{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# About \n",
    "A quick notebook to test how to\n",
    "- get data from AWS DynamoDB (using aws boto python library)\n",
    "- clean up data with *Pandas*\n",
    "- plot data with the specificities of time series (automatic time scalling)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Prerequisites\n",
    "\n",
    "## Install AWS CLI\n",
    "Allows boto library to use the CLI credentials to authenticate.\n",
    "\n",
    "## Install boto lib\n",
    "pip install boto3\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Retrieve data from dynamoDB\n",
    "\n",
    "## Connect with boto3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from boto3 import resource\n",
    "from boto3.dynamodb.conditions import Key\n",
    "\n",
    "# The boto3 dynamoDB resource\n",
    "dynamodb_resource = resource('dynamodb')\n",
    "\n",
    "my_table_name = 'iot_data2'\n",
    "table = dynamodb_resource.Table(my_table_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get table metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'status': u'ACTIVE', 'primary_key_name': {u'KeyType': u'HASH', u'AttributeName': u'deviceid'}, 'num_items': 252, 'bytes_size': 33137, 'global_secondary_indices': None}\n",
      "number of entries: 252\n"
     ]
    }
   ],
   "source": [
    "def get_table_metadata(table_name):\n",
    "    \"\"\"\n",
    "    Get some metadata about chosen table.\n",
    "    \"\"\"\n",
    "    table = dynamodb_resource.Table(table_name)\n",
    "\n",
    "    return {\n",
    "        'num_items': table.item_count,\n",
    "        'primary_key_name': table.key_schema[0],\n",
    "        'status': table.table_status,\n",
    "        'bytes_size': table.table_size_bytes,\n",
    "        'global_secondary_indices': table.global_secondary_indexes\n",
    "    }\n",
    "\n",
    "# Get data from our table\n",
    "my_table_name = 'iot_data2'\n",
    "table_metadata = get_table_metadata(my_table_name)\n",
    "print table_metadata\n",
    "print \"number of entries:\", table_metadata['num_items']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apparté: dicts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "myRaspberry\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "jsonData = '{\"name\":\"myRaspberry\", \"color\":\"red\", \"id\":42}'\n",
    "jsonToPython = json.loads(jsonData)\n",
    "print jsonToPython[\"name\"]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Query and scan table\n",
    "\n",
    "*Code originally shared by Martina Pugliese (https://martinapugliese.github.io/interacting-with-a-dynamodb-via-boto3/). Thank you !*\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scan_table(table_name, filter_key=None, filter_value=None):\n",
    "    \"\"\"\n",
    "    Perform a scan operation on table.\n",
    "    Can specify filter_key (col name) and its value to be filtered.\n",
    "    \"\"\"\n",
    "    table = dynamodb_resource.Table(table_name)\n",
    "\n",
    "    if filter_key and filter_value:\n",
    "        filtering_exp = Key(filter_key).eq(filter_value)\n",
    "        response = table.scan(FilterExpression=filtering_exp)\n",
    "    else:\n",
    "        response = table.scan()\n",
    "\n",
    "    return response\n",
    "\n",
    "\n",
    "def query_table(table_name, filter_key=None, filter_value=None):\n",
    "    \"\"\"\n",
    "    Perform a query operation on the table. \n",
    "    Can specify filter_key (col name) and its value to be filtered.\n",
    "    \"\"\"\n",
    "    table = dynamodb_resource.Table(table_name)\n",
    "\n",
    "    if filter_key and filter_value:\n",
    "        filtering_exp = Key(filter_key).eq(filter_value)\n",
    "        response = table.query(KeyConditionExpression=filtering_exp)\n",
    "    else:\n",
    "        response = table.query()\n",
    "\n",
    "    return response\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{u'Count': 262, u'Items': [{u'timestamp': u'1528731454.09', u'data': {u'timestamp': u'1528731454.09', u'device_id': u'olivepi', u'temperature': u'44.5', u'comments': u'dummy value', u'sequence': Decimal('25')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731460.23', u'data': {u'timestamp': u'1528731460.23', u'device_id': u'olivepi', u'temperature': u'44.5', u'comments': u'dummy value', u'sequence': Decimal('26')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731466.37', u'data': {u'timestamp': u'1528731466.37', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('27')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731472.55', u'data': {u'timestamp': u'1528731472.55', u'device_id': u'olivepi', u'temperature': u'45.1', u'comments': u'dummy value', u'sequence': Decimal('28')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731478.69', u'data': {u'timestamp': u'1528731478.69', u'device_id': u'olivepi', u'temperature': u'45.1', u'comments': u'dummy value', u'sequence': Decimal('29')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731484.82', u'data': {u'timestamp': u'1528731484.82', u'device_id': u'olivepi', u'temperature': u'44.5', u'comments': u'dummy value', u'sequence': Decimal('30')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731491.0', u'data': {u'timestamp': u'1528731491.0', u'device_id': u'olivepi', u'temperature': u'44.5', u'comments': u'dummy