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1. Shard Calculation

Query:

Response:

Calculated Shard: 68 (from laksa041)

2. Crawled Status Check

Query:

curl -X POST \
  'http://laksa068.int.ahrefs:8124/' \
  -H 'Content-Type: text/plain' \
  -H 'X-ClickHouse-Database: crawler3' \
  -H 'Authorization: Basic YXBpOg==' \
  -d 'SELECT getAhrefsURLFromUnparsed(src_unparsed) AS found_url, ifNull(toUnixTimestamp(download_stamp), 0) AS crawl_time, ifNull(toUnixTimestamp(props_url_first_seen), 0) AS first_indexed_time, download_http_code AS http_code, src_unparsed AS src_unparsed, src_root_hash AS src_root_hash, history_drop_reason AS history_drop_reason, meta_title AS meta_title, meta_descriptions AS meta_descriptions, attrs_boilerpipe_text AS attrs_boilerpipe_text, attrs_markdown AS attrs_markdown, attrs_readable_markdown AS attrs_readable_markdown, meta_canonical AS meta_canonical FROM crawler3.page_info_local FINAL PREWHERE (src_root_hash, src_unparsed) IN ((getAhrefsRootHashFromUnparsed(getAhrefsUnparsedNoserviceFromURL(\'https://pandas.pydata.org/docs/getting_started/install.html\')), getAhrefsUnparsedNoserviceFromURL(\'https://pandas.pydata.org/docs/getting_started/install.html\'))) FORMAT JSONEachRow'

Response:

{"found_url":"https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html","crawl_time":1775232469,"first_indexed_time":1580522412,"http_code":200,"src_unparsed":"org,pydata!pandas,\/docs\/getting_started\/install.html s443","src_root_hash":"10355827761381095868","history_drop_reason":null,"meta_title":"Installation — pandas 3.0.2 documentation","meta_descriptions":[],"attrs_boilerpipe_text":"The pandas development team officially distributes pandas for installation\nthrough the following methods:\nAvailable on\nconda-forge\nfor installation with the conda package manager.\nAvailable on\nPyPI\nfor installation with pip.\nAvailable on\nGithub\nfor installation from source.\nNote\npandas may be installable from other sources besides the ones listed above,\nbut they are\nnot\nmanaged by the pandas development team.\nPython version support\n#\nSee\nPython support policy\n.\nInstalling pandas\n#\nInstalling with Conda\n#\nFor users working with the\nConda\npackage manager,\npandas can be installed from the\nconda-forge\nchannel.\nconda\ninstall\n-c\nconda-forge\npandas\nTo install the Conda package manager on your system, the\nMiniforge distribution\nis recommended.\nAdditionally, it is recommended to install and run pandas from a virtual environment.\nconda\ncreate\n-c\nconda-forge\n-n\nname_of_my_env\npython\npandas\n# On Linux or MacOS\nsource\nactivate\nname_of_my_env\n# On Windows\nactivate\nname_of_my_env\nTip\nFor users that are new to Python, the easiest way to install Python, pandas, and the\npackages that make up the\nPyData\nstack such as\nSciPy\n,\nNumPy\nand\nMatplotlib\nis with\nAnaconda\n, a cross-platform\n(Linux, macOS, Windows) Python distribution for data analytics and\nscientific computing.\nHowever, pandas from Anaconda is\nnot\nofficially managed by the pandas development team.\nInstalling with pip\n#\nFor users working with the\npip\npackage manager,\npandas can be installed from\nPyPI\n.\npip\ninstall\npandas\npandas can also be installed with sets of optional dependencies to enable certain functionality. For example,\nto install pandas with the optional dependencies to read Excel files.\npip\ninstall\n\"pandas[excel]\"\nThe full list of extras that can be installed can be found in the\ndependency section.\nAdditionally, it is recommended to install and run pandas from a virtual environment, for example,\nusing the Python standard library’s\nvenv\nInstalling from source\n#\nSee the\ncontributing guide\nfor complete instructions on building from the git source tree.\nFurther, see\ncreating a development environment\nif you wish to create\na pandas development environment.\nInstalling the development version of pandas\n#\nInstalling the development version is the quickest way to:\nTry a new feature that will be shipped in the next release (that is, a feature from a pull-request that was recently merged to the main branch).\nCheck whether a bug you encountered has been fixed since the last release.\nThe development version is usually uploaded daily to the scientific-python-nightly-wheels\nindex from the PyPI registry of anaconda.org. You can install it by running.\npip\ninstall\n--pre\n--extra-index-url\nhttps:\/\/pypi.anaconda.org\/scientific-python-nightly-wheels\/simple\npandas\nNote\nYou might be required to uninstall an existing version of pandas to install the development version.\npip\nuninstall\npandas\n-y\nRunning the test suite\n#\nIf pandas has been installed\nfrom source\n, running\npytest\npandas\nwill run all of pandas unit tests.\nThe unit tests can also be run from the pandas module itself with the\ntest()\nfunction. The packages required to run the tests\ncan be installed with\npip\ninstall\n\"pandas[test]\"\n.\nNote\nTest failures are not necessarily indicative of a broken pandas installation.\nDependencies\n#\nRequired dependencies\n#\npandas requires the following dependencies.\nPackage\nMinimum supported version\nNumPy\n1.26.0\npython-dateutil\n2.8.2\ntzdata\n*\n\/\n*\ntzdata\nis only required on Windows and Pyodide (Emscripten).\nGenerally, the minimum supported version is ~2 years old from the release date of a major or minor pandas version.\nOptional dependencies\n#\npandas has many optional dependencies that are only used for specific methods.\nFor example,\npandas.read_hdf()\nrequires the\npytables\npackage, while\nDataFrame.to_markdown()\nrequires the\ntabulate\npackage. If the\noptional dependency is not installed, pandas will raise an\nImportError\nwhen\nthe method requiring that dependency is called.\nWith pip, optional pandas dependencies can be installed or managed in a file (e.g. requirements.txt or pyproject.toml)\nas optional extras (e.g.\npandas[performance,\naws]\n). All optional dependencies can be installed with\npandas[all]\n,\nand specific sets of dependencies are listed in the sections below.\nGenerally, the minimum supported version is ~1 years old from the release date of a major or minor pandas version.\nOlder versions of optional dependencies may still work, but they are not tested or considered supported.\nPerformance dependencies (recommended)\n#\nNote\nYou are highly encouraged to install these libraries, as they provide speed improvements, especially\nwhen working with large data sets.\nInstallable with\npip\ninstall\n\"pandas[performance]\"\nDependency\nMinimum Version\npip extra\nNotes\nnumexpr\n2.10.2\nperformance\nAccelerates certain numerical operations by using multiple cores as well as smart chunking and caching to achieve large speedups\nbottleneck\n1.4.2\nperformance\nAccelerates certain types of\nnan\nby using specialized cython routines to achieve large speedup.\nnumba\n0.60.0\nperformance\nAlternative execution engine for operations that accept\nengine=\"numba\"\nusing a JIT compiler that translates Python functions to optimized machine code using the LLVM compiler.\nVisualization\n#\nInstallable with\npip\ninstall\n\"pandas[plot,\noutput-formatting]\"\n.\nDependency\nMinimum Version\npip extra\nNotes\nmatplotlib\n3.8.3\nplot\nPlotting library\nJinja2\n3.1.3\noutput-formatting\nConditional formatting with DataFrame.style\ntabulate\n0.9.0\noutput-formatting\nPrinting in Markdown-friendly format (see\ntabulate\n)\nComputation\n#\nInstallable with\npip\ninstall\n\"pandas[computation]\"\n.\nDependency\nMinimum Version\npip extra\nNotes\nSciPy\n1.14.1\ncomputation\nMiscellaneous statistical functions\nxarray\n2024.10.0\ncomputation\npandas-like API for N-dimensional data\nExcel files\n#\nInstallable with\npip\ninstall\n\"pandas[excel]\"\n.\nDependency\nMinimum Version\npip extra\nNotes\nxlrd\n2.0.1\nexcel\nReading for xls files\nxlsxwriter\n3.2.0\nexcel\nWriting for xlsx files\nopenpyxl\n3.1.5\nexcel\nReading \/ writing for Excel 2010 xlsx\/xlsm\/xltx\/xltm files\npyxlsb\n1.0.10\nexcel\nReading for xlsb files\npython-calamine\n0.3.0\nexcel\nReading for xls\/xlsx\/xlsm\/xlsb\/xla\/xlam\/ods files\nodfpy\n1.4.1\nexcel\nReading \/ writing for OpenDocument 1.2 files\nHTML\n#\nInstallable with\npip\ninstall\n\"pandas[html]\"\n.\nDependency\nMinimum Version\npip extra\nNotes\nBeautifulSoup4\n4.12.3\nhtml\nHTML parser for read_html\nhtml5lib\n1.1\nhtml\nHTML parser for read_html\nlxml\n4.9.2\nhtml\nHTML parser for read_html\nOne of the following combinations of libraries is needed to use the\ntop-level\nread_html()\nfunction:\nBeautifulSoup4\nand\nhtml5lib\nBeautifulSoup4\nand\nlxml\nBeautifulSoup4\nand\nhtml5lib\nand\nlxml\nOnly\nlxml\n, although see\nHTML Table Parsing\nfor reasons as to why you should probably\nnot\ntake this approach.\nWarning\nif you install\nBeautifulSoup4\nyou must install either\nlxml\nor\nhtml5lib\nor both.\nread_html()\nwill\nnot\nwork with\nonly\nBeautifulSoup4\ninstalled.\nYou are highly encouraged to read\nHTML Table Parsing gotchas\n.\nIt explains issues surrounding the installation and\nusage of the above three libraries.\nXML\n#\nInstallable with\npip\ninstall\n\"pandas[xml]\"\n.\nDependency\nMinimum Version\npip extra\nNotes\nlxml\n5.3.0\nxml\nXML parser for read_xml and tree builder for to_xml\nSQL databases\n#\nTraditional drivers are installable with\npip\ninstall\n\"pandas[postgresql,\nmysql,\nsql-other]\"\nDependency\nMinimum Version\npip extra\nNotes\nSQLAlchemy\n2.0.36\npostgresql,\nmysql,\nsql-other\nSQL support for databases other than sqlite\npsycopg2\n2.9.10\npostgresql\nPostgreSQL engine for sqlalchemy\npymysql\n1.1.1\nmysql\nMySQL engine for sqlalchemy\nadbc-driver-postgresql\n1.2.0\npostgresql\nADBC Driver for PostgreSQL\nadbc-driver-sqlite\n1.2.0\nsql-other\nADBC Driver for SQLite\nOther data sources\n#\nInstallable with\npip\ninstall\n\"pandas[hdf5,\nparquet,\niceberg,\nfeather,\nspss,\nexcel]\"\nDependency\nMinimum Version\npip extra\nNotes\nPyTables\n3.10.1\nhdf5\nHDF5-based reading \/ writing\nzlib\nhdf5\nCompression for HDF5\nfastparquet\n2024.11.0\nParquet reading \/ writing (pyarrow is default)\npyarrow\n13.0.0\nparquet, feather\nParquet, ORC, and feather reading \/ writing\nPyIceberg\n0.8.1\niceberg\nApache Iceberg reading \/ writing\npyreadstat\n1.2.8\nspss\nSPSS files (.sav) reading\nodfpy\n1.4.1\nexcel\nOpen document format (.odf, .ods, .odt) reading \/ writing\nWarning\nIf you want to use\nread_orc()\n, it is highly recommended to install pyarrow using conda.\nread_orc()\nmay fail if pyarrow was installed from pypi, and\nread_orc()\nis\nnot compatible with Windows OS.\nAccess data in the cloud\n#\nInstallable with\npip\ninstall\n\"pandas[fss,\naws,\ngcp]\"\nDependency\nMinimum Version\npip extra\nNotes\nfsspec\n2024.10.0\nfss, gcp, aws\nHandling files aside from simple local and HTTP (required\ndependency of s3fs, gcsfs).\ngcsfs\n2024.10.0\ngcp\nGoogle Cloud Storage access\ns3fs\n2024.10.0\naws\nAmazon S3 access\nClipboard\n#\nInstallable with\npip\ninstall\n\"pandas[clipboard]\"\n.\nDependency\nMinimum Version\npip extra\nNotes\nPyQt4\n\/\nPyQt5\n5.15.9\nclipboard\nClipboard I\/O\nqtpy\n2.4.2\nclipboard\nClipboard I\/O\nNote\nDepending on operating system, system-level packages may need to installed.\nFor clipboard to operate on Linux one of the CLI tools\nxclip\nor\nxsel\nmust be installed on your system.