GEG_EDA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "22da74d8-a501-40b2-83de-725646f93272",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install altair -q --user"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1722bfd4-f257-4c09-a1dc-00f202b029c2",
   "metadata": {},
   "source": [
    "# GEG EDA\n",
    "\n",
    "<INTRO STUFF>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "defe4b11-7b7d-4fd1-9392-e2eb8f1155d9",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Getting data on Evergiven timeframe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07c2861b-9150-463a-9512-46923f3ae65d",
   "metadata": {},
   "source": [
    "import json\n",
    "#set params\n",
    "BEGIN = \"2021-03-01\"\n",
    "END = \"2021-04-30\"\n",
    "\n",
    "HEIGHT = 400\n",
    "WIDTH = 400\n",
    "\n",
    "bq_params = {\"BEGIN\": BEGIN, \"END\": END}\n",
    "bq_params = json.dumps(bq_params, indent=4, sort_keys=True, default=str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e8c4be05-53d1-4030-9e9b-82a49d0d0b5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Query complete after 0.00s: 100%|██████████| 9/9 [00:00<00:00, 3853.09query/s]                        \n",
      "Downloading: 100%|██████████| 70369292/70369292 [00:47<00:00, 1494310.07rows/s]\n"
     ]
    }
   ],
   "source": [
    "%%bigquery april_events --params {\"BEGIN\": \"2021-03-01\", \"END\": \"2021-04-30\"}\n",
    "WITH geg_data AS ((\n",
    "    SELECT \n",
    "    groupId, \n",
    "    name, \n",
    "    a.entity as stringVal, \n",
    "    a.numMentions, \n",
    "    a.avgSalience, \n",
    "    eventTime, \n",
    "    polarity, \n",
    "    magnitude, \n",
    "    score \n",
    "    from (\n",
    "    \tSELECT polarity, \n",
    "        magnitude, \n",
    "        score, \n",
    "        FARM_FINGERPRINT(url) groupId, \n",
    "        CONCAT('Entity',entity.type) name, \n",
    "        FORMAT_TIMESTAMP(\"%Y-%m-%dT%X%Ez\", date, \"UTC\") eventTime, \n",
    "    \tentity.mid mid FROM `gdelt-bq.gdeltv2.geg_gcnlapi`, UNNEST(entities) entity WHERE entity.mid is not null \n",
    "       and lang='en' and DATE(date) >= @BEGIN and DATE(date) <= @END\n",
    "  ) b JOIN (\n",
    "     SELECT APPROX_TOP_COUNT(entities.name, 1)[OFFSET(0)].value entity, \n",
    "        entities.mid mid, sum(entities.numMentions) as numMentions, \n",
    "        avg(entities.avgSalience) as avgSalience\n",
    "      FROM `gdelt-bq.gdeltv2.geg_gcnlapi`, \n",
    "     unnest(entities) entities where entities.mid is not null and lang='en' \n",
    "     and DATE(date) >= @BEGIN and DATE(date) <= @END group by entities.mid\n",
    "  ) a USING(mid)))\n",
    "\n",
    "select * from geg_data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1604245c-6eea-42b7-b221-41f107e6d9bb",
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   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>groupId</th>\n",
       "      <th>name</th>\n",
       "      <th>stringVal</th>\n",
       "      <th>numMentions</th>\n",
       "      <th>avgSalience</th>\n",
       "      <th>eventTime</th>\n",
       "      <th>polarity</th>\n",
       "      <th>magnitude</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7830976867991912493</td>\n",
       "      <td>EntityLOCATION</td>\n",
       "      <td>Gilbert Station Road</td>\n",
       "      <td>5</td>\n",
       "      <td>0.001257</td>\n",
       "      <td>2021-03-21T11:46:56+00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3100410252706223831</td>\n",
       "      <td>EntityORGANIZATION</td>\n",
       "      <td>UBS</td>\n",
       "      <td>8848</td>\n",
       "      <td>0.018045</td>\n",
       "      <td>2021-03-21T21:32:33+00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2297817263941679196</td>\n",
       "      <td>EntityLOCATION</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>205042</td>\n",
       "      <td>0.010001</td>\n",
       "      <td>2021-03-21T00:02:03+00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-5374852001300381844</td>\n",
       "      <td>EntityOTHER</td>\n",
       "      <td>Kolelas</td>\n",
       "      <td>1338</td>\n",
       "      <td>0.047422</td>\n",
       "      <td>2021-03-21T16:32:38+00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>-0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-6754983596130560644</td>\n",
       "      <td>EntityOTHER</td>\n",
       "      <td>Kolelas</td>\n",
       "      <td>1338</td>\n",
       "      <td>0.047422</td>\n",
