pandas各版本对依赖库的最低版本要求

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Pandas 2.1.0 (Aug 30, 2023)#

Increased minimum version for Python

pandas 2.1.0 supports Python 3.9 and higher.

Increased minimum versions for dependencies

Some minimum supported versions of dependencies were updated. If installed, we now require:

Package

Minimum Version

Required

Changed

numpy

1.22.4

X

X

mypy (dev)

1.4.1

X

beautifulsoup4

4.11.1

X

bottleneck

1.3.4

X

dataframe-api-compat

0.1.7

X

fastparquet

0.8.1

X

fsspec

2022.05.0

X

hypothesis

6.46.1

X

gcsfs

2022.05.0

X

jinja2

3.1.2

X

lxml

4.8.0

X

numba

0.55.2

X

numexpr

2.8.0

X

openpyxl

3.0.10

X

pandas-gbq

0.17.5

X

psycopg2

2.9.3

X

pyreadstat

1.1.5

X

pyqt5

5.15.6

X

pytables

3.7.0

X

pytest

7.3.2

X

python-snappy

0.6.1

X

pyxlsb

1.0.9

X

s3fs

2022.05.0

X

scipy

1.8.1

X

sqlalchemy

1.4.36

X

tabulate

0.8.10

X

xarray

2022.03.0

X

xlsxwriter

3.0.3

X

zstandard

0.17.0

X

For optional libraries the general recommendation is to use the latest version.

See Dependencies and Optional dependencies for more.

Required dependencies

pandas requires the following dependencies.

Package

Minimum supported version

NumPy

1.22.4

python-dateutil

2.8.2

pytz

2020.1

tzdata

2022.1

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.

If using 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.

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

2.8.0

performance

Accelerates certain numerical operations by using multiple cores as well as smart chunking and caching to achieve large speedups

bottleneck

1.3.4

performance

Accelerates certain types of nan by using specialized cython routines to achieve large speedup.

numba

0.55.2

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

3.6.1

plot

Plotting library

Jinja2

3.1.2

output-formatting

Conditional formatting with DataFrame.style

tabulate

0.8.10

output-formatting

Printing in Markdown-friendly format (see tabulate)

Computation

Installable with pip install "pandas[computation]".

Dependency

Minimum Version

pip extra

Notes

SciPy

1.8.1

computation

Miscellaneous statistical functions

xarray

2022.03.0

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 Excel

xlsxwriter

3.0.3

excel

Writing Excel

openpyxl

3.0.10

excel

Reading / writing for xlsx files

pyxlsb

1.0.9

excel

Reading for xlsb files

HTML

Installable with pip install "pandas[html]".

Dependency

Minimum Version

pip extra

Notes

BeautifulSoup4

4.11.1

html

HTML parser for read_html

html5lib

1.1

html

HTML parser for read_html

lxml

4.8.0

html

HTML parser for read_html

One of the following combinations of libraries is needed to use the top-level read_html() function:

Warning

XML

Installable with pip install "pandas[xml]".

Dependency

Minimum Version

pip extra

Notes

lxml

4.8.0

xml

XML parser for read_xml and tree builder for to_xml

SQL databases

Installable with pip install "pandas[postgresql, mysql, sql-other]".

Dependency

Minimum Version

pip extra

Notes

SQLAlchemy

1.4.36

postgresql, mysql, sql-other

SQL support for databases other than sqlite

psycopg2

2.9.3

postgresql

PostgreSQL engine for sqlalchemy

pymysql

1.0.2

mysql

MySQL engine for sqlalchemy

Other data sources

Installable with pip install "pandas[hdf5, parquet, feather, spss, excel]"

Dependency

Minimum Version

pip extra

Notes

PyTables

3.7.0

hdf5

HDF5-based reading / writing

blosc

1.21.0

hdf5

Compression for HDF5; only available on conda

zlib

hdf5

Compression for HDF5

fastparquet

0.8.1

Parquet reading / writing (pyarrow is default)

pyarrow

7.0.0

parquet, feather

Parquet, ORC, and feather reading / writing

pyreadstat

1.1.5

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

2022.05.0

fss, gcp, aws

Handling files aside from simple local and HTTP (required dependency of s3fs, gcsfs).

gcsfs

2022.05.0

gcp

Google Cloud Storage access

pandas-gbq

0.17.5

gcp

Google Big Query access

s3fs

2022.05.0

aws

Amazon S3 access

Clipboard

Installable with pip install "pandas[clipboard]".

