The easiest way to install pandas is to install it as part of the Anaconda distribution, a cross platform distribution for data analysis and scientific computing. The Conda package manager is the recommended installation method for most users.
Instructions for installing from source, PyPI, or a development version are also provided.
Python version support#Officially Python 3.9, 3.10, 3.11 and 3.12.
Installing pandas# Installing with Anaconda#For users that are new to Python, the easiest way to install Python, pandas, and the packages that make up the PyData stack (SciPy, NumPy, Matplotlib, and more) is with Anaconda, a cross-platform (Linux, macOS, Windows) Python distribution for data analytics and scientific computing. Installation instructions for Anaconda can be found here.
Installing with Miniconda#For users experienced with Python, the recommended way to install pandas with Miniconda. Miniconda allows you to create a minimal, self-contained Python installation compared to Anaconda and use the Conda package manager to install additional packages and create a virtual environment for your installation. Installation instructions for Miniconda can be found here.
The next step is to create a new conda environment. A conda environment is like a virtualenv that allows you to specify a specific version of Python and set of libraries. Run the following commands from a terminal window.
conda create -c conda-forge -n name_of_my_env python pandas
This will create a minimal environment with only Python and pandas installed. To put your self inside this environment run.
source activate name_of_my_env # On Windows activate name_of_my_envInstalling from PyPI#
pandas can be installed via pip from PyPI.
Note
You must have pip>=19.3 to install from PyPI.
Note
It is recommended to install and run pandas from a virtual environment, for example, using the Python standard libraryâs venv
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.
Handling ImportErrors#If you encounter an ImportError, it usually means that Python couldnât find pandas in the list of available libraries. Python internally has a list of directories it searches through, to find packages. You can obtain these directories with.
One way you could be encountering this error is if you have multiple Python installations on your system and you donât have pandas installed in the Python installation youâre currently using. In Linux/Mac you can run which python on your terminal and it will tell you which Python installation youâre using. If itâs something like â/usr/bin/pythonâ, youâre using the Python from the system, which is not recommended.
It is highly recommended to use conda, for quick installation and for package and dependency updates. You can find simple installation instructions for pandas in this document.
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 https://pypi.anaconda.org/scientific-python-nightly-wheels/simple pandas
Note that you might be required to uninstall an existing version of pandas to install the development version.
Running the test suite#pandas is equipped with an exhaustive set of unit tests. The packages required to run the tests can be installed with pip install "pandas[test]". To run the tests from a Python terminal.
>>> import pandas as pd >>> pd.test() running: pytest -m "not slow and not network and not db" /home/user/anaconda3/lib/python3.9/site-packages/pandas ============================= test session starts ============================== platform linux -- Python 3.9.7, pytest-6.2.5, py-1.11.0, pluggy-1.0.0 rootdir: /home/user plugins: dash-1.19.0, anyio-3.5.0, hypothesis-6.29.3 collected 154975 items / 4 skipped / 154971 selected ........................................................................ [ 0%] ........................................................................ [ 99%] ....................................... [100%] ==================================== ERRORS ==================================== =================================== FAILURES =================================== =============================== warnings summary =============================== =========================== short test summary info ============================ = 1 failed, 146194 passed, 7402 skipped, 1367 xfailed, 5 xpassed, 197 warnings, 10 errors in 1090.16s (0:18:10) =
Note
This is just an example of what information is shown. Test failures are not necessarily indicative of a broken pandas installation.
Dependencies# Required dependencies#pandas requires the following dependencies.
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.
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
2.8.4
performance
Accelerates certain numerical operations by using multiple cores as well as smart chunking and caching to achieve large speedups
1.3.6
performance
Accelerates certain types of nan by using specialized cython routines to achieve large speedup.
0.56.4
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.
Installable with pip install "pandas[plot, output-formatting]".
Dependency
Minimum Version
pip extra
Notes
matplotlib
3.6.3
plot
Plotting library
Jinja2
3.1.2
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
1.10.0
computation
Miscellaneous statistical functions
xarray
2022.12.0
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
3.0.5
excel
Writing Excel
openpyxl
3.1.0
excel
Reading / writing for xlsx files
pyxlsb
1.0.10
excel
Reading for xlsb files
python-calamine
0.1.7
excel
Reading for xls/xlsx/xlsb/ods files
HTML#Installable with pip install "pandas[html]".
Dependency
Minimum Version
pip extra
Notes
BeautifulSoup4
4.11.2
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 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.
Installable with pip install "pandas[xml]".
Dependency
Minimum Version
pip extra
Notes
lxml
4.9.2
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
2.0.0
postgresql, mysql, sql-other
SQL support for databases other than sqlite
psycopg2
2.9.6
postgresql
PostgreSQL engine for sqlalchemy
pymysql
1.0.2
mysql
MySQL engine for sqlalchemy
adbc-driver-postgresql
0.8.0
postgresql
ADBC Driver for PostgreSQL
adbc-driver-sqlite
0.8.0
sql-other
ADBC Driver for SQLite
Other data sources#Installable with pip install "pandas[hdf5, parquet, feather, spss, excel]"
Dependency
Minimum Version
pip extra
Notes
PyTables
3.8.0
hdf5
HDF5-based reading / writing
blosc
1.21.3
hdf5
Compression for HDF5; only available on conda
zlib
hdf5
Compression for HDF5
fastparquet
2022.12.0
Parquet reading / writing (pyarrow is default)
pyarrow
10.0.1
parquet, feather
Parquet, ORC, and feather reading / writing
pyreadstat
1.2.0
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.
Installable with pip install "pandas[fss, aws, gcp]"
Dependency
Minimum Version
pip extra
Notes
fsspec
2022.11.0
fss, gcp, aws
Handling files aside from simple local and HTTP (required dependency of s3fs, gcsfs).
gcsfs
2022.11.0
gcp
Google Cloud Storage access
pandas-gbq
0.19.0
gcp
Google Big Query access
s3fs
2022.11.0
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
2.3.0
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.
Installable with pip install "pandas[compression]"
Dependency
Minimum Version
pip extra
Notes
Zstandard
0.19.0
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
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