numpy | fundamental package for scientific computing | Data Manipulation library

 by   numpy Python Version: 2.0.0rc1 License: BSD-3-Clause

kandi X-RAY | numpy Summary

kandi X-RAY | numpy Summary

numpy is a Python library typically used in Utilities, Data Manipulation, Numpy applications. numpy has build file available, it has a Permissive License and it has medium support. However numpy has 152 bugs and it has 5 vulnerabilities. You can install using 'pip install numpy' or download it from GitHub, PyPI.

NumPy is the fundamental package for scientific computing with Python.

            kandi-support Support

              numpy has a medium active ecosystem.
              It has 23755 star(s) with 8200 fork(s). There are 587 watchers for this library.
              There were 5 major release(s) in the last 6 months.
              There are 1995 open issues and 9504 have been closed. On average issues are closed in 341 days. There are 170 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of numpy is 2.0.0rc1

            kandi-Quality Quality

              numpy has 152 bugs (5 blocker, 0 critical, 140 major, 7 minor) and 1954 code smells.

            kandi-Security Security

              numpy has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              numpy code analysis shows 5 unresolved vulnerabilities (5 blocker, 0 critical, 0 major, 0 minor).
              There are 26 security hotspots that need review.

            kandi-License License

              numpy 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

              numpy 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.
              numpy saves you 126151 person hours of effort in developing the same functionality from scratch.
              It has 132876 lines of code, 10561 functions and 454 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed numpy and discovered the below as its top functions. This is intended to give you an instant insight into numpy implemented functionality, and help decide if they suit your requirements.
            • Create a configuration object .
            • Create a row from a text file .
            • Analyze the group .
            • Einsum operator .
            • Analyze block .
            • Pad an array with a given padding .
            • Compute the gradient of a function .
            • Calculate the percentile of an array .
            • Computes the einsum path .
            • Read data from a file .
            Get all kandi verified functions for this library.

            numpy Key Features

            No Key Features are available at this moment for numpy.

            numpy Examples and Code Snippets

            Advanced indexing
            Pythondot img1Lines of Code : 522dot img1License : Permissive (BSD-3-Clause)
            copy iconCopy
            Integer array indexing allows selection of arbitrary items in the array
            based on their *N*-dimensional index. Each integer array represents a number
            of indices into that dimension.
            Negative values are permitted in the index arrays and work as they   
            Pythondot img2Lines of Code : 435dot img2License : Permissive (BSD-3-Clause)
            copy iconCopy
            The :ref:`array interface protocol ` defines a way for
            array-like objects to re-use each other's data buffers. Its implementation
            relies on the existence of the following attributes or methods:
            -  ``__array_interface__``: a Python dictionary contai  
            Quick Start
            Pythondot img3Lines of Code : 288dot img3License : Permissive (BSD-3-Clause)
            copy iconCopy
            And I do not intend to export the build to other users or target a
            different CPU than what the host has.
            Set `native` for baseline, or manually specify the CPU features in case of option
            `native` isn't supported by your platform::
                python setup  
            numpy - conf-source
            Pythondot img4Lines of Code : 293dot img4License : Non-SPDX (BSD 3-Clause "New" or "Revised" License)
            copy iconCopy
            import os
            import re
            import sys
            import importlib
            # Minimum version, enforced by sphinx
            needs_sphinx = '4.3'
            # This is a nasty hack to use platform-agnostic names for types in the
            # documentation.
            # must be kept alive to hold the patched names
            numpy - gen features
            Pythondot img5Lines of Code : 159dot img5License : Non-SPDX (BSD 3-Clause "New" or "Revised" License)
            copy iconCopy
            Generate CPU features tables from CCompilerOpt
            from os import sys, path
            from numpy.distutils.ccompiler_opt import CCompilerOpt
            class FakeCCompilerOpt(CCompilerOpt):
                # disable caching no need for it
                conf_nocache = True
                def __init  
            numpy - build index
            Pythondot img6Lines of Code : 85dot img6License : Non-SPDX (BSD 3-Clause "New" or "Revised" License)
            copy iconCopy
            Scan the directory of nep files and extract their metadata.  The
            metadata is passed to Jinja for filling out the toctrees for various NEP
            import os
            import jinja2
            import glob
            import re
            def render(tpl_path, context):
                path, fi  
            ValueError: could not convert string to float: 'Null' (Pandas)
            Pythondot img7Lines of Code : 4dot img7License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
  [2, 'QTY'] = float('nan')
  [2, 'QTY'] = np.nan
            copy iconCopy
            import netCDF4 as nc
            import numpy as np
            import matplotlib.pyplot as plt
            import as ccrs
            import pandas as pd
            def road_map():    
                # Open the file for highway metadata to read csv data
                highway_metadata = pd.read_csv('miles
            Pandas finding a text in row and assign a dummy variable value based on this
            Pythondot img9Lines of Code : 34dot img9License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            import numpy as np
            d = {'listings':listings, 'scripting':scripting, 'medical':medical}
            for k,v in d.items():
                df[k] = df['input'].str.contains('|'.join(v))
            arr = df[list(d)].to_numpy()
            tmp = np.zeros(arr.shape, dtype='int8')
            I don't understand how the second bracket works
            Pythondot img10Lines of Code : 22dot img10License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            y = np.array([1,2,1,3])
            array([True, False, True, False])
            x = np.array([[1,2],[3,4],[5,6],[7,8]])
                array([[1, 2],
                       [3, 4],
                       [5, 6],
                       [7, 8]])

