Revise python/README.md. Move DEVELOPMENT.md to Sphinx docs. Other cleaning

Change-Id: I3fa58de325357048c1a5aaea74cfb366fa30d25d
This commit is contained in:
Wes McKinney committed 2017-04-22 17:45:42 -04:00
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@@ -202,7 +202,7 @@ Now, we can build pyarrow:
python setup.py build_ext --inplace
Running C++ unit tests with Python
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
----------------------------------
Getting ``python-test.exe`` to run is a bit tricky because your
``%PYTHONPATH%`` must be configured given the active conda environment:
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@@ -35,11 +35,11 @@ structures.
:caption: Getting Started
install
development
pandas
filesystems
parquet
api
development
getting_involved
.. toctree::
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@@ -37,115 +37,14 @@ Install the latest version from PyPI:
pip install pyarrow
.. note::
Currently there are only binary artifcats available for Linux and MacOS.
Currently there are only binary artifacts available for Linux and MacOS.
Otherwise this will only pull the python sources and assumes an existing
installation of the C++ part of Arrow.
To retrieve the binary artifacts, you'll need a recent ``pip`` version that
supports features like the ``manylinux1`` tag.
installation of the C++ part of Arrow. To retrieve the binary artifacts,
you'll need a recent ``pip`` version that supports features like the
``manylinux1`` tag.
Building from source
--------------------
Installing from source
----------------------
First, clone the master git repository:
.. code-block:: bash
git clone https://github.com/apache/arrow.git arrow
System requirements
~~~~~~~~~~~~~~~~~~~
Building pyarrow requires:
* A C++11 compiler
* Linux: gcc >= 4.8 or clang >= 3.5
* OS X: XCode 6.4 or higher preferred
* `CMake <https://cmake.org/>`_
Python requirements
~~~~~~~~~~~~~~~~~~~
You will need Python (CPython) 2.7, 3.4, or 3.5 installed. Earlier releases and
are not being targeted.
.. note::
This library targets CPython only due to an emphasis on interoperability with
pandas and NumPy, which are only available for CPython.
The build requires NumPy, Cython, and a few other Python dependencies:
.. code-block:: bash
pip install cython
cd arrow/python
pip install -r requirements.txt
Installing Arrow C++ library
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
First, you should choose an installation location for Arrow C++. In the future
using the default system install location will work, but for now we are being
explicit:
.. code-block:: bash
export ARROW_HOME=$HOME/local
Now, we build Arrow:
.. code-block:: bash
cd arrow/cpp
mkdir dev-build
cd dev-build
cmake -DCMAKE_INSTALL_PREFIX=$ARROW_HOME ..
make
# Use sudo here if $ARROW_HOME requires it
make install
To get the optional Parquet support, you should also build and install
`parquet-cpp <https://github.com/apache/parquet-cpp/blob/master/README.md>`_.
Install `pyarrow`
~~~~~~~~~~~~~~~~~
.. code-block:: bash
cd arrow/python
# --with-parquet enables the Apache Parquet support in PyArrow
# --with-jemalloc enables the jemalloc allocator support in PyArrow
# --build-type=release disables debugging information and turns on
# compiler optimizations for native code
python setup.py build_ext --with-parquet --with-jemalloc --build-type=release install
python setup.py install
.. warning::
On XCode 6 and prior there are some known OS X `@rpath` issues. If you are
unable to import pyarrow, upgrading XCode may be the solution.
.. note::
In development installations, you will also need to set a correct
``LD_LIBRARY_PATH``. This is most probably done with
``export LD_LIBRARY_PATH=$ARROW_HOME/lib:$LD_LIBRARY_PATH``.
.. code-block:: python
In [1]: import pyarrow
In [2]: pyarrow.array([1,2,3])
Out[2]:
<pyarrow.array.Int64Array object at 0x7f899f3e60e8>
[
1,
2,
3
]
See :ref:`development`.