fhoffa/bq-sample.ipynb

## bq-sample.ipynb
{
 "metadata": {
  "name": "Untitled4"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import bq\nimport datetime\nimport pandas as pd\nclient = bq.Client.Get()",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "query = \"\"\"\nSELECT HOUR(DTimestamp) hour, AVG(Data)\nFROM [io_sensor_data.moscone_io13]\nWHERE SensorType='temperature'\nGROUP BY hour\nORDER BY hour\n\"\"\"\nfields, data = client.ReadSchemaAndRows(client.Query(query)['configuration']['query']['destinationTable'], max_rows = 1000)\ndata[0:3]",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\rWaiting on bqjob_r526e159a8558fe18_00000140e610e6ab_3 ... (0s) Current status: DONE   "
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\n"
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": "[[u'0', u'112.47283733613693'],\n [u'1', u'109.25338134551203'],\n [u'2', u'112.28235492080853']]"
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "plot(data)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": "[<matplotlib.lines.Line2D at 0x4238910>,\n <matplotlib.lines.Line2D at 0x4238a90>]"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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eiEikGPBERCLFgCciEikGPBGRSDHgiYhEigFPRCRSDHgiIpFiwBMRiRQDnohI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\nDxWnjDMdIQZnAAAAAElFTkSuQmCC\n",
       "text": "<matplotlib.figure.Figure at 0x3628f50>"
      }
     ],
     "prompt_number": 7
    }
   ],
   "metadata": {}
  }
 ]
}
	{
	"metadata": {
	"name": "Untitled4"
	},
	"nbformat": 3,
	"nbformat_minor": 0,
	"worksheets": [
	{
	"cells": [
	{
	"cell_type": "code",
	"collapsed": false,
	"input": "import bq\nimport datetime\nimport pandas as pd\nclient = bq.Client.Get()",
	"language": "python",
	"metadata": {},
	"outputs": [],
	"prompt_number": 3
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": "query = \"\"\"\nSELECT HOUR(DTimestamp) hour, AVG(Data)\nFROM [io_sensor_data.moscone_io13]\nWHERE SensorType='temperature'\nGROUP BY hour\nORDER BY hour\n\"\"\"\nfields, data = client.ReadSchemaAndRows(client.Query(query)['configuration']['query']['destinationTable'], max_rows = 1000)\ndata[0:3]",
	"language": "python",
	"metadata": {},
	"outputs": [
	{
	"output_type": "stream",
	"stream": "stdout",
	"text": "\rWaiting on bqjob_r526e159a8558fe18_00000140e610e6ab_3 ... (0s) Current status: DONE "
	},
	{
	"output_type": "stream",
	"stream": "stdout",
	"text": "\n"
	},
	{
	"metadata": {},
	"output_type": "pyout",
	"prompt_number": 6,
	"text": "[[u'0', u'112.47283733613693'],\n [u'1', u'109.25338134551203'],\n [u'2', u'112.28235492080853']]"
	}
	],
	"prompt_number": 6
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": "plot(data)",
	"language": "python",
	"metadata": {},
	"outputs": [
	{
	"metadata": {},
	"output_type": "pyout",
	"prompt_number": 7,
	"text": "[<matplotlib.lines.Line2D at 0x4238910>,\n <matplotlib.lines.Line2D at 0x4238a90>]"
	},
	{
	"metadata": {},
	"output_type": "display_data",
	"png": 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eiEikGPBERCLFgCciEikGPBGRSDHgiYhEigFPRCRSDHgiIpFiwBMRiRQDnohI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\nDxWnjDMdIQZnAAAAAElFTkSuQmCC\n",
	"text": "<matplotlib.figure.Figure at 0x3628f50>"
	}
	],
	"prompt_number": 7
	}
	],
	"metadata": {}
	}
	]
	}