Python 3.6.8 was the last bugfix release for 3.6.x. Following the release of 3.6.8, we plan to provide security fixes for Python 3.6 as needed through 2021, five years following its initial release. Security fix releases are source-only releases; binary installers are not provided. It is strongly recommended to use 64-bit Python if possible. Getting the right compiler is especially confusing for Windows users. Over time, Python has been built using a variety of different Windows C compilers. This guide should help clarify which version of Python uses which compiler by default.
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¶
Pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language. Install pandas now! ? Best Python Book for Beginners: ?Top 3 Programming Languages for Robotics: ?Best on-budget Earbuds for.
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.
Stamp id3 tag editor for mac. 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
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¶
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 | Download / Repository Link | Install method | |
---|---|---|---|
Debian | stable | sudoapt-getinstallpython3-pandas | |
Debian & Ubuntu | unstable (latest packages) | sudoapt-getinstallpython3-pandas | |
Ubuntu | stable | sudoapt-getinstallpython3-pandas | |
OpenSuse | stable | zypperinpython3-pandas | |
Fedora | stable | dnfinstallpython3-pandas | |
Centos/RHEL | stable | yuminstallpython3-pandas |
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 conda
methods 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¶
![Download pandas for python 3.6 macros Download pandas for python 3.6 macros](/uploads/1/3/3/2/133276439/144727173.png)
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.
numexpr
uses 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
nan
evaluations.bottleneck
uses specialized cython routines to achieve large speedups. If installed,must be Version 1.2.1 or higher.
Download Pandas For Python 3.7
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.Max for live free download mac. 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 Os sierra with word for mac. | 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
Pandas In Python 3
- 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.
Pandas is a common Python tool for data manipulation and analysis.This task explains how to use Navigator to set up and begin working withPandas in your choice of terminal, Python, IPython, or Jupyter Notebook.The steps are similar forinstalling and opening nearly any package.
- Start Navigator.
- Click the Environments tab.
- Click the Create button. When prompted, enter a descriptive name for theenvironment, such as “Pandas.”
- Select a Python version to run in the environment.
- Click OK.The new environment appears in the environments list.
- Click the name of the new environment to activate it.The environment is highlighted with a green background.
- In the list above the packages table, select All to filter the tableto show all packages in all channels.
- In the Search Packages box, type
Pandas
.Pandas appears as a package available for installation. - Select the checkbox in front of the Pandas package name.
- In the menu that appears, select Mark for specific version installation.
- In the list that appears, select the Pandas version you want to install.
- Click the Apply button.A progress bar appears below the Packages pane while Pandas and itsdependencies are installed.
- To begin using your new environment, click the Environments tab.
- Click the arrow button next to the Pandas environment name.
- In the list that appears, select the tool to use to open Pandas:Terminal, Python, IPython, or Jupyter Notebook.