Dependency

Minimum Version

pip extra

Notes

PyQt4/PyQt5

5.15.6

clipboard

Clipboard I/O

qtpy

2.2.0

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

0.17.0

compression

Zstandard compression

Consortium Standard

Installable with pip install "pandas[consortium-standard]"

Dependency

Minimum Version

pip extra

Notes

dataframe-api-compat

0.1.7

consortium-standard

Consortium Standard-compatible implementation based on pandas

Pandas in 2.0.0 (April 3, 2023)  支持win7的最高pandas版本

Python version support

Officially Python 3.8, 3.9, 3.10 and 3.11.

Increased minimum versions for dependencies

Some minimum supported versions of dependencies were updated. If installed, we now require:

Package

Minimum Version

Required

Changed

mypy (dev)

1.0

X

pytest (dev)

7.0.0

X

pytest-xdist (dev)

2.2.0

X

hypothesis (dev)

6.34.2

X

python-dateutil

2.8.2

X

X

tzdata

2022.1

X

X

For optional libraries the general recommendation is to use the latest version. The following table lists the lowest version per library that is currently being tested throughout the development of pandas. Optional libraries below the lowest tested version may still work, but are not considered supported.

Package

Minimum Version

Changed

pyarrow

7.0.0

X

matplotlib

3.6.1

X

fastparquet

0.6.3

X

xarray

0.21.0

X

See Dependencies and Optional dependencies for more.

Dependencies

Required dependencies

pandas requires the following dependencies.

Package

Minimum supported version

NumPy

1.20.3

python-dateutil

2.8.2

pytz

2020.1

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.

If using 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]>=1.5.0``). All optional dependencies can be installed with pandas[all], and specific sets of dependencies are listed in the sections below.

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

2.7.3

performance

Accelerates certain numerical operations by using uses multiple cores as well as smart chunking and caching to achieve large speedups

bottleneck

1.3.2

performance

Accelerates certain types of nan by using specialized cython routines to achieve large speedup.

numba

0.53.1

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

3.6.1

plot

Plotting library

Jinja2

3.0.0

output_formatting

Conditional formatting with DataFrame.style

tabulate

0.8.9

output_formatting

Printing in Markdown-friendly format (see tabulate)

Computation

Installable with pip install "pandas[computation]".

Dependency

Minimum Version

pip extra

Notes

SciPy

1.7.1

computation

Miscellaneous statistical functions

xarray

0.21.0

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 Excel

xlsxwriter

1.4.3

excel

Writing Excel

openpyxl

3.0.7

excel

Reading / writing for xlsx files

pyxlsb

1.0.8

excel

Reading for xlsb files

HTML

Installable with pip install "pandas[html]".

Dependency

Minimum Version

pip extra

Notes

BeautifulSoup4

4.9.3

html

HTML parser for read_html

html5lib

1.1

html

HTML parser for read_html

lxml

4.6.3

html

HTML parser for read_html

One of the following combinations of libraries is needed to use the top-level read_html() function:

Warning

XML

Installable with pip install "pandas[xml]".

Dependency

Minimum Version

pip extra

Notes

lxml

4.6.3

xml

XML parser for read_xml and tree builder for to_xml

SQL databases

Installable with pip install "pandas[postgresql, mysql, sql-other]".

Dependency

Minimum Version

pip extra

Notes

SQLAlchemy

1.4.16

postgresql, mysql, sql-other

SQL support for databases other than sqlite

psycopg2

2.8.6

postgresql

PostgreSQL engine for sqlalchemy

pymysql

1.0.2

mysql

MySQL engine for sqlalchemy

Other data sources

Installable with pip install "pandas[hdf5, parquet, feather, spss, excel]"

Dependency

Minimum Version

pip extra

Notes

PyTables

3.6.1

hdf5

HDF5-based reading / writing

blosc

1.21.0

hdf5

Compression for HDF5; only available on conda

zlib

hdf5

Compression for HDF5

fastparquet

0.6.3

Parquet reading / writing (pyarrow is default)

pyarrow

7.0.0

parquet, feather

Parquet, ORC, and feather reading / writing

pyreadstat

1.1.2

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. The following is a summary of the environment in which read_orc() can work.

