python msgpack | Serialization library

 by   msgpack Python Version: v1.0.5 License: Non-SPDX

kandi X-RAY | msgpack-python Summary

kandi X-RAY | msgpack-python Summary

msgpack-python is a Python library typically used in Utilities, Serialization applications. msgpack-python has no bugs, it has no vulnerabilities, it has build file available and it has medium support. However msgpack-python has a Non-SPDX License. You can install using 'pip install msgpack-python' or download it from GitHub, PyPI.

MessagePack is an efficient binary serialization format. It lets you exchange data among multiple languages like JSON. But it's faster and smaller. This package provides CPython bindings for reading and writing MessagePack data.

            kandi-support Support

              msgpack-python has a medium active ecosystem.
              It has 1755 star(s) with 224 fork(s). There are 47 watchers for this library.
              There were 1 major release(s) in the last 12 months.
              There are 8 open issues and 262 have been closed. On average issues are closed in 143 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of msgpack-python is v1.0.5

            kandi-Quality Quality

              msgpack-python has 0 bugs and 0 code smells.

            kandi-Security Security

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

            kandi-License License

              msgpack-python has a Non-SPDX License.
              Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

            kandi-Reuse Reuse

              msgpack-python 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 are not available. Examples and code snippets are available.
              msgpack-python saves you 853 person hours of effort in developing the same functionality from scratch.
              It has 2188 lines of code, 215 functions and 25 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed msgpack-python and discovered the below as its top functions. This is intended to give you an instant insight into msgpack-python implemented functionality, and help decide if they suit your requirements.
            • Unpack a binary stream
            • Create a new Timestamp instance from bytes
            • Feed data into the buffer
            • Get the raw data
            • Unpack a memoryview from an object
            • Read n bytes from the buffer
            • Convert to UNIX time
            • Convert to Unix time
            • Returns a datetime object
            • Return True if there are data in buffer
            • Unpack binary data
            • Reserve n bytes
            • Reads an array from the stream
            • Read a header
            • Pack data
            • Profile a function
            • Ensure that the source file exists
            • Run cythonize
            Get all kandi verified functions for this library.

            msgpack-python Key Features

            No Key Features are available at this moment for msgpack-python.

            msgpack-python Examples and Code Snippets

            Getting Started
            Pythondot img1Lines of Code : 137dot img1no licencesLicense : No License
            copy iconCopy
            ├── configs
            │   ├──
            │   ├── dev.json
            │   └── main.json
            ├── input
            ├── log
            │   └──
            ├── ngram
            │   └──
            ├── requirements.txt
            ├── results
            ├── trained
            │   ├── trai  
            MessagePack for Python-How to use-Packing/unpacking of custom data type
            Pythondot img2Lines of Code : 21dot img2License : Non-SPDX (NOASSERTION)
            copy iconCopy
            import datetime
            import msgpack
            useful_dict = {
                "id": 1,
            def decode_datetime(obj):
                if '__datetime__' in obj:
                    obj = datetime.datetime.strptime(obj["as_str"], "%Y%m%dT%H:%M:%S.%f")
                return o  
            MessagePack for Python-How to use-Extended types
            Pythondot img3Lines of Code : 19dot img3License : Non-SPDX (NOASSERTION)
            copy iconCopy
            >>> import msgpack
            >>> import array
            >>> def default(obj):
            ...     if isinstance(obj, array.array) and obj.typecode == 'd':
            ...         return msgpack.ExtType(42, obj.tostring())
            ...     raise TypeError("Unknown type: %r" %   
            No such file or directory: '/opt/anaconda3/lib/python3.8/site-packages/rtree/lib'
            Pythondot img4Lines of Code : 4dot img4License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            python is /opt/anaconda3/bin/python
            python is /usr/local/bin/python
            python is /usr/bin/python
            mamba fails to create env
            Pythondot img5Lines of Code : 24dot img5License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            name: nbdev
              - fastai
              - defaults
              - conda-forge
              - _r-mutex
              - _tflow_select
              - absl-py
              - alabaster
            name: nbdev
              - fastai
              - defaults
              - conda-forge
              - p
            copy iconCopy
               The code below implements a simple ZeroMQ and MessagePack RPC server.
               Written by Wojciech M. Zabolotny, or
               The code is based on a post: "Simple object based R
            Can't install alpaca_trade_api
            Pythondot img7Lines of Code : 41dot img7License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            $ pip install alpaca-trade-api
            Collecting alpaca-trade-api
                  Downloading alpaca_trade_api-1.2.1-py3-none-any.whl (39 kB)
                Collecting urllib3<2,>1.24
                  Downloading urllib3-1.26.4-py2.py3-none-any.whl (153 kB)
            Dask map_partitions fails when function depends on large array
            Pythondot img8Lines of Code : 6dot img8License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            def do_something(partition, big_array):
                # some operations involving partition and big_array
            array_on_workers = client.scatter(big_array)
            dask_dataframe.map_partitions(do_something, array_on_workers)
            Jupyter Notebook Cannot Connect to Kernel, Likely due to Zipline / AssertionError
            Pythondot img9Lines of Code : 10dot img9License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            # Create environment
            conda create -n zipline_env python=3.6 ipykernel
            # Activate environment, make sure you can see it in jupyter notebooks
            conda activate zipline_env
            python -m ipykernel install --user --name=zipline_env
            # Install Ziplin
            Apache Superset TypeError while starting with OAuth authentication enabled
            Pythondot img10Lines of Code : 14dot img10License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            OAUTH_PROVIDERS = [{
                    "name": "github",
                    "icon": "fa-github",
                    "remote_app": {
                        "client_id": "" ,
                        "client_secret": "",
                        "api_base_url": "",

