examples/trump_approval_ratings.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "\n",
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "import urllib.request\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    import kusto_pandas\n",
    "except:\n",
    "    sys.path.insert(0, os.path.abspath(os.path.join(os.path.abspath(\"\"), '..')))\n",
    "    import kusto_pandas\n",
    "from kusto_pandas import Wrap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>president</th>\n",
       "      <th>subgroup</th>\n",
       "      <th>modeldate</th>\n",
       "      <th>startdate</th>\n",
       "      <th>enddate</th>\n",
       "      <th>pollster</th>\n",
       "      <th>grade</th>\n",
       "      <th>samplesize</th>\n",
       "      <th>population</th>\n",
       "      <th>weight</th>\n",
       "      <th>...</th>\n",
       "      <th>disapprove</th>\n",
       "      <th>adjusted_approve</th>\n",
       "      <th>adjusted_disapprove</th>\n",
       "      <th>multiversions</th>\n",
       "      <th>tracking</th>\n",
       "      <th>url</th>\n",
       "      <th>poll_id</th>\n",
       "      <th>question_id</th>\n",
       "      <th>createddate</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>All polls</td>\n",
       "      <td>1/20/2021</td>\n",
       "      <td>1/20/2017</td>\n",
       "      <td>1/22/2017</td>\n",
       "      <td>Morning Consult</td>\n",
       "      <td>B/C</td>\n",
       "      <td>1992.0</td>\n",
       "      <td>rv</td>\n",
       "      <td>0.680029</td>\n",
       "      <td>...</td>\n",
       "      <td>37.0</td>\n",
       "      <td>45.686784</td>\n",
       "      <td>38.055805</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://static.politico.com/9b/13/82a3baf542ae9...</td>\n",
       "      <td>49249</td>\n",
       "      <td>77261</td>\n",
       "      <td>1/23/2017</td>\n",
       "      <td>11:47:59 20 Jan 2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>All polls</td>\n",
       "      <td>1/20/2021</td>\n",
       "      <td>1/20/2017</td>\n",
       "      <td>1/22/2017</td>\n",
       "      <td>Gallup</td>\n",
       "      <td>B</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>a</td>\n",
       "      <td>0.262323</td>\n",
       "      <td>...</td>\n",
       "      <td>45.0</td>\n",
       "      <td>45.861441</td>\n",
       "      <td>43.539189</td>\n",
       "      <td>NaN</td>\n",
       "      <td>T</td>\n",
       "      <td>http://www.gallup.com/poll/201617/gallup-daily...</td>\n",
       "      <td>49253</td>\n",
       "      <td>77265</td>\n",
       "      <td>1/23/2017</td>\n",
       "      <td>11:47:59 20 Jan 2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>All polls</td>\n",
       "      <td>1/20/2021</td>\n",
       "      <td>1/20/2017</td>\n",
       "      <td>1/24/2017</td>\n",
       "      <td>Ipsos</td>\n",
       "      <td>B-</td>\n",
       "      <td>1632.0</td>\n",
       "      <td>a</td>\n",
       "      <td>0.153481</td>\n",
       "      <td>...</td>\n",
       "      <td>45.2</td>\n",
       "      <td>43.451563</td>\n",
       "      <td>43.780389</td>\n",
       "      <td>NaN</td>\n",
       "      <td>T</td>\n",
       "      <td>http://polling.reuters.com/#poll/CP3_2/</td>\n",
       "      <td>49426</td>\n",
       "      <td>77599</td>\n",
       "      <td>3/1/2017</td>\n",
       "      <td>11:47:59 20 Jan 2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>All polls</td>\n",
       "      <td>1/20/2021</td>\n",
       "      <td>1/21/2017</td>\n",
       "      <td>1/23/2017</td>\n",
       "      <td>Gallup</td>\n",
       "      <td>B</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>a</td>\n",
       "      <td>0.242845</td>\n",
       "      <td>...</td>\n",
       "      <td>46.0</td>\n",
       "      <td>45.861441</td>\n",
       "      <td>44.539189</td>\n",
       "      <td>NaN</td>\n",
       "      <td>T</td>\n",
       "      <td>http://www.gallup.com/poll/201617/gallup-daily...</td>\n",
       "      <td>49262</td>\n",
       "      <td>77274</td>\n",