    System

    Conda

    PyPI

    Linux

    Successful

    Failed

    macOS

    Successful

    Failed

    Windows

    Failed

    Failed

Access data in the cloud

Installable with pip install "pandas[fss, aws, gcp]"

Dependency

Minimum Version

pip extra

Notes

fsspec

2021.7.0

fss, gcp, aws

Handling files aside from simple local and HTTP (required dependency of s3fs, gcsfs).

gcsfs

2021.7.0

gcp

Google Cloud Storage access

pandas-gbq

0.15.0

gcp

Google Big Query access

s3fs

2021.08.0

aws

Amazon S3 access

Clipboard

Installable with pip install "pandas[clipboard]".

Dependency

Minimum Version

pip extra

Notes

PyQt4/PyQt5

5.15.1

clipboard

Clipboard I/O

qtpy

2.2.0

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

brotli

0.7.0

compression

Brotli compression

python-snappy

0.6.0

compression

Snappy compression

Zstandard

0.15.2

compression

Zstandard compression

Pandas 1.5.0 (September 19, 2022)

Python version support

Officially Python 3.8, 3.9 and 3.10.

Increased minimum versions for dependencies

Some minimum supported versions of dependencies were updated. If installed, we now require:

Package

Minimum Version

Required

Changed

numpy

1.20.3

X

X

mypy (dev)

0.971

X

beautifulsoup4

4.9.3

X

blosc

1.21.0

X

bottleneck

1.3.2

X

fsspec

2021.07.0

X

hypothesis

6.13.0

X

gcsfs

2021.07.0

X

jinja2

3.0.0

X

lxml

4.6.3

X

numba

0.53.1

X

numexpr

2.7.3

X

openpyxl

3.0.7

X

pandas-gbq

0.15.0

X

psycopg2

2.8.6

X

pymysql

1.0.2

X

pyreadstat

1.1.2

X

pyxlsb

1.0.8

X

s3fs

2021.08.0

X

scipy

1.7.1

X

sqlalchemy

1.4.16

X

tabulate

0.8.9

X

xarray

0.19.0

X

xlsxwriter

1.4.3

X

For optional libraries the general recommendation is to use the latest version. The following table lists the lowest version per library that is currently being tested throughout the development of pandas. Optional libraries below the lowest tested version may still work, but are not considered supported.

Package

Minimum Version

Changed

beautifulsoup4

4.9.3

X

blosc

1.21.0

X

bottleneck

1.3.2

X

brotlipy

0.7.0

fastparquet

0.4.0

fsspec

2021.08.0

X

html5lib

1.1

hypothesis

6.13.0

X

gcsfs

2021.08.0

X

jinja2

3.0.0

X

lxml

4.6.3

X

matplotlib

3.3.2

numba

0.53.1

X

numexpr

2.7.3

X

odfpy

1.4.1

openpyxl

3.0.7

X

pandas-gbq

0.15.0

X

psycopg2

2.8.6

X

pyarrow

1.0.1

pymysql

1.0.2

X

pyreadstat

1.1.2

X

pytables

3.6.1

python-snappy

0.6.0

pyxlsb

1.0.8

X

s3fs

2021.08.0

X

scipy

1.7.1

X

sqlalchemy

1.4.16

X

tabulate

0.8.9

X

tzdata

2022a

xarray

0.19.0

X

xlrd

2.0.1

xlsxwriter

1.4.3

X

xlwt

1.3.0

zstandard

0.15.2

See Dependencies and Optional dependencies for more.