value', u'sequence': Decimal('31')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731497.14', u'data': {u'timestamp': u'1528731497.14', u'device_id': u'olivepi', u'temperature': u'44.5', u'comments': u'dummy value', u'sequence': Decimal('32')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731503.43', u'data': {u'timestamp': u'1528731503.43', u'device_id': u'olivepi', u'temperature': u'45.1', u'comments': u'dummy value', u'sequence': Decimal('33')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731509.57', u'data': {u'timestamp': u'1528731509.57', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('34')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731515.7', u'data': {u'timestamp': u'1528731515.7', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('35')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731521.89', u'data': {u'timestamp': u'1528731521.89', u'device_id': u'olivepi', u'temperature': u'45.1', u'comments': u'dummy value', u'sequence': Decimal('36')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731528.08', u'data': {u'timestamp': u'1528731528.08', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('37')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731534.21', u'data': {u'timestamp': u'1528731534.21', u'device_id': u'olivepi', u'temperature': u'44.5', u'comments': u'dummy value', u'sequence': Decimal('38')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731540.4', u'data': {u'timestamp': u'1528731540.4', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('39')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731546.59', u'data': {u'timestamp': u'1528731546.59', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('40')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731552.72', u'data': {u'timestamp': u'1528731552.72', u'device_id': u'olivepi', u'temperature': u'44.5', u'comments': u'dummy value', u'sequence': Decimal('41')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731558.86', u'data': {u'timestamp': u'1528731558.86', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('42')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731565.0', u'data': {u'timestamp': u'1528731565.0', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('43')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731571.13', u'data': {u'timestamp': u'1528731571.13', u'device_id': u'olivepi', u'temperature': u'44.5', u'comments': u'dummy value', u'sequence': Decimal('44')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731577.27', u'data': {u'timestamp': u'1528731577.27', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('45')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731583.41', u'data': {u'timestamp': u'1528731583.41', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('46')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731589.54', u'data': {u'timestamp': u'1528731589.54', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('47')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731595.68', u'data': {u'timestamp': u'1528731595.68', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('48')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731601.82', u'data': {u'timestamp': u'1528731601.82', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('49')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731608.0', u'data': {u'timestamp': u'1528731608.0', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('50')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731614.14', u'data': {u'timestamp': u'1528731614.14', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('51')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731620.27', u'data': {u'timestamp': u'1528731620.27', u'device_id': u'olivepi', u'temperature': u'44.5', u'comments': u'dummy value', u'sequence': Decimal('52')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731626.41', u'data': {u'timestamp': u'1528731626.41', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('53')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731632.55', u'data': {u'timestamp': u'1528731632.55', u'device_id': u'olivepi', u'temperature': u'44.5', u'comments': u'dummy value', u'sequence': Decimal('54')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731638.68', u'data': {u'timestamp': u'1528731638.68', u'device_id': u'olivepi', u'temperature': u'43.5', u'comments': u'dummy value', u'sequence': Decimal('55')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731644.82', u'data': {u'timestamp': u'1528731644.82', u'device_id': u'olivepi', u'temperature': u'43.5', u'comments': u'dummy value', u'sequence': Decimal('56')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731650.96', u'data': {u'timestamp': u'1528731650.96', u'device_id': u'olivepi', u'temperature': u'44.5', u'comments': u'dummy value', u'sequence': Decimal('57')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731657.09', u'data': {u'timestamp': u'1528731657.09', u'device_id': u'olivepi', u'temperature': u'43.5', u'comments': u'dummy