       "      <td>2021-03-21T17:01:45+00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>-0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70369287</th>\n",
       "      <td>-1191005947168842791</td>\n",
       "      <td>EntityLOCATION</td>\n",
       "      <td>U.S.</td>\n",
       "      <td>4350763</td>\n",
       "      <td>0.016365</td>\n",
       "      <td>2021-03-09T01:18:31+00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>-0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70369288</th>\n",
       "      <td>2970384450307155624</td>\n",
       "      <td>EntityLOCATION</td>\n",
       "      <td>U.S.</td>\n",
       "      <td>4350763</td>\n",
       "      <td>0.016365</td>\n",
       "      <td>2021-03-09T16:34:09+00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>-0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70369289</th>\n",
       "      <td>4391024216151471084</td>\n",
       "      <td>EntityLOCATION</td>\n",
       "      <td>U.S.</td>\n",
       "      <td>4350763</td>\n",
       "      <td>0.016365</td>\n",
       "      <td>2021-03-09T07:18:18+00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>70369290</th>\n",
       "      <td>-4657659261277881748</td>\n",
       "      <td>EntityLOCATION</td>\n",
       "      <td>U.S.</td>\n",
       "      <td>4350763</td>\n",
       "      <td>0.016365</td>\n",
       "      <td>2021-03-09T07:46:49+00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70369291</th>\n",
       "      <td>3126909358210772309</td>\n",
       "      <td>EntityLOCATION</td>\n",
       "      <td>U.S.</td>\n",
       "      <td>4350763</td>\n",
       "      <td>0.016365</td>\n",
       "      <td>2021-03-09T07:18:22+00:00</td>\n",
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       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
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       "  </tbody>\n",
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       "<p>70369292 rows × 9 columns</p>\n",
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      ],
      "text/plain": [
       "                      groupId                name             stringVal  \\\n",
       "0         7830976867991912493      EntityLOCATION  Gilbert Station Road   \n",
       "1         3100410252706223831  EntityORGANIZATION                   UBS   \n",
       "2        -2297817263941679196      EntityLOCATION           Los Angeles   \n",
       "3        -5374852001300381844         EntityOTHER               Kolelas   \n",
       "4        -6754983596130560644         EntityOTHER               Kolelas   \n",
       "...                       ...                 ...                   ...   \n",
       "70369287 -1191005947168842791      EntityLOCATION                  U.S.   \n",
       "70369288  2970384450307155624      EntityLOCATION                  U.S.   \n",
       "70369289  4391024216151471084      EntityLOCATION                  U.S.   \n",
       "70369290 -4657659261277881748      EntityLOCATION                  U.S.   \n",
       "70369291  3126909358210772309      EntityLOCATION                  U.S.   \n",
       "\n",
       "          numMentions  avgSalience                  eventTime  polarity  \\\n",
       "0                   5     0.001257  2021-03-21T11:46:56+00:00       0.0   \n",
       "1                8848     0.018045  2021-03-21T21:32:33+00:00       0.0   \n",
       "2              205042     0.010001  2021-03-21T00:02:03+00:00       0.0   \n",
       "3                1338     0.047422  2021-03-21T16:32:38+00:00       0.0   \n",
       "4                1338     0.047422  2021-03-21T17:01:45+00:00       0.0   \n",
       "...               ...          ...                        ...       ...   \n",
       "70369287      4350763     0.016365  2021-03-09T01:18:31+00:00       0.0   \n",
       "70369288      4350763     0.016365  2021-03-09T16:34:09+00:00       0.0   \n",
       "70369289      4350763     0.016365  2021-03-09T07:18:18+00:00       0.0   \n",
       "70369290      4350763     0.016365  2021-03-09T07:46:49+00:00       0.0   \n",
       "70369291      4350763     0.016365  2021-03-09T07:18:22+00:00       0.0   \n",
       "\n",
       "          magnitude  score  \n",
       "0               4.5    0.0  \n",
       "1               4.5    0.0  \n",
       "2               4.5    0.0  \n",
       "3               4.5   -0.3  \n",
       "4               4.5   -0.3  \n",
       "...             ...    ...  \n",
       "70369287        7.5   -0.1  \n",
       "70369288        7.5   -0.4  \n",
       "70369289        8.0    0.0  \n",
       "70369290        8.0    0.0  \n",
       "70369291        8.0    0.0  \n",