Dependencies

Package

Minimum supported version

NumPy

1.20.3

python-dateutil

2.8.1

pytz

2020.1

Recommended dependencies

  • numexpr: for accelerating certain numerical operations. numexpr uses multiple cores as well as smart chunking and caching to achieve large speedups. If installed, must be Version 2.7.3 or higher.

  • bottleneck: for accelerating certain types of nan evaluations. bottleneck uses specialized cython routines to achieve large speedups. If installed, must be Version 1.3.2 or higher.

Note

You are highly encouraged to install these libraries, as they provide speed improvements, especially when working with large data sets.

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.

Timezones

Dependency

Minimum Version

Notes

tzdata

2022.1(pypi)/ 2022a(for system tzdata)

Allows the use of zoneinfo timezones with pandas. Note: You only need to install the pypi package if your system does not already provide the IANA tz database. However, the minimum tzdata version still applies, even if it is not enforced through an error.

If you would like to keep your system tzdata version updated, it is recommended to use the tzdata package from conda-forge.

Visualization

Dependency

Minimum Version

Notes

matplotlib

3.3.2

Plotting library

Jinja2

3.0.0

Conditional formatting with DataFrame.style

tabulate

0.8.9

Printing in Markdown-friendly format (see tabulate)

Computation

Dependency

Minimum Version

Notes

SciPy

1.7.1

Miscellaneous statistical functions

numba

0.53.1

Alternative execution engine for rolling operations (see Enhancing Performance)

xarray

0.19.0

pandas-like API for N-dimensional data

Excel files

Dependency

Minimum Version

Notes

xlrd

2.0.1

Reading Excel

xlwt

1.3.0

Writing Excel

xlsxwriter

1.4.3

Writing Excel

openpyxl

3.0.7

Reading / writing for xlsx files

pyxlsb

1.0.8

Reading for xlsb files

HTML

Dependency

Minimum Version

Notes

BeautifulSoup4

4.9.3

HTML parser for read_html

html5lib

1.1

HTML parser for read_html

lxml

4.6.3

HTML parser for read_html

One of the following combinations of libraries is needed to use the top-level read_html() function:

Warning

XML

Dependency

Minimum Version

Notes

lxml

4.5.0

XML parser for read_xml and tree builder for to_xml

SQL databases

Dependency

Minimum Version

Notes

SQLAlchemy

1.4.16

SQL support for databases other than sqlite

psycopg2

2.8.6

PostgreSQL engine for sqlalchemy

pymysql

1.0.2

MySQL engine for sqlalchemy

Other data sources

Dependency

Minimum Version

Notes

PyTables

3.6.1

HDF5-based reading / writing

blosc

1.21.0

Compression for HDF5

zlib

Compression for HDF5

fastparquet

0.4.0

Parquet reading / writing

pyarrow

1.0.1

Parquet, ORC, and feather reading / writing

pyreadstat

1.1.2

SPSS files (.sav) reading

Warning

  • If you want to use read_orc(), it is highly recommended to install pyarrow using conda. The following is a summary of the environment in which read_orc() can work.

    System

    Conda

    PyPI

    Linux

    Successful

    Failed(pyarrow==3.0 Successful)

    macOS

    Successful

    Failed

    Windows

    Failed

    Failed

Access data in the cloud

Dependency

Minimum Version

Notes

fsspec

2021.7.0

Handling files aside from simple local and HTTP

gcsfs

2021.7.0

Google Cloud Storage access

pandas-gbq

0.15.0

Google Big Query access

s3fs

2021.08.0

Amazon S3 access

Clipboard

Dependency

Minimum Version

Notes

PyQt4/PyQt5

Clipboard I/O

qtpy

Clipboard I/O

xclip

Clipboard I/O on linux

xsel

Clipboard I/O on linux

Compression

Dependency

Minimum Version

Notes

brotli

0.7.0

Brotli compression

python-snappy

0.6.0

Snappy compression

Zstandard

0.15.2

Zstandard compression

Pandas 1.4.0 (January 22, 2022)

Increased minimum version for Python

pandas 1.4.0 supports Python 3.8 and higher.