value', u'sequence': Decimal('58')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731663.23', u'data': {u'timestamp': u'1528731663.23', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('59')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731669.37', u'data': {u'timestamp': u'1528731669.37', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('60')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731675.5', u'data': {u'timestamp': u'1528731675.5', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('61')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731681.64', u'data': {u'timestamp': u'1528731681.64', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('62')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731687.78', u'data': {u'timestamp': u'1528731687.78', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('63')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731693.96', u'data': {u'timestamp': u'1528731693.96', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('64')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731700.1', u'data': {u'timestamp': u'1528731700.1', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('65')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731706.24', u'data': {u'timestamp': u'1528731706.24', u'device_id': u'olivepi', u'temperature': u'43.5', u'comments': u'dummy value', u'sequence': Decimal('66')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731712.37', u'data': {u'timestamp': u'1528731712.37', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('67')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731718.51', u'data': {u'timestamp': u'1528731718.51', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('68')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731724.64', u'data': {u'timestamp': u'1528731724.64', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('69')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731730.78', u'data': {u'timestamp': u'1528731730.78', u'device_id': u'olivepi', u'temperature': u'44.5', u'comments': u'dummy value', u'sequence': Decimal('70')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731736.92', u'data': {u'timestamp': u'1528731736.92', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('71')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731743.05', u'data': {u'timestamp': u'1528731743.05', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('72')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731749.19', u'data': {u'timestamp': u'1528731749.19', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('73')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731755.33', u'data': {u'timestamp': u'1528731755.33', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('74')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731761.46', u'data': {u'timestamp': u'1528731761.46', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('75')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731767.6', u'data': {u'timestamp': u'1528731767.6', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('76')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731773.73', u'data': {u'timestamp': u'1528731773.73', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('77')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731779.87', u'data': {u'timestamp': u'1528731779.87', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('78')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731786.06', u'data': {u'timestamp': u'1528731786.06', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('79')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731792.19', u'data': {u'timestamp': u'1528731792.19', u'device_id': u'olivepi', u'temperature': u'44.5', u'comments': u'dummy value', u'sequence': Decimal('80')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731798.33', u'data': {u'timestamp': u'1528731798.33', u'device_id': u'olivepi', u'temperature': u'44.5', u'comments': u'dummy 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u'olivepi', u'temperature': u'44.5', u'comments': u'dummy value', u'sequence': Decimal('86')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731835.25', u'data': {u'timestamp': u'1528731835.25', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('87')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731841.38', u'data': {u'timestamp': u'1528731841.38', u'device_id': u'olivepi', u'temperature': u'43.5', u'comments': u'dummy value', u'sequence': Decimal('88')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731847.57', u'data': {u'timestamp': u'1528731847.57', u'device_id': u'olivepi', u'temperature': u'44.5', u'comments': u'dummy value', u'sequence': Decimal('89')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731853.76', u'data': {u'timestamp': u'1528731853.76', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('90')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528731859.89', 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u'1528732790.93', u'data': {u'timestamp': u'1528732790.93', u'device_id': u'olivepi', u'temperature': u'45.1', u'comments': u'dummy value', u'sequence': Decimal('242')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528732797.06', u'data': {u'timestamp': u'1528732797.06', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('243')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528732803.2', u'data': {u'timestamp': u'1528732803.2', u'device_id': u'olivepi', u'temperature': u'45.1', u'comments': u'dummy