       "\n",
       "[70369292 rows x 9 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "april_events"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "608e95a4-691c-4c93-b709-0ad273a41ea8",
   "metadata": {},
   "source": [
    "### Get a count of the entity values for this period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "06959d02-7626-4ffe-ae8c-19984c6e9d7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import altair as alt\n",
    "\n",
    "\n",
    "def return_top_count_categories(data, group_field, depth):\n",
    "    ### Get the top counts for stuff\n",
    "    entity_count = data.groupby(group_field).count()\n",
    "    top_n = entity_count.sort_values('groupId', ascending=False)[0:depth]\n",
    "    other_count = entity_count.sort_values('groupId', ascending=False)['groupId'][depth+1::].sum()\n",
    "    top_n = top_n.reset_index()\n",
    "    if isinstance(group_field, list):\n",
    "        top_n.append({'stringVal': 'Other', 'name': 'Other', 'eventTime': other_count}, ignore_index=True)\n",
    "    else:\n",
    "    \ttop_n = top_n.append({group_field: 'Other', 'eventTime': other_count}, ignore_index=True)\n",
    "    return(top_n)                  \n",
    "                         \n",
    "def create_entity_count_charts(count_field, data, salience_filter=0, entity_term_filter=None, top_n=20):\n",
    "    \n",
    "    data = data[data['avgSalience'] >= salience_filter]\n",
    "    if entity_term_filter:\n",
    "        data=data[data[count_field].str.contains(entity_term_filter, case=False)]\n",
    "    if top_n > 0:\n",
    "    \tentity_count = return_top_count_categories(data, count_field, top_n)\n",
    "    else:\n",
    "        entity_count = data.groupby(count_field).count()\n",
    "\n",
    "    entity_count = entity_count.reset_index()\n",
    "    entity_count = entity_count.sort_values('eventTime', ascending=False)\n",
    "    entity_count['count'] = entity_count['eventTime']\n",
    "\n",
    "    count_chart = alt.Chart(entity_count).mark_bar().encode(\n",
    "        x={'field': f'{count_field}', 'type': 'ordinal', 'sort': 'y',},\n",
    "        y={'field': 'count', 'type': 'quantitative'},\n",
    "        tooltip=[count_field, 'count']\n",
    "    ).properties(\n",
    "        title=f\"{count_field} Counts for March 2021\",\n",
    "        height=HEIGHT,\n",
    "        width=WIDTH\n",
    "    )\n",
    "    \n",
    "    return count_chart\n",
    "\n",
    "\n",
    "# another one for the chart that uses colors for the categories\n",
    "\n",
    "def create_entity_count_charts_color(data, salience_filter=0, entity_term_filter=None, top_n=20):\n",
    "    \n",
    "    data = data[data['avgSalience'] >= salience_filter]\n",
    "    if entity_term_filter:\n",
    "        data=data[data['stringVal'].str.contains(entity_term_filter, case=False)]\n",
    "    if top_n > 0:\n",
    "        entity_count = return_top_count_categories(data, ['stringVal', 'name'], top_n)\n",
    "    else:\n",
    "    \tentity_count = data.groupby(['stringVal', 'name']).count()\n",
    "\n",
    "    entity_count = entity_count.reset_index()\n",
    "    entity_count = entity_count.sort_values('eventTime', ascending=False)\n",
    "    entity_count['count'] = entity_count['eventTime']\n",
    "\n",
    "    count_chart = alt.Chart(entity_count).mark_bar().encode(\n",
    "        x={'field': 'stringVal', 'type':'ordinal', 'sort': 'y'},\n",
    "        y={'field': 'count', 'type': 'quantitative'},\n",
    "        tooltip=['name', 'stringVal', 'count'],\n",
    "        color='name:N',\n",
    "    ).properties(\n",
    "        title=f\"Combined Counts for March 2021\",\n",
    "        height=HEIGHT,\n",
    "        width=WIDTH*2\n",
    "    )\n",
    "    \n",
    "    return count_chart\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "90e032be-fab8-4ecc-b621-378a91b1ccb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "## EverGiven is EntityOTHER - maybe we monitor that steam?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a1de6f05-f7d1-4694-a19b-8f668b6a1a23",
   "metadata": {},
   "outputs": [
