sakura.ipynb

{
    "cells": [
        {
            "cell_type": "code", 
            "outputs": [], 
            "metadata": {
                "collapsed": true
            }, 
            "source": "# The code was removed by DSX for sharing.", 
            "execution_count": 1
        }, 
        {
            "cell_type": "code", 
            "outputs": [
                {
                    "text": "Collecting cloudant\n  Downloading cloudant-2.3.1.tar.gz (48kB)\n\u001b[K    100% |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 51kB 2.7MB/s \n\u001b[?25hRequirement already satisfied (use --upgrade to upgrade): requests<3.0.0,>=2.7.0 in /usr/local/src/conda3_runtime.v4/4.1.1/lib/python3.5/site-packages (from cloudant)\nBuilding wheels for collected packages: cloudant\n  Running setup.py bdist_wheel for cloudant ... \u001b[?25l-\b \b\\\b \bdone\n\u001b[?25h  Stored in directory: /gpfs/fs01/user/sbcf-fd41f5fdd05671-30a815acdc92/.cache/pip/wheels/40/93/a6/d9913de4cbec6ea134416d49fabedff7899376100a620ad6f7\nSuccessfully built cloudant\nInstalling collected packages: cloudant\nSuccessfully installed cloudant-2.3.1\n", 
                    "output_type": "stream", 
                    "name": "stdout"
                }
            ], 
            "metadata": {
                "scrolled": true, 
                "collapsed": false
            }, 
            "source": "!pip install --user cloudant", 
            "execution_count": 3
        }, 
        {
            "cell_type": "code", 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/plain": "['_replicator', '_users', '_warehouser', 'nodered', 'sakura_iot']"
                    }, 
                    "metadata": {}, 
                    "execution_count": 9
                }
            ], 
            "metadata": {
                "collapsed": false
            }, 
            "source": "from cloudant.client import Cloudant\nfrom cloudant.result import Result\n\nclient = Cloudant(credentials_1[\"username\"], credentials_1[\"password\"], url=credentials_1[\"url\"])\nclient.connect()\nclient.all_dbs()", 
            "execution_count": 9
        }, 
        {
            "cell_type": "code", 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/plain": "                                _id                                _rev  \\\n0  01bbc09c49c51ca2af1c2f0077beba1c  1-c2c566f48808c07480ea5dfcc43aa094   \n1  01bbc09c49c51ca2af1c2f0077eb709a  1-8349dcf6ff522970582dcaee397c79a5   \n2  0f31258932e7aafcb9e590e2792c1a73  1-e9d93e0459e4e53385259a215f94e227   \n3  0f31258932e7aafcb9e590e27957193c  1-132eff6caf6b3e7151a36ef63c28d814   \n4  0f31258932e7aafcb9e590e27971db80  1-4901832867318a17569247b3e5a7fd7c   \n\n                         datetime  light        module  temperature  \n0  2017-02-14T04:36:57.140104706Z    413  ujCvrJMT5MhW     1.524994  \n1  2017-02-14T04:41:13.952045814Z    401  ujCvrJMT5MhW     0.710113  \n2  2017-02-14T04:48:01.817835267Z    400  ujCvrJMT5MhW     0.607300  \n3  2017-02-14T04:51:03.091761397Z    413  ujCvrJMT5MhW     0.710113  \n4  2017-02-14T04:54:04.378442787Z    412  ujCvrJMT5MhW     1.322510  ", 
                        "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>_id</th>\n      <th>_rev</th>\n      <th>datetime</th>\n      <th>light</th>\n      <th>module</th>\n      <th>temperature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>01bbc09c49c51ca2af1c2f0077beba1c</td>\n      <td>1-c2c566f48808c07480ea5dfcc43aa094</td>\n      <td>2017-02-14T04:36:57.140104706Z</td>\n      <td>413</td>\n      <td>ujCvrJMT5MhW</td>\n      <td>1.524994</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>01bbc09c49c51ca2af1c2f0077eb709a</td>\n      <td>1-8349dcf6ff522970582dcaee397c79a5</td>\n      <td>2017-02-14T04:41:13.952045814Z</td>\n      <td>401</td>\n      <td>ujCvrJMT5MhW</td>\n      <td>0.710113</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0f31258932e7aafcb9e590e2792c1a73</td>\n      <td>1-e9d93e0459e4e53385259a215f94e227</td>\n      <td>2017-02-14T04:48:01.817835267Z</td>\n      <td>400</td>\n      <td>ujCvrJMT5MhW</td>\n      <td>0.607300</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0f31258932e7aafcb9e590e27957193c</td>\n      <td>1-132eff6caf6b3e7151a36ef63c28d814</td>\n      <td>2017-02-14T04:51:03.091761397Z</td>\n      <td>413</td>\n      <td>ujCvrJMT5MhW</td>\n      <td>0.710113</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0f31258932e7aafcb9e590e27971db80</td>\n      <td>1-4901832867318a17569247b3e5a7fd7c</td>\n      <td>2017-02-14T04:54:04.378442787Z</td>\n      <td>412</td>\n      <td>ujCvrJMT5MhW</td>\n      <td>1.322510</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
                    }, 
                    "metadata": {}, 
                    "execution_count": 10
                }
            ], 
            "metadata": {
                "collapsed": false
            }, 
            "source": "import pandas as pd, json\n\ndb = client['sakura_iot']\nresult_collection = Result(db.all_docs, include_docs=True)\ndf = pd.DataFrame([item['doc'] for item in result_collection])\ndf.head()", 
            "execution_count": 10
        }, 
        {
            "cell_type": "code", 
            "outputs": [], 
            "metadata": {
                "collapsed": true
            }, 
            "source": "", 
            "execution_count": null
        }
    ], 
    "nbformat": 4, 
    "metadata": {
        "kernelspec": {
            "display_name": "Python 3.5 (Experimental) with Spark 1.6", 
            "name": "python3", 
            "language": "python"
        }, 
        "language_info": {
            "mimetype": "text/x-python", 
            "version": "3.5.2", 
            "file_extension": ".py", 
            "name": "python", 
            "pygments_lexer": "ipython3", 
            "nbconvert_exporter": "python", 
            "codemirror_mode": {
                "version": 3, 
                "name": "ipython"
            }
        }
    }, 
    "nbformat_minor": 0
}