value', u'sequence': Decimal('244')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528732809.39', u'data': {u'timestamp': u'1528732809.39', u'device_id': u'olivepi', u'temperature': u'44.5', u'comments': u'dummy value', u'sequence': Decimal('245')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528732815.52', u'data': {u'timestamp': u'1528732815.52', u'device_id': u'olivepi', u'temperature': u'44.5', u'comments': u'dummy value', u'sequence': Decimal('246')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528732821.66', u'data': {u'timestamp': u'1528732821.66', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('247')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528732827.8', u'data': {u'timestamp': u'1528732827.8', u'device_id': u'olivepi', u'temperature': u'45.1', u'comments': u'dummy value', u'sequence': Decimal('248')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528732833.93', u'data': {u'timestamp': u'1528732833.93', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('249')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528732840.07', u'data': {u'timestamp': u'1528732840.07', u'device_id': u'olivepi', u'temperature': u'45.1', u'comments': u'dummy value', u'sequence': Decimal('250')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528732846.21', u'data': {u'timestamp': u'1528732846.21', u'device_id': u'olivepi', u'temperature': u'45.1', u'comments': u'dummy value', u'sequence': Decimal('251')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528732852.39', u'data': {u'timestamp': u'1528732852.39', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('252')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528732858.53', u'data': {u'timestamp': u'1528732858.53', u'device_id': u'olivepi', u'temperature': u'45.1', u'comments': u'dummy value', u'sequence': Decimal('253')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528732864.66', u'data': {u'timestamp': u'1528732864.66', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('254')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528732870.8', u'data': {u'timestamp': u'1528732870.8', u'device_id': u'olivepi', u'temperature': u'44.5', u'comments': u'dummy value', u'sequence': Decimal('255')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528732876.94', u'data': {u'timestamp': u'1528732876.94', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('256')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528732883.07', u'data': {u'timestamp': u'1528732883.07', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('257')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528732889.21', u'data': {u'timestamp': u'1528732889.21', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('258')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528732895.35', u'data': {u'timestamp': u'1528732895.35', u'device_id': u'olivepi', u'temperature': u'44.5', u'comments': u'dummy value', u'sequence': Decimal('259')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528732901.48', u'data': {u'timestamp': u'1528732901.48', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('260')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528732907.62', u'data': {u'timestamp': u'1528732907.62', u'device_id': u'olivepi', u'temperature': u'45.1', u'comments': u'dummy value', u'sequence': Decimal('261')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528732913.75', u'data': {u'timestamp': u'1528732913.75', u'device_id': u'olivepi', u'temperature': u'45.1', u'comments': u'dummy value', u'sequence': Decimal('262')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528732919.94', u'data': {u'timestamp': u'1528732919.94', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('263')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528732926.12', u'data': {u'timestamp': u'1528732926.12', u'device_id': u'olivepi', u'temperature': u'44.5', u'comments': u'dummy value', u'sequence': Decimal('264')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528732932.25', u'data': {u'timestamp': u'1528732932.25', u'device_id': u'olivepi', u'temperature': u'44.5', u'comments': u'dummy value', u'sequence': Decimal('265')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528732938.39', u'data': {u'timestamp': u'1528732938.39', u'device_id': u'olivepi', u'temperature': u'45.1', u'comments': u'dummy value', u'sequence': Decimal('266')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528732944.52', u'data': {u'timestamp': u'1528732944.52', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('267')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528732950.66', u'data': {u'timestamp': u'1528732950.66', u'device_id': u'olivepi', u'temperature': u'44.5', u'comments': u'dummy value', u'sequence': Decimal('268')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528732956.8', u'data': {u'timestamp': u'1528732956.8', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('269')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528732962.93', u'data': {u'timestamp': u'1528732962.93', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('270')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528732969.07', u'data': {u'timestamp': u'1528732969.07', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('271')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528732975.21', u'data': {u'timestamp': u'1528732975.21', u'device_id': u'olivepi', u'temperature': u'45.1', u'comments': u'dummy