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.. grid-item-card:: Working with conda?
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pandas can be installed via conda from `conda-forge <https://anaconda.org/conda-forge/pandas>`__.
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.. code-block:: bash
conda install -c conda-forge pandas
.. grid-item-card:: Prefer pip?
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pandas can be installed via pip from `PyPI <https://pypi.org/project/pandas>`__.
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.. code-block:: bash
pip install pandas
.. grid-item-card:: In-depth instructions?
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Installing a specific version? Installing from source? Check the advanced
installation page.
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.. button-ref:: install
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Learn more
When working with tabular data, such as data stored in spreadsheets or databases, pandas is the right tool for you. pandas will help you to explore, clean, and process your data. In pandas, a data table is called a :class:`DataFrame`.
pandas supports the integration with many file formats or data sources out of the box (csv, excel, sql, json, parquet,…). The ability to import data from each of these
data sources is provided by functions with the prefix, read_*. Similarly, the to_* methods are used to store data.
Selecting or filtering specific rows and/or columns? Filtering the data on a particular condition? Methods for slicing, selecting, and extracting the data you need are available in pandas.
pandas provides plotting for your data right out of the box with the power of Matplotlib. Simply pick the plot type (scatter, bar, boxplot,...) corresponding to your data.
There's no need to loop over all rows of your data table to do calculations. Column data manipulations work elementwise in pandas. Adding a column to a :class:`DataFrame` based on existing data in other columns is straightforward.
Basic statistics (mean, median, min, max, counts...) are easily calculable across data frames. These, or even custom aggregations, can be applied on the entire data set, a sliding window of the data, or grouped by categories. The latter is also known as the split-apply-combine approach.
- Change the structure of your data table in a variety of ways. You can use :func:`~pandas.melt` to reshape your data from a wide format to a long and tidy one. Use :func:`~pandas.pivot`
- to go from long to wide format. With aggregations built-in, a pivot table can be created with a single command.
Multiple tables can be concatenated column wise or row wise with pandas' database-like join and merge operations.
pandas has great support for time series and has an extensive set of tools for working with dates, times, and time-indexed data.
Data sets often contain more than just numerical data. pandas provides a wide range of functions to clean textual data and extract useful information from it.
Are you familiar with other software for manipulating tabular data? Learn the pandas-equivalent operations compared to software you already know:
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The `R programming language <https://www.r-project.org/>`__ provides a
``data.frame`` data structure as well as packages like
`tidyverse <https://www.tidyverse.org>`__ which use and extend ``data.frame``
for convenient data handling functionalities similar to pandas.
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Learn more
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Already familiar with ``SELECT``, ``GROUP BY``, ``JOIN``, etc.?
Many SQL manipulations have equivalents in pandas.
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.. button-ref:: compare_with_sql
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Learn more
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The ``data set`` included in the `STATA <https://en.wikipedia.org/wiki/Stata>`__
statistical software suite corresponds to the pandas ``DataFrame``.
Many of the operations known from STATA have an equivalent in pandas.
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Learn more
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Users of `Excel <https://en.wikipedia.org/wiki/Microsoft_Excel>`__
or other spreadsheet programs will find that many of the concepts are
transferable to pandas.
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Learn more
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`SAS <https://en.wikipedia.org/wiki/SAS_(software)>`__, the statistical software suite,
uses the ``data set`` structure, which closely corresponds pandas' ``DataFrame``.
Also SAS vectorized operations such as filtering or string processing operations
have similar functions in pandas.
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.. button-ref:: compare_with_sas
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Learn more
For a quick overview of pandas functionality, see :ref:`10 Minutes to pandas<10min>`.
You can also reference the pandas cheat sheet for a succinct guide for manipulating data with pandas.
The community produces a wide variety of tutorials available online. Some of the material is enlisted in the community contributed :ref:`communitytutorials`.
Try our experimental JupyterLite live shell with pandas, powered by Pyodide.
Please note it can take a while (>30 seconds) before the shell is initialized and ready to run commands.
Running it requires a reasonable amount of bandwidth and resources (>70 MiB on the first load), so it may not work properly on all devices or networks.
.. replite::
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:prompt: Try pandas online!
:execute: False
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import pandas as pd
df = pd.DataFrame({"num_legs": [2, 4], "num_wings": [2, 0]}, index=["falcon", "dog"])
df
.. toctree::
:maxdepth: 2
:hidden:
install
overview
intro_tutorials/index
comparison/index
tutorials