Increased minimum versions for dependencies

Some minimum supported versions of dependencies were updated. If installed, we now require:

Package

Minimum Version

Required

Changed

numpy

1.18.5

X

X

pytz

2020.1

X

X

python-dateutil

2.8.1

X

X

bottleneck

1.3.1

X

numexpr

2.7.1

X

pytest (dev)

6.0

mypy (dev)

0.930

X

For optional libraries the general recommendation is to use the latest version. The following table lists the lowest version per library that is currently being tested throughout the development of pandas. Optional libraries below the lowest tested version may still work, but are not considered supported.

Package

Minimum Version

Changed

beautifulsoup4

4.8.2

X

fastparquet

0.4.0

fsspec

0.7.4

gcsfs

0.6.0

lxml

4.5.0

X

matplotlib

3.3.2

X

numba

0.50.1

X

openpyxl

3.0.3

X

pandas-gbq

0.14.0

X

pyarrow

1.0.1

X

pymysql

0.10.1

X

pytables

3.6.1

X

s3fs

0.4.0

scipy

1.4.1

X

sqlalchemy

1.4.0

X

tabulate

0.8.7

xarray

0.15.1

X

xlrd

2.0.1

X

xlsxwriter

1.2.2

X

xlwt

1.3.0

See Dependencies and Optional dependencies for more.

Dependencies

Package

Minimum supported version

NumPy

1.18.5

python-dateutil

2.8.1

pytz

2020.1

Recommended dependencies

  • numexpr: for accelerating certain numerical operations. numexpr uses multiple cores as well as smart chunking and caching to achieve large speedups. If installed, must be Version 2.7.1 or higher.

  • bottleneck: for accelerating certain types of nan evaluations. bottleneck uses specialized cython routines to achieve large speedups. If installed, must be Version 1.3.1 or higher.

Note

You are highly encouraged to install these libraries, as they provide speed improvements, especially when working with large data sets.

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.

Visualization

Dependency

Minimum Version

Notes

matplotlib

3.3.2

Plotting library

Jinja2

2.11

Conditional formatting with DataFrame.style

tabulate

0.8.7

Printing in Markdown-friendly format (see tabulate)

Computation

Dependency

Minimum Version

Notes

SciPy

1.4.1

Miscellaneous statistical functions

numba

0.50.1

Alternative execution engine for rolling operations (see Enhancing Performance)

xarray

0.15.1

pandas-like API for N-dimensional data

Excel files

Dependency

Minimum Version

Notes

xlrd

2.0.1

Reading Excel

xlwt

1.3.0

Writing Excel

xlsxwriter

1.2.2

Writing Excel

openpyxl

3.0.3

Reading / writing for xlsx files

pyxlsb

1.0.6

Reading for xlsb files

HTML

Dependency

Minimum Version

Notes

BeautifulSoup4

4.8.2

HTML parser for read_html

html5lib

1.1

HTML parser for read_html

lxml

4.5.0

HTML parser for read_html

One of the following combinations of libraries is needed to use the top-level read_html() function:

Warning

XML

Dependency

Minimum Version

Notes

lxml

4.5.0

XML parser for read_xml and tree builder for to_xml

SQL databases

Dependency

Minimum Version

Notes

SQLAlchemy

1.4.0

SQL support for databases other than sqlite

psycopg2

2.8.4

PostgreSQL engine for sqlalchemy

pymysql

0.10.1

MySQL engine for sqlalchemy

Other data sources

Dependency

Minimum Version

Notes

PyTables

3.6.1

HDF5-based reading / writing

blosc

1.20.1

Compression for HDF5

zlib

Compression for HDF5

fastparquet

0.4.0

Parquet reading / writing

pyarrow

1.0.1

Parquet, ORC, and feather reading / writing

pyreadstat

1.1.0

SPSS files (.sav) reading

Warning

  • If you want to use read_orc(), it is highly recommended to install pyarrow using conda. The following is a summary of the environment in which read_orc() can work.