\nCompression\n#\nInstallable with\npip\ninstall\n\"pandas[compression]\"\nDependency\nMinimum Version\npip extra\nNotes\nZstandard\n0.19.0\ncompression\nZstandard compression\nTimezone\n#\nInstallable with\npip\ninstall\n\"pandas[timezone]\"\nDependency\nMinimum Version\npip extra\nNotes\npytz\n2024.2\ntimezone\nAlternative timezone library to\nzoneinfo\n.","attrs_markdown":"[Skip to main content](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#main-content)\n\nBack to top\n\nAnnouncement: pandas 3.0 released! Read more [in the blog post](https:\/\/pandas.pydata.org\/community\/blog\/pandas-3.0.html)\n\n[![pandas 3.0.2 documentation - Home](https:\/\/pandas.pydata.org\/docs\/_static\/pandas.svg) ![pandas 3.0.2 documentation - Home](https:\/\/pandas.pydata.org\/static\/img\/pandas_white.svg)](https:\/\/pandas.pydata.org\/docs\/index.html)\n\n- [Getting started](https:\/\/pandas.pydata.org\/docs\/getting_started\/index.html)\n- [User Guide](https:\/\/pandas.pydata.org\/docs\/user_guide\/index.html)\n- [API reference](https:\/\/pandas.pydata.org\/docs\/reference\/index.html)\n- [Development](https:\/\/pandas.pydata.org\/docs\/development\/index.html)\n- [Release notes](https:\/\/pandas.pydata.org\/docs\/whatsnew\/index.html)\n\nSearch `Ctrl`\\+`K`\n\n3\\.0 (stable)\n\n[dev](https:\/\/pandas.pydata.org\/docs\/dev\/getting_started\/install.html)[3\\.0 (stable)](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html)[2\\.3](https:\/\/pandas.pydata.org\/pandas-docs\/version\/2.3\/getting_started\/install.html)[2\\.2](https:\/\/pandas.pydata.org\/pandas-docs\/version\/2.2\/getting_started\/install.html)[2\\.1](https:\/\/pandas.pydata.org\/pandas-docs\/version\/2.1\/getting_started\/install.html)[2\\.0](https:\/\/pandas.pydata.org\/pandas-docs\/version\/2.0\/getting_started\/install.html)[1\\.5](https:\/\/pandas.pydata.org\/pandas-docs\/version\/1.5\/getting_started\/install.html)[1\\.4](https:\/\/pandas.pydata.org\/pandas-docs\/version\/1.4\/getting_started\/install.html)[1\\.3](https:\/\/pandas.pydata.org\/pandas-docs\/version\/1.3\/getting_started\/install.html)[1\\.2](https:\/\/pandas.pydata.org\/pandas-docs\/version\/1.2\/getting_started\/install.html)[1\\.1](https:\/\/pandas.pydata.org\/pandas-docs\/version\/1.1\/getting_started\/install.html)[1\\.0](https:\/\/pandas.pydata.org\/pandas-docs\/version\/1.0\/getting_started\/install.html)\n\n- [GitHub](https:\/\/github.com\/pandas-dev\/pandas)\n- [X](https:\/\/x.com\/pandas_dev)\n- [Mastodon](https:\/\/fosstodon.org\/@pandas_dev)\n\nSearch `Ctrl`\\+`K`\n\n- [Getting started](https:\/\/pandas.pydata.org\/docs\/getting_started\/index.html)\n- [User Guide](https:\/\/pandas.pydata.org\/docs\/user_guide\/index.html)\n- [API reference](https:\/\/pandas.pydata.org\/docs\/reference\/index.html)\n- [Development](https:\/\/pandas.pydata.org\/docs\/development\/index.html)\n- [Release notes](https:\/\/pandas.pydata.org\/docs\/whatsnew\/index.html)\n\n3\\.0 (stable)\n\n[dev](https:\/\/pandas.pydata.org\/docs\/dev\/getting_started\/install.html)[3\\.0 (stable)](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html)[2\\.3](https:\/\/pandas.pydata.org\/pandas-docs\/version\/2.3\/getting_started\/install.html)[2\\.2](https:\/\/pandas.pydata.org\/pandas-docs\/version\/2.2\/getting_started\/install.html)[2\\.1](https:\/\/pandas.pydata.org\/pandas-docs\/version\/2.1\/getting_started\/install.html)[2\\.0](https:\/\/pandas.pydata.org\/pandas-docs\/version\/2.0\/getting_started\/install.html)[1\\.5](https:\/\/pandas.pydata.org\/pandas-docs\/version\/1.5\/getting_started\/install.html)[1\\.4](https:\/\/pandas.pydata.org\/pandas-docs\/version\/1.4\/getting_started\/install.html)[1\\.3](https:\/\/pandas.pydata.org\/pandas-docs\/version\/1.3\/getting_started\/install.html)[1\\.2](https:\/\/pandas.pydata.org\/pandas-docs\/version\/1.2\/getting_started\/install.html)[1\\.1](https:\/\/pandas.pydata.org\/pandas-docs\/version\/1.1\/getting_started\/install.html)[1\\.0](https:\/\/pandas.pydata.org\/pandas-docs\/version\/1.0\/getting_started\/install.html)\n\n- [GitHub](https:\/\/github.com\/pandas-dev\/pandas)\n- [X](https:\/\/x.com\/pandas_dev)\n- [Mastodon](https:\/\/fosstodon.org\/@pandas_dev)\n\n- [Installation](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html)\n- [Package overview](https:\/\/pandas.pydata.org\/docs\/getting_started\/overview.html)\n- [Getting started tutorials](https:\/\/pandas.pydata.org\/docs\/getting_started\/intro_tutorials\/index.html)\n  - [What kind of data does pandas handle?](https:\/\/pandas.pydata.org\/docs\/getting_started\/intro_tutorials\/01_table_oriented.html)\n  - [How do I read and write tabular data?](https:\/\/pandas.pydata.org\/docs\/getting_started\/intro_tutorials\/02_read_write.html)\n  - [How do I select a subset of a `DataFrame`?](https:\/\/pandas.pydata.org\/docs\/getting_started\/intro_tutorials\/03_subset_data.html)\n  - [How do I create plots in pandas?](https:\/\/pandas.pydata.org\/docs\/getting_started\/intro_tutorials\/04_plotting.html)\n  - [How to create new columns derived from existing columns](https:\/\/pandas.pydata.org\/docs\/getting_started\/intro_tutorials\/05_add_columns.html)\n  - [How to calculate summary statistics](https:\/\/pandas.pydata.org\/docs\/getting_started\/intro_tutorials\/06_calculate_statistics.html)\n  - [How to reshape the layout of tables](https:\/\/pandas.pydata.org\/docs\/getting_started\/intro_tutorials\/07_reshape_table_layout.html)\n  - [How to combine data from multiple tables](https:\/\/pandas.pydata.org\/docs\/getting_started\/intro_tutorials\/08_combine_dataframes.html)\n  - [How to handle time series data with ease](https:\/\/pandas.pydata.org\/docs\/getting_started\/intro_tutorials\/09_timeseries.html)\n  - [How to manipulate textual data](https:\/\/pandas.pydata.org\/docs\/getting_started\/intro_tutorials\/10_text_data.html)\n- [Comparison with other tools](https:\/\/pandas.pydata.org\/docs\/getting_started\/comparison\/index.html)\n  - [Comparison with R \/ R libraries](https:\/\/pandas.pydata.org\/docs\/getting_started\/comparison\/comparison_with_r.html)\n  - [Comparison with SQL](https:\/\/pandas.pydata.org\/docs\/getting_started\/comparison\/comparison_with_sql.html)\n  - [Comparison with spreadsheets](https:\/\/pandas.pydata.org\/docs\/getting_started\/comparison\/comparison_with_spreadsheets.html)\n  - [Comparison with SAS](https:\/\/pandas.pydata.org\/docs\/getting_started\/comparison\/comparison_with_sas.html)\n  - [Comparison with Stata](https:\/\/pandas.pydata.org\/docs\/getting_started\/comparison\/comparison_with_stata.html)\n  - [Comparison with SPSS](https:\/\/pandas.pydata.org\/docs\/getting_started\/comparison\/comparison_with_spss.html)\n- [Community tutorials](https:\/\/pandas.pydata.org\/docs\/getting_started\/tutorials.html)\n\n- [Getting started](https:\/\/pandas.pydata.org\/docs\/getting_started\/index.html)\n- Installation\n\n# Installation[\\#](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#installation \"Link to this heading\")\nThe pandas development team officially distributes pandas for installation through the following methods:\n\n- Available on [conda-forge](https:\/\/anaconda.org\/conda-forge\/pandas) for installation with the conda package manager.\n- Available on [PyPI](https:\/\/pypi.org\/project\/pandas\/) for installation with pip.\n- Available on [Github](https:\/\/github.com\/pandas-dev\/pandas) for installation from source.\n\nNote\n\npandas may be installable from other sources besides the ones listed above, but they are **not** managed by the pandas development team.\n\n## Python version support[\\#](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#python-version-support \"Link to this heading\")\nSee [Python support policy](https:\/\/pandas.pydata.org\/docs\/development\/policies.html#policies-python-support).\n\n## Installing pandas[\\#](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#installing-pandas \"Link to this heading\")\n### Installing with Conda[\\#](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#installing-with-conda \"Link to this heading\")\nFor users working with the [Conda](https:\/\/conda.io\/en\/latest\/) package manager, pandas can be installed from the `conda-forge` channel.\n```\nconda install -c conda-forge pandas\n```\nTo install the Conda package manager on your system, the [Miniforge distribution](https:\/\/github.com\/conda-forge\/miniforge?tab=readme-ov-file#install) is recommended.\n\nAdditionally, it is recommended to install and run pandas from a virtual environment.\n```\nconda create -c conda-forge -n name_of_my_env python pandas\n# On Linux or MacOS\nsource activate name_of_my_env\n# On Windows\nactivate name_of_my_env\n```\nTip\n\nFor users that are new to Python, the easiest way to install Python, pandas, and the packages that make up the [PyData](https:\/\/pydata.org\/) stack such as [SciPy](https:\/\/scipy.org\/), [NumPy](https:\/\/numpy.org\/) and [Matplotlib](https:\/\/matplotlib.org\/) is with [Anaconda](https:\/\/docs.anaconda.com\/anaconda\/install\/), a cross-platform (Linux, macOS, Windows) Python distribution for data analytics and scientific computing.\n\nHowever, pandas from Anaconda is **not** officially managed by the pandas development team.\n\n### Installing with pip[\\#](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#installing-with-pip \"Link to this heading\")\nFor users working with the [pip](https:\/\/pip.pypa.io\/en\/stable\/) package manager, pandas can be installed from [PyPI](https:\/\/pypi.org\/project\/pandas\/).\n```\npip install pandas\n```\npandas can also be installed with sets of optional dependencies to enable certain functionality. For example, to install pandas with the optional dependencies to read Excel files.\n```\npip install \"pandas[excel]\"\n```\nThe full list of extras that can be installed can be found in the [dependency section.](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#install-optional-dependencies)\n\nAdditionally, it is recommended to install and run pandas from a virtual environment, for example, using the Python standard library’s [venv](https:\/\/docs.python.org\/3\/library\/venv.html)\n\n### Installing from source[\\#](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#installing-from-source \"Link to this heading\")\nSee the [contributing guide](https:\/\/pandas.pydata.org\/docs\/development\/contributing.html#contributing) for complete instructions on building from the git source tree. Further, see [creating a development environment](https:\/\/pandas.pydata.org\/docs\/development\/contributing_environment.html#contributing-environment) if you wish to create a pandas development environment.\n\n### Installing the development version of pandas[\\#](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#installing-the-development-version-of-pandas \"Link to this heading\")\nInstalling the development version is the quickest way to:\n\n- Try a new feature that will be shipped in the next release (that is, a feature from a pull-request that was recently merged to the main branch).\n- Check whether a bug you encountered has been fixed since the last release.\n\nThe development version is usually uploaded daily to the scientific-python-nightly-wheels index from the PyPI registry of anaconda.org. You can install it by running.\n```\npip install --pre --extra-index-url https:\/\/pypi.anaconda.org\/scientific-python-nightly-wheels\/simple pandas\n```\nNote\n\nYou might be required to uninstall an existing version of pandas to install the development version.\n```\npip uninstall pandas -y\n```\n\n## Running the test suite[\\#](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#running-the-test-suite \"Link to this heading\")\nIf pandas has been installed [from source](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#install-source), running `pytest pandas` will run all of pandas unit tests.\n\nThe unit tests can also be run from the pandas module itself with the [`test()`](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.test.html#pandas.test \"pandas.test\") function. The packages required to run the tests can be installed with `pip install \"pandas[test]\"`.\n\nNote\n\nTest failures are not necessarily indicative of a broken pandas installation.\n\n## Dependencies[\\#](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#dependencies \"Link to this heading\")\n### Required dependencies[\\#](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#required-dependencies \"Link to this heading\")\npandas requires the following dependencies.\n\n| Package | Minimum supported version |\n|---|---|\n| [NumPy](https:\/\/numpy.org\/) | 1\\.26.0 |\n| [python-dateutil](https:\/\/dateutil.readthedocs.io\/en\/stable\/) | 2\\.8.2 |\n| [tzdata](https:\/\/pypi.org\/project\/tzdata\/) \\* | \/ |\n\n\\* `tzdata` is only required on Windows and Pyodide (Emscripten).\n\nGenerally, the minimum supported version is ~2 years old from the release date of a major or minor pandas version.\n\n### Optional dependencies[\\#](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#optional-dependencies \"Link to this heading\")\npandas has many optional dependencies that are only used for specific methods. For example, [`pandas.read_hdf()`](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.read_hdf.html#pandas.read_hdf \"pandas.read_hdf\") requires the `pytables` package, while [`DataFrame.to_markdown()`](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.to_markdown.html#pandas.DataFrame.to_markdown \"pandas.DataFrame.to_markdown\") requires the `tabulate` package. If the optional dependency is not installed, pandas will raise an `ImportError` when the method requiring that dependency is called.\n\nWith pip, optional pandas dependencies can be installed or managed in a file (e.g. requirements.txt or pyproject.toml) as optional extras (e.g. `pandas[performance, aws]`). All optional dependencies can be installed with `pandas[all]`, and specific sets of dependencies are listed in the sections below.\n\nGenerally, the minimum supported version is ~1 years old from the release date of a major or minor pandas version. Older versions of optional dependencies may still work, but they are not tested or considered supported.\n\n#### Performance dependencies (recommended)[\\#](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#performance-dependencies-recommended \"Link to this heading\")\nNote\n\nYou are highly encouraged to install these libraries, as they provide speed improvements, especially when working with large data sets.