            Community Discussions


            UnsatisfiableError on importing environment pywin32==300 (Requested package -> Available versions)
            Asked 2021-Dec-03 at 14:58

            Good day

            I am getting an error while importing my environment:



            Answered 2021-Dec-03 at 09:22

            Build tags in you environment.yml are quite strict requirements to satisfy and most often not needed. In your case, changing the yml file to



            dask-yarn job fails with dumps_msgpack ImportError while reading parquet
            Asked 2021-Apr-29 at 13:56

            I am trying to do a simple read and count of a small parquet file (10K records) using dask-yarn on an AWS EMR cluster with one master and one worker node, both are m5.xlarge instances.

            I am trying to execute the following code just to test my cluster:



            Answered 2021-Apr-29 at 12:43

            Your dask and distributed versions have gone out of sync, 2021.4.0 versus 2021.4.1. Updating dask should fix this. Note that you need to ensure that the exact same versions are also in the environment you are using for YARN.



            Updating packages in conda
            Asked 2021-Apr-14 at 20:26

            I have a problem with updating packages in conda. The list of my installed packages is:



            Answered 2021-Apr-14 at 20:26

            Channel pypi means that the package was installed with pip. You may need to upgrade it with pip as well



            Jupyter Notebook Cannot Connect to Kernel, Likely due to Zipline / AssertionError
            Asked 2021-Apr-12 at 04:17

            All of my virtual environments work fine, except for one in which the jupyter notebook won't connect for kernel. This environment has Zipline in it, so I expect there is some dependency that is a problem there, even though I installed all packages with Conda.

            I've read the question and answers here, and unfortunately downgrading tornado to 5.1.1 didn't work nor do I get ValueErrors. I am, however, getting an AssertionError that appears related to the Class NSProcessInfo.

            I'm on an M1 Mac. Log from terminal showing the error below, and my environment file is below that. Can someone help me get this kernel working? Thank you!



            Answered 2021-Apr-04 at 18:14

            Figured it out.

            What works:



            Salt states. If variables have some word in stdout
            Asked 2021-Mar-10 at 10:54

            There is a web page with a large piece of text on it.

            I want to configure the state to perform a certain action if curl returns an error.

            If the variable doesn't contain 'StatusDescription : OK'

            How can I set up a check for a piece of text that is inside a variable



            Answered 2021-Mar-10 at 10:54

            I want to configure the state to perform a certain action if curl returns an error.

            There is a Salt state called http which can query a URL and return the status. Using this (instead of curl) we can check for the status code(s) (200, 201, etc.), as well as matching text. Then we can use requisites to run subsequent states depending on the success/failure of the http.query.