value', u'sequence': Decimal('272')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528732981.39', u'data': {u'timestamp': u'1528732981.39', u'device_id': u'olivepi', u'temperature': u'44.5', u'comments': u'dummy value', u'sequence': Decimal('273')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528732987.53', u'data': {u'timestamp': u'1528732987.53', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('274')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528732993.72', u'data': {u'timestamp': u'1528732993.72', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('275')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1528732999.85', u'data': {u'timestamp': u'1528732999.85', u'device_id': u'olivepi', u'temperature': u'44.0', u'comments': u'dummy value', u'sequence': Decimal('276')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1529502906.13', u'data': {u'timestamp': u'1529502906.13', u'device_id': u'olivepi', u'temperature': Decimal('22.380053494288752'), u'comments': u'dummy value', u'sequence': Decimal('0')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1529502907.3', u'data': {u'timestamp': u'1529502907.3', u'device_id': u'olivepi', u'temperature': Decimal('22.275242909879132'), u'comments': u'dummy value', u'sequence': Decimal('1')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1529502908.47', u'data': {u'timestamp': u'1529502908.47', u'device_id': u'olivepi', u'temperature': Decimal('23.897990065310385'), u'comments': u'dummy value', u'sequence': Decimal('2')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1529502909.58', u'data': {u'timestamp': u'1529502909.58', u'device_id': u'olivepi', u'temperature': Decimal('21.93856468530984'), u'comments': u'dummy value', u'sequence': Decimal('3')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1529502910.7', u'data': {u'timestamp': u'1529502910.7', u'device_id': u'olivepi', u'temperature': Decimal('21.893106491222763'), u'comments': u'dummy value', u'sequence': Decimal('4')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1529503033.84', u'data': {u'timestamp': u'1529503033.84', u'device_id': u'olivepi', u'temperature': Decimal('22.764429182717574'), u'comments': u'dummy value', u'sequence': Decimal('0')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1529503034.95', u'data': {u'timestamp': u'1529503034.95', u'device_id': u'olivepi', u'temperature': Decimal('24.7207185848546'), u'comments': u'dummy value', u'sequence': Decimal('1')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1529503036.12', u'data': {u'timestamp': u'1529503036.12', u'device_id': u'olivepi', u'temperature': Decimal('23.383048594001643'), u'comments': u'dummy value', u'sequence': Decimal('2')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1529503037.24', u'data': {u'timestamp': u'1529503037.24', u'device_id': u'olivepi', u'temperature': Decimal('24.005756901163764'), u'comments': u'dummy value', u'sequence': Decimal('3')}, u'deviceid': u'olivepi'}, {u'timestamp': u'1529503038.35', u'data': {u'timestamp': u'1529503038.35', u'device_id': u'olivepi', u'temperature': Decimal('22.68491202044252'), u'comments': u'dummy value', u'sequence': Decimal('4')}, u'deviceid': u'olivepi'}], u'ScannedCount': 262, 'ResponseMetadata': {'RetryAttempts': 0, 'HTTPStatusCode': 200, 'RequestId': 'S3BG183E31QB7JUN8S6N8V3V8VVV4KQNSO5AEMVJF66Q9ASUAAJG', 'HTTPHeaders': {'x-amzn-requestid': 'S3BG183E31QB7JUN8S6N8V3V8VVV4KQNSO5AEMVJF66Q9ASUAAJG', 'content-length': '57931', 'server': 'Server', 'connection': 'keep-alive', 'x-amz-crc32': '377734902', 'date': 'Wed, 20 Jun 2018 14:15:54 GMT', 'content-type': 'application/x-amz-json-1.0'}}}\n"
     ]
    }
   ],
   "source": [
    "full_scan = scan_table(my_table_name)\n",
    "\n",
    "# Uncomment next line to see the raw data fetched\n",
    "print full_scan\n",
    "\n",
    "#jsonified_full_scan = json.load(full_scan)\n",
    "#print(json.dumps(jsonified_full_scan, indent=2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Iterate results and transform into series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "timestamps = []\n",
    "temperatures = []\n",
    "\n",
    "for data_point in full_scan['Items']:\n",
    "    ts = data_point['data']['timestamp']\n",
    "    measure = data_point['data']['temperature']\n",
    "    #print ts, measure\n",
    "    timestamps.append(ts)\n",
    "    temperatures.append(measure)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clean up data with Pandas\n",
    "\n",
    "## Create a dataframe "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temp</th>\n",
       "      <th>ts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>44.5</td>\n",
       "      <td>1528731454.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44.5</td>\n",
       "      <td>1528731460.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>44.0</td>\n",
       "      <td>1528731466.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45.1</td>\n",
       "      <td>1528731472.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>45.1</td>\n",