       "      <td>1/24/2017</td>\n",
       "      <td>11:47:59 20 Jan 2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>All polls</td>\n",
       "      <td>1/20/2021</td>\n",
       "      <td>1/22/2017</td>\n",
       "      <td>1/24/2017</td>\n",
       "      <td>Gallup</td>\n",
       "      <td>B</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>a</td>\n",
       "      <td>0.227380</td>\n",
       "      <td>...</td>\n",
       "      <td>45.0</td>\n",
       "      <td>46.861441</td>\n",
       "      <td>43.539189</td>\n",
       "      <td>NaN</td>\n",
       "      <td>T</td>\n",
       "      <td>http://www.gallup.com/poll/201617/gallup-daily...</td>\n",
       "      <td>49236</td>\n",
       "      <td>77248</td>\n",
       "      <td>1/25/2017</td>\n",
       "      <td>11:47:59 20 Jan 2021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      president   subgroup  modeldate  startdate    enddate         pollster  \\\n",
       "0  Donald Trump  All polls  1/20/2021  1/20/2017  1/22/2017  Morning Consult   \n",
       "1  Donald Trump  All polls  1/20/2021  1/20/2017  1/22/2017           Gallup   \n",
       "2  Donald Trump  All polls  1/20/2021  1/20/2017  1/24/2017            Ipsos   \n",
       "3  Donald Trump  All polls  1/20/2021  1/21/2017  1/23/2017           Gallup   \n",
       "4  Donald Trump  All polls  1/20/2021  1/22/2017  1/24/2017           Gallup   \n",
       "\n",
       "  grade  samplesize population    weight  ...  disapprove  adjusted_approve  \\\n",
       "0   B/C      1992.0         rv  0.680029  ...        37.0         45.686784   \n",
       "1     B      1500.0          a  0.262323  ...        45.0         45.861441   \n",
       "2    B-      1632.0          a  0.153481  ...        45.2         43.451563   \n",
       "3     B      1500.0          a  0.242845  ...        46.0         45.861441   \n",
       "4     B      1500.0          a  0.227380  ...        45.0         46.861441   \n",
       "\n",
       "   adjusted_disapprove  multiversions  tracking  \\\n",
       "0            38.055805            NaN       NaN   \n",
       "1            43.539189            NaN         T   \n",
       "2            43.780389            NaN         T   \n",
       "3            44.539189            NaN         T   \n",
       "4            43.539189            NaN         T   \n",
       "\n",
       "                                                 url poll_id question_id  \\\n",
       "0  http://static.politico.com/9b/13/82a3baf542ae9...   49249       77261   \n",
       "1  http://www.gallup.com/poll/201617/gallup-daily...   49253       77265   \n",
       "2            http://polling.reuters.com/#poll/CP3_2/   49426       77599   \n",
       "3  http://www.gallup.com/poll/201617/gallup-daily...   49262       77274   \n",
       "4  http://www.gallup.com/poll/201617/gallup-daily...   49236       77248   \n",
       "\n",
       "   createddate             timestamp  \n",
       "0    1/23/2017  11:47:59 20 Jan 2021  \n",
       "1    1/23/2017  11:47:59 20 Jan 2021  \n",
       "2     3/1/2017  11:47:59 20 Jan 2021  \n",
       "3    1/24/2017  11:47:59 20 Jan 2021  \n",
       "4    1/25/2017  11:47:59 20 Jan 2021  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def dowload_dataset_if_necessary(url, filename):\n",
    "    if not os.path.exists(filename):\n",
    "        urllib.request.urlretrieve (url, filename)\n",
    "url = \"https://projects.fivethirtyeight.com/trump-approval-data/approval_polllist.csv\"\n",
    "filename = \"approval_polllist.csv\"\n",
    "dowload_dataset_if_necessary(url, filename)\n",
    "data = pd.read_csv(filename)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['president', 'subgroup', 'modeldate', 'startdate', 'enddate',\n",
       "       'pollster', 'grade', 'samplesize', 'population', 'weight', 'influence',\n",
       "       'approve', 'disapprove', 'adjusted_approve', 'adjusted_disapprove',\n",
       "       'multiversions', 'tracking', 'url', 'poll_id', 'question_id',\n",
