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        "author": "Quantopian Inc.",
        "author_email": "opensource@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: Apache Software License",
            "Natural Language :: English",
            "Programming Language :: C",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: Implementation :: CPython",
            "Topic :: Scientific/Engineering"
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        "description": "warp_prism\n==========\n\nQuickly move data from postgres to numpy or pandas.\n\nAPI\n---\n\n``to_arrays(query, *, bind=None)``\n``````````````````````````````````\n\n.. code-block::\n\n   Run the query returning a the results as np.ndarrays.\n\n   Parameters\n   ----------\n   query : sa.sql.Selectable\n       The query to run. This can be a select or a table.\n   bind : sa.Engine, optional\n       The engine used to create the connection. If not provided\n       ``query.bind`` will be used.\n\n   Returns\n   -------\n   arrays : dict[str, (np.ndarray, np.ndarray)]\n       A map from column name to the result arrays. The first array holds the\n       values and the second array is a boolean mask for NULLs. The values\n       where the mask is False are 0 interpreted by the type.\n\n\n``to_dataframe(query, *, bind=None, null_values=None)``\n```````````````````````````````````````````````````````\n\n.. code-block::\n\n   Run the query returning a the results as a pd.DataFrame.\n\n   Parameters\n   ----------\n   query : sa.sql.Selectable\n       The query to run. This can be a select or a table.\n   bind : sa.Engine, optional\n       The engine used to create the connection. If not provided\n       ``query.bind`` will be used.\n   null_values : dict[str, any]\n       The null values to use for each column. This falls back to\n       ``warp_prism.null_values`` for columns that are not specified.\n\n   Returns\n   -------\n   df : pd.DataFrame\n       A pandas DataFrame holding the results of the query. The columns\n       of the DataFrame will be named the same and be in the same order as the\n       query.\n\n\n``register_odo_dataframe_edge()``\n`````````````````````````````````\n\n.. code-block::\n\n   Register an odo edge for sqlalchemy selectable objects to dataframe.\n\n   This edge will have a lower cost that the default edge so it will be\n   selected as the fasted path.\n\n   If the selectable is not in a postgres database, it will fallback to the\n   default odo edge.\n\n\nComparisons\n-----------\n\nA quick comparison between ``warp_prism``, ``odo``, and ``pd.read_sql_table``.\n\nIn this example we will read real data for VIX from quandl stored in a local\npostgres database using ``warp_prism``, ``odo``, and ``pd.read_sql_table``.\nAfter that, we will use ``odo`` to create a table with two float columns and\n1000000 rows and query it with the tree tools again.\n\n.. code-block:: python\n\n   In [1]: import warp_prism\n\n   In [2]: from odo import odo, resource\n\n   In [3]: import pandas as pd\n\n   In [4]: table = resource(\n      ...:     'postgresql://localhost/bz::yahoo_index_vix',\n      ...:     schema='quandl',\n      ...: )\n\n   In [5]: warp_prism.to_dataframe(table).head()\n   Out[5]:\n      asof_date      open_       high        low      close  volume  \\\n   0 2016-01-08  22.959999  27.080000  22.480000  27.010000     0.0\n   1 2015-12-04  17.430000  17.650000  14.690000  14.810000     0.0\n   2 2015-10-29  14.800000  15.460000  14.330000  14.610000     0.0\n   3 2015-12-21  19.639999  20.209999  18.700001  18.700001     0.0\n   4 2015-10-26  14.760000  15.430000  14.680000  15.290000     0.0\n\n      adjusted_close                  timestamp\n   0       27.010000 2016-01-11 23:14:54.682220\n   1       14.810000 2016-01-11 23:14:54.682220\n   2       14.610000 2016-01-11 23:14:54.682220\n   3       18.700001 2016-01-11 23:14:54.682220\n   4       15.290000 2016-01-11 23:14:54.682220\n\n   In [6]: odo(table, pd.DataFrame).head()\n   Out[6]:\n      asof_date      open_       high        low      close  volume  \\\n   0 2016-01-08  22.959999  27.080000  22.480000  27.010000     0.0\n   1 2015-12-04  17.430000  17.650000  14.690000  14.810000     0.0\n   2 2015-10-29  14.800000  15.460000  14.330000  14.610000     0.0\n   3 2015-12-21  19.639999  20.209999  18.700001  18.700001     0.0\n   4 2015-10-26  14.760000  15.430000  14.680000  15.290000     0.0\n\n      adjusted_close                  timestamp\n   0       27.010000 2016-01-11 23:14:54.682220\n   1       14.810000 2016-01-11 23:14:54.682220\n   2       14.610000 2016-01-11 23:14:54.682220\n   3       18.700001 2016-01-11 23:14:54.682220\n   4       15.290000 2016-01-11 23:14:54.682220\n\n   In [7]: pd.read_sql_table(table.name, table.bind, table.schema).head()\n   Out[7]:\n      asof_date      open_       high        low      close  volume  \\\n   0 2016-01-08  22.959999  27.080000  22.480000  27.010000     0.0\n   1 2015-12-04  17.430000  17.650000  14.690000  14.810000     0.0\n   2 2015-10-29  14.800000  15.460000  14.330000  14.610000     0.0\n   3 2015-12-21  19.639999  20.209999  18.700001  18.700001     0.0\n   4 2015-10-26  14.760000  15.430000  14.680000  15.290000     0.0\n\n      adjusted_close                  timestamp\n   0       27.010000 2016-01-11 23:14:54.682220\n   1       14.810000 2016-01-11 23:14:54.682220\n   2       14.610000 2016-01-11 23:14:54.682220\n   3       18.700001 2016-01-11 23:14:54.682220\n   4       15.290000 2016-01-11 23:14:54.682220\n\n   In [8]: len(warp_prism.to_dataframe(table))\n   Out[8]: 6565\n\n   In [9]: %timeit warp_prism.to_dataframe(table)\n   100 loops, best of 3: 7.55 ms per loop\n\n   In [10]: %timeit odo(table, pd.DataFrame)\n   10 loops, best of 3: 49.9 ms per loop\n\n   In [11]: %timeit pd.read_sql_table(table.name, table.bind, table.schema)\n   10 loops, best of 3: 61.8 ms per loop\n\n   In [12]: big_table = odo(\n       ...:     pd.DataFrame({\n       ...:         'a': np.random.rand(1000000),\n       ...:         'b': np.random.rand(1000000)},\n       ...:     ),\n       ...:     'postgresql://localhost/test::largefloattest',\n       ...: )\n\n   In [13]: %timeit warp_prism.to_dataframe(big_table)\n   1 loop, best of 3: 248 ms per loop\n\n   In [14]: %timeit odo(big_table, pd.DataFrame)\n   1 loop, best of 3: 1.51 s per loop\n\n   In [15]: %timeit pd.read_sql_table(big_table.name, big_table.bind)\n   1 loop, best of 3: 1.9 s per loop\n\n\nInstallation\n------------\n\nWarp Prism can be pip installed but requires numpy to build its C extensions:\n\n.. code-block::\n\n   $ pip install numpy\n   $ pip install warp_prism\n\n\nLicense\n-------\n\nWarp Prism is licensed under the Apache 2.0.\n\nWarp Prism is sponsored by `Quantopian <https://www.quantopian.com>`_ where it\nis used to fetch data for use in `Zipline <http://www.zipline.io/>`_ through the\n`Pipeline API <https://www.quantopian.com/tutorials/pipeline>`_ or interactively\nwith `Blaze <http://blaze.readthedocs.io/en/latest/index.html>`_.\n",
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