\n\nInstallable with `pip install \"pandas[performance]\"`\n\n| Dependency | Minimum Version | pip extra | Notes |\n|---|---|---|---|\n| [numexpr](https:\/\/github.com\/pydata\/numexpr) | 2\\.10.2 | performance | Accelerates certain numerical operations by using multiple cores as well as smart chunking and caching to achieve large speedups |\n| [bottleneck](https:\/\/github.com\/pydata\/bottleneck) | 1\\.4.2 | performance | Accelerates certain types of `nan` by using specialized cython routines to achieve large speedup. |\n| [numba](https:\/\/github.com\/numba\/numba) | 0\\.60.0 | performance | Alternative execution engine for operations that accept `engine=\"numba\"` using a JIT compiler that translates Python functions to optimized machine code using the LLVM compiler. |\n\n#### Visualization[\\#](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#visualization \"Link to this heading\")\nInstallable with `pip install \"pandas[plot, output-formatting]\"`.\n\n| Dependency | Minimum Version | pip extra | Notes |\n|---|---|---|---|\n| [matplotlib](https:\/\/github.com\/matplotlib\/matplotlib) | 3\\.8.3 | plot | Plotting library |\n| [Jinja2](https:\/\/github.com\/pallets\/jinja) | 3\\.1.3 | output-formatting | Conditional formatting with DataFrame.style |\n| [tabulate](https:\/\/github.com\/astanin\/python-tabulate) | 0\\.9.0 | output-formatting | Printing in Markdown-friendly format (see [tabulate](https:\/\/github.com\/astanin\/python-tabulate)) |\n\n#### Computation[\\#](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#computation \"Link to this heading\")\nInstallable with `pip install \"pandas[computation]\"`.\n\n| Dependency | Minimum Version | pip extra | Notes |\n|---|---|---|---|\n| [SciPy](https:\/\/github.com\/scipy\/scipy) | 1\\.14.1 | computation | Miscellaneous statistical functions |\n| [xarray](https:\/\/github.com\/pydata\/xarray) | 2024\\.10.0 | computation | pandas-like API for N-dimensional data |\n\n#### Excel files[\\#](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#excel-files \"Link to this heading\")\nInstallable with `pip install \"pandas[excel]\"`.\n\n| Dependency | Minimum Version | pip extra | Notes |\n|---|---|---|---|\n| [xlrd](https:\/\/github.com\/python-excel\/xlrd) | 2\\.0.1 | excel | Reading for xls files |\n| [xlsxwriter](https:\/\/github.com\/jmcnamara\/XlsxWriter) | 3\\.2.0 | excel | Writing for xlsx files |\n| [openpyxl](https:\/\/github.com\/theorchard\/openpyxl) | 3\\.1.5 | excel | Reading \/ writing for Excel 2010 xlsx\/xlsm\/xltx\/xltm files |\n| [pyxlsb](https:\/\/github.com\/willtrnr\/pyxlsb) | 1\\.0.10 | excel | Reading for xlsb files |\n| [python-calamine](https:\/\/github.com\/dimastbk\/python-calamine) | 0\\.3.0 | excel | Reading for xls\/xlsx\/xlsm\/xlsb\/xla\/xlam\/ods files |\n| [odfpy](https:\/\/github.com\/eea\/odfpy) | 1\\.4.1 | excel | Reading \/ writing for OpenDocument 1.2 files |\n\n#### HTML[\\#](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#html \"Link to this heading\")\nInstallable with `pip install \"pandas[html]\"`.\n\n| Dependency | Minimum Version | pip extra | Notes |\n|---|---|---|---|\n| [BeautifulSoup4](https:\/\/github.com\/wention\/BeautifulSoup4) | 4\\.12.3 | html | HTML parser for read\\_html |\n| [html5lib](https:\/\/github.com\/html5lib\/html5lib-python) | 1\\.1 | html | HTML parser for read\\_html |\n| [lxml](https:\/\/github.com\/lxml\/lxml) | 4\\.9.2 | html | HTML parser for read\\_html |\n\nOne of the following combinations of libraries is needed to use the top-level [`read_html()`](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.read_html.html#pandas.read_html \"pandas.read_html\") function:\n\n- [BeautifulSoup4](https:\/\/www.crummy.com\/software\/BeautifulSoup) and [html5lib](https:\/\/github.com\/html5lib\/html5lib-python)\n- [BeautifulSoup4](https:\/\/www.crummy.com\/software\/BeautifulSoup) and [lxml](https:\/\/lxml.de\/)\n- [BeautifulSoup4](https:\/\/www.crummy.com\/software\/BeautifulSoup) and [html5lib](https:\/\/github.com\/html5lib\/html5lib-python) and [lxml](https:\/\/lxml.de\/)\n- Only [lxml](https:\/\/lxml.de\/), although see [HTML Table Parsing](https:\/\/pandas.pydata.org\/docs\/user_guide\/io.html#io-html-gotchas) for reasons as to why you should probably **not** take this approach.\n\nWarning\n\n- if you install [BeautifulSoup4](https:\/\/www.crummy.com\/software\/BeautifulSoup) you must install either [lxml](https:\/\/lxml.de\/) or [html5lib](https:\/\/github.com\/html5lib\/html5lib-python) or both. [`read_html()`](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.read_html.html#pandas.read_html \"pandas.read_html\") will **not** work with *only* [BeautifulSoup4](https:\/\/www.crummy.com\/software\/BeautifulSoup) installed.\n- You are highly encouraged to read [HTML Table Parsing gotchas](https:\/\/pandas.pydata.org\/docs\/user_guide\/io.html#io-html-gotchas). It explains issues surrounding the installation and usage of the above three libraries.\n\n#### XML[\\#](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#xml \"Link to this heading\")\nInstallable with `pip install \"pandas[xml]\"`.\n\n| Dependency | Minimum Version | pip extra | Notes |\n|---|---|---|---|\n| [lxml](https:\/\/github.com\/lxml\/lxml) | 5\\.3.0 | xml | XML parser for read\\_xml and tree builder for to\\_xml |\n\n#### SQL databases[\\#](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#sql-databases \"Link to this heading\")\nTraditional drivers are installable with `pip install \"pandas[postgresql, mysql, sql-other]\"`\n\n| Dependency | Minimum Version | pip extra | Notes |\n|---|---|---|---|\n| [SQLAlchemy](https:\/\/github.com\/sqlalchemy\/sqlalchemy) | 2\\.0.36 | postgresql, mysql, sql-other | SQL support for databases other than sqlite |\n| [psycopg2](https:\/\/github.com\/psycopg\/psycopg2) | 2\\.9.10 | postgresql | PostgreSQL engine for sqlalchemy |\n| [pymysql](https:\/\/github.com\/PyMySQL\/PyMySQL) | 1\\.1.1 | mysql | MySQL engine for sqlalchemy |\n| [adbc-driver-postgresql](https:\/\/github.com\/apache\/arrow-adbc) | 1\\.2.0 | postgresql | ADBC Driver for PostgreSQL |\n| [adbc-driver-sqlite](https:\/\/github.com\/apache\/arrow-adbc) | 1\\.2.0 | sql-other | ADBC Driver for SQLite |\n\n#### Other data sources[\\#](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#other-data-sources \"Link to this heading\")\nInstallable with `pip install \"pandas[hdf5, parquet, iceberg, feather, spss, excel]\"`\n\n| Dependency | Minimum Version | pip extra | Notes |\n|---|---|---|---|\n| [PyTables](https:\/\/github.com\/PyTables\/PyTables) | 3\\.10.1 | hdf5 | HDF5-based reading \/ writing |\n| [zlib](https:\/\/github.com\/madler\/zlib) |   | hdf5 | Compression for HDF5 |\n| [fastparquet](https:\/\/github.com\/dask\/fastparquet) | 2024\\.11.0 |   | Parquet reading \/ writing (pyarrow is default) |\n| [pyarrow](https:\/\/github.com\/apache\/arrow) | 13\\.0.0 | parquet, feather | Parquet, ORC, and feather reading \/ writing |\n| [PyIceberg](https:\/\/py.iceberg.apache.org\/) | 0\\.8.1 | iceberg | Apache Iceberg reading \/ writing |\n| [pyreadstat](https:\/\/github.com\/Roche\/pyreadstat) | 1\\.2.8 | spss | SPSS files (.sav) reading |\n| [odfpy](https:\/\/github.com\/eea\/odfpy) | 1\\.4.1 | excel | Open document format (.odf, .ods, .odt) reading \/ writing |\n\nWarning\n\n- If you want to use [`read_orc()`](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.read_orc.html#pandas.read_orc \"pandas.read_orc\"), it is highly recommended to install pyarrow using conda. [`read_orc()`](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.read_orc.html#pandas.read_orc \"pandas.read_orc\") may fail if pyarrow was installed from pypi, and [`read_orc()`](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.read_orc.html#pandas.read_orc \"pandas.read_orc\") is not compatible with Windows OS.\n\n#### Access data in the cloud[\\#](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#access-data-in-the-cloud \"Link to this heading\")\nInstallable with `pip install \"pandas[fss, aws, gcp]\"`\n\n| Dependency | Minimum Version | pip extra | Notes |\n|---|---|---|---|\n| [fsspec](https:\/\/github.com\/fsspec) | 2024\\.10.0 | fss, gcp, aws | Handling files aside from simple local and HTTP (required dependency of s3fs, gcsfs). |\n| [gcsfs](https:\/\/github.com\/fsspec\/gcsfs) | 2024\\.10.0 | gcp | Google Cloud Storage access |\n| [s3fs](https:\/\/github.com\/fsspec\/s3fs) | 2024\\.10.0 | aws | Amazon S3 access |\n\n#### Clipboard[\\#](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#clipboard \"Link to this heading\")\nInstallable with `pip install \"pandas[clipboard]\"`.\n\n| Dependency | Minimum Version | pip extra | Notes |\n|---|---|---|---|\n| [PyQt4](https:\/\/pypi.org\/project\/PyQt4\/)\/[PyQt5](https:\/\/pypi.org\/project\/PyQt5\/) | 5\\.15.9 | clipboard | Clipboard I\/O |\n| [qtpy](https:\/\/github.com\/spyder-ide\/qtpy) | 2\\.4.2 | clipboard | Clipboard I\/O |\n\nNote\n\nDepending on operating system, system-level packages may need to installed. For clipboard to operate on Linux one of the CLI tools `xclip` or `xsel` must be installed on your system.\n\n#### Compression[\\#](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#compression \"Link to this heading\")\nInstallable with `pip install \"pandas[compression]\"`\n\n| Dependency | Minimum Version | pip extra | Notes |\n|---|---|---|---|\n| [Zstandard](https:\/\/github.com\/facebook\/zstd) | 0\\.19.0 | compression | Zstandard compression |\n\n#### Timezone[\\#](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#timezone \"Link to this heading\")\nInstallable with `pip install \"pandas[timezone]\"`\n\n| Dependency | Minimum Version | pip extra | Notes |\n|---|---|---|---|\n| [pytz](https:\/\/github.com\/stub42\/pytz) | 2024\\.2 | timezone | Alternative timezone library to `zoneinfo`. |\n\n[previous Getting started](https:\/\/pandas.pydata.org\/docs\/getting_started\/index.html \"previous page\")\n\n[next Package overview](https:\/\/pandas.pydata.org\/docs\/getting_started\/overview.html \"next page\")\n\nOn this page\n\n- [Python version support](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#python-version-support)\n- [Installing pandas](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#installing-pandas)\n  - [Installing with Conda](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#installing-with-conda)\n  - [Installing with pip](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#installing-with-pip)\n  - [Installing from source](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#installing-from-source)\n  - [Installing the development version of pandas](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#installing-the-development-version-of-pandas)\n- [Running the test suite](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#running-the-test-suite)\n- [Dependencies](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#dependencies)\n  - [Required dependencies](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#required-dependencies)\n  - [Optional dependencies](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#optional-dependencies)\n    - [Performance dependencies (recommended)](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#performance-dependencies-recommended)\n    - [Visualization](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#visualization)\n    - [Computation](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#computation)\n    - [Excel files](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#excel-files)\n    - [HTML](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#html)\n    - [XML](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#xml)\n    - [SQL databases](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#sql-databases)\n    - [Other data sources](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#other-data-sources)\n    - [Access data in the cloud](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#access-data-in-the-cloud)\n    - [Clipboard](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#clipboard)\n    - [Compression](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#compression)\n    - [Timezone](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#timezone)\n\n© 2026, pandas via [NumFOCUS, Inc.](https:\/\/numfocus.org\/) Hosted by [OVHcloud](https:\/\/www.ovhcloud.com\/).\n\nCreated using [Sphinx](https:\/\/www.sphinx-doc.org\/) 9.0.4.\n\nBuilt with the [PyData Sphinx Theme](https:\/\/pydata-sphinx-theme.readthedocs.io\/en\/stable\/index.html) 0.16.1.","attrs_readable_markdown":"The pandas development team officially distributes pandas for installation through the following methods:\n\n- Available on [conda-forge](https:\/\/anaconda.org\/conda-forge\/pandas) for installation with the conda package manager.\n- Available on [PyPI](https:\/\/pypi.org\/project\/pandas\/) for installation with pip.\n- Available on [Github](https:\/\/github.com\/pandas-dev\/pandas) for installation from source.\n\nNote\n\npandas may be installable from other sources besides the ones listed above, but they are **not** managed by the pandas development team.\n\n## Python version support[\\#](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#python-version-support \"Link to this heading\")\nSee [Python support policy](https:\/\/pandas.pydata.org\/docs\/development\/policies.html#policies-python-support).