            I have added a check for status code of 200, you can omit - status: 200 if you don't care about the status code.



            Salt stack: how to set up a package installation check with Chocolate (state)
            Asked 2021-Feb-02 at 06:08

            During the execution of the state, the manager "Chocolate" installs the package RealVNC (Program Files\Real VNC) Next, I create a key in the registry. I want the keys to be created only if the VNC installation was successful. I have tried



            Answered 2021-Feb-02 at 06:08

            The requisites that we mention, such as require, watch, etc. should be references to other states that are "scheduled" to run on the targets.

            About watch

            Even though the path is created by installing the "realvnc" package, there is no Salt state to handle the path. That is why it is showing the error.

            Quoting from documentation for watch:

            A watch requisite is used to add additional behavior when there are changes in other states.

            So if you want to watch on a file, then there should be a state that "handles" the file using the file module.

            About require

            Also, while specifying require you are referring to pkg module, so Saltstack would look for pkg.installed for realvnc. Whereas you are using chocolatey.installed.

            So with below modifications it should work fine.



            "ImportError: No module named seaborn" in Azure ML
            Asked 2020-Oct-22 at 16:57

            Created a new compute instance in Azure ML and trained a model with out any issue. I wanted to draw a pairplot using seaborn but I keep getting the error "ImportError: No module named seaborn"

            I ran !conda list and I can see seaborn in the list



            Answered 2020-Sep-07 at 04:17

            I just did the following and wasn't able to reproduce your error:

            1. make a new compute instance
            2. open it up using JupyterLab
            3. open a new terminal
            4. conda activate azureml_py36
            5. conda install seaborn -y
            6. open a new notebook and run import seaborn as sns
            1. Are you using the kernel, Python 3.6 - AzureML (i.e. the azureml_py36 conda env)?
            2. Have you tried restarting the kernel and/or creating a new compute instance?



            Import rasterio failed. Reason: image not found
            Asked 2020-Sep-22 at 05:37

            I'm going to use rasterio in python. I downloaded rasterio via



            Answered 2020-Sep-22 at 05:37

            I've got some experience with rasterio, but I am not nearly a master with it. If I remember correctly, rasterio requires you to have installed the program GDAL(both binaries and python utilities), and some other dependencies listed on the PyPi page. I don't use conda at the moment, I like to use the regular python 3.8 installer with pip. Given what I'm seeing with your installation, I would uninstall rasterio and follow a different installation procedure.

            I follow the instructions listed here:
            This page also has separate instructions for those using Anaconda.

            The GDAL installation is by far the most annoying but once it's done, the hard part is over. The python utilities for both rasterio and gdal can be found here:
            The second link is also provided on the PyPi page but I like to keep it bookmarked because there's a lot of good resources there!



            how to download all the python packages mentioned in the requirement.txt to a folder in linux?
            Asked 2020-Jun-30 at 21:01

            I want to download all the python packages mentioned in the requirement.txt to a folder in Linux. I don't want to install them. I just need to download them.

            python version is 3.6

            list of packages in the requirement.txt



            Answered 2020-Jun-30 at 21:01

            The documentation gives what you want : pip download

            pip download does the same resolution and downloading as pip install, but instead of installing the dependencies, it collects the downloaded distributions into the directory provided


            So you may try these option with pip download :



            GPU Dask Cuda cluster: client.submit
            Asked 2020-Jun-13 at 15:37

            I am quite familiar with Dask distributed for CPUs. I'd like to explore a transition to running my code on GPU cores. When I submit a task to the LocalCUDACluster I get this error:



            Answered 2020-Jun-13 at 15:37

            It looks like this question has an answer in the comments. I'm going to copy a response from Nick Becker

            Dask's distributed scheduler is single threaded (CPU and GPU), and Dask-CUDA uses a one worker per GPU model. This means that each task assigned to a given GPU will run serially, but that the task itself will use the GPU for parallelized computation. You may want to look at the Dask documentation and explore Dask.Array (which also supports GPU arrays).


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


            No vulnerabilities reported

            Install msgpack-python

            You can install using 'pip install msgpack-python' or download it from GitHub, PyPI.
            You can use msgpack-python 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.


            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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