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\"name\": 150374.0, \"numMentions\": 150374.0, \"avgSalience\": 150374.0, \"eventTime\": 150374, \"polarity\": 150374.0, \"magnitude\": 150374.0, \"score\": 150374.0, \"count\": 150374}, {\"index\": 25, \"stringVal\": \"White House\", \"groupId\": 149631.0, \"name\": 149631.0, \"numMentions\": 149631.0, \"avgSalience\": 149631.0, \"eventTime\": 149631, \"polarity\": 149631.0, \"magnitude\": 149631.0, \"score\": 149631.0, \"count\": 149631}, {\"index\": 26, \"stringVal\": \"European Union\", \"groupId\": 147130.0, \"name\": 147130.0, \"numMentions\": 147130.0, \"avgSalience\": 147130.0, \"eventTime\": 147130, \"polarity\": 147130.0, \"magnitude\": 147130.0, \"score\": 147130.0, \"count\": 147130}, {\"index\": 27, \"stringVal\": \"Washington\", \"groupId\": 137102.0, \"name\": 137102.0, \"numMentions\": 137102.0, \"avgSalience\": 137102.0, \"eventTime\": 137102, \"polarity\": 137102.0, \"magnitude\": 137102.0, \"score\": 137102.0, \"count\": 137102}, {\"index\": 28, \"stringVal\": \"Japan\", \"groupId\": 133319.0, \"name\": 133319.0, \"numMentions\": 133319.0, \"avgSalience\": 133319.0, \"eventTime\": 133319, \"polarity\": 133319.0, \"magnitude\": 133319.0, \"score\": 133319.0, \"count\": 133319}, {\"index\": 29, \"stringVal\": \"Centers for Disease Control and Prevention\", \"groupId\": 131382.0, \"name\": 131382.0, \"numMentions\": 131382.0, \"avgSalience\": 131382.0, \"eventTime\": 131382, \"polarity\": 131382.0, \"magnitude\": 131382.0, \"score\": 131382.0, \"count\": 131382}, {\"index\": 30, \"stringVal\": \"Congress\", \"groupId\": 129434.0, \"name\": 129434.0, \"numMentions\": 129434.0, \"avgSalience\": 129434.0, \"eventTime\": 129434, \"polarity\": 129434.0, \"magnitude\": 129434.0, \"score\": 129434.0, \"count\": 129434}, {\"index\": 31, \"stringVal\": \"Los Angeles\", \"groupId\": 127706.0, \"name\": 127706.0, \"numMentions\": 127706.0, \"avgSalience\": 127706.0, \"eventTime\": 127706, \"polarity\": 127706.0, \"magnitude\": 127706.0, \"score\": 127706.0, \"count\": 127706}, {\"index\": 32, \"stringVal\": \"AstraZeneca\", \"groupId\": 117965.0, \"name\": 117965.0, \"numMentions\": 117965.0, \"avgSalience\": 117965.0, \"eventTime\": 117965, \"polarity\": 117965.0, \"magnitude\": 117965.0, \"score\": 117965.0, \"count\": 117965}, {\"index\": 33, \"stringVal\": \"Pfizer\", \"groupId\": 114951.0, \"name\": 114951.0, \"numMentions\": 114951.0, \"avgSalience\": 114951.0, \"eventTime\": 114951, \"polarity\": 114951.0, \"magnitude\": 114951.0, \"score\": 114951.0, \"count\": 114951}, {\"index\": 34, \"stringVal\": \"CNN\", \"groupId\": 107022.0, \"name\": 107022.0, \"numMentions\": 107022.0, \"avgSalience\": 107022.0, \"eventTime\": 107022, \"polarity\": 107022.0, \"magnitude\": 107022.0, \"score\": 107022.0, \"count\": 107022}, {\"index\": 35, \"stringVal\": \"Washington, D.C.\", \"groupId\": 106997.0, \"name\": 106997.0, \"numMentions\": 106997.0, \"avgSalience\": 106997.0, \"eventTime\": 106997, \"polarity\": 106997.0, \"magnitude\": 106997.0, \"score\": 106997.0, \"count\": 106997}, {\"index\": 36, \"stringVal\": \"Italy\", \"groupId\": 104587.0, \"name\": 104587.0, \"numMentions\": 104587.0, \"avgSalience\": 104587.0, \"eventTime\": 104587, \"polarity\": 104587.0, \"magnitude\": 104587.0, \"score\": 104587.0, \"count\": 104587}, {\"index\": 37, \"stringVal\": \"Africa\", \"groupId\": 102408.0, \"name\": 102408.0, \"numMentions\": 102408.0, \"avgSalience\": 102408.0, \"eventTime\": 102408, \"polarity\": 102408.0, \"magnitude\": 102408.0, \"score\": 102408.0, \"count\": 102408}, {\"index\": 38, \"stringVal\": \"Getty Images\", \"groupId\": 102280.0, \"name\": 102280.0, \"numMentions\": 102280.0, \"avgSalience\": 102280.0, \"eventTime\": 102280, \"polarity\": 102280.0, \"magnitude\": 102280.0, \"score\": 102280.0, \"count\": 102280}, {\"index\": 39, \"stringVal\": \"English\", \"groupId\": 99994.0, \"name\": 99994.0, \"numMentions\": 99994.0, \"avgSalience\": 99994.0, \"eventTime\": 99994, \"polarity\": 99994.0, \"magnitude\": 99994.0, \"score\": 99994.0, \"count\": 99994}, {\"index\": 40, \"stringVal\": \"Brazil\", \"groupId\": 95715.0, \"name\": 95715.0, \"numMentions\": 95715.0, \"avgSalience\": 95715.0, \"eventTime\": 95715, \"polarity\": 95715.0, \"magnitude\": 95715.0, \"score\": 95715.0, \"count\": 95715}, {\"index\": 41, \"stringVal\": \"England\", \"groupId\": 95545.0, \"name\": 95545.0, \"numMentions\": 95545.0, \"avgSalience\": 95545.0, \"eventTime\": 95545, \"polarity\": 95545.0, \"magnitude\": 95545.0, \"score\": 95545.0, \"count\": 95545}, {\"index\": 42, \"stringVal\": \"United Nations\", \"groupId\": 93991.0, \"name\": 93991.0, \"numMentions\": 93991.0, \"avgSalience\": 93991.0, \"eventTime\": 93991, \"polarity\": 93991.0, \"magnitude\": 93991.0, \"score\": 93991.0, \"count\": 93991}, {\"index\": 43, \"stringVal\": \"Israel\", \"groupId\": 91636.0, \"name\": 91636.0, \"numMentions\": 91636.0, \"avgSalience\": 91636.0, \"eventTime\": 91636, \"polarity\": 91636.0, \"magnitude\": 91636.0, \"score\": 