       "       'createddate', 'timestamp'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>president</th>\n",
       "      <th>subgroup</th>\n",
       "      <th>modeldate</th>\n",
       "      <th>startdate</th>\n",
       "      <th>enddate</th>\n",
       "      <th>pollster</th>\n",
       "      <th>grade</th>\n",
       "      <th>samplesize</th>\n",
       "      <th>population</th>\n",
       "      <th>weight</th>\n",
       "      <th>...</th>\n",
       "      <th>disapprove</th>\n",
       "      <th>adjusted_approve</th>\n",
       "      <th>adjusted_disapprove</th>\n",
       "      <th>multiversions</th>\n",
       "      <th>tracking</th>\n",
       "      <th>url</th>\n",
       "      <th>poll_id</th>\n",
       "      <th>question_id</th>\n",
       "      <th>createddate</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>All polls</td>\n",
       "      <td>1/20/2021</td>\n",
       "      <td>1/20/2017</td>\n",
       "      <td>1/22/2017</td>\n",
       "      <td>Morning Consult</td>\n",
       "      <td>B/C</td>\n",
       "      <td>1992.0</td>\n",
       "      <td>rv</td>\n",
       "      <td>0.680029</td>\n",
       "      <td>...</td>\n",
       "      <td>37.0</td>\n",
       "      <td>45.686784</td>\n",
       "      <td>38.055805</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://static.politico.com/9b/13/82a3baf542ae9...</td>\n",
       "      <td>49249</td>\n",
       "      <td>77261</td>\n",
       "      <td>1/23/2017</td>\n",
       "      <td>11:47:59 20 Jan 2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>All polls</td>\n",
       "      <td>1/20/2021</td>\n",
       "      <td>1/20/2017</td>\n",
       "      <td>1/22/2017</td>\n",
       "      <td>Gallup</td>\n",
       "      <td>B</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>a</td>\n",
       "      <td>0.262323</td>\n",
       "      <td>...</td>\n",
       "      <td>45.0</td>\n",
       "      <td>45.861441</td>\n",
       "      <td>43.539189</td>\n",
       "      <td>NaN</td>\n",
       "      <td>T</td>\n",
       "      <td>http://www.gallup.com/poll/201617/gallup-daily...</td>\n",
       "      <td>49253</td>\n",
       "      <td>77265</td>\n",
       "      <td>1/23/2017</td>\n",
       "      <td>11:47:59 20 Jan 2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>All polls</td>\n",
       "      <td>1/20/2021</td>\n",
       "      <td>1/20/2017</td>\n",
       "      <td>1/24/2017</td>\n",
       "      <td>Ipsos</td>\n",
       "      <td>B-</td>\n",
       "      <td>1632.0</td>\n",
       "      <td>a</td>\n",
       "      <td>0.153481</td>\n",
       "      <td>...</td>\n",
       "      <td>45.2</td>\n",
       "      <td>43.451563</td>\n",
       "      <td>43.780389</td>\n",
       "      <td>NaN</td>\n",
       "      <td>T</td>\n",
       "      <td>http://polling.reuters.com/#poll/CP3_2/</td>\n",
       "      <td>49426</td>\n",
       "      <td>77599</td>\n",
       "      <td>3/1/2017</td>\n",
       "      <td>11:47:59 20 Jan 2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>All polls</td>\n",
       "      <td>1/20/2021</td>\n",
       "      <td>1/21/2017</td>\n",
       "      <td>1/23/2017</td>\n",
       "      <td>Gallup</td>\n",
       "      <td>B</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>a</td>\n",
       "      <td>0.242845</td>\n",
       "      <td>...</td>\n",
       "      <td>46.0</td>\n",
       "      <td>45.861441</td>\n",
       "      <td>44.539189</td>\n",
       "      <td>NaN</td>\n",
       "      <td>T</td>\n",
       "      <td>http://www.gallup.com/poll/201617/gallup-daily...</td>\n",
       "      <td>49262</td>\n",
       "      <td>77274</td>\n",
       "      <td>1/24/2017</td>\n",
       "      <td>11:47:59 20 Jan 2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>All polls</td>\n",
       "      <td>1/20/2021</td>\n",
       "      <td>1/22/2017</td>\n",
       "      <td>1/24/2017</td>\n",
       "      <td>Gallup</td>\n",
       "      <td>B</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>a</td>\n",
       "      <td>0.227380</td>\n",
