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+# pandas: powerful Python data analysis toolkit
+
+![Travis-CI Build Status](https://travis-ci.org/pydata/pandas.png)
+
+## What is it
+**pandas** is a Python package providing fast, flexible, and expressive data
+structures designed to make working with "relational" or "labeled" data both
+easy and intuitive. It aims to be the fundamental high-level building block for
+doing practical, **real world** data analysis in Python. Additionally, it has
+the broader goal of becoming **the most powerful and flexible open source data
+analysis / manipulation tool available in any language**. It is already well on
+its way toward this goal.
+
+## Main Features
+Here are just a few of the things that pandas does well:
+
+  - Easy handling of [**missing data**][missing-data] (represented as
+    `NaN`) in floating point as well as non-floating point data
+  - Size mutability: columns can be [**inserted and
+    deleted**][insertion-deletion] from DataFrame and higher dimensional
+    objects
+  - Automatic and explicit [**data alignment**][alignment]: objects can
+    be explicitly aligned to a set of labels, or the user can simply
+    ignore the labels and let `Series`, `DataFrame`, etc. automatically
+    align the data for you in computations
+  - Powerful, flexible [**group by**][groupby] functionality to perform
+    split-apply-combine operations on data sets, for both aggregating
+    and transforming data
+  - Make it [**easy to convert**][conversion] ragged,
+    differently-indexed data in other Python and NumPy data structures
+    into DataFrame objects
+  - Intelligent label-based [**slicing**][slicing], [**fancy
+    indexing**][fancy-indexing], and [**subsetting**][subsetting] of
+    large data sets
+  - Intuitive [**merging**][merging] and [**joining**][joining] data
+    sets
+  - Flexible [**reshaping**][reshape] and [**pivoting**][pivot-table] of
+    data sets
+  - [**Hierarchical**][mi] labeling of axes (possible to have multiple
+    labels per tick)
+  - Robust IO tools for loading data from [**flat files**][flat-files]
+    (CSV and delimited), [**Excel files**][excel], [**databases**][db],
+    and saving/loading data from the ultrafast [**HDF5 format**][hdfstore]
+  - [**Time series**][timeseries]-specific functionality: date range
+    generation and frequency conversion, moving window statistics,
+    moving window linear regressions, date shifting and lagging, etc.
+
+
+   [missing-data]: http://pandas.pydata.org/pandas-docs/stable/missing_data.html#working-with-missing-data
+   [insertion-deletion]: http://pandas.pydata.org/pandas-docs/stable/dsintro.html#column-selection-addition-deletion
+   [alignment]: http://pandas.pydata.org/pandas-docs/stable/dsintro.html?highlight=alignment#intro-to-data-structures
+   [groupby]: http://pandas.pydata.org/pandas-docs/stable/groupby.html#group-by-split-apply-combine
+   [conversion]: http://pandas.pydata.org/pandas-docs/stable/dsintro.html#dataframe
+   [slicing]: http://pandas.pydata.org/pandas-docs/stable/indexing.html#slicing-ranges
+   [fancy-indexing]: http://pandas.pydata.org/pandas-docs/stable/indexing.html#advanced-indexing-with-ix
+   [subsetting]: http://pandas.pydata.org/pandas-docs/stable/indexing.html#boolean-indexing
+   [merging]: http://pandas.pydata.org/pandas-docs/stable/merging.html#database-style-dataframe-joining-merging
+   [joining]: http://pandas.pydata.org/pandas-docs/stable/merging.html#joining-on-index
+   [reshape]: http://pandas.pydata.org/pandas-docs/stable/reshaping.html#reshaping-and-pivot-tables
+   [pivot-table]: http://pandas.pydata.org/pandas-docs/stable/reshaping.html#pivot-tables-and-cross-tabulations
+   [mi]: http://pandas.pydata.org/pandas-docs/stable/indexing.html#hierarchical-indexing-multiindex
+   [flat-files]: http://pandas.pydata.org/pandas-docs/stable/io.html#csv-text-files
+   [excel]: http://pandas.pydata.org/pandas-docs/stable/io.html#excel-files
+   [db]: http://pandas.pydata.org/pandas-docs/stable/io.html#sql-queries
+   [hdfstore]: http://pandas.pydata.org/pandas-docs/stable/io.html#hdf5-pytables
+   [timeseries]: http://pandas.pydata.org/pandas-docs/stable/timeseries.html#time-series-date-functionality
+
+## Where to get it
+The source code is currently hosted on GitHub at:
+http://github.com/pydata/pandas
+
+Binary installers for the latest released version are available at the Python
+package index
+
+    http://pypi.python.org/pypi/pandas/
+
+And via `easy_install`:
+
+```sh
+easy_install pandas
+```
+
+or  `pip`:
+
+```sh
+pip install pandas
+```
+
+## Dependencies
+- [NumPy](http://www.numpy.org): 1.6.1 or higher
+- [python-dateutil](http://labix.org/python-dateutil): 1.5 or higher
+- [pytz](http://pytz.sourceforge.net)
+    - Needed for time zone support with ``pandas.date_range``
+
+### Highly Recommended Dependencies
+- [numexpr](http://code.google.com/p/numexpr/)
+   - Needed to accelerate some expression evaluation operations
+   - Required by PyTables
+- [bottleneck](http://berkeleyanalytics.com/bottleneck)
+   - Needed to accelerate certain numerical operations
+
+### Optional dependencies
+- [Cython](http://www.cython.org): Only necessary to build development version. Version 0.17.1 or higher.