       "      <td>1528731478.69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   temp             ts\n",
       "0  44.5  1528731454.09\n",
       "1  44.5  1528731460.23\n",
       "2  44.0  1528731466.37\n",
       "3  45.1  1528731472.55\n",
       "4  45.1  1528731478.69"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "dat = pd.DataFrame({\"temp\": temperatures,\"ts\": timestamps})\n",
    "\n",
    "# Check the 5 first entries\n",
    "dat.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Force the typing of data\n",
    "We need to make sure that timestamps values are typed into dates. This will ease later plotting (\n",
    "e.g. graphics are scalled properly on time axis)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "temp    object\n",
       "ts      object\n",
       "dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pandas can show the types of DataFrame series\n",
    "# by default it returns untyped object!\n",
    "dat.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Option 1: Convert using standard functions:\n",
    "from datetime import datetime\n",
    "\n",
    "utc_dates = map(datetime.fromtimestamp, map(float,dat['ts']))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "temp            object\n",
       "ts      datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Option 2: Convert using pandas\n",
    "dat['ts'] = pd.to_datetime(dat['ts'], unit='s')\n",
    "dat.dtypes\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "temp           float64\n",
       "ts      datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Option 2: Convert using pandas\n",
    "dat['temp'] = pd.to_numeric(dat['temp'])\n",
    "dat.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic stats\n",
    "\n",
    "Basic statistics to ensure that data is interpreted properly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43.350167263088515"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat['temp'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45.1"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat['temp'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21.893106491222763"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat['temp'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>262.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>43.350167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.081001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>21.893106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>44.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>44.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>44.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>45.100000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             temp\n",
       "count  262.000000\n",
       "mean    43.350167\n",
       "std      4.081001\n",
       "min     21.893106\n",
       "25%     44.000000\n",
       "50%     44.000000\n",
       "75%     44.500000\n",
       "max     45.100000"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# All in one\n",
    "dat.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Display the raw serie with pyplot\n",
    "We are printing raw data just to quick ceck the values. Time representation is wrong."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5c20430810>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from matplotlib import pyplot\n",
    "pyplot.plot(dat['temp'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Display with matplotlib (time scalling comes for free)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x1600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "\n",
    "\n",
    "colors = ['#2678B2', '#AFC8E7', '#FD7F28', '#FDBB7D', \n",
    "          '#339E34', '#9ADE8D', '#D42A2F', '#FD9898', \n",
    "          '#9369BB', '#C5B1D4', '#8B564C', '#C39C95', \n",
    "          '#E179C1', '#F6B7D2', '#7F7F7F', '#C7C7C7']\n",
    "\n",
    "\n",
    "# Define plot\n",
    "fig, ax = plt.subplots(figsize=(15, 8), dpi=200)\n",
    "ax.plot(dat['ts'], dat['temp'], c=colors[0])\n",
    "\n",
    "# Add mean line\n",
    "ax.plot((dat['ts'].min(), dat['ts'].max()), (dat['temp'].mean(), dat['temp'].mean()), c=colors[4])\n",
    "\n",
    "\n",
    "# Optional manage orientation of dates values on x axis\n",
    "#fig.autofmt_xdate()\n",
    "\n",
    "# Presentation\n",
    "plt.title('IOT temperature records')\n",
    "plt.ylabel('Temperature - celsius')\n",
    "plt.margins(0)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# References\n",
    "\n",
    "DynamoDB\n",
    "- https://martinapugliese.github.io/interacting-with-a-dynamodb-via-boto3/\n",
    "\n",
    "Plot\n",
    "- https://blog.webkid.io/analysing-data-with-jupyter-notebooks-and-pandas/\n",
    "\n",
    "\n",
    "Python basics:\n",
    "- https://openclassrooms.com/courses/apprenez-a-programmer-en-python/les-dictionnaires-2\n",
    "- https://openclassrooms.com/courses/apprenez-a-programmer-en-python/les-listes-et-tuples-2-2\n",
    "- https://code.tutsplus.com/tutorials/how-to-work-with-json-data-using-python--cms-25758\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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