            Community Discussions


            Why is `np.sum(range(N))` very slow?
            Asked 2022-Mar-29 at 14:31

            I saw a video about speed of loops in python, where it was explained that doing sum(range(N)) is much faster than manually looping through range and adding the variables together, since the former runs in C due to built-in functions being used, while in the latter the summation is done in (slow) python. I was curious what happens when adding numpy to the mix. As I expected np.sum(np.arange(N)) is the fastest, but sum(np.arange(N)) and np.sum(range(N)) are even slower than doing the naive for loop.

            Why is this?

            Here's the script I used to test, some comments about the supposed cause of slowing done where I know (taken mostly from the video) and the results I got on my machine (python 3.10.0, numpy 1.21.2):

            updated script:



            Answered 2021-Oct-16 at 17:42

            From the cpython source code for sum sum initially seems to attempt a fast path that assumes all inputs are the same type. If that fails it will just iterate:



            Installing scipy and scikit-learn on apple m1
            Asked 2022-Mar-22 at 06:21

            The installation on the m1 chip for the following packages: Numpy 1.21.1, pandas 1.3.0, torch 1.9.0 and a few other ones works fine for me. They also seem to work properly while testing them. However when I try to install scipy or scikit-learn via pip this error appears:

            ERROR: Failed building wheel for numpy

            Failed to build numpy

            ERROR: Could not build wheels for numpy which use PEP 517 and cannot be installed directly

            Why should Numpy be build again when I have the latest version from pip already installed?

            Every previous installation was done using python3.9 -m pip install ... on Mac OS 11.3.1 with the apple m1 chip.

            Maybe somebody knows how to deal with this error or if its just a matter of time.



            Answered 2021-Aug-02 at 14:33

            Please see this note of scikit-learn about

            Installing on Apple Silicon M1 hardware

            The recently introduced macos/arm64 platform (sometimes also known as macos/aarch64) requires the open source community to upgrade the build configuation and automation to properly support it.

            At the time of writing (January 2021), the only way to get a working installation of scikit-learn on this hardware is to install scikit-learn and its dependencies from the conda-forge distribution, for instance using the miniforge installers:


            The following issue tracks progress on making it possible to install scikit-learn from PyPI with pip:




            How could I speed up my written python code: spheres contact detection (collision) using spatial searching
            Asked 2022-Mar-13 at 15:43

            I am working on a spatial search case for spheres in which I want to find connected spheres. For this aim, I searched around each sphere for spheres that centers are in a (maximum sphere diameter) distance from the searching sphere’s center. At first, I tried to use scipy related methods to do so, but scipy method takes longer times comparing to equivalent numpy method. For scipy, I have determined the number of K-nearest spheres firstly and then find them by cKDTree.query, which lead to more time consumption. However, it is slower than numpy method even by omitting the first step with a constant value (it is not good to omit the first step in this case). It is contrary to my expectations about scipy spatial searching speed. So, I tried to use some list-loops instead some numpy lines for speeding up using numba prange. Numba run the code a little faster, but I believe that this code can be optimized for better performances, perhaps by vectorization, using other alternative numpy modules or using numba in another way. I have used iteration on all spheres due to prevent probable memory leaks and …, where number of spheres are high.



            Answered 2022-Feb-14 at 10:23

            Have you tried FLANN?