91636.0, \"count\": 91636}, {\"index\": 44, \"stringVal\": \"Georgia\", \"groupId\": 91369.0, \"name\": 91369.0, \"numMentions\": 91369.0, \"avgSalience\": 91369.0, \"eventTime\": 91369, \"polarity\": 91369.0, \"magnitude\": 91369.0, \"score\": 91369.0, \"count\": 91369}, {\"index\": 45, \"stringVal\": \"God\", \"groupId\": 91215.0, \"name\": 91215.0, \"numMentions\": 91215.0, \"avgSalience\": 91215.0, \"eventTime\": 91215, \"polarity\": 91215.0, \"magnitude\": 91215.0, \"score\": 91215.0, \"count\": 91215}, {\"index\": 46, \"stringVal\": \"Minnesota\", \"groupId\": 90179.0, \"name\": 90179.0, \"numMentions\": 90179.0, \"avgSalience\": 90179.0, \"eventTime\": 90179, \"polarity\": 90179.0, \"magnitude\": 90179.0, \"score\": 90179.0, \"count\": 90179}, {\"index\": 47, \"stringVal\": \"Johnson & Johnson\", \"groupId\": 89853.0, \"name\": 89853.0, \"numMentions\": 89853.0, \"avgSalience\": 89853.0, \"eventTime\": 89853, \"polarity\": 89853.0, \"magnitude\": 89853.0, \"score\": 89853.0, \"count\": 89853}, {\"index\": 48, \"stringVal\": \"South Africa\", \"groupId\": 86906.0, \"name\": 86906.0, \"numMentions\": 86906.0, \"avgSalience\": 86906.0, \"eventTime\": 86906, \"polarity\": 86906.0, \"magnitude\": 86906.0, \"score\": 86906.0, \"count\": 86906}, {\"index\": 49, \"stringVal\": \"Mexico\", \"groupId\": 86698.0, \"name\": 86698.0, \"numMentions\": 86698.0, \"avgSalience\": 86698.0, \"eventTime\": 86698, \"polarity\": 86698.0, \"magnitude\": 86698.0, \"score\": 86698.0, \"count\": 86698}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.VConcatChart(...)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "category_count_chart = create_entity_count_charts('name', april_events, salience_filter=0, entity_term_filter=None, top_n=100)\n",
    "entity_count_chart = create_entity_count_charts('stringVal', april_events, salience_filter=0, entity_term_filter=None, top_n=50)\n",
    "top_chart = create_entity_count_charts_color(april_events, salience_filter=0, entity_term_filter=None, top_n=50)\n",
    "alt.data_transformers.disable_max_rows()\n",
    "\n",
    "alt.vconcat(\n",
    "    top_chart,\n",
    "    alt.hconcat(category_count_chart, entity_count_chart),\n",
    ")\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55082a9d-ecfe-401a-b743-ffe45a318280",
   "metadata": {},
   "source": [
    "## Timeseries Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f2a90927-442d-43f2-b350-323484ad8b00",
   "metadata": {},
   "outputs": [],
   "source": [
    "april_events[april_events.stringVal == 'Ever Given']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "01fffd5e-0d6c-4937-94f8-b151099a2c4d",
   "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>eventTime</th>\n",
       "      <th>stringVal</th>\n",
       "      <th>numMentions</th>\n",
       "      <th>polarity</th>\n",
       "      <th>magnitude</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-03-01</td>\n",
       "      <td>#NationalWorkoutBuddyDay</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-03-01</td>\n",
       "      <td>&amp;nbsp</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-03-01</td>\n",
       "      <td>'Homework</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.7</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-03-01</td>\n",
       "      <td>'T' For Texas</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60.6</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-03-01</td>\n",
       "      <td>...And Call Me Conrad</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11881467</th>\n",
       "      <td>2021-04-30</td>\n",
       "      <td>許書桓</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>-0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11881468</th>\n",
       "      <td>2021-04-30</td>\n",
       "      <td>連線商業銀行</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11881469</th>\n",
       "      <td>2021-04-30</td>\n",
       "      <td>陳潤秋</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>-0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11881470</th>\n",
       "      <td>2021-04-30</td>\n",
       "      <td>韓志正</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11881471</th>\n",
       "      <td>2021-04-30</td>\n",
       "      <td>도시락</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11881472 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          eventTime                 stringVal  numMentions  polarity  \\\n",
       "0        2021-03-01  #NationalWorkoutBuddyDay          5.0       0.0   \n",
       "1        2021-03-01                     &nbsp         51.0       0.0   \n",
