Как загрузить пандас в питон

Installation#

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 and 3.11.

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_env

Installing from PyPI#

pandas can be installed via pip from PyPI.

You must have pip>=19.3 to install from PyPI.

It is recommended to install and run pandas from a virtual environment, for example, using the Python standard library’s 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.

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 couldn’t 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 don’t have pandas installed in the Python installation you’re currently using. In Linux/Mac you can run which python on your terminal and it will tell you which Python installation you’re using. 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 .

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 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) = 

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.

Minimum supported version

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Installation#

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. This is the recommended installation method for most users.

Instructions for installing from source, PyPI, ActivePython, various Linux distributions, or a development version are also provided.

Python version support#

Officially Python 3.8, 3.9, 3.10 and 3.11.

Installing pandas#

Installing with Anaconda#

Installing pandas and the rest of the NumPy and SciPy stack can be a little difficult for inexperienced users.

The simplest way to install not only pandas, but Python and the most popular packages that make up the SciPy stack (IPython, NumPy, Matplotlib, …) is with Anaconda, a cross-platform (Linux, macOS, Windows) Python distribution for data analytics and scientific computing.

After running the installer, the user will have access to pandas and the rest of the SciPy stack without needing to install anything else, and without needing to wait for any software to be compiled.

Installation instructions for Anaconda can be found here.

A full list of the packages available as part of the Anaconda distribution can be found here.

Another advantage to installing Anaconda is that you don’t need admin rights to install it. Anaconda can install in the user’s home directory, which makes it trivial to delete Anaconda if you decide (just delete that folder).

Installing with Miniconda#

The previous section outlined how to get pandas installed as part of the Anaconda distribution. However this approach means you will install well over one hundred packages and 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 internet bandwidth, then installing pandas with Miniconda may be a better solution.

Conda is the package manager that the Anaconda 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 a minimal self contained Python installation, and then use the Conda command to install additional packages.

First you will need Conda to be installed and downloading and running the Miniconda will do this for you. The installer 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 -n name_of_my_env python 

This will create a minimal environment with only Python installed in it. To put your self inside this environment run:

source activate name_of_my_env 

On Windows the command is:

The final step required is to install pandas. This can be done with the following command:

To install a specific pandas version:

To install other packages, IPython for example:

To install the full Anaconda distribution:

If you need packages that are available to pip but not conda, then install pip, and then use pip to install those packages:

conda install pip pip install django 

Installing from PyPI#

pandas can be installed via pip from PyPI.

You must have pip>=19.3 to install from PyPI.

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.

The full list of extras that can be installed can be found in the dependency section.

Installing with ActivePython#

Installation instructions for ActivePython can be found here. Versions 2.7, 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.

Download / Repository Link

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Getting started

The next steps provides the easiest and recommended way to set up your environment to use pandas. Other installation options can be found in the advanced installation page.

1. Download Anaconda for your operating system and the latest Python version, run the installer, and follow the steps. Please note:
   • It is not needed (and discouraged) to install Anaconda as root or administrator.
   • When asked if you wish to initialize Anaconda3, answer yes.
   • Restart the terminal after completing the installation.

Detailed instructions on how to install Anaconda can be found in the Anaconda documentation.

In the Anaconda prompt (or terminal in Linux or macOS), start JupyterLab:

In JupyterLab, create a new (Python 3) notebook:

In the first cell of the notebook, you can import pandas and check the version with:

Now you are ready to use pandas, and you can write your code in the next cells.

Tutorials

You can learn more about pandas in the tutorials, and more about JupyterLab in the JupyterLab documentation.

Books

The book we recommend to learn pandas is Python for Data Analysis, by Wes McKinney, creator of pandas.

Videos

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