            This code doesn't solve your problem completely. It simply finds the nearest 50 neighbors to each point in your 500000 point dataset:



            Error while downloading the requirements using pip install (setup command: use_2to3 is invalid.)
            Asked 2022-Mar-05 at 07:13

            version pip 21.2.4 python 3.6

            The command:



            Answered 2021-Nov-19 at 13:30

            It looks like setuptools>=58 breaks support for use_2to3:

            setuptools changelog for v58

            So you should update setuptools to setuptools<58 or avoid using packages with use_2to3 in the setup parameters.

            I was having the same problem, pip==19.3.1



            TypeError: load() missing 1 required positional argument: 'Loader' in Google Colab
            Asked 2022-Mar-04 at 11:01

            I am trying to do a regular import in Google Colab.
            This import worked up until now.
            If I try:



            Answered 2021-Oct-15 at 21:11

            Found the problem.
            I was installing pandas_profiling, and this package updated pyyaml to version 6.0 which is not compatible with the current way Google Colab imports packages.
            So just reverting back to pyyaml version 5.4.1 solved the problem.

            For more information check versions of pyyaml here.
            See this issue and formal answers in GitHub

            For reverting back to pyyaml version 5.4.1 in your code, add the next line at the end of your packages installations:



            How do I calculate square root in Python?
            Asked 2022-Feb-17 at 03:40

            I need to calculate the square root of some numbers, for example √9 = 3 and √2 = 1.4142. How can I do it in Python?

            The inputs will probably be all positive integers, and relatively small (say less than a billion), but just in case they're not, is there anything that might break?


            Note: This is an attempt at a canonical question after a discussion on Meta about an existing question with the same title.



            Answered 2022-Feb-04 at 19:44
            Option 1: math.sqrt()

            The math module from the standard library has a sqrt function to calculate the square root of a number. It takes any type that can be converted to float (which includes int) as an argument and returns a float.



            Using a pip requirements file in a conda yml file throws AttributeError: 'FileNotFoundError'
            Asked 2022-Jan-23 at 13:29

            I have a requirements.txt like



            Answered 2022-Jan-23 at 13:29
            Changes to Pip Behavior in 21.2.1

            A recent change in the Pip code has changed its behavior to be more strict with respect to file: URI syntax. As pointed out by a PyPA member and Pip developer, the syntax file:requirements.txt is not a valid URI according to the RFC8089 specification.

            Instead, one must either drop the file: scheme altogether:



            Problem with memory allocation in Julia code
            Asked 2022-Jan-19 at 09:34

            I used a function in Python/Numpy to solve a problem in combinatorial game theory.



            Answered 2022-Jan-19 at 09:34

            The original code can be re-written in the following way:



            Efficient summation in Python
            Asked 2022-Jan-16 at 12:49

            I am trying to efficiently compute a summation of a summation in Python:

            WolframAlpha is able to compute it too a high n value: sum of sum.

            I have two approaches: a for loop method and an np.sum method. I thought the np.sum approach would be faster. However, they are the same until a large n, after which the np.sum has overflow errors and gives the wrong result.

            I am trying to find the fastest way to compute this sum.



            Answered 2022-Jan-16 at 12:49

            (fastest methods, 3 and 4, are at the end)

            In a fast NumPy method you need to specify dtype=np.object so that NumPy does not convert Python int to its own dtypes (np.int64 or others). It will now give you correct results (checked it up to N=100000).



            NumPy 1.21.2 may not yet support Python 3.10
            Asked 2021-Nov-27 at 20:37

            Python 3.10 is released and when I try to install NumPy it gives me this: NumPy 1.21.2 may not yet support Python 3.10.. what should I do?



            Answered 2021-Oct-06 at 12:26

            If on Windows, numpy has not yet released a precompiled wheel for Python 3.10. However you can try the unofficial wheels available at . Specifically look for

            • numpy‑1.21.2+mkl‑cp310‑cp310‑win_amd64.whl or
            • numpy‑1.21.2+mkl‑cp310‑cp310‑win32.whl

            depending on you system architecture.

            After downloading the file go to the download directory and run pip install ".whl".)

            (I have personally installed numpy‑1.21.2+mkl‑cp310‑cp310‑win_amd64.whl and it worked for me.)


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


            No vulnerabilities reported

            Install numpy

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


            The NumPy project welcomes your expertise and enthusiasm!. Small improvements or fixes are always appreciated; issues labeled as "good first issue" may be a good starting point. If you are considering larger contributions to the source code, please contact us through the mailing list first.
            Find more information at:

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