       "2        2021-03-01                 'Homework         40.0       0.0   \n",
       "3        2021-03-01             'T' For Texas        132.0       0.0   \n",
       "4        2021-03-01     ...And Call Me Conrad          6.0       0.0   \n",
       "...             ...                       ...          ...       ...   \n",
       "11881467 2021-04-30                       許書桓          4.0       0.0   \n",
       "11881468 2021-04-30                    連線商業銀行          6.0       0.0   \n",
       "11881469 2021-04-30                       陳潤秋          1.0       0.0   \n",
       "11881470 2021-04-30                       韓志正          3.0       0.0   \n",
       "11881471 2021-04-30                       도시락          2.0       0.0   \n",
       "\n",
       "          magnitude  score  \n",
       "0               5.3   0.10  \n",
       "1               5.4   0.00  \n",
       "2               9.7   0.25  \n",
       "3              60.6   0.20  \n",
       "4              64.0   0.00  \n",
       "...             ...    ...  \n",
       "11881467        9.5  -0.40  \n",
       "11881468        1.6   0.00  \n",
       "11881469        2.7  -0.20  \n",
       "11881470        4.2   0.00  \n",
       "11881471        2.5   0.20  \n",
       "\n",
       "[11881472 rows x 6 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import datetime\n",
    "\n",
    "import pandas as pd\n",
    "def create_ts_chart_data(data, filter_val=[\"Ever Given\", \"Beverly Hills\", \"Donald Trump\"]):\n",
    "    if filter_val != None:\n",
    "    \tdata = data[data.stringVal.str.contains('|'.join(filter_val))]\n",
    "    data['eventTime'] = pd.to_datetime(data['eventTime']).dt.date\n",
    "    avg_pol = data.groupby(['eventTime', 'stringVal'])['polarity'].mean()\n",
    "    avg_mag = data.groupby(['eventTime', 'stringVal'])['magnitude'].mean()\n",
    "    avg_score = data.groupby(['eventTime', 'stringVal'])['score'].mean()\n",
    "    count = data.groupby(['eventTime', 'stringVal'])['numMentions'].sum()\n",
    "    return pd.DataFrame([count, avg_pol, avg_mag, avg_score]).transpose().reset_index()\n",
    "\n",
    "\n",
    "ts_data = create_ts_chart_data(april_events, filter_val=None)\n",
    "\n",
    "\n",
    "ts_data['eventTime'] = ts_data['eventTime'].astype('datetime64[ns]')\n",
    "ts_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "fdc796ee-9da7-4d07-a7cf-1ffe266e166c",
   "metadata": {},
   "outputs": [],
   "source": [
    "## A little more pre-processing - get top 50 events partitioned by date\n",
    "\n",
    "ts_data_top50 = ts_data.assign(rnk=ts_data\n",
    "                                   .groupby('eventTime')['numMentions'].rank(method='min', ascending=False)).query('rnk <= 25')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "353d1cd5-75e0-4702-bdc7-3775e608cb61",
   "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>eventTime</th>\n",
       "      <th>stringVal</th>\n",
       "      <th>numMentions</th>\n",
       "      <th>polarity</th>\n",
       "      <th>magnitude</th>\n",
       "      <th>score</th>\n",
       "      <th>rnk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1552</th>\n",
       "      <td>2021-03-01</td>\n",
       "      <td>AP</td>\n",
       "      <td>3.320628e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.157895</td>\n",
       "      <td>-0.157093</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14110</th>\n",
       "      <td>2021-03-01</td>\n",
       "      <td>Australia</td>\n",
       "      <td>1.797192e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.819173</td>\n",
       "      <td>-0.045149</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27586</th>\n",
       "      <td>2021-03-01</td>\n",
       "      <td>COVID-19</td>\n",
       "      <td>3.386235e+10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.823083</td>\n",
       "      <td>-0.064856</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28290</th>\n",
       "      <td>2021-03-01</td>\n",
       "      <td>California</td>\n",
       "      <td>2.386322e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.851965</td>\n",
       "      <td>-0.066189</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28911</th>\n",
       "      <td>2021-03-01</td>\n",
       "      <td>Canada</td>\n",
       "      <td>2.014735e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.858203</td>\n",
       "      <td>-0.045467</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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>11841118</th>\n",
       "      <td>2021-04-30</td>\n",
