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        "description": "pytd\n====\n\n|Build Status| |Build status| |PyPI version| |docs status|\n\n**pytd** provides user-friendly interfaces to Treasure Data\u2019s `REST\nAPIs <https://github.com/treasure-data/td-client-python>`__, `Presto\nquery\nengine <https://support.treasuredata.com/hc/en-us/articles/360001457427-Presto-Query-Engine-Introduction>`__,\nand `Plazma primary\nstorage <https://www.slideshare.net/treasure-data/td-techplazma>`__.\n\nThe seamless connection allows your Python code to efficiently\nread/write a large volume of data from/to Treasure Data. Eventually,\npytd makes your day-to-day data analytics work more productive.\n\nInstallation\n------------\n\n.. code:: sh\n\n   pip install pytd\n\nUsage\n-----\n\n-  `Documentation <https://pytd-doc.readthedocs.io/>`__\n-  `Sample usage on Google\n   Colaboratory <https://colab.research.google.com/drive/1ps_ChU-H2FvkeNlj1e1fcOebCt4ryN11>`__\n\nSet your `API\nkey <https://support.treasuredata.com/hc/en-us/articles/360000763288-Get-API-Keys>`__\nand\n`endpoint <https://support.treasuredata.com/hc/en-us/articles/360001474288-Sites-and-Endpoints>`__\nto the environment variables, ``TD_API_KEY`` and ``TD_API_SERVER``,\nrespectively, and create a client instance:\n\n.. code:: py\n\n   import pytd\n\n   client = pytd.Client(database='sample_datasets')\n   # or, hard-code your API key, endpoint, and/or query engine:\n   # >>> pytd.Client(apikey='1/XXX', endpoint='https://api.treasuredata.com/', database='sample_datasets', default_engine='presto')\n\nQuery in Treasure Data\n~~~~~~~~~~~~~~~~~~~~~~\n\nIssue Presto query and retrieve the result:\n\n.. code:: py\n\n   client.query('select symbol, count(1) as cnt from nasdaq group by 1 order by 1')\n   # {'columns': ['symbol', 'cnt'], 'data': [['AAIT', 590], ['AAL', 82], ['AAME', 9252], ..., ['ZUMZ', 2364]]}\n\nIn case of Hive:\n\n.. code:: py\n\n   client.query('select hivemall_version()', engine='hive')\n   # {'columns': ['_c0'], 'data': [['0.6.0-SNAPSHOT-201901-r01']]} (as of Feb, 2019)\n\nIt is also possible to explicitly initialize ``pytd.Client`` for Hive:\n\n.. code:: py\n\n   client_hive = pytd.Client(database='sample_datasets', default_engine='hive')\n   client_hive.query('select hivemall_version()')\n\nWrite data to Treasure Data\n~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nData represented as ``pandas.DataFrame`` can be written to Treasure Data\nas follows:\n\n.. code:: py\n\n   import pandas as pd\n\n   df = pd.DataFrame(data={'col1': [1, 2], 'col2': [3, 10]})\n   client.load_table_from_dataframe(df, 'takuti.foo', writer='bulk_import', if_exists='overwrite')\n\nFor the ``writer`` option, pytd supports three different ways to ingest\ndata to Treasure Data:\n\n1. **Bulk Import API**: ``bulk_import`` (default)\n\n   -  Convert data into a CSV file and upload in the batch fashion.\n\n2. **Presto INSERT INTO query**: ``insert_into``\n\n   -  Insert every single row in ``DataFrame`` by issuing an INSERT INTO\n      query through the Presto query engine.\n   -  Recommended only for a small volume of data.\n\n3. `td-spark <https://support.treasuredata.com/hc/en-us/articles/360001487167-Apache-Spark-Driver-td-spark-FAQs>`__:\n   ``spark``\n\n   -  Local customized Spark instance directly writes ``DataFrame`` to\n      Treasure Data\u2019s primary storage system.\n\nEnabling Spark Writer\n^^^^^^^^^^^^^^^^^^^^^\n\nSince td-spark gives special access to the main storage system via\n`PySpark <https://spark.apache.org/docs/latest/api/python/index.html>`__,\nfollow the instructions below:\n\n1. Contact support@treasuredata.com to activate the permission to your\n   Treasure Data account.\n2. Install pytd with ``[spark]`` option if you use the third option:\n   ``pip install pytd[spark]``\n\nIf you want to use existing td-spark JAR file, creating ``SparkWriter``\nwith ``td_spark_path`` option would be helpful.\n\n.. code:: py\n\n   from pytd.writer import SparkWriter\n\n   writer = SparkWriter(apikey='1/XXX', endpoint='https://api.treasuredata.com/', td_spark_path='/path/to/td-spark-assembly.jar')\n   client.load_table_from_dataframe(df, 'mydb.bar', writer=writer, if_exists='overwrite')\n\nHow to replace pandas-td\n------------------------\n\n**pytd** offers\n`pandas-td <https://github.com/treasure-data/pandas-td>`__-compatible\nfunctions that provide the same functionalities more efficiently. If you\nare still using pandas-td, we recommend you to switch to **pytd** as\nfollows.\n\nFirst, install the package from PyPI:\n\n.. code:: sh\n\n   pip install pytd\n   # or, `pip install pytd[spark]` if you wish to use `to_td`\n\nNext, make the following modifications on the import statements.\n\n*Before:*\n\n.. code:: python\n\n   import pandas_td as td\n\n.. code:: python\n\n   In [1]: %%load_ext pandas_td.ipython\n\n*After:*\n\n.. code:: python\n\n   import pytd.pandas_td as td\n\n.. code:: python\n\n   In [1]: %%load_ext pytd.pandas_td.ipython\n\nConsequently, all ``pandas_td`` code should keep running correctly with\n``pytd``. 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