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pandas | powerful data analysis / manipulation library

 by   pandas-dev Python Version: 1.5.2 License: BSD-3-Clause

 by   pandas-dev Python Version: 1.5.2 License: BSD-3-Clause

kandi X-RAY | pandas Summary

pandas is a Python library typically used in Data Science, Pandas applications. pandas has build file available, it has a Permissive License and it has high support. However pandas has 2481 bugs and it has 4 vulnerabilities. You can install using 'pip install pandas' or download it from GitHub, PyPI.
pandas is a Python package that provides fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way towards this goal.
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Support
Quality
Quality
Security
Security
License
License
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kandi-support Support

  • pandas has a highly active ecosystem.
  • It has 36647 star(s) with 15679 fork(s). There are 1117 watchers for this library.
  • There were 5 major release(s) in the last 6 months.
  • There are 3534 open issues and 20265 have been closed. On average issues are closed in 505 days. There are 146 open pull requests and 0 closed requests.
  • It has a positive sentiment in the developer community.
  • The latest version of pandas is 1.5.2
pandas Support
Best in #Python
Average in #Python
pandas Support
Best in #Python
Average in #Python

quality kandi Quality

  • pandas has 2481 bugs (7 blocker, 0 critical, 2300 major, 174 minor) and 3690 code smells.
pandas Quality
Best in #Python
Average in #Python
pandas Quality
Best in #Python
Average in #Python

securitySecurity

  • pandas has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • pandas code analysis shows 4 unresolved vulnerabilities (4 blocker, 0 critical, 0 major, 0 minor).
  • There are 22 security hotspots that need review.
pandas Security
Best in #Python
Average in #Python
pandas Security
Best in #Python
Average in #Python

license License

  • pandas 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.
pandas License
Best in #Python
Average in #Python
pandas License
Best in #Python
Average in #Python

buildReuse

  • pandas 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.
  • pandas saves you 667226 person hours of effort in developing the same functionality from scratch.
  • It has 330153 lines of code, 22612 functions and 1317 files.
  • It has high code complexity. Code complexity directly impacts maintainability of the code.
pandas Reuse
Best in #Python
Average in #Python
pandas Reuse
Best in #Python
Average in #Python
Top functions reviewed by kandi - BETA

kandi has reviewed pandas and discovered the below as its top functions. This is intended to give you an instant insight into pandas implemented functionality, and help decide if they suit your requirements.

  • Write the table to a LaTeX file .
    • Convert argument to datetime index .
      • Add numeric operations .
        • Normalize JSON data .
          • Read data from a JSON file .
            • Convert wide to long .
              • Merge two DataFrames .
                • Read data from an XML file .
                  • Create a loc indexer .
                    • Cut an array .

                      Get all kandi verified functions for this library.

                      Get all kandi verified functions for this library.

                      pandas Key Features

                      Easy handling of missing data (represented as NaN, NA, or NaT) in floating point as well as non-floating point data

                      Size mutability: columns can be inserted and deleted from DataFrame and higher dimensional objects

                      Automatic and explicit data alignment: objects can be explicitly aligned to a set of labels, or the user can simply ignore the labels and let Series, DataFrame, etc. automatically align the data for you in computations

                      Powerful, flexible group by functionality to perform split-apply-combine operations on data sets, for both aggregating and transforming data

                      Make it easy to convert ragged, differently-indexed data in other Python and NumPy data structures into DataFrame objects

                      Intelligent label-based slicing, fancy indexing, and subsetting of large data sets

                      Intuitive merging and joining data sets

                      Flexible reshaping and pivoting of data sets

                      Hierarchical labeling of axes (possible to have multiple labels per tick)

                      Robust IO tools for loading data from flat files (CSV and delimited), Excel files, databases, and saving/loading data from the ultrafast HDF5 format

                      Time series-specific functionality: date range generation and frequency conversion, moving window statistics, date shifting and lagging

                      pandas Examples and Code Snippets

                      See all related Code Snippets

                      Community Discussions

                      Trending Discussions on pandas
                      • Installing scipy and scikit-learn on apple m1
                      • Error while downloading the requirements using pip install (setup command: use_2to3 is invalid.)
                      • Mapping complex JSON to Pandas Dataframe
                      • AttributeError: Can't get attribute 'new_block' on <module 'pandas.core.internals.blocks'>
                      • How to update pandas DataFrame.drop() for Future Warning - all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only
                      • Cannot set up a conda environment with python 3.10
                      • ImportError: cannot import name 'ABCIndexClass' from 'pandas.core.dtypes.generic'
                      • Merge two pandas DataFrame based on partial match
                      • Create a new column in a Pandas DataFrame from existing column names
                      • After conda update, python kernel crashes when matplotlib is used
                      Trending Discussions on pandas

                      QUESTION

                      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.

                      ANSWER

                      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:

                      https://github.com/conda-forge/miniforge

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

                      https://github.com/scikit-learn/scikit-learn/issues/19137

                      Source https://stackoverflow.com/questions/68620927

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

                      Vulnerabilities

                      No vulnerabilities reported

                      Install pandas

                      To install pandas from source you need Cython in addition to the normal dependencies above. Cython can be installed from PyPI:.

                      Support

                      The official documentation is hosted on PyData.org: https://pandas.pydata.org/pandas-docs/stable.

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                      Install
                      • pip install pandas

                      Clone
                      • https://github.com/pandas-dev/pandas.git

                      • gh repo clone pandas-dev/pandas

                      • git@github.com:pandas-dev/pandas.git

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