       "      <td>Senate</td>\n",
       "      <td>1.365383e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.029434</td>\n",
       "      <td>-0.172267</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11856785</th>\n",
       "      <td>2021-04-30</td>\n",
       "      <td>Texas</td>\n",
       "      <td>1.245798e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.734963</td>\n",
       "      <td>-0.081005</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11865737</th>\n",
       "      <td>2021-04-30</td>\n",
       "      <td>U.S.</td>\n",
       "      <td>9.243631e+10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.267872</td>\n",
       "      <td>-0.091457</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11866067</th>\n",
       "      <td>2021-04-30</td>\n",
       "      <td>UK</td>\n",
       "      <td>1.175500e+10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.513301</td>\n",
       "      <td>-0.078459</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11875086</th>\n",
       "      <td>2021-04-30</td>\n",
       "      <td>White House</td>\n",
       "      <td>1.010060e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.737631</td>\n",
       "      <td>-0.151839</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>1525 rows × 7 columns</p>\n",
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       "          eventTime    stringVal   numMentions  polarity  magnitude     score  \\\n",
       "1552     2021-03-01           AP  3.320628e+09       0.0  13.157895 -0.157093   \n",
       "14110    2021-03-01    Australia  1.797192e+09       0.0  11.819173 -0.045149   \n",
       "27586    2021-03-01     COVID-19  3.386235e+10       0.0  11.823083 -0.064856   \n",
       "28290    2021-03-01   California  2.386322e+09       0.0  13.851965 -0.066189   \n",
       "28911    2021-03-01       Canada  2.014735e+09       0.0  13.858203 -0.045467   \n",
       "...             ...          ...           ...       ...        ...       ...   \n",
       "11841118 2021-04-30       Senate  1.365383e+09       0.0  17.029434 -0.172267   \n",
       "11856785 2021-04-30        Texas  1.245798e+09       0.0  16.734963 -0.081005   \n",
       "11865737 2021-04-30         U.S.  9.243631e+10       0.0  14.267872 -0.091457   \n",
       "11866067 2021-04-30           UK  1.175500e+10       0.0  14.513301 -0.078459   \n",
       "11875086 2021-04-30  White House  1.010060e+09       0.0  18.737631 -0.151839   \n",
       "\n",
       "           rnk  \n",
       "1552       9.0  \n",
       "14110     16.0  \n",
       "27586      2.0  \n",
       "28290     12.0  \n",
       "28911     14.0  \n",
       "...        ...  \n",
       "11841118  18.0  \n",
       "11856785  21.0  \n",
       "11865737   1.0  \n",
       "11866067   4.0  \n",
       "11875086  24.0  \n",
       "\n",
       "[1525 rows x 7 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "ts_data_top50"
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\"rnk\": 19.0}, {\"eventTime\": \"2021-04-28T00:00:00\", \"stringVal\": \"U.S.\", \"numMentions\": 102886843424.0, \"polarity\": 0.0, \"magnitude\": 13.41595060893099, \"score\": -0.08895889715832205, \"rnk\": 1.0}, {\"eventTime\": \"2021-04-28T00:00:00\", \"stringVal\": \"UK\", \"numMentions\": 13023230562.0, \"polarity\": 0.0, \"magnitude\": 13.2833497644868, \"score\": -0.050542228064410125, \"rnk\": 4.0}, {\"eventTime\": \"2021-04-28T00:00:00\", \"stringVal\": \"White House\", \"numMentions\": 1017220931.0, \"polarity\": 0.0, \"magnitude\": 18.54500670241287, \"score\": -0.13039544235924933, \"rnk\": 23.0}, {\"eventTime\": \"2021-04-29T00:00:00\", \"stringVal\": \"AP\", \"numMentions\": 3095049564.0, \"polarity\": 0.0, \"magnitude\": 13.586775542720574, \"score\": -0.17386974706233818, \"rnk\": 10.0}, {\"eventTime\": \"2021-04-29T00:00:00\", \"stringVal\": \"Australia\", \"numMentions\": 1607610889.0, \"polarity\": 0.0, \"magnitude\": 12.252547260493431, \"score\": -0.026529958346683754, \"rnk\": 18.0}, {\"eventTime\": \"2021-04-29T00:00:00\", \"stringVal\": \"COVID-19\", \"numMentions\": 39447088236.0, \"polarity\": 0.0, \"magnitude\": 13.212271524401503, \"score\": -0.0571296695181242, \"rnk\": 2.0}, {\"eventTime\": \"2021-04-29T00:00:00\", \"stringVal\": \"California\", \"numMentions\": 2654354267.0, \"polarity\": 0.0, \"magnitude\": 13.374970691676436, \"score\": -0.04579914028917546, \"rnk\": 13.0}, {\"eventTime\": \"2021-04-29T00:00:00\", \"stringVal\": \"Canada\", \"numMentions\": 2063296109.0, \"polarity\": 0.0, \"magnitude\": 