\n\n## Installing pandas[\\#](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#installing-pandas \"Link to this heading\")\n### Installing with Conda[\\#](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#installing-with-conda \"Link to this heading\")\nFor users working with the [Conda](https:\/\/conda.io\/en\/latest\/) package manager, pandas can be installed from the `conda-forge` channel.\n```\nconda install -c conda-forge pandas\n```\nTo install the Conda package manager on your system, the [Miniforge distribution](https:\/\/github.com\/conda-forge\/miniforge?tab=readme-ov-file#install) is recommended.\n\nAdditionally, it is recommended to install and run pandas from a virtual environment.\n```\nconda create -c conda-forge -n name_of_my_env python pandas\n# On Linux or MacOS\nsource activate name_of_my_env\n# On Windows\nactivate name_of_my_env\n```\nTip\n\nFor users that are new to Python, the easiest way to install Python, pandas, and the packages that make up the [PyData](https:\/\/pydata.org\/) stack such as [SciPy](https:\/\/scipy.org\/), [NumPy](https:\/\/numpy.org\/) and [Matplotlib](https:\/\/matplotlib.org\/) is with [Anaconda](https:\/\/docs.anaconda.com\/anaconda\/install\/), a cross-platform (Linux, macOS, Windows) Python distribution for data analytics and scientific computing.\n\nHowever, pandas from Anaconda is **not** officially managed by the pandas development team.\n\n### Installing with pip[\\#](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#installing-with-pip \"Link to this heading\")\nFor users working with the [pip](https:\/\/pip.pypa.io\/en\/stable\/) package manager, pandas can be installed from [PyPI](https:\/\/pypi.org\/project\/pandas\/).\n```\npip install pandas\n```\npandas can also be installed with sets of optional dependencies to enable certain functionality. For example, to install pandas with the optional dependencies to read Excel files.\n```\npip install \"pandas[excel]\"\n```\nThe full list of extras that can be installed can be found in the [dependency section.](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#install-optional-dependencies)\n\nAdditionally, it is recommended to install and run pandas from a virtual environment, for example, using the Python standard library’s [venv](https:\/\/docs.python.org\/3\/library\/venv.html)\n\n### Installing from source[\\#](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#installing-from-source \"Link to this heading\")\nSee the [contributing guide](https:\/\/pandas.pydata.org\/docs\/development\/contributing.html#contributing) for complete instructions on building from the git source tree. Further, see [creating a development environment](https:\/\/pandas.pydata.org\/docs\/development\/contributing_environment.html#contributing-environment) if you wish to create a pandas development environment.\n\n### Installing the development version of pandas[\\#](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#installing-the-development-version-of-pandas \"Link to this heading\")\nInstalling the development version is the quickest way to:\n\n- Try a new feature that will be shipped in the next release (that is, a feature from a pull-request that was recently merged to the main branch).\n- Check whether a bug you encountered has been fixed since the last release.\n\nThe development version is usually uploaded daily to the scientific-python-nightly-wheels index from the PyPI registry of anaconda.org. You can install it by running.\n```\npip install --pre --extra-index-url https:\/\/pypi.anaconda.org\/scientific-python-nightly-wheels\/simple pandas\n```\nNote\n\nYou might be required to uninstall an existing version of pandas to install the development version.\n```\npip uninstall pandas -y\n```\n\n## Running the test suite[\\#](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#running-the-test-suite \"Link to this heading\")\nIf pandas has been installed [from source](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#install-source), running `pytest pandas` will run all of pandas unit tests.\n\nThe unit tests can also be run from the pandas module itself with the [`test()`](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.test.html#pandas.test \"pandas.test\") function. The packages required to run the tests can be installed with `pip install \"pandas[test]\"`.\n\nNote\n\nTest failures are not necessarily indicative of a broken pandas installation.\n\n## Dependencies[\\#](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#dependencies \"Link to this heading\")\n### Required dependencies[\\#](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#required-dependencies \"Link to this heading\")\npandas requires the following dependencies.\n\n| Package | Minimum supported version |\n|---|---|\n| [NumPy](https:\/\/numpy.org\/) | 1\\.26.0 |\n| [python-dateutil](https:\/\/dateutil.readthedocs.io\/en\/stable\/) | 2\\.8.2 |\n| [tzdata](https:\/\/pypi.org\/project\/tzdata\/) \\* | \/ |\n\n\\* `tzdata` is only required on Windows and Pyodide (Emscripten).\n\nGenerally, the minimum supported version is ~2 years old from the release date of a major or minor pandas version.\n\n### Optional dependencies[\\#](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#optional-dependencies \"Link to this heading\")\npandas has many optional dependencies that are only used for specific methods. For example, [`pandas.read_hdf()`](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.read_hdf.html#pandas.read_hdf \"pandas.read_hdf\") requires the `pytables` package, while [`DataFrame.to_markdown()`](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.to_markdown.html#pandas.DataFrame.to_markdown \"pandas.DataFrame.to_markdown\") requires the `tabulate` package. If the optional dependency is not installed, pandas will raise an `ImportError` when the method requiring that dependency is called.\n\nWith pip, optional pandas dependencies can be installed or managed in a file (e.g. requirements.txt or pyproject.toml) as optional extras (e.g. `pandas[performance, aws]`). All optional dependencies can be installed with `pandas[all]`, and specific sets of dependencies are listed in the sections below.\n\nGenerally, the minimum supported version is ~1 years old from the release date of a major or minor pandas version. Older versions of optional dependencies may still work, but they are not tested or considered supported.\n\n#### Performance dependencies (recommended)[\\#](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#performance-dependencies-recommended \"Link to this heading\")\nNote\n\nYou are highly encouraged to install these libraries, as they provide speed improvements, especially when working with large data sets.\n\nInstallable with `pip install \"pandas[performance]\"`\n\n| Dependency | Minimum Version | pip extra | Notes |\n|---|---|---|---|\n| [numexpr](https:\/\/github.com\/pydata\/numexpr) | 2\\.10.2 | performance | Accelerates certain numerical operations by using multiple cores as well as smart chunking and caching to achieve large speedups |\n| [bottleneck](https:\/\/github.com\/pydata\/bottleneck) | 1\\.4.2 | performance | Accelerates certain types of `nan` by using specialized cython routines to achieve large speedup. |\n| [numba](https:\/\/github.com\/numba\/numba) | 0\\.60.0 | performance | Alternative execution engine for operations that accept `engine=\"numba\"` using a JIT compiler that translates Python functions to optimized machine code using the LLVM compiler. |\n\n#### Visualization[\\#](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#visualization \"Link to this heading\")\nInstallable with `pip install \"pandas[plot, output-formatting]\"`.\n\n| Dependency | Minimum Version | pip extra | Notes |\n|---|---|---|---|\n| [matplotlib](https:\/\/github.com\/matplotlib\/matplotlib) | 3\\.8.3 | plot | Plotting library |\n| [Jinja2](https:\/\/github.com\/pallets\/jinja) | 3\\.1.3 | output-formatting | Conditional formatting with DataFrame.style |\n| [tabulate](https:\/\/github.com\/astanin\/python-tabulate) | 0\\.9.0 | output-formatting | Printing in Markdown-friendly format (see [tabulate](https:\/\/github.com\/astanin\/python-tabulate)) |\n\n#### Computation[\\#](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#computation \"Link to this heading\")\nInstallable with `pip install \"pandas[computation]\"`.\n\n| Dependency | Minimum Version | pip extra | Notes |\n|---|---|---|---|\n| [SciPy](https:\/\/github.com\/scipy\/scipy) | 1\\.14.1 | computation | Miscellaneous statistical functions |\n| [xarray](https:\/\/github.com\/pydata\/xarray) | 2024\\.10.0 | computation | pandas-like API for N-dimensional data |\n\n#### Excel files[\\#](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#excel-files \"Link to this heading\")\nInstallable with `pip install \"pandas[excel]\"`.\n\n| Dependency | Minimum Version | pip extra | Notes |\n|---|---|---|---|\n| [xlrd](https:\/\/github.com\/python-excel\/xlrd) | 2\\.0.1 | excel | Reading for xls files |\n| [xlsxwriter](https:\/\/github.com\/jmcnamara\/XlsxWriter) | 3\\.2.0 | excel | Writing for xlsx files |\n| [openpyxl](https:\/\/github.com\/theorchard\/openpyxl) | 3\\.1.5 | excel | Reading \/ writing for Excel 2010 xlsx\/xlsm\/xltx\/xltm files |\n| [pyxlsb](https:\/\/github.com\/willtrnr\/pyxlsb) | 1\\.0.10 | excel | Reading for xlsb files |\n| [python-calamine](https:\/\/github.com\/dimastbk\/python-calamine) | 0\\.3.0 | excel | Reading for xls\/xlsx\/xlsm\/xlsb\/xla\/xlam\/ods files |\n| [odfpy](https:\/\/github.com\/eea\/odfpy) | 1\\.4.1 | excel | Reading \/ writing for OpenDocument 1.2 files |\n\n#### HTML[\\#](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#html \"Link to this heading\")\nInstallable with `pip install \"pandas[html]\"`.\n\n| Dependency | Minimum Version | pip extra | Notes |\n|---|---|---|---|\n| [BeautifulSoup4](https:\/\/github.com\/wention\/BeautifulSoup4) | 4\\.12.3 | html | HTML parser for read\\_html |\n| [html5lib](https:\/\/github.com\/html5lib\/html5lib-python) | 1\\.1 | html | HTML parser for read\\_html |\n| [lxml](https:\/\/github.com\/lxml\/lxml) | 4\\.9.2 | html | HTML parser for read\\_html |\n\nOne of the following combinations of libraries is needed to use the top-level [`read_html()`](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.read_html.html#pandas.read_html \"pandas.read_html\") function:\n\n- [BeautifulSoup4](https:\/\/www.crummy.com\/software\/BeautifulSoup) and [html5lib](https:\/\/github.com\/html5lib\/html5lib-python)\n- [BeautifulSoup4](https:\/\/www.crummy.com\/software\/BeautifulSoup) and [lxml](https:\/\/lxml.de\/)\n- [BeautifulSoup4](https:\/\/www.crummy.com\/software\/BeautifulSoup) and [html5lib](https:\/\/github.com\/html5lib\/html5lib-python) and [lxml](https:\/\/lxml.de\/)\n- Only [lxml](https:\/\/lxml.de\/), although see [HTML Table Parsing](https:\/\/pandas.pydata.org\/docs\/user_guide\/io.html#io-html-gotchas) for reasons as to why you should probably **not** take this approach.\n\nWarning\n\n- if you install [BeautifulSoup4](https:\/\/www.crummy.com\/software\/BeautifulSoup) you must install either [lxml](https:\/\/lxml.de\/) or [html5lib](https:\/\/github.com\/html5lib\/html5lib-python) or both. [`read_html()`](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.read_html.html#pandas.read_html \"pandas.read_html\") will **not** work with *only* [BeautifulSoup4](https:\/\/www.crummy.com\/software\/BeautifulSoup) installed.\n- You are highly encouraged to read [HTML Table Parsing gotchas](https:\/\/pandas.pydata.org\/docs\/user_guide\/io.html#io-html-gotchas). It explains issues surrounding the installation and usage of the above three libraries.\n\n#### XML[\\#](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#xml \"Link to this heading\")\nInstallable with `pip install \"pandas[xml]\"`.