+- [SciPy](http://www.scipy.org): miscellaneous statistical functions
+- [PyTables](http://www.pytables.org): necessary for HDF5-based storage
+- [matplotlib](http://matplotlib.sourceforge.net/): for plotting
+- [statsmodels](http://statsmodels.sourceforge.net/)
+   - Needed for parts of `pandas.stats`
+- [openpyxl](http://packages.python.org/openpyxl/), [xlrd/xlwt](http://www.python-excel.org/)
+   - openpyxl version 1.6.1 or higher, for writing .xlsx files
+   - xlrd >= 0.9.0
+   - Needed for Excel I/O
+- [boto](https://pypi.python.org/pypi/boto): necessary for Amazon S3 access.
+- One of the following combinations of libraries is needed to use the
+  top-level [`pandas.read_html`][read-html-docs] function:
+  - [BeautifulSoup4][BeautifulSoup4] and [html5lib][html5lib] (Any
+    recent version of [html5lib][html5lib] is okay.)
+  - [BeautifulSoup4][BeautifulSoup4] and [lxml][lxml]
+  - [BeautifulSoup4][BeautifulSoup4] and [html5lib][html5lib] and [lxml][lxml]
+  - Only [lxml][lxml], although see [HTML reading gotchas][html-gotchas]
+    for reasons as to why you should probably **not** take this approach.
+
+#### Notes about HTML parsing libraries
+- If you install [BeautifulSoup4][BeautifulSoup4] you must install
+  either [lxml][lxml] or [html5lib][html5lib] or both.
+  `pandas.read_html` will **not** work with *only* `BeautifulSoup4`
+  installed.
+- You are strongly encouraged to read [HTML reading
+  gotchas][html-gotchas]. It explains issues surrounding the
+  installation and usage of the above three libraries.
+- You may need to install an older version of
+  [BeautifulSoup4][BeautifulSoup4]:
+    - Versions 4.2.1, 4.1.3 and 4.0.2 have been confirmed for 64 and
+      32-bit Ubuntu/Debian
+- Additionally, if you're using [Anaconda][Anaconda] you should
+  definitely read [the gotchas about HTML parsing][html-gotchas]
+  libraries
+- If you're on a system with `apt-get` you can do
+
+  ```sh
+  sudo apt-get build-dep python-lxml
+  ```
+
+  to get the necessary dependencies for installation of [lxml][lxml].
+  This will prevent further headaches down the line.
+
+   [html5lib]: https://github.com/html5lib/html5lib-python "html5lib"
+   [BeautifulSoup4]: http://www.crummy.com/software/BeautifulSoup "BeautifulSoup4"
+   [lxml]: http://lxml.de
+   [Anaconda]: https://store.continuum.io/cshop/anaconda
+   [NumPy]: http://numpy.scipy.org/
+   [html-gotchas]: http://pandas.pydata.org/pandas-docs/stable/gotchas.html#html-table-parsing
+   [read-html-docs]: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.io.html.read_html.html#pandas.io.html.read_html
+
+## Installation from sources
+To install pandas from source you need Cython in addition to the normal
+dependencies above. Cython can be installed from pypi:
+
+```sh
+pip install cython
+```
+
+In the `pandas` directory (same one where you found this file after
+cloning the git repo), execute:
+
+```sh
+python setup.py install
+```
+
+or for installing in [development mode](http://www.pip-installer.org/en/latest/usage.html):
+
+```sh
+python setup.py develop
+```
+
+Alternatively, you can use `pip` if you want all the dependencies pulled
+in automatically (the `-e` option is for installing it in [development
+mode](http://www.pip-installer.org/en/latest/usage.html)):
+
+```sh
+pip install -e .
+```
+
+On Windows, you will need to install MinGW and execute:
+
+```sh
+python setup.py build --compiler=mingw32
+python setup.py install
+```
+
+See http://pandas.pydata.org/ for more information.
+
+## License
+BSD
+
+## Documentation
+The official documentation is hosted on PyData.org: http://pandas.pydata.org/
+
+The Sphinx documentation should provide a good starting point for learning how
+to use the library. Expect the docs to continue to expand as time goes on.
+
+## Background
+Work on ``pandas`` started at AQR (a quantitative hedge fund) in 2008 and
+has been under active development since then.
+
+## Discussion and Development
+Since pandas development is related to a number of other scientific
+Python projects, questions are welcome on the scipy-user mailing
+list. Specialized discussions or design issues should take place on
+the pystatsmodels mailing list / Google group, where
+``scikits.statsmodels`` and other libraries will also be discussed:
+
+http://groups.google.com/group/pystatsmodels