MacOS graphical install¶. Download the graphical macOS installer for your version of Python. RECOMMENDED: Verify data integrity with SHA-256.For more information on hashes, see What about cryptographic hash verification? Double-click the downloaded file and click continue to start the installation. I recommend you to install pyenv if you use homebrew, because config file of anaconda without pyenv is the same as that of homebrew and anaconda without pyenv breaks the config file. Environment Mac OS X 10.14.2 Mojave.
The easiest way to install pandas is to install itas part of the Anaconda distribution, across platform distribution for data analysis and scientific computing.This is the recommended installation method for most users.
Instructions for installing from source,PyPI, ActivePython, various Linux distributions, or adevelopment version are also provided.
Python version support¶
Officially Python 3.6.1 and above, 3.7, 3.8, and 3.9.
Installing pandas¶
Installing with Anaconda¶
Installing pandas and the rest of the NumPy andSciPy stack can be a littledifficult for inexperienced users.
The simplest way to install not only pandas, but Python and the most popularpackages that make up the SciPy stack(IPython, NumPy,Matplotlib, …) is withAnaconda, a cross-platform(Linux, Mac OS X, Windows) Python distribution for data analytics andscientific computing.
After running the installer, the user will have access to pandas and therest of the SciPy stack without needing to installanything else, and without needing to wait for any software to be compiled.
Installation instructions for Anacondacan be found here.
A full list of the packages available as part of theAnaconda distributioncan be found here.
Another advantage to installing Anaconda is that you don’t needadmin rights to install it. Anaconda can install in the user’s home directory,which makes it trivial to delete Anaconda if you decide (just deletethat folder).
Installing with Miniconda¶
The previous section outlined how to get pandas installed as part of theAnaconda distribution.However this approach means you will install well over one hundred packagesand involves downloading the installer which is a few hundred megabytes in size.
If you want to have more control on which packages, or have a limited internetbandwidth, then installing pandas withMiniconda may be a better solution.
Conda is the package manager that theAnaconda distribution is built upon.It is a package manager that is both cross-platform and language agnostic(it can play a similar role to a pip and virtualenv combination).
Miniconda allows you to create aminimal self contained Python installation, and then use theConda command to install additional packages.
First you will need Conda to be installed anddownloading and running the Minicondawill do this for you. The installercan be found here
Anaconda Python Mac
The next step is to create a new conda environment. A conda environment is like avirtualenv that allows you to specify a specific version of Python and set of libraries.Run the following commands from a terminal window:
This will create a minimal environment with only Python installed in it.To put your self inside this environment run:
On Windows the command is:
The final step required is to install pandas. This can be done with thefollowing command:
To install a specific pandas version:
To install other packages, IPython for example:
To install the full Anacondadistribution:
If you need packages that are available to pip but not conda, theninstall pip, and then use pip to install those packages:
Installing from PyPI¶
pandas can be installed via pip fromPyPI.
Installing with ActivePython¶
Anaconda Install For All Users Mac
Installation instructions forActivePython can be foundhere. Versions2.7, 3.5 and 3.6 include pandas.
Installing using your Linux distribution’s package manager.¶
The commands in this table will install pandas for Python 3 from your distribution.
Distribution | Status | Download / Repository Link | Install method |
|---|---|---|---|
Debian | stable |
| |
Debian & Ubuntu | unstable (latest packages) |
| |
Ubuntu | stable |
| |
OpenSuse | stable |
| |
Fedora | stable |
| |
Centos/RHEL | stable |
|
However, the packages in the linux package managers are often a few versions behind, soto get the newest version of pandas, it’s recommended to install using the pip or condamethods described above.
Handling ImportErrors¶
If you encounter an ImportError, it usually means that Python couldn’t find pandas in the list of availablelibraries. Python internally has a list of directories it searches through, to find packages. You canobtain these directories with:
One way you could be encountering this error is if you have multiple Python installations on your systemand you don’t have pandas installed in the Python installation you’re currently using.In Linux/Mac you can run whichpython on your terminal and it will tell you which Python installation you’reusing. If it’s something like “/usr/bin/python”, you’re 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: installation instructions </getting_started.html>.
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.
Running the test suite¶
pandas is equipped with an exhaustive set of unit tests, covering about 97% ofthe code base as of this writing. To run it on your machine to verify thateverything is working (and that you have all of the dependencies, soft and hard,installed), make sure you have pytest >= 5.0.1 and Hypothesis >= 3.58, then run:
Dependencies¶
Package | Minimum supported version |
|---|---|
24.2.0 | |
1.15.4 | |
2.7.3 | |
2017.2 |
Recommended dependencies¶
numexpr: for accelerating certain numerical operations.
numexpruses multiple cores as well as smart chunking and caching to achieve large speedups.If installed, must be Version 2.6.2 or higher.bottleneck: for accelerating certain types of
nanevaluations.bottleneckuses 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, especiallywhen 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, whileDataFrame.to_markdown() requires the tabulate package. If theoptional dependency is not installed, pandas will raise an ImportError whenthe method requiring that dependency is called.
Dependency | Minimum Version | Notes |
|---|---|---|
BeautifulSoup4 | 4.6.0 | HTML parser for read_html (see note) |
Jinja2 | Conditional formatting with DataFrame.style | |
PyQt4 | Clipboard I/O | |
PyQt5 | Clipboard I/O | |
PyTables | 3.4.3 | HDF5-based reading / writing |
SQLAlchemy | 1.1.4 | SQL support for databases other than sqlite |
SciPy | 0.19.0 | Miscellaneous statistical functions |
XLsxWriter | 0.9.8 | Excel writing |
blosc | Compression for HDF5 | |
fsspec | 0.7.4 | Handling files aside from local and HTTP |
fastparquet | 0.3.2 | Parquet reading / writing |
gcsfs | 0.6.0 | Google Cloud Storage access |
html5lib | HTML parser for read_html (see note) | |
lxml | 3.8.0 | HTML parser for read_html (see note) |
matplotlib | 2.2.2 | Visualization |
numba | 0.46.0 | Alternative execution engine for rolling operations |
openpyxl | 2.5.7 | Reading / writing for xlsx files |
pandas-gbq | 0.12.0 | Google Big Query access |
psycopg2 | PostgreSQL engine for sqlalchemy | |
pyarrow | 0.12.0 | Parquet, ORC (requires 0.13.0), and feather reading / writing |
pymysql | 0.7.11 | MySQL engine for sqlalchemy |
pyreadstat | SPSS files (.sav) reading | |
pytables | 3.4.3 | HDF5 reading / writing |
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 | 0.8.2 | pandas-like API for N-dimensional data |
xclip | Clipboard I/O on linux | |
xlrd | 1.1.0 | Excel reading |
xlwt | 1.2.0 | 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 thetop-level read_html() function:
BeautifulSoup4 and html5lib
BeautifulSoup4 and lxml
BeautifulSoup4 and html5lib and lxml
Only lxml, although see HTML Table Parsingfor reasons as to why you should probably not take this approach.
Warning
if you install BeautifulSoup4 you must install eitherlxml or html5lib or both.
read_html()will not work with onlyBeautifulSoup4 installed.You are highly encouraged to read HTML Table Parsing gotchas.It explains issues surrounding the installation andusage of the above three libraries.