       "      <td>...</td>\n",
       "      <td>45.0</td>\n",
       "      <td>46.861441</td>\n",
       "      <td>43.539189</td>\n",
       "      <td>NaN</td>\n",
       "      <td>T</td>\n",
       "      <td>http://www.gallup.com/poll/201617/gallup-daily...</td>\n",
       "      <td>49236</td>\n",
       "      <td>77248</td>\n",
       "      <td>1/25/2017</td>\n",
       "      <td>11:47:59 20 Jan 2021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "<kusto_pandas.kusto_pandas.Wrap at 0x2a7cc0c3d60>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w = Wrap(data)\n",
    "w.take(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>president</th>\n",
       "      <th>count_</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<kusto_pandas.kusto_pandas.Wrap at 0x2a7bb18ac40>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.summarize(\"count() by president\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count_</th>\n",
       "      <th>min_startdate</th>\n",
       "      <th>max_startdate</th>\n",
       "      <th>dcount_pollster</th>\n",
       "      <th>avg_disapprove</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16500</td>\n",
       "      <td>1/1/2018</td>\n",
       "      <td>9/9/2020</td>\n",
       "      <td>93</td>\n",
       "      <td>53.31449</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<kusto_pandas.kusto_pandas.Wrap at 0x2a7cc73a5b0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.summarize(\"count(), min(startdate), max(startdate), dcount(pollster), avg(disapprove)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>pollster</th>\n",
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       "      <th>count_</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>YouGov</td>\n",
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       "      <td>4582</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Ipsos</td>\n",
       "      <td>B-</td>\n",
       "      <td>2717</td>\n",
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       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Morning Consult</td>\n",
       "      <td>B/C</td>\n",
       "      <td>2353</td>\n",
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       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Rasmussen Reports/Pulse Opinion Research</td>\n",
       "      <td>C+</td>\n",
       "      <td>2012</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>HarrisX</td>\n",
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       "      <td>896</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Gallup</td>\n",
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       "      <td>859</td>\n",
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       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>SurveyMonkey</td>\n",
       "      <td>D-</td>\n",
       "      <td>494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Marist College</td>\n",
       "      <td>A+</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Quinnipiac University</td>\n",
       "      <td>B+</td>\n",
       "      <td>140</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>American Research Group</td>\n",
       "      <td>B</td>\n",
       "      <td>127</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "<kusto_pandas.kusto_pandas.Wrap at 0x2a7cc744220>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.summarize(\"count() by pollster, grade\").top(10, \"count_\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>...</th>\n",
       "      <th>disapprove</th>\n",
       "      <th>adjusted_approve</th>\n",
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       "      <th>timestamp</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>All polls</td>\n",
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       "      <td>1/20/2017</td>\n",
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       "      <td>37.0</td>\n",
       "      <td>45.686784</td>\n",
       "      <td>38.055805</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://static.politico.com/9b/13/82a3baf542ae9...</td>\n",