    System

    Conda

    PyPI

    Linux

    Successful

    Failed(pyarrow==3.0 Successful)

    macOS

    Successful

    Failed

    Windows

    Failed

    Failed

Access data in the cloud

Dependency

Minimum Version

Notes

fsspec

0.7.4

Handling files aside from simple local and HTTP

gcsfs

0.6.0

Google Cloud Storage access

pandas-gbq

0.14.0

Google Big Query access

s3fs

0.4.0

Amazon S3 access

Clipboard

Dependency

Minimum Version

Notes

PyQt4/PyQt5

Clipboard I/O

qtpy

Clipboard I/O

xclip

Clipboard I/O on linux

xsel

Clipboard I/O on linux

Compression

Dependency

Minimum Version

Notes

brotli

0.7.0

Brotli compression

python-snappy

0.6.0

Snappy compression

Zstandard

0.15.2

Zstandard compression

Pandas 1.3.0 (July 2, 2021)

​​​​​​Pandas 1.3.0 (July 2, 2021)

Python version support

Officially Python 3.7.1 and above, 3.8, and 3.9.

Increased minimum versions for dependencies

Some minimum supported versions of dependencies were updated. If installed, we now require:

Package

Minimum Version

Required

Changed

numpy

1.17.3

X

X

pytz

2017.3

X

python-dateutil

2.7.3

X

bottleneck

1.2.1

numexpr

2.7.0

X

pytest (dev)

6.0

X

mypy (dev)

0.812

X

setuptools

38.6.0

X

For optional libraries the general recommendation is to use the latest version. The following table lists the lowest version per library that is currently being tested throughout the development of pandas. Optional libraries below the lowest tested version may still work, but are not considered supported.

Package

Minimum Version

Changed

beautifulsoup4

4.6.0

fastparquet

0.4.0

X

fsspec

0.7.4

gcsfs

0.6.0

lxml

4.3.0

matplotlib

2.2.3

numba

0.46.0

openpyxl

3.0.0

X

pyarrow

0.17.0

X

pymysql

0.8.1

X

pytables

3.5.1

s3fs

0.4.0

scipy

1.2.0

sqlalchemy

1.3.0

X

tabulate

0.8.7

X

xarray

0.12.0

xlrd

1.2.0

xlsxwriter

1.0.2

xlwt

1.3.0

pandas-gbq

0.12.0

Dependencies

Package

Minimum supported version

NumPy

1.17.3

python-dateutil

2.7.3

pytz

2017.3

Recommended dependencies

  • numexpr: for accelerating certain numerical operations. numexpr uses multiple cores as well as smart chunking and caching to achieve large speedups. If installed, must be Version 2.7.0 or higher.

  • bottleneck: for accelerating certain types of nan evaluations. bottleneck uses specialized cython routines to achieve large speedups. If installed, must be Version 1.2.1 or higher.

Note

You are highly encouraged to install these libraries, as they provide speed improvements, especially when working with large data sets.

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.

Visualization

Dependency

Minimum Version

Notes

setuptools

38.6.0

Utils for entry points of plotting backend

matplotlib

2.2.3

Plotting library

Jinja2

2.10

Conditional formatting with DataFrame.style

tabulate

0.8.7

Printing in Markdown-friendly format (see tabulate)

Computation

Dependency

Minimum Version

Notes

SciPy

1.12.0

Miscellaneous statistical functions

numba

0.46.0

Alternative execution engine for rolling operations (see Enhancing Performance)

xarray

0.12.3

pandas-like API for N-dimensional data

Excel files

Dependency

Minimum Version

Notes

xlrd

1.2.0

Reading Excel

xlwt

1.3.0

Writing Excel

xlsxwriter

1.0.2

Writing Excel

openpyxl

3.0.0

Reading / writing for xlsx files

pyxlsb

1.0.6

Reading for xlsb files

HTML

Dependency

Minimum Version

Notes

BeautifulSoup4

4.6.0

HTML parser for read_html

html5lib

1.0.1

HTML parser for read_html

lxml

4.3.0

HTML parser for read_html

One of the following combinations of libraries is needed to use the top-level read_html() function:

Warning

XML

Dependency

Minimum Version

Notes

lxml

4.3.0

XML parser for read_xml and tree builder for to_xml

SQL databases

Dependency

Minimum Version

Notes

SQLAlchemy

1.3.0

SQL support for databases other than sqlite

psycopg2

2.7

PostgreSQL engine for sqlalchemy

pymysql

0.8.1

MySQL engine for sqlalchemy

Other data sources

Dependency

Minimum Version

Notes

PyTables

3.5.1

HDF5-based reading / writing

blosc

1.17.0

Compression for HDF5

zlib

Compression for HDF5

fastparquet

0.4.0

Parquet reading / writing

pyarrow

0.17.0

Parquet, ORC, and feather reading / writing

pyreadstat

SPSS files (.sav) reading

Warning

  • If you want to use read_orc(), it is highly recommended to install pyarrow using conda. The following is a summary of the environment in which read_orc() can work.

    System

    Conda

    PyPI

    Linux

    Successful

    Failed(pyarrow==3.0 Successful)

    macOS

    Successful

    Failed

    Windows

    Failed

    Failed

Access data in the cloud

Dependency

Minimum Version

Notes

fsspec

0.7.4

Handling files aside from simple local and HTTP

gcsfs

0.6.0

Google Cloud Storage access

pandas-gbq

0.12.0

Google Big Query access

s3fs

0.4.0

Amazon S3 access

Clipboard

Dependency

Minimum Version

Notes

PyQt4/PyQt5

Clipboard I/O

qtpy

Clipboard I/O

xclip

Clipboard I/O on linux

xsel

Clipboard I/O on linux

Pandas 1.2.0 (December 26, 2020)

What’s new in 1.2.0 (December 26, 2020) — pandas 1.3.5 documentation

Increased minimum version for Python

pandas 1.2.0 supports Python 3.7.1 and higher (GH35214).

Increased minimum versions for dependencies

Some minimum supported versions of dependencies were updated (GH35214). If installed, we now require:

Package

Minimum Version

Required

Changed

numpy

1.16.5

X

X

pytz

2017.3

X

X

python-dateutil

2.7.3

X

bottleneck

1.2.1

numexpr

2.6.8

X

pytest (dev)

5.0.1

X

mypy (dev)

0.782

X

For optional libraries the general recommendation is to use the latest version. The following table lists the lowest version per library that is currently being tested throughout the development of pandas. Optional libraries below the lowest tested version may still work, but are not considered supported.

Package

Minimum Version

Changed

beautifulsoup4

4.6.0

fastparquet

0.3.2

fsspec

0.7.4

gcsfs

0.6.0

lxml

4.3.0

X

matplotlib

2.2.3

X

numba

0.46.0

openpyxl

2.6.0

X

pyarrow

0.15.0

X

pymysql

0.7.11

X

pytables

3.5.1

X

s3fs

0.4.0

scipy

1.2.0

sqlalchemy

1.2.8

X

xarray

0.12.3

X

xlrd

1.2.0

X

xlsxwriter

1.0.2

X

xlwt

1.3.0

X

pandas-gbq

0.12.0

See Dependencies and Optional dependencies for more.

Dependencies

Package

Minimum supported version

setuptools

24.2.0

NumPy

1.16.5

python-dateutil

2.7.3

pytz

2017.3

Recommended dependencies

  • numexpr: for accelerating certain numerical operations. numexpr uses multiple cores as well as smart chunking and caching to achieve large speedups. If installed, must be Version 2.6.8 or higher.

  • bottleneck: for accelerating certain types of nan evaluations. bottleneck uses specialized cython routines to achieve large speedups. If installed, must be Version 1.2.1 or higher.

Note

You are highly encouraged to install these libraries, as they provide speed improvements, especially when working with large data sets.

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.