\n\n| Dependency | Minimum Version | pip extra | Notes |\n|---|---|---|---|\n| [lxml](https:\/\/github.com\/lxml\/lxml) | 5\\.3.0 | xml | XML parser for read\\_xml and tree builder for to\\_xml |\n\n#### SQL databases[\\#](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#sql-databases \"Link to this heading\")\nTraditional drivers are installable with `pip install \"pandas[postgresql, mysql, sql-other]\"`\n\n| Dependency | Minimum Version | pip extra | Notes |\n|---|---|---|---|\n| [SQLAlchemy](https:\/\/github.com\/sqlalchemy\/sqlalchemy) | 2\\.0.36 | postgresql, mysql, sql-other | SQL support for databases other than sqlite |\n| [psycopg2](https:\/\/github.com\/psycopg\/psycopg2) | 2\\.9.10 | postgresql | PostgreSQL engine for sqlalchemy |\n| [pymysql](https:\/\/github.com\/PyMySQL\/PyMySQL) | 1\\.1.1 | mysql | MySQL engine for sqlalchemy |\n| [adbc-driver-postgresql](https:\/\/github.com\/apache\/arrow-adbc) | 1\\.2.0 | postgresql | ADBC Driver for PostgreSQL |\n| [adbc-driver-sqlite](https:\/\/github.com\/apache\/arrow-adbc) | 1\\.2.0 | sql-other | ADBC Driver for SQLite |\n\n#### Other data sources[\\#](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#other-data-sources \"Link to this heading\")\nInstallable with `pip install \"pandas[hdf5, parquet, iceberg, feather, spss, excel]\"`\n\n| Dependency | Minimum Version | pip extra | Notes |\n|---|---|---|---|\n| [PyTables](https:\/\/github.com\/PyTables\/PyTables) | 3\\.10.1 | hdf5 | HDF5-based reading \/ writing |\n| [zlib](https:\/\/github.com\/madler\/zlib) |   | hdf5 | Compression for HDF5 |\n| [fastparquet](https:\/\/github.com\/dask\/fastparquet) | 2024\\.11.0 |   | Parquet reading \/ writing (pyarrow is default) |\n| [pyarrow](https:\/\/github.com\/apache\/arrow) | 13\\.0.0 | parquet, feather | Parquet, ORC, and feather reading \/ writing |\n| [PyIceberg](https:\/\/py.iceberg.apache.org\/) | 0\\.8.1 | iceberg | Apache Iceberg reading \/ writing |\n| [pyreadstat](https:\/\/github.com\/Roche\/pyreadstat) | 1\\.2.8 | spss | SPSS files (.sav) reading |\n| [odfpy](https:\/\/github.com\/eea\/odfpy) | 1\\.4.1 | excel | Open document format (.odf, .ods, .odt) reading \/ writing |\n\nWarning\n\n- If you want to use [`read_orc()`](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.read_orc.html#pandas.read_orc \"pandas.read_orc\"), it is highly recommended to install pyarrow using conda. [`read_orc()`](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.read_orc.html#pandas.read_orc \"pandas.read_orc\") may fail if pyarrow was installed from pypi, and [`read_orc()`](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.read_orc.html#pandas.read_orc \"pandas.read_orc\") is not compatible with Windows OS.\n\n#### Access data in the cloud[\\#](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#access-data-in-the-cloud \"Link to this heading\")\nInstallable with `pip install \"pandas[fss, aws, gcp]\"`\n\n| Dependency | Minimum Version | pip extra | Notes |\n|---|---|---|---|\n| [fsspec](https:\/\/github.com\/fsspec) | 2024\\.10.0 | fss, gcp, aws | Handling files aside from simple local and HTTP (required dependency of s3fs, gcsfs). |\n| [gcsfs](https:\/\/github.com\/fsspec\/gcsfs) | 2024\\.10.0 | gcp | Google Cloud Storage access |\n| [s3fs](https:\/\/github.com\/fsspec\/s3fs) | 2024\\.10.0 | aws | Amazon S3 access |\n\n#### Clipboard[\\#](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#clipboard \"Link to this heading\")\nInstallable with `pip install \"pandas[clipboard]\"`.\n\n| Dependency | Minimum Version | pip extra | Notes |\n|---|---|---|---|\n| [PyQt4](https:\/\/pypi.org\/project\/PyQt4\/)\/[PyQt5](https:\/\/pypi.org\/project\/PyQt5\/) | 5\\.15.9 | clipboard | Clipboard I\/O |\n| [qtpy](https:\/\/github.com\/spyder-ide\/qtpy) | 2\\.4.2 | clipboard | Clipboard I\/O |\n\nNote\n\nDepending on operating system, system-level packages may need to installed. For clipboard to operate on Linux one of the CLI tools `xclip` or `xsel` must be installed on your system.\n\n#### Compression[\\#](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#compression \"Link to this heading\")\nInstallable with `pip install \"pandas[compression]\"`\n\n| Dependency | Minimum Version | pip extra | Notes |\n|---|---|---|---|\n| [Zstandard](https:\/\/github.com\/facebook\/zstd) | 0\\.19.0 | compression | Zstandard compression |\n\n#### Timezone[\\#](https:\/\/pandas.pydata.org\/docs\/getting_started\/install.html#timezone \"Link to this heading\")\nInstallable with `pip install \"pandas[timezone]\"`\n\n| Dependency | Minimum Version | pip extra | Notes |\n|---|---|---|---|\n| [pytz](https:\/\/github.com\/stub42\/pytz) | 2024\\.2 | timezone | Alternative timezone library to `zoneinfo`. |","meta_canonical":null}

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Boilerpipe Text	The pandas development team officially distributes pandas for installation through the following methods: Available on conda-forge for installation with the conda package manager. Available on PyPI for installation with pip. Available on Github for installation from source. Note pandas may be installable from other sources besides the ones listed above, but they are not managed by the pandas development team. Python version support # See Python support policy . Installing pandas # Installing with Conda # For users working with the Conda package manager, pandas can be installed from the conda-forge channel. conda install -c conda-forge pandas To install the Conda package manager on your system, the Miniforge distribution is recommended. Additionally, it is recommended to install and run pandas from a virtual environment. conda create -c conda-forge -n name_of_my_env python pandas # On Linux or MacOS source activate name_of_my_env # On Windows activate name_of_my_env Tip For users that are new to Python, the easiest way to install Python, pandas, and the packages that make up the PyData stack such as SciPy , NumPy and Matplotlib is with Anaconda , a cross-platform (Linux, macOS, Windows) Python distribution for data analytics and scientific computing. However, pandas from Anaconda is not officially managed by the pandas development team. Installing with pip # For users working with the pip package manager, pandas can be installed from PyPI . pip install pandas pandas can also be installed with sets of optional dependencies to enable certain functionality. For example, to install pandas with the optional dependencies to read Excel files. pip install "pandas[excel]" The full list of extras that can be installed can be found in the dependency section. Additionally, it is recommended to install and run pandas from a virtual environment, for example, using the Python standard library’s venv Installing from source # See the contributing guide for complete instructions on building from the git source tree. Further, see creating a development environment if you wish to create a pandas development environment. Installing the development version of pandas # Installing the development version is the quickest way to: Try a new feature that will be shipped in the next release (that is, a feature from a pull-request that was recently merged to the main branch). Check whether a bug you encountered has been fixed since the last release. The development version is usually uploaded daily to the scientific-python-nightly-wheels index from the PyPI registry of anaconda.org. You can install it by running. pip install --pre --extra-index-url https://pypi.anaconda.org/scientific-python-nightly-wheels/simple pandas Note You might be required to uninstall an existing version of pandas to install the development version. pip uninstall pandas -y Running the test suite # If pandas has been installed from source , running pytest pandas will run all of pandas unit tests. The unit tests can also be run from the pandas module itself with the test() function. The packages required to run the tests can be installed with pip install "pandas[test]" . Note Test failures are not necessarily indicative of a broken pandas installation. Dependencies # Required dependencies # pandas requires the following dependencies. Package Minimum supported version NumPy 1.26.0 python-dateutil 2.8.2 tzdata * / * tzdata is only required on Windows and Pyodide (Emscripten). Generally, the minimum supported version is ~2 years old from the release date of a major or minor pandas version. Optional dependencies # pandas has many optional dependencies that are only used for specific methods. For example, pandas.read_hdf() requires the pytables package, while DataFrame.to_markdown() requires the tabulate package. If the optional dependency is not installed, pandas will raise an ImportError when the method requiring that dependency is called. With pip, optional pandas dependencies can be installed or managed in a file (e.g. requirements.txt or pyproject.toml) as optional extras (e.g. pandas[performance, aws] ). All optional dependencies can be installed with pandas[all] , and specific sets of dependencies are listed in the sections below. Generally, the minimum supported version is ~1 years old from the release date of a major or minor pandas version. Older versions of optional dependencies may still work, but they are not tested or considered supported. Performance dependencies (recommended) # Note You are highly encouraged to install these libraries, as they provide speed improvements, especially when working with large data sets. Installable with pip install "pandas[performance]" Dependency Minimum Version pip extra Notes numexpr 2.10.2 performance Accelerates certain numerical operations by using multiple cores as well as smart chunking and caching to achieve large speedups bottleneck 1.4.2 performance Accelerates certain types of nan by using specialized cython routines to achieve large speedup. numba 0.60.0 performance Alternative execution engine for operations that accept engine="numba" using a JIT compiler that translates Python functions to optimized machine code using the LLVM compiler. Visualization # Installable with pip install "pandas[plot, output-formatting]" . Dependency Minimum Version pip extra Notes matplotlib 3.8.3 plot Plotting library Jinja2 3.1.3 output-formatting Conditional formatting with DataFrame.style tabulate 0.9.0 output-formatting Printing in Markdown-friendly format (see tabulate ) Computation # Installable with pip install "pandas[computation]" . Dependency Minimum Version pip extra Notes SciPy 1.14.1 computation Miscellaneous statistical functions xarray 2024.10.0 computation pandas-like API for N-dimensional data Excel files # Installable with pip install "pandas[excel]" . Dependency Minimum Version pip extra Notes xlrd 2.0.1 excel Reading for xls files xlsxwriter 3.2.0 excel Writing for xlsx files openpyxl 3.1.5 excel Reading / writing for Excel 2010 xlsx/xlsm/xltx/xltm files pyxlsb 1.0.10 excel Reading for xlsb files python-calamine 0.3.0 excel Reading for xls/xlsx/xlsm/xlsb/xla/xlam/ods files odfpy 1.4.1 excel Reading / writing for OpenDocument 1.2 files HTML # Installable with pip install "pandas[html]" . Dependency Minimum Version pip extra Notes BeautifulSoup4 4.12.3 html HTML parser for read_html html5lib 1.1 html HTML parser for read_html lxml 4.9.2 html HTML parser for read_html One of the following combinations of libraries is needed to use the top-level read_html() function: BeautifulSoup4 and html5lib BeautifulSoup4 and lxml BeautifulSoup4 and html5lib and lxml Only lxml , although see HTML Table Parsing for reasons as to why you should probably not take this approach. Warning if you install BeautifulSoup4 you must install either lxml or html5lib or both. read_html() will not work with only BeautifulSoup4 installed. You are highly encouraged to read HTML Table Parsing gotchas . It explains issues surrounding the installation and usage of the above three libraries. XML # Installable with pip install "pandas[xml]" . Dependency Minimum Version pip extra Notes lxml 5.3.0 xml XML parser for read_xml and tree builder for to_xml SQL databases # Traditional drivers are installable with pip install "pandas[postgresql, mysql, sql-other]" Dependency Minimum Version pip extra Notes SQLAlchemy 2.0.36 postgresql, mysql, sql-other SQL support for databases other than sqlite psycopg2 2.9.10 postgresql PostgreSQL engine for sqlalchemy pymysql 1.1.1 mysql MySQL engine for sqlalchemy adbc-driver-postgresql 1.2.0 postgresql ADBC Driver for PostgreSQL adbc-driver-sqlite 1.2.0 sql-other ADBC Driver for SQLite Other data sources # Installable with pip install "pandas[hdf5, parquet, iceberg, feather, spss, excel]" Dependency Minimum Version pip extra Notes PyTables 3.10.1 hdf5 HDF5-based reading / writing zlib hdf5 Compression for HDF5 fastparquet 2024.11.0 Parquet reading / writing (pyarrow is default) pyarrow 13.0.0 parquet, feather Parquet, ORC, and feather reading / writing PyIceberg 0.8.1 iceberg Apache Iceberg reading / writing pyreadstat 1.2.8 spss SPSS files (.sav) reading odfpy 1.4.1 excel Open document format (.odf, .ods, .odt) reading / writing Warning If you want to use read_orc() , it is highly recommended to install pyarrow using conda. read_orc() may fail if pyarrow was installed from pypi, and read_orc() is not compatible with Windows OS. Access data in the cloud # Installable with pip install "pandas[fss, aws, gcp]" Dependency Minimum Version pip extra Notes fsspec 2024.10.0 fss, gcp, aws Handling files aside from simple local and HTTP (required dependency of s3fs, gcsfs). gcsfs 2024.10.0 gcp Google Cloud Storage access s3fs 2024.10.0 aws Amazon S3 access Clipboard # Installable with pip install "pandas[clipboard]" . Dependency Minimum Version pip extra Notes PyQt4 / PyQt5 5.15.9 clipboard Clipboard I/O qtpy 2.4.2 clipboard Clipboard I/O Note Depending on operating system, system-level packages may need to installed. For clipboard to operate on Linux one of the CLI tools xclip or xsel must be installed on your system. Compression # Installable with pip install "pandas[compression]" Dependency Minimum Version pip extra Notes Zstandard 0.19.0 compression Zstandard compression Timezone # Installable with pip install "pandas[timezone]" Dependency Minimum Version pip extra Notes pytz 2024.2 timezone Alternative timezone library to zoneinfo .