       "      <td>49249</td>\n",
       "      <td>77261</td>\n",
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       "      <td>Donald Trump</td>\n",
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       "      <td>B</td>\n",
       "      <td>1500.0</td>\n",
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       "      <td>0.262323</td>\n",
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       "      <td>45.0</td>\n",
       "      <td>45.861441</td>\n",
       "      <td>43.539189</td>\n",
       "      <td>NaN</td>\n",
       "      <td>T</td>\n",
       "      <td>http://www.gallup.com/poll/201617/gallup-daily...</td>\n",
       "      <td>49253</td>\n",
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       "    <tr>\n",
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       "      <td>Donald Trump</td>\n",
       "      <td>All polls</td>\n",
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       "      <td>1/20/2017</td>\n",
       "      <td>2017-01-24</td>\n",
       "      <td>Ipsos</td>\n",
       "      <td>B-</td>\n",
       "      <td>1632.0</td>\n",
       "      <td>a</td>\n",
       "      <td>0.153481</td>\n",
       "      <td>...</td>\n",
       "      <td>45.2</td>\n",
       "      <td>43.451563</td>\n",
       "      <td>43.780389</td>\n",
       "      <td>NaN</td>\n",
       "      <td>T</td>\n",
       "      <td>http://polling.reuters.com/#poll/CP3_2/</td>\n",
       "      <td>49426</td>\n",
       "      <td>77599</td>\n",
       "      <td>3/1/2017</td>\n",
       "      <td>11:47:59 20 Jan 2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>All polls</td>\n",
       "      <td>1/20/2021</td>\n",
       "      <td>1/21/2017</td>\n",
       "      <td>2017-01-23</td>\n",
       "      <td>Gallup</td>\n",
       "      <td>B</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>a</td>\n",
       "      <td>0.242845</td>\n",
       "      <td>...</td>\n",
       "      <td>46.0</td>\n",
       "      <td>45.861441</td>\n",
       "      <td>44.539189</td>\n",
       "      <td>NaN</td>\n",
       "      <td>T</td>\n",
       "      <td>http://www.gallup.com/poll/201617/gallup-daily...</td>\n",
       "      <td>49262</td>\n",
       "      <td>77274</td>\n",
       "      <td>1/24/2017</td>\n",
       "      <td>11:47:59 20 Jan 2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>All polls</td>\n",
       "      <td>1/20/2021</td>\n",
       "      <td>1/22/2017</td>\n",
       "      <td>2017-01-24</td>\n",
       "      <td>Gallup</td>\n",
       "      <td>B</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>a</td>\n",
       "      <td>0.227380</td>\n",
       "      <td>...</td>\n",
       "      <td>45.0</td>\n",
       "      <td>46.861441</td>\n",
       "      <td>43.539189</td>\n",
       "      <td>NaN</td>\n",
       "      <td>T</td>\n",
       "      <td>http://www.gallup.com/poll/201617/gallup-daily...</td>\n",
       "      <td>49236</td>\n",
       "      <td>77248</td>\n",
       "      <td>1/25/2017</td>\n",
       "      <td>11:47:59 20 Jan 2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16495</th>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>Voters</td>\n",
       "      <td>1/20/2021</td>\n",
       "      <td>1/15/2021</td>\n",
       "      <td>2021-01-17</td>\n",
       "      <td>Quinnipiac University</td>\n",
       "      <td>B+</td>\n",
       "      <td>1131.0</td>\n",
       "      <td>rv</td>\n",
       "      <td>1.345482</td>\n",
       "      <td>...</td>\n",
       "      <td>61.0</td>\n",
       "      <td>36.758206</td>\n",
       "      <td>59.198940</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://poll.qu.edu/national/release-detail?Re...</td>\n",
       "      <td>74214</td>\n",
       "      <td>139262</td>\n",
       "      <td>1/18/2021</td>\n",
       "      <td>11:52:51 20 Jan 2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16496</th>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>Voters</td>\n",
       "      <td>1/20/2021</td>\n",
       "      <td>1/14/2021</td>\n",