Dependency

Minimum Version

Notes

BeautifulSoup4

4.6.0

HTML parser for read_html (see note)

Jinja2

2.10

Conditional formatting with DataFrame.style

PyQt4

Clipboard I/O

PyQt5

Clipboard I/O

PyTables

3.5.1

HDF5-based reading / writing

SQLAlchemy

1.3.0

SQL support for databases other than sqlite

SciPy

1.12.0

Miscellaneous statistical functions

xlsxwriter

1.0.2

Excel writing

blosc

1.17.0

Compression for HDF5

fsspec

0.7.4

Handling files aside from local and HTTP

fastparquet

0.4.0

Parquet reading / writing

gcsfs

0.6.0

Google Cloud Storage access

html5lib

1.0.1

HTML parser for read_html (see note)

lxml

4.3.0

HTML parser for read_html (see note)

matplotlib

2.2.3

Visualization

numba

0.46.0

Alternative execution engine for rolling operations

openpyxl

2.6.0

Reading / writing for xlsx files

pandas-gbq

0.12.0

Google Big Query access

psycopg2

2.7

PostgreSQL engine for sqlalchemy

pyarrow

0.15.0

Parquet, ORC, and feather reading / writing

pymysql

0.8.1

MySQL engine for sqlalchemy

pyreadstat

SPSS files (.sav) reading

pyxlsb

1.0.6

Reading for xlsb files

qtpy

Clipboard I/O

s3fs

0.4.0

Amazon S3 access

tabulate

0.8.3

Printing in Markdown-friendly format (see tabulate)

xarray

0.12.3

pandas-like API for N-dimensional data

xclip

Clipboard I/O on linux

xlrd

1.2.0

Excel reading

xlwt

1.3.0

Excel writing

xsel

Clipboard I/O on linux

zlib

Compression for HDF5

Optional dependencies for parsing HTML

One of the following combinations of libraries is needed to use the top-level read_html() function:

Warning

Pandas 1.1.0 (July 28, 2020)

Python version support

Officially Python 3.6.1 and above, 3.7, 3.8, and 3.9.

Increased minimum versions for dependencies

Some minimum supported versions of dependencies were updated (GH 33718, GH 29766, GH 29723, pytables >= 3.4.3). If installed, we now require:

Package

Minimum Version

Required

Changed

numpy

1.15.4

X

X

pytz

2015.4

X

python-dateutil

2.7.3

X

X

bottleneck

1.2.1

numexpr

2.6.2

pytest (dev)

4.0.2

For optional libraries the general recommendation is to use the latest version. The following table lists the lowest version per library that is currently being tested throughout the development of pandas. Optional libraries below the lowest tested version may still work, but are not considered supported.

Package

Minimum Version

Changed

beautifulsoup4

4.6.0

fastparquet

0.3.2

fsspec

0.7.4

gcsfs

0.6.0

X

lxml

3.8.0

matplotlib

2.2.2

numba

0.46.0

openpyxl

2.5.7

pyarrow

0.13.0

pymysql

0.7.1

pytables

3.4.3

X

s3fs

0.4.0

X

scipy

1.2.0

X

sqlalchemy

1.1.4

xarray

0.8.2

xlrd

1.1.0

xlsxwriter

0.9.8

xlwt

1.2.0

pandas-gbq

1.2.0

X

See Dependencies and Optional dependencies for more.

Pandas 1.0.0 (January 29, 2020)¶

Increased minimum version for Python

pandas 1.0.0 supports Python 3.6.1 and higher (GH29212).

Increased minimum versions for dependencies

Some minimum supported versions of dependencies were updated (GH29766, GH29723). If installed, we now require:

Package

Minimum Version

Required

Changed

numpy

1.13.3

X

pytz

2015.4

X

python-dateutil

2.6.1

X

bottleneck

1.2.1

numexpr

2.6.2

pytest (dev)

4.0.2

For optional libraries the general recommendation is to use the latest version. The following table lists the lowest version per library that is currently being tested throughout the development of pandas. Optional libraries below the lowest tested version may still work, but are not considered supported.

Package

Minimum Version

Changed

beautifulsoup4

4.6.0

fastparquet

0.3.2

X

gcsfs

0.2.2

lxml

3.8.0

matplotlib

2.2.2

numba

0.46.0

X

openpyxl

2.5.7

X

pyarrow

0.13.0

X

pymysql

0.7.1

pytables

3.4.2

s3fs

0.3.0

X

scipy

0.19.0

sqlalchemy

1.1.4

xarray

0.8.2

xlrd

1.1.0

xlsxwriter

0.9.8

xlwt

1.2.0

See Dependencies and Optional dependencies for more.

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