Markdown	[Skip to main content](https://pandas.pydata.org/docs/getting_started/install.html#main-content) Back to top Announcement: pandas 3.0 released! Read more [in the blog post](https://pandas.pydata.org/community/blog/pandas-3.0.html) [![pandas 3.0.2 documentation - Home](https://pandas.pydata.org/docs/_static/pandas.svg) ![pandas 3.0.2 documentation - Home](https://pandas.pydata.org/static/img/pandas_white.svg)](https://pandas.pydata.org/docs/index.html) - [Getting started](https://pandas.pydata.org/docs/getting_started/index.html) - [User Guide](https://pandas.pydata.org/docs/user_guide/index.html) - [API reference](https://pandas.pydata.org/docs/reference/index.html) - [Development](https://pandas.pydata.org/docs/development/index.html) - [Release notes](https://pandas.pydata.org/docs/whatsnew/index.html) Search `Ctrl`\+`K` 3\.0 (stable) [dev](https://pandas.pydata.org/docs/dev/getting_started/install.html)[3\.0 (stable)](https://pandas.pydata.org/docs/getting_started/install.html)[2\.3](https://pandas.pydata.org/pandas-docs/version/2.3/getting_started/install.html)[2\.2](https://pandas.pydata.org/pandas-docs/version/2.2/getting_started/install.html)[2\.1](https://pandas.pydata.org/pandas-docs/version/2.1/getting_started/install.html)[2\.0](https://pandas.pydata.org/pandas-docs/version/2.0/getting_started/install.html)[1\.5](https://pandas.pydata.org/pandas-docs/version/1.5/getting_started/install.html)[1\.4](https://pandas.pydata.org/pandas-docs/version/1.4/getting_started/install.html)[1\.3](https://pandas.pydata.org/pandas-docs/version/1.3/getting_started/install.html)[1\.2](https://pandas.pydata.org/pandas-docs/version/1.2/getting_started/install.html)[1\.1](https://pandas.pydata.org/pandas-docs/version/1.1/getting_started/install.html)[1\.0](https://pandas.pydata.org/pandas-docs/version/1.0/getting_started/install.html) - 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[GitHub](https://github.com/pandas-dev/pandas) - [X](https://x.com/pandas_dev) - [Mastodon](https://fosstodon.org/@pandas_dev) - [Installation](https://pandas.pydata.org/docs/getting_started/install.html) - [Package overview](https://pandas.pydata.org/docs/getting_started/overview.html) - [Getting started tutorials](https://pandas.pydata.org/docs/getting_started/intro_tutorials/index.html) - [What kind of data does pandas handle?](https://pandas.pydata.org/docs/getting_started/intro_tutorials/01_table_oriented.html) - [How do I read and write tabular data?](https://pandas.pydata.org/docs/getting_started/intro_tutorials/02_read_write.html) - [How do I select a subset of a `DataFrame`?](https://pandas.pydata.org/docs/getting_started/intro_tutorials/03_subset_data.html) - [How do I create plots in pandas?](https://pandas.pydata.org/docs/getting_started/intro_tutorials/04_plotting.html) - [How to create new columns derived from existing columns](https://pandas.pydata.org/docs/getting_started/intro_tutorials/05_add_columns.html) - [How to calculate summary statistics](https://pandas.pydata.org/docs/getting_started/intro_tutorials/06_calculate_statistics.html) - [How to reshape the layout of tables](https://pandas.pydata.org/docs/getting_started/intro_tutorials/07_reshape_table_layout.html) - [How to combine data from multiple tables](https://pandas.pydata.org/docs/getting_started/intro_tutorials/08_combine_dataframes.html) - [How to handle time series data with ease](https://pandas.pydata.org/docs/getting_started/intro_tutorials/09_timeseries.html) - [How to manipulate textual data](https://pandas.pydata.org/docs/getting_started/intro_tutorials/10_text_data.html) - [Comparison with other tools](https://pandas.pydata.org/docs/getting_started/comparison/index.html) - [Comparison with R / R libraries](https://pandas.pydata.org/docs/getting_started/comparison/comparison_with_r.html) - [Comparison with SQL](https://pandas.pydata.org/docs/getting_started/comparison/comparison_with_sql.html) - [Comparison with spreadsheets](https://pandas.pydata.org/docs/getting_started/comparison/comparison_with_spreadsheets.html) - [Comparison with SAS](https://pandas.pydata.org/docs/getting_started/comparison/comparison_with_sas.html) - [Comparison with Stata](https://pandas.pydata.org/docs/getting_started/comparison/comparison_with_stata.html) - [Comparison with SPSS](https://pandas.pydata.org/docs/getting_started/comparison/comparison_with_spss.html) - [Community tutorials](https://pandas.pydata.org/docs/getting_started/tutorials.html) - [Getting started](https://pandas.pydata.org/docs/getting_started/index.html) - Installation # Installation[\#](https://pandas.pydata.org/docs/getting_started/install.html#installation "Link to this heading") The pandas development team officially distributes pandas for installation through the following methods: - Available on [conda-forge](https://anaconda.org/conda-forge/pandas) for installation with the conda package manager. - Available on [PyPI](https://pypi.org/project/pandas/) for installation with pip. - Available on [Github](https://github.com/pandas-dev/pandas) for installation from source. Note pandas may be installable from other sources besides the ones listed above, but they are not managed by the pandas development team. ## Python version support[\#](https://pandas.pydata.org/docs/getting_started/install.html#python-version-support "Link to this heading") See [Python support policy](https://pandas.pydata.org/docs/development/policies.html#policies-python-support). ## Installing pandas[\#](https://pandas.pydata.org/docs/getting_started/install.html#installing-pandas "Link to this heading") ### Installing with Conda[\#](https://pandas.pydata.org/docs/getting_started/install.html#installing-with-conda "Link to this heading") For users working with the [Conda](https://conda.io/en/latest/) package manager, pandas can be installed from the `conda-forge` channel. ``` conda install -c conda-forge pandas ``` To install the Conda package manager on your system, the [Miniforge distribution](https://github.com/conda-forge/miniforge?tab=readme-ov-file#install) is recommended. Additionally, it is recommended to install and run pandas from a virtual environment. ``` conda create -c conda-forge -n name_of_my_env python pandas # On Linux or MacOS source activate name_of_my_env # On Windows activate name_of_my_env ``` Tip For users that are new to Python, the easiest way to install Python, pandas, and the packages that make up the [PyData](https://pydata.org/) stack such as [SciPy](https://scipy.org/), [NumPy](https://numpy.org/) and [Matplotlib](https://matplotlib.org/) is with [Anaconda](https://docs.anaconda.com/anaconda/install/), a cross-platform (Linux, macOS, Windows) Python distribution for data analytics and scientific computing. However, pandas from Anaconda is not officially managed by the pandas development team. ### Installing with pip[\#](https://pandas.pydata.org/docs/getting_started/install.html#installing-with-pip "Link to this heading") For users working with the [pip](https://pip.pypa.io/en/stable/) package manager, pandas can be installed from [PyPI](https://pypi.org/project/pandas/). ``` pip install pandas ``` pandas can also be installed with sets of optional dependencies to enable certain functionality. For example, to install pandas with the optional dependencies to read Excel files. ``` pip install "pandas[excel]" ``` The full list of extras that can be installed can be found in the [dependency section.](https://pandas.pydata.org/docs/getting_started/install.html#install-optional-dependencies) Additionally, it is recommended to install and run pandas from a virtual environment, for example, using the Python standard library’s [venv](https://docs.python.org/3/library/venv.html) ### Installing from source[\#](https://pandas.pydata.org/docs/getting_started/install.html#installing-from-source "Link to this heading") See the [contributing guide](https://pandas.pydata.org/docs/development/contributing.html#contributing) for complete instructions on building from the git source tree. Further, see [creating a development environment](https://pandas.pydata.org/docs/development/contributing_environment.html#contributing-environment) if you wish to create a pandas development environment. ### Installing the development version of pandas[\#](https://pandas.pydata.org/docs/getting_started/install.html#installing-the-development-version-of-pandas "Link to this heading") Installing the development version is the quickest way to: - Try a new feature that will be shipped in the next release (that is, a feature from a pull-request that was recently merged to the main branch). - Check whether a bug you encountered has been fixed since the last release. The development version is usually uploaded daily to the scientific-python-nightly-wheels index from the PyPI registry of anaconda.org. You can install it by running. ``` pip install --pre --extra-index-url https://pypi.anaconda.org/scientific-python-nightly-wheels/simple pandas ``` Note You might be required to uninstall an existing version of pandas to install the development version. ``` pip uninstall pandas -y ``` ## Running the test suite[\#](https://pandas.pydata.org/docs/getting_started/install.html#running-the-test-suite "Link to this heading") If pandas has been installed [from source](https://pandas.pydata.org/docs/getting_started/install.html#install-source), running `pytest pandas` will run all of pandas unit tests. The unit tests can also be run from the pandas module itself with the [`test()`](https://pandas.pydata.org/docs/reference/api/pandas.test.html#pandas.test "pandas.test") function. The packages required to run the tests can be installed with `pip install "pandas[test]"`. Note Test failures are not necessarily indicative of a broken pandas installation. ## Dependencies[\#](https://pandas.pydata.org/docs/getting_started/install.html#dependencies "Link to this heading") ### Required dependencies[\#](https://pandas.pydata.org/docs/getting_started/install.html#required-dependencies "Link to this heading") pandas requires the following dependencies. \| Package \| Minimum supported version \| \|---\|---\| \| [NumPy](https://numpy.org/) \| 1\.26.0 \| \| [python-dateutil](https://dateutil.readthedocs.io/en/stable/) \| 2\.8.2 \| \| [tzdata](https://pypi.org/project/tzdata/) \* \| / \| \* `tzdata` is only required on Windows and Pyodide (Emscripten). Generally, the minimum supported version is ~2 years old from the release date of a major or minor pandas version. ### Optional dependencies[\#](https://pandas.pydata.org/docs/getting_started/install.html#optional-dependencies "Link to this heading") pandas has many optional dependencies that are only used for specific methods. For example, [`pandas.read_hdf()`](https://pandas.pydata.org/docs/reference/api/pandas.read_hdf.html#pandas.read_hdf "pandas.read_hdf") requires the `pytables` package, while [`DataFrame.to_markdown()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_markdown.html#pandas.DataFrame.to_markdown "pandas.DataFrame.to_markdown") requires the `tabulate` package. If the optional dependency is not installed, pandas will raise an `ImportError` when the method requiring that dependency is called. With pip, optional pandas dependencies can be installed or managed in a file (e.g. requirements.txt or pyproject.toml) as optional extras (e.g. `pandas[performance, aws]`). All optional dependencies can be installed with `pandas[all]`, and specific sets of dependencies are listed in the sections below. Generally, the minimum supported version is ~1 years old from the release date of a major or minor pandas version. Older versions of optional dependencies may still work, but they are not tested or considered supported. #### Performance dependencies (recommended)[\#](https://pandas.pydata.org/docs/getting_started/install.html#performance-dependencies-recommended "Link to this heading") Note You are highly encouraged to install these libraries, as they provide speed improvements, especially when working with large data sets. Installable with `pip install "pandas[performance]"` \| Dependency \| Minimum Version \| pip extra \| Notes \| \|---\|---\|---\|---\| \| [numexpr](https://github.com/pydata/numexpr) \| 2\.10.2 \| performance \| Accelerates certain numerical operations by using multiple cores as well as smart chunking and caching to achieve large speedups \| \| [bottleneck](https://github.com/pydata/bottleneck) \| 1\.4.2 \| performance \| Accelerates certain types of `nan` by using specialized cython routines to achieve large speedup. \| \| [numba](https://github.com/numba/numba) \| 0\.60.0 \| performance \| Alternative execution engine for operations that accept `engine="numba"` using a JIT compiler that translates Python functions to optimized machine code using the LLVM compiler. \| #### Visualization[\#](https://pandas.pydata.org/docs/getting_started/install.html#visualization "Link to this heading") Installable with `pip install "pandas[plot, output-formatting]"`. \| Dependency \| Minimum Version \| pip extra \| Notes \| \|---\|---\|---\|---\| \| [matplotlib](https://github.com/matplotlib/matplotlib) \| 3\.8.3 \| plot \| Plotting library \| \| [Jinja2](https://github.com/pallets/jinja) \| 3\.1.3 \| output-formatting \| Conditional formatting with DataFrame.style \| \| [tabulate](https://github.com/astanin/python-tabulate) \| 0\.9.0 \| output-formatting \| Printing in Markdown-friendly format (see [tabulate](https://github.com/astanin/python-tabulate)) \| #### Computation[\#](https://pandas.pydata.org/docs/getting_started/install.html#computation "Link to this heading") Installable with `pip install "pandas[computation]"`. \| Dependency \| Minimum Version \| pip extra \| Notes \| \|---\|---\|---\|---\| \| [SciPy](https://github.com/scipy/scipy) \| 