       "      <td>2021-01-18</td>\n",
       "      <td>Rasmussen Reports/Pulse Opinion Research</td>\n",
       "      <td>C+</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>lv</td>\n",
       "      <td>0.569744</td>\n",
       "      <td>...</td>\n",
       "      <td>48.0</td>\n",
       "      <td>46.837601</td>\n",
       "      <td>48.988128</td>\n",
       "      <td>NaN</td>\n",
       "      <td>T</td>\n",
       "      <td>http://www.rasmussenreports.com/public_content...</td>\n",
       "      <td>74221</td>\n",
       "      <td>139287</td>\n",
       "      <td>1/19/2021</td>\n",
       "      <td>11:52:51 20 Jan 2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16497</th>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>Voters</td>\n",
       "      <td>1/20/2021</td>\n",
       "      <td>1/15/2021</td>\n",
       "      <td>2021-01-17</td>\n",
       "      <td>Morning Consult</td>\n",
       "      <td>B/C</td>\n",
       "      <td>1993.0</td>\n",
       "      <td>rv</td>\n",
       "      <td>0.093102</td>\n",
       "      <td>...</td>\n",
       "      <td>58.0</td>\n",
       "      <td>39.495997</td>\n",
       "      <td>58.343779</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://assets.morningconsult.com/wp-uploads/2...</td>\n",
       "      <td>74218</td>\n",
       "      <td>139267</td>\n",
       "      <td>1/19/2021</td>\n",
       "      <td>11:52:51 20 Jan 2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16498</th>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>Voters</td>\n",
       "      <td>1/20/2021</td>\n",
       "      <td>1/16/2021</td>\n",
       "      <td>2021-01-19</td>\n",
       "      <td>American Research Group</td>\n",
       "      <td>B</td>\n",
       "      <td>990.0</td>\n",
       "      <td>rv</td>\n",
       "      <td>1.314191</td>\n",
       "      <td>...</td>\n",
       "      <td>67.0</td>\n",
       "      <td>32.325063</td>\n",
       "      <td>63.443719</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://americanresearchgroup.com/economy/</td>\n",
       "      <td>74226</td>\n",
       "      <td>139307</td>\n",
       "      <td>1/20/2021</td>\n",
       "      <td>11:52:51 20 Jan 2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16499</th>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>Voters</td>\n",
       "      <td>1/20/2021</td>\n",
       "      <td>1/16/2021</td>\n",
       "      <td>2021-01-19</td>\n",
       "      <td>YouGov</td>\n",
       "      <td>B</td>\n",
       "      <td>1155.0</td>\n",
       "      <td>rv</td>\n",
       "      <td>0.944349</td>\n",
       "      <td>...</td>\n",
       "      <td>55.0</td>\n",
       "      <td>43.555458</td>\n",
       "      <td>54.288382</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://docs.cdn.yougov.com/y9zsit5bzd/weeklyt...</td>\n",
       "      <td>74225</td>\n",
       "      <td>139305</td>\n",
       "      <td>1/20/2021</td>\n",
       "      <td>11:52:51 20 Jan 2021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16500 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "<kusto_pandas.kusto_pandas.Wrap at 0x2a7cf28e970>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w = w.extend(\"enddate = todatetime(enddate)\")\n",
    "w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='bin_enddate'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(w.summarize(\"avg(approve), avg(disapprove) by bin(enddate, 7d)\")\n",
    ".render(\"timechart\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='pollster'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(w.summarize(\"count() by pollster\")\n",
    " .top(\"10 by count_\")\n",
    " .render(\"barchart\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='pollster'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(w\n",
    " .summarize(\"num_polls= count(), avg(approve), avg(disapprove) by pollster\")\n",
    " .top(10, \"num_polls\")\n",
    " .project_away(\"num_polls\")\n",
    " .render(\"barchart\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}