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        "description": "pg-utils: PostgreSQL Utils\n==========================\n\n.. image:: https://badge.fury.io/py/pg-utils.svg\n    :target: https://badge.fury.io/py/pg-utils\n\n.. image:: https://readthedocs.org/projects/pg-utils/badge/?version=latest\n    :target: http://pg-utils.readthedocs.org/en/latest/?badge=latest\n    :alt: Documentation Status\n\nWhen analyzing large datasets, it can often be useful to let the database do as much of the analysis as possible. While `Pandas is great at manipulating datasets that are large enough to fit on one machine, but possibly not large enough to fit into memory <http://stackoverflow.com/a/14268804/554546>`_, concerns over performance and data security can sometimes make analysis in the database more convenient.\n\nThis package is built for use with PostgreSQL. Support for other databases *might* follow (but don't hold your breath).\n\nWait, what? Why?!?\n------------------\n\nLet's illustrate with a few examples. To begin with, making a connection is simple:\n\n::\n\n    In [1]: from pg_utils import connection, table\n\n    In [2]: conn = connection.Connection()\n\nThe environment variables ``pg_username``, ``pg_password``, ``pg_hostname``, and ``pg_database`` can be used to store values for the corresponding connection information. Of course, any of the username, password, hostname, or database can be overridden.\n\nMoving on, let's build a simple table consisting of one million rows with one column chosen randomly and another sampled from the standard normal distribution (via the `Box-Muller transform <https://en.wikipedia.org/wiki/Box%E2%80%93Muller_transform>`_).\n\n::\n\n    In [1]: from pg_utils import table\n\n    In [2]: t = table.Table.create(\"pg_utils_test\",\n       ...:         \"\"\"create table pg_utils_test as\n                        select random() as x,\n       ...:             sqrt(-2 * ln(u1))*cos(2*PI()*u2) as y\n       ...:             from(\n       ...:                 select random() as u1, random() as u2\n       ...:                 from generate_series(1, 1000000)\n       ...:             )a\"\"\")\n\nThe object ``t`` is a metadata object. It doesn't hold any of the actual data within the table. However, we have a limited subset of the Pandas API that works via the database. For example:\n\n::\n\n    In [3]: t.describe()\n    Out[3]:\n                          x               y\n    count    1000000.000000  1000000.000000\n    mean           0.499628       -0.000075\n    std_dev        0.288671        0.999630\n    minimum        0.000001       -4.589661\n    25%            0.249420       -0.672603\n    50%            0.499709        0.000695\n    75%            0.749733        0.673413\n    maximum        0.999999        4.867347\n\n\nNote that *none* of those calculations are done locally. They're all done in the database.\n\nAlso, ``t.x.distplot(bins=1000)`` and ``t.y.distplot(bins=1000)`` produce the following histograms (with KDEs):\n\n.. image:: img/x.png\n\n.. image:: img/y.png\n\nNote\n----\n\nThe ``Table`` API has significantly changed from ``0.3.x`` to ``0.4.0``. In particular, schema and connection parameters are now optional (replaced with your username and a fresh connection, respectively). This leaves the table name as the only required parameter for many of these methods. If ``table_name`` is already qualified with a schema (eg ``\"foo.bar\"``), then pg-utils will Do The Right Thing and just set ``schema=\"foo\"`` and ``table_name=\"bar\"``.\n\nGoals\n-----\n\nThe goals for this package are:\n\n* Providing a simple ``Connection`` object that builds easy connections to the database based off of environment variables (overridden with parameters, if specified).\n\n* Mocking Pandas Series and DataFrame objects with metadata constructs of ``Column`` and ``Table`` (respectively). Columns and Tables will implement some parts of the Pandas API that do calculations in the database.\n\n* Possibly other tools for automation of simple tasks and conveniently displaying metadata (if needed).\n\nNon-Goals\n---------\n\nThis package will never be:\n\n* An object-relational mapper (ORM). The only SQL-ish operations will be those that are implemented and that mock SQL-ish bits of the Pandas API for Series and/or DataFrames.\n\nInstallation\n------------\n\nIt's up on PyPI. So, just do\n\n::\n\n    pip install pg-utils\n\nfor the base package, or\n\n::\n\n    pip install pg-utils[graphics]\n\nto install `Seaborn <https://stanford.edu/~mwaskom/software/seaborn/>`_ for graphical visualizations.\n\n\n",
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