1\.14.1 \| computation \| Miscellaneous statistical functions \| \| [xarray](https://github.com/pydata/xarray) \| 2024\.10.0 \| computation \| pandas-like API for N-dimensional data \| #### Excel files[\#](https://pandas.pydata.org/docs/getting_started/install.html#excel-files "Link to this heading") Installable with `pip install "pandas[excel]"`. \| Dependency \| Minimum Version \| pip extra \| Notes \| \|---\|---\|---\|---\| \| [xlrd](https://github.com/python-excel/xlrd) \| 2\.0.1 \| excel \| Reading for xls files \| \| [xlsxwriter](https://github.com/jmcnamara/XlsxWriter) \| 3\.2.0 \| excel \| Writing for xlsx files \| \| [openpyxl](https://github.com/theorchard/openpyxl) \| 3\.1.5 \| excel \| Reading / writing for Excel 2010 xlsx/xlsm/xltx/xltm files \| \| [pyxlsb](https://github.com/willtrnr/pyxlsb) \| 1\.0.10 \| excel \| Reading for xlsb files \| \| [python-calamine](https://github.com/dimastbk/python-calamine) \| 0\.3.0 \| excel \| Reading for xls/xlsx/xlsm/xlsb/xla/xlam/ods files \| \| [odfpy](https://github.com/eea/odfpy) \| 1\.4.1 \| excel \| Reading / writing for OpenDocument 1.2 files \| #### HTML[\#](https://pandas.pydata.org/docs/getting_started/install.html#html "Link to this heading") Installable with `pip install "pandas[html]"`. \| Dependency \| Minimum Version \| pip extra \| Notes \| \|---\|---\|---\|---\| \| [BeautifulSoup4](https://github.com/wention/BeautifulSoup4) \| 4\.12.3 \| html \| HTML parser for read\_html \| \| [html5lib](https://github.com/html5lib/html5lib-python) \| 1\.1 \| html \| HTML parser for read\_html \| \| [lxml](https://github.com/lxml/lxml) \| 4\.9.2 \| html \| HTML parser for read\_html \| One of the following combinations of libraries is needed to use the top-level [`read_html()`](https://pandas.pydata.org/docs/reference/api/pandas.read_html.html#pandas.read_html "pandas.read_html") function: - [BeautifulSoup4](https://www.crummy.com/software/BeautifulSoup) and [html5lib](https://github.com/html5lib/html5lib-python) - [BeautifulSoup4](https://www.crummy.com/software/BeautifulSoup) and [lxml](https://lxml.de/) - [BeautifulSoup4](https://www.crummy.com/software/BeautifulSoup) and [html5lib](https://github.com/html5lib/html5lib-python) and [lxml](https://lxml.de/) - Only [lxml](https://lxml.de/), although see [HTML Table Parsing](https://pandas.pydata.org/docs/user_guide/io.html#io-html-gotchas) for reasons as to why you should probably not take this approach. Warning - if you install [BeautifulSoup4](https://www.crummy.com/software/BeautifulSoup) you must install either [lxml](https://lxml.de/) or [html5lib](https://github.com/html5lib/html5lib-python) or both. [`read_html()`](https://pandas.pydata.org/docs/reference/api/pandas.read_html.html#pandas.read_html "pandas.read_html") will not work with only [BeautifulSoup4](https://www.crummy.com/software/BeautifulSoup) installed. - You are highly encouraged to read [HTML Table Parsing gotchas](https://pandas.pydata.org/docs/user_guide/io.html#io-html-gotchas). It explains issues surrounding the installation and usage of the above three libraries. #### XML[\#](https://pandas.pydata.org/docs/getting_started/install.html#xml "Link to this heading") Installable with `pip install "pandas[xml]"`. \| Dependency \| Minimum Version \| pip extra \| Notes \| \|---\|---\|---\|---\| \| [lxml](https://github.com/lxml/lxml) \| 5\.3.0 \| xml \| XML parser for read\_xml and tree builder for to\_xml \| #### SQL databases[\#](https://pandas.pydata.org/docs/getting_started/install.html#sql-databases "Link to this heading") Traditional drivers are installable with `pip install "pandas[postgresql, mysql, sql-other]"` \| Dependency \| Minimum Version \| pip extra \| Notes \| \|---\|---\|---\|---\| \| [SQLAlchemy](https://github.com/sqlalchemy/sqlalchemy) \| 2\.0.36 \| postgresql, mysql, sql-other \| SQL support for databases other than sqlite \| \| [psycopg2](https://github.com/psycopg/psycopg2) \| 2\.9.10 \| postgresql \| PostgreSQL engine for sqlalchemy \| \| [pymysql](https://github.com/PyMySQL/PyMySQL) \| 1\.1.1 \| mysql \| MySQL engine for sqlalchemy \| \| [adbc-driver-postgresql](https://github.com/apache/arrow-adbc) \| 1\.2.0 \| postgresql \| ADBC Driver for PostgreSQL \| \| [adbc-driver-sqlite](https://github.com/apache/arrow-adbc) \| 1\.2.0 \| sql-other \| ADBC Driver for SQLite \| #### Other data sources[\#](https://pandas.pydata.org/docs/getting_started/install.html#other-data-sources "Link to this heading") Installable with `pip install "pandas[hdf5, parquet, iceberg, feather, spss, excel]"` \| Dependency \| Minimum Version \| pip extra \| Notes \| \|---\|---\|---\|---\| \| [PyTables](https://github.com/PyTables/PyTables) \| 3\.10.1 \| hdf5 \| HDF5-based reading / writing \| \| [zlib](https://github.com/madler/zlib) \| \| hdf5 \| Compression for HDF5 \| \| [fastparquet](https://github.com/dask/fastparquet) \| 2024\.11.0 \| \| Parquet reading / writing (pyarrow is default) \| \| [pyarrow](https://github.com/apache/arrow) \| 13\.0.0 \| parquet, feather \| Parquet, ORC, and feather reading / writing \| \| [PyIceberg](https://py.iceberg.apache.org/) \| 0\.8.1 \| iceberg \| Apache Iceberg reading / writing \| \| [pyreadstat](https://github.com/Roche/pyreadstat) \| 1\.2.8 \| spss \| SPSS files (.sav) reading \| \| [odfpy](https://github.com/eea/odfpy) \| 1\.4.1 \| excel \| Open document format (.odf, .ods, .odt) reading / writing \| Warning - If you want to use [`read_orc()`](https://pandas.pydata.org/docs/reference/api/pandas.read_orc.html#pandas.read_orc "pandas.read_orc"), it is highly recommended to install pyarrow using conda. [`read_orc()`](https://pandas.pydata.org/docs/reference/api/pandas.read_orc.html#pandas.read_orc "pandas.read_orc") may fail if pyarrow was installed from pypi, and [`read_orc()`](https://pandas.pydata.org/docs/reference/api/pandas.read_orc.html#pandas.read_orc "pandas.read_orc") is not compatible with Windows OS. #### Access data in the cloud[\#](https://pandas.pydata.org/docs/getting_started/install.html#access-data-in-the-cloud "Link to this heading") Installable with `pip install "pandas[fss, aws, gcp]"` \| Dependency \| Minimum Version \| pip extra \| Notes \| \|---\|---\|---\|---\| \| [fsspec](https://github.com/fsspec) \| 2024\.10.0 \| fss, gcp, aws \| Handling files aside from simple local and HTTP (required dependency of s3fs, gcsfs). \| \| [gcsfs](https://github.com/fsspec/gcsfs) \| 2024\.10.0 \| gcp \| Google Cloud Storage access \| \| [s3fs](https://github.com/fsspec/s3fs) \| 2024\.10.0 \| aws \| Amazon S3 access \| #### Clipboard[\#](https://pandas.pydata.org/docs/getting_started/install.html#clipboard "Link to this heading") Installable with `pip install "pandas[clipboard]"`. \| Dependency \| Minimum Version \| pip extra \| Notes \| \|---\|---\|---\|---\| \| [PyQt4](https://pypi.org/project/PyQt4/)/[PyQt5](https://pypi.org/project/PyQt5/) \| 5\.15.9 \| clipboard \| Clipboard I/O \| \| [qtpy](https://github.com/spyder-ide/qtpy) \| 2\.4.2 \| clipboard \| Clipboard I/O \| Note Depending on operating system, system-level packages may need to installed. For clipboard to operate on Linux one of the CLI tools `xclip` or `xsel` must be installed on your system. #### Compression[\#](https://pandas.pydata.org/docs/getting_started/install.html#compression "Link to this heading") Installable with `pip install "pandas[compression]"` \| Dependency \| Minimum Version \| pip extra \| Notes \| \|---\|---\|---\|---\| \| [Zstandard](https://github.com/facebook/zstd) \| 0\.19.0 \| compression \| Zstandard compression \| #### Timezone[\#](https://pandas.pydata.org/docs/getting_started/install.html#timezone "Link to this heading") Installable with `pip install "pandas[timezone]"` \| Dependency \| Minimum Version \| pip extra \| Notes \| \|---\|---\|---\|---\| \| [pytz](https://github.com/stub42/pytz) \| 2024\.2 \| timezone \| Alternative timezone library to `zoneinfo`. \| [previous Getting started](https://pandas.pydata.org/docs/getting_started/index.html "previous page") [next Package overview](https://pandas.pydata.org/docs/getting_started/overview.html "next page") On this page - [Python version support](https://pandas.pydata.org/docs/getting_started/install.html#python-version-support) - [Installing pandas](https://pandas.pydata.org/docs/getting_started/install.html#installing-pandas) - [Installing with Conda](https://pandas.pydata.org/docs/getting_started/install.html#installing-with-conda) - [Installing with pip](https://pandas.pydata.org/docs/getting_started/install.html#installing-with-pip) - [Installing from source](https://pandas.pydata.org/docs/getting_started/install.html#installing-from-source) - [Installing the development version of pandas](https://pandas.pydata.org/docs/getting_started/install.html#installing-the-development-version-of-pandas) - [Running the test suite](https://pandas.pydata.org/docs/getting_started/install.html#running-the-test-suite) - [Dependencies](https://pandas.pydata.org/docs/getting_started/install.html#dependencies) - [Required dependencies](https://pandas.pydata.org/docs/getting_started/install.html#required-dependencies) - [Optional dependencies](https://pandas.pydata.org/docs/getting_started/install.html#optional-dependencies) - [Performance dependencies (recommended)](https://pandas.pydata.org/docs/getting_started/install.html#performance-dependencies-recommended) - [Visualization](https://pandas.pydata.org/docs/getting_started/install.html#visualization) - [Computation](https://pandas.pydata.org/docs/getting_started/install.html#computation) - [Excel files](https://pandas.pydata.org/docs/getting_started/install.html#excel-files) - [HTML](https://pandas.pydata.org/docs/getting_started/install.html#html) - [XML](https://pandas.pydata.org/docs/getting_started/install.html#xml) - [SQL databases](https://pandas.pydata.org/docs/getting_started/install.html#sql-databases) - [Other data sources](https://pandas.pydata.org/docs/getting_started/install.html#other-data-sources) - [Access data in the cloud](https://pandas.pydata.org/docs/getting_started/install.html#access-data-in-the-cloud) - [Clipboard](https://pandas.pydata.org/docs/getting_started/install.html#clipboard) - [Compression](https://pandas.pydata.org/docs/getting_started/install.html#compression) - [Timezone](https://pandas.pydata.org/docs/getting_started/install.html#timezone) © 2026, pandas via [NumFOCUS, Inc.](https://numfocus.org/) Hosted by [OVHcloud](https://www.ovhcloud.com/). 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Readable Markdown	The pandas development team officially distributes pandas for installation through the following methods: - Available on [conda-forge](https://anaconda.org/conda-forge/pandas) for installation with the conda package manager. - Available on [PyPI](https://pypi.org/project/pandas/) for installation with pip. - Available on [Github](https://github.com/pandas-dev/pandas) for installation from source. Note pandas may be installable from other sources besides the ones listed above, but they are not managed by the pandas development team. ## Python version support[\#](https://pandas.pydata.org/docs/getting_started/install.html#python-version-support "Link to this heading") See [Python support policy](https://pandas.pydata.org/docs/development/policies.html#policies-python-support). ## Installing pandas[\#](https://pandas.pydata.org/docs/getting_started/install.html#installing-pandas "Link to this heading") ### Installing with Conda[\#](https://pandas.pydata.org/docs/getting_started/install.html#installing-with-conda "Link to this heading") For users working with the [Conda](https://conda.io/en/latest/) package manager, pandas can be installed from the `conda-forge` channel. ``` conda install -c conda-forge pandas ``` To install the Conda package manager on your system, the [Miniforge distribution](https://github.com/conda-forge/miniforge?tab=readme-ov-file#install) is recommended. Additionally, it is recommended to install and run pandas from a virtual environment. ``` conda create -c conda-forge -n name_of_my_env python pandas # On Linux or MacOS source activate name_of_my_env # On Windows activate name_of_my_env ``` Tip For users that are new to Python, the easiest way to install Python, pandas, and the packages that make up the [PyData](https://pydata.org/) stack such as [SciPy](https://scipy.org/), [NumPy](https://numpy.org/) and [Matplotlib](https://matplotlib.org/) is with [Anaconda](https://docs.anaconda.com/anaconda/install/), a cross-platform (Linux, macOS, Windows) Python distribution for data analytics and scientific computing. However, pandas from Anaconda is not officially managed by the pandas development team. ### Installing with pip[\#](https://pandas.pydata.org/docs/getting_started/install.html#installing-with-pip "Link to this heading") For users working with the [pip](https://pip.pypa.io/en/stable/) package manager, pandas can be installed from [PyPI](https://pypi.org/project/pandas/). ``` pip install pandas ``` pandas can also be installed with sets of optional dependencies to enable certain functionality. For example, to install pandas with the optional dependencies to read Excel files. ``` pip install "pandas[excel]" ``` The full list of extras that can be installed can be found in the [dependency section.](https://pandas.pydata.org/docs/getting_started/install.html#install-optional-dependencies) Additionally, it is recommended to install and run pandas from a virtual environment, for example, using the Python standard library’s [venv](https://docs.python.org/3/library/venv.html) ### Installing from source[\#](https://pandas.pydata.org/docs/getting_started/install.html#installing-from-source "Link to this heading") See the [contributing guide](https://pandas.pydata.org/docs/development/contributing.html#contributing) for complete instructions on building from the git source tree. Further, see [creating a development environment](https://pandas.pydata.org/docs/development/contributing_environment.html#contributing-environment) if you wish to create a pandas development environment. ### Installing the development version of pandas[\#](https://pandas.pydata.org/docs/getting_started/install.html#installing-the-development-version-of-pandas "Link to this heading") Installing the development version is the quickest way to: - Try a new feature that will be shipped in the next release (that is, a feature from a pull-request that was recently merged to the main branch). - Check whether a bug you encountered has been fixed since the last release. The development version is usually uploaded daily to the scientific-python-nightly-wheels index from the PyPI registry of anaconda.org. You can install it by running. ``` pip install --pre --extra-index-url https://pypi.anaconda.org/scientific-python-nightly-wheels/simple pandas ``` Note You might be required to uninstall an existing version of pandas to install the development version. ``` pip uninstall pandas -y ``` ## Running the test suite[\#](https://pandas.pydata.org/docs/getting_started/install.html#running-the-test-suite "Link to this heading") If pandas has been installed [from source](https://pandas.pydata.org/docs/getting_started/install.html#install-source), running `pytest pandas` will run all of pandas unit tests. The unit tests can also be run from the pandas module itself with the [`test()`](https://pandas.pydata.org/docs/reference/api/pandas.test.html#pandas.test "pandas.test") function. The packages required to run the tests can be installed with `pip install "pandas[test]"`. Note Test failures are not necessarily indicative of a broken pandas installation. ## Dependencies[\#](https://pandas.pydata.org/docs/getting_started/install.html#dependencies "Link to this heading") ### Required dependencies[\#](https://pandas.pydata.org/docs/getting_started/install.html#required-dependencies "Link to this heading") pandas requires the following dependencies. \| Package \| Minimum supported version \| \|---\|---\| \| [NumPy](https://numpy.org/) \| 1\.26.0 \| \| [python-dateutil](https://dateutil.readthedocs.io/en/stable/) \| 2\.8.2 \| \| [tzdata](https://pypi.org/project/tzdata/) \* \| / \| \* `tzdata` is only required on Windows and Pyodide (Emscripten). Generally, the minimum supported version is ~2 years old from the release date of a major or minor pandas version. ### Optional dependencies[\#](https://pandas.pydata.org/docs/getting_started/install.html#optional-dependencies "Link to this heading") pandas has many optional dependencies that are only used for specific methods. For example, [`pandas.read_hdf()`](https://pandas.pydata.org/docs/reference/api/pandas.read_hdf.html#pandas.read_hdf "pandas.read_hdf") requires the `pytables` package, while [`DataFrame.to_markdown()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_markdown.html#pandas.DataFrame.to_markdown "pandas.DataFrame.to_markdown") requires the `tabulate` package. If the optional dependency is not installed, pandas will raise an `ImportError` when the method requiring that dependency is called. With pip, optional pandas dependencies can be installed or managed in a file (e.g. requirements.txt or pyproject.toml) as optional extras (e.g. `pandas[performance, aws]`). All optional dependencies can be installed with `pandas[all]`, and specific sets of dependencies are listed in the sections below. Generally, the minimum supported version is ~1 years old from the release date of a major or minor pandas version. Older versions of optional dependencies may still work, but they are not tested or considered supported. #### Performance dependencies (recommended)[\#](https://pandas.pydata.org/docs/getting_started/install.html#performance-dependencies-recommended "Link to this heading") Note You are highly encouraged to install these libraries, as they provide speed improvements, especially when working with large data sets. Installable with `pip install "pandas[performance]"` \| Dependency \| Minimum Version \| pip extra \| Notes \| \|---\|---\|---\|---\| \| [numexpr](https://github.com/pydata/numexpr) \| 2\.10.2 \| performance \| Accelerates certain numerical operations by using multiple cores as well as smart chunking and caching to achieve large speedups \| \| [bottleneck](https://github.com/pydata/bottleneck) \| 1\.4.2 \| performance \| Accelerates certain types of `nan` by using specialized cython routines to achieve large speedup. \| \| [numba](https://github.com/numba/numba) \| 0\.60.0 \| performance \| Alternative execution engine for operations that accept `engine="numba"` using a JIT compiler that translates Python functions to optimized machine code using the LLVM compiler. \| #### Visualization[\#](https://pandas.pydata.org/docs/getting_started/install.html#visualization "Link to this heading") Installable with `pip install "pandas[plot, output-formatting]"`. \| Dependency \| Minimum Version \| pip extra \| Notes \| \|---\|---\|---\|---\| \| [matplotlib](https://github.com/matplotlib/matplotlib) \| 3\.8.3 \| plot \| Plotting library \| \| [Jinja2](https://github.com/pallets/jinja) \| 3\.1.3 \| output-formatting \| Conditional formatting with DataFrame.style \| \| [tabulate](https://github.com/astanin/python-tabulate) \| 0\.9.0 \| output-formatting \| Printing in Markdown-friendly format (see [tabulate](https://github.com/astanin/python-tabulate)) \| #### Computation[\#](https://pandas.pydata.org/docs/getting_started/install.html#computation "Link to this heading") Installable with `pip install "pandas[computation]"`. \| Dependency \| Minimum Version \| pip extra \| Notes \| \|---\|---\|---\|---\| \| [SciPy](https://github.com/scipy/scipy) \| 1\.14.1 \| computation \| Miscellaneous statistical functions \| \| [xarray](https://github.com/pydata/xarray) \| 2024\.10.0 \| computation \| pandas-like API for N-dimensional data \| #### Excel files[\#](https://pandas.pydata.org/docs/getting_started/install.html#excel-files "Link to this heading") Installable with `pip install "pandas[excel]"`. \| Dependency \| Minimum Version \| pip extra \| Notes \| \|---\|---\|---\|---\| \| [xlrd](https://github.com/python-excel/xlrd) \| 2\.0.1 \| excel \| Reading for xls files \| \| [xlsxwriter](https://github.com/jmcnamara/XlsxWriter) \| 3\.2.0 \| excel \| Writing for xlsx files \| \| [openpyxl](https://github.com/theorchard/openpyxl) \| 3\.1.5 \| excel \| Reading / writing for Excel 2010 xlsx/xlsm/xltx/xltm files \| \| [pyxlsb](https://github.com/willtrnr/pyxlsb) \| 1\.0.10 \| excel \| Reading for xlsb files \| \| [python-calamine](https://github.com/dimastbk/python-calamine) \| 0\.3.0 \| excel \| Reading for xls/xlsx/xlsm/xlsb/xla/xlam/ods files \| \| [odfpy](https://github.com/eea/odfpy) \| 1\.4.1 \| excel \| Reading / writing for OpenDocument 1.2 files \| #### HTML[\#](https://pandas.pydata.org/docs/getting_started/install.html#html "Link to this heading") Installable with `pip install "pandas[html]"`. \| Dependency \| Minimum Version \| pip extra \| Notes \| \|---\|---\|---\|---\| \| [BeautifulSoup4](https://github.com/wention/BeautifulSoup4) \| 4\.12.3 \| html \| HTML parser for read\_html \| \| [html5lib](https://github.com/html5lib/html5lib-python) \| 1\.1 \| html \| HTML parser for read\_html \| \| [lxml](https://github.com/lxml/lxml) \| 4\.9.2 \| html \| HTML parser for read\_html \| One of the following combinations of libraries is needed to use the top-level [`read_html()`](https://pandas.pydata.org/docs/reference/api/pandas.read_html.html#pandas.read_html "pandas.read_html") function: - [BeautifulSoup4](https://www.crummy.com/software/BeautifulSoup) and [html5lib](https://github.com/html5lib/html5lib-python) - [BeautifulSoup4](https://www.crummy.com/software/BeautifulSoup) and [lxml](https://lxml.de/) - [BeautifulSoup4](https://www.crummy.com/software/BeautifulSoup) and [html5lib](https://github.com/html5lib/html5lib-python) and [lxml](https://lxml.de/) - Only [lxml](https://lxml.de/), although see [HTML Table Parsing](https://pandas.pydata.org/docs/user_guide/io.html#io-html-gotchas) for reasons as to why you should probably not take this approach. Warning - if you install [BeautifulSoup4](https://www.crummy.com/software/BeautifulSoup) you must install either [lxml](https://lxml.de/) or [html5lib](https://github.com/html5lib/html5lib-python) or both. [`read_html()`](https://pandas.pydata.org/docs/reference/api/pandas.read_html.html#pandas.read_html "pandas.read_html") will not work with only [BeautifulSoup4](https://www.crummy.com/software/BeautifulSoup) installed. - You are highly encouraged to read [HTML Table Parsing gotchas](https://pandas.pydata.org/docs/user_guide/io.html#io-html-gotchas). It explains issues surrounding the installation and usage of the above three libraries. #### XML[\#](https://pandas.pydata.org/docs/getting_started/install.html#xml "Link to this heading") Installable with `pip install "pandas[xml]"`. \| Dependency \| Minimum Version \| pip extra \| Notes \| \|---\|---\|---\|---\| \| [lxml](https://github.com/lxml/lxml) \| 5\.3.0 \| xml \| XML parser for read\_xml and tree builder for to\_xml \| #### SQL databases[\#](https://pandas.pydata.org/docs/getting_started/install.html#sql-databases "Link to this heading") Traditional drivers are installable with `pip install "pandas[postgresql, mysql, sql-other]"` \| Dependency \| Minimum Version \| pip extra \| Notes \| \|---\|---\|---\|---\| \| [SQLAlchemy](https://github.com/sqlalchemy/sqlalchemy) \| 2\.0.36 \| postgresql, mysql, sql-other \| SQL support for databases other than sqlite \| \| [psycopg2](https://github.com/psycopg/psycopg2) \| 2\.9.10 \| postgresql \| PostgreSQL engine for sqlalchemy \| \| [pymysql](https://github.com/PyMySQL/PyMySQL) \| 1\.1.1 \| mysql \| MySQL engine for sqlalchemy \| \| [adbc-driver-postgresql](https://github.com/apache/arrow-adbc) \| 1\.2.0 \| postgresql \| ADBC Driver for PostgreSQL \| \| [adbc-driver-sqlite](https://github.com/apache/arrow-adbc) \| 1\.2.0 \| sql-other \| ADBC Driver for SQLite \| #### Other data sources[\#](https://pandas.pydata.org/docs/getting_started/install.html#other-data-sources "Link to this heading") Installable with `pip install "pandas[hdf5, parquet, iceberg, feather, spss, excel]"` \| Dependency \| Minimum Version \| pip extra \| Notes \| \|---\|---\|---\|---\| \| [PyTables](https://github.com/PyTables/PyTables) \| 3\.10.1 \| hdf5 \| HDF5-based reading / writing \| \| [zlib](https://github.com/madler/zlib) \| \| hdf5 \| Compression for HDF5 \| \| [fastparquet](https://github.com/dask/fastparquet) \| 2024\.11.0 \| \| Parquet reading / writing (pyarrow is default) \| \| [pyarrow](https://github.com/apache/arrow) \| 13\.0.0 \| parquet, feather \| Parquet, ORC, and feather reading / writing \| \| [PyIceberg](https://py.iceberg.apache.org/) \| 0\.8.1 \| iceberg \| Apache Iceberg reading / writing \| \| [pyreadstat](https://github.com/Roche/pyreadstat) \| 1\.2.8 \| spss \| SPSS files (.sav) reading \| \| [odfpy](https://github.com/eea/odfpy) \| 1\.4.1 \| excel \| Open document format (.odf, .ods, .odt) reading / writing \| Warning - If you want to use [`read_orc()`](https://pandas.pydata.org/docs/reference/api/pandas.read_orc.html#pandas.read_orc "pandas.read_orc"), it is highly recommended to install pyarrow using conda. [`read_orc()`](https://pandas.pydata.org/docs/reference/api/pandas.read_orc.html#pandas.read_orc "pandas.read_orc") may fail if pyarrow was installed from pypi, and [`read_orc()`](https://pandas.pydata.org/docs/reference/api/pandas.read_orc.html#pandas.read_orc "pandas.read_orc") is not compatible with Windows OS. #### Access data in the cloud[\#](https://pandas.pydata.org/docs/getting_started/install.html#access-data-in-the-cloud "Link to this heading") Installable with `pip install "pandas[fss, aws, gcp]"` \| Dependency \| Minimum Version \| pip extra \| Notes \| \|---\|---\|---\|---\| \| [fsspec](https://github.com/fsspec) \| 2024\.10.0 \| fss, gcp, aws \| Handling files aside from simple local and HTTP (required dependency of s3fs, gcsfs). \| \| [gcsfs](https://github.com/fsspec/gcsfs) \| 2024\.10.0 \| gcp \| Google Cloud Storage access \| \| [s3fs](https://github.com/fsspec/s3fs) \| 2024\.10.0 \| aws \| Amazon S3 access \| #### Clipboard[\#](https://pandas.pydata.org/docs/getting_started/install.html#clipboard "Link to this heading") Installable with `pip install "pandas[clipboard]"`. \| Dependency \| Minimum Version \| pip extra \| Notes \| \|---\|---\|---\|---\| \| [PyQt4](https://pypi.org/project/PyQt4/)/[PyQt5](https://pypi.org/project/PyQt5/) \| 5\.15.9 \| clipboard \| Clipboard I/O \| \| [qtpy](https://github.com/spyder-ide/qtpy) \| 2\.4.2 \| clipboard \| Clipboard I/O \| Note Depending on operating system, system-level packages may need to installed. For clipboard to operate on Linux one of the CLI tools `xclip` or `xsel` must be installed on your system. #### Compression[\#](https://pandas.pydata.org/docs/getting_started/install.html#compression "Link to this heading") Installable with `pip install "pandas[compression]"` \| Dependency \| Minimum Version \| pip extra \| Notes \| \|---\|---\|---\|---\| \| [Zstandard](https://github.com/facebook/zstd) \| 0\.19.0 \| compression \| Zstandard compression \| #### Timezone[\#](https://pandas.pydata.org/docs/getting_started/install.html#timezone "Link to this heading") Installable with `pip install "pandas[timezone]"` \| Dependency \| Minimum Version \| pip extra \| Notes \| \|---\|---\|---\|---\| \| [pytz](https://github.com/stub42/pytz) \| 2024\.2 \| timezone \| Alternative timezone library to `zoneinfo`. \|
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