13.804172238566462, \"score\": -0.028109120085584378, \"rnk\": 15.0}, {\"eventTime\": \"2021-04-29T00:00:00\", \"stringVal\": \"China\", \"numMentions\": 6631502717.0, \"polarity\": 0.0, \"magnitude\": 15.747018303483568, \"score\": -0.07400118086990749, \"rnk\": 5.0}, {\"eventTime\": \"2021-04-29T00:00:00\", \"stringVal\": \"Congress\", \"numMentions\": 1545339856.0, \"polarity\": 0.0, \"magnitude\": 17.789478171896317, \"score\": -0.11843451568894953, \"rnk\": 19.0}, {\"eventTime\": \"2021-04-29T00:00:00\", \"stringVal\": \"Democrats\", \"numMentions\": 4051897028.0, \"polarity\": 0.0, \"magnitude\": 19.524332897928716, \"score\": -0.17178578092927785, \"rnk\": 9.0}, {\"eventTime\": \"2021-04-29T00:00:00\", \"stringVal\": \"Donald Trump\", \"numMentions\": 5814922250.0, \"polarity\": 0.0, \"magnitude\": 15.822325581395349, \"score\": -0.1935813953488372, \"rnk\": 7.0}, {\"eventTime\": \"2021-04-29T00:00:00\", \"stringVal\": \"Europe\", \"numMentions\": 2905668571.0, \"polarity\": 0.0, \"magnitude\": 16.548601472134596, \"score\": -0.02121976866456362, \"rnk\": 11.0}, {\"eventTime\": \"2021-04-29T00:00:00\", \"stringVal\": \"European Union\", \"numMentions\": 1149473545.0, \"polarity\": 0.0, \"magnitude\": 18.760585490029698, \"score\": -0.12897751378871447, \"rnk\": 20.0}, {\"eventTime\": \"2021-04-29T00:00:00\", \"stringVal\": \"Facebook\", \"numMentions\": 2052381597.0, \"polarity\": 0.0, \"magnitude\": 13.496682464454977, \"score\": -0.034958248702324536, \"rnk\": 16.0}, {\"eventTime\": \"2021-04-29T00:00:00\", \"stringVal\": \"George Floyd\", \"numMentions\": 1068066374.0, \"polarity\": 0.0, \"magnitude\": 29.65659978880676, \"score\": -0.18521647307286168, \"rnk\": 25.0}, {\"eventTime\": \"2021-04-29T00:00:00\", \"stringVal\": \"Germany\", \"numMentions\": 1100386914.0, \"polarity\": 0.0, \"magnitude\": 11.94661679700782, \"score\": -0.0638898333900034, \"rnk\": 23.0}, {\"eventTime\": \"2021-04-29T00:00:00\", \"stringVal\": \"India\", \"numMentions\": 4577938529.0, \"polarity\": 0.0, \"magnitude\": 14.236024371667938, \"score\": -0.07265803503427266, \"rnk\": 8.0}, {\"eventTime\": \"2021-04-29T00:00:00\", \"stringVal\": \"Joe Biden\", \"numMentions\": 18066696960.0, \"polarity\": 0.0, \"magnitude\": 14.991414459857081, \"score\": -0.14062631357713326, \"rnk\": 3.0}, {\"eventTime\": \"2021-04-29T00:00:00\", \"stringVal\": \"London\", \"numMentions\": 1074438596.0, \"polarity\": 0.0, \"magnitude\": 12.54966755319149, \"score\": -0.03660239361702128, \"rnk\": 24.0}, {\"eventTime\": \"2021-04-29T00:00:00\", \"stringVal\": \"New York\", \"numMentions\": 2753454606.0, \"polarity\": 0.0, \"magnitude\": 14.557525773195875, \"score\": -0.04984932593180016, \"rnk\": 12.0}, {\"eventTime\": \"2021-04-29T00:00:00\", \"stringVal\": \"Republican\", \"numMentions\": 6188155334.0, \"polarity\": 0.0, \"magnitude\": 18.405120576260572, \"score\": -0.17339492640150328, \"rnk\": 6.0}, {\"eventTime\": \"2021-04-29T00:00:00\", \"stringVal\": \"Russia\", \"numMentions\": 1799917165.0, \"polarity\": 0.0, \"magnitude\": 19.158604977216964, \"score\": -0.13957237995092883, \"rnk\": 17.0}, {\"eventTime\": \"2021-04-29T00:00:00\", \"stringVal\": \"Senate\", \"numMentions\": 2133346267.0, \"polarity\": 0.0, \"magnitude\": 19.637094594594593, \"score\": -0.15495495495495495, \"rnk\": 14.0}, {\"eventTime\": \"2021-04-29T00:00:00\", \"stringVal\": \"Texas\", \"numMentions\": 1141221659.0, \"polarity\": 0.0, \"magnitude\": 21.127556200145033, \"score\": -0.06102248005801306, \"rnk\": 21.0}, {\"eventTime\": \"2021-04-29T00:00:00\", \"stringVal\": \"U.S.\", \"numMentions\": 103613420845.0, \"polarity\": 0.0, \"magnitude\": 13.947096367835396, \"score\": -0.08398488347680035, \"rnk\": 1.0}, {\"eventTime\": \"2021-04-29T00:00:00\", \"stringVal\": \"UK\", \"numMentions\": 11221462548.0, \"polarity\": 0.0, \"magnitude\": 11.389422832443428, \"score\": -0.03778286295448767, \"rnk\": 4.0}, {\"eventTime\": \"2021-04-29T00:00:00\", \"stringVal\": \"White House\", \"numMentions\": 1118499713.0, \"polarity\": 0.0, \"magnitude\": 21.84056690033526, \"score\": -0.1450472416946053, \"rnk\": 22.0}, {\"eventTime\": \"2021-04-30T00:00:00\", \"stringVal\": \"AP\", \"numMentions\": 3355521509.0, \"polarity\": 0.0, \"magnitude\": 14.253461027877869, \"score\": -0.14551488716100894, \"rnk\": 9.0}, {\"eventTime\": \"2021-04-30T00:00:00\", \"stringVal\": \"Australia\", \"numMentions\": 1408727203.0, \"polarity\": 0.0, \"magnitude\": 15.429945155393053, \"score\": -0.042742230347349176, \"rnk\": 17.0}, {\"eventTime\": \"2021-04-30T00:00:00\", \"stringVal\": \"COVID-19\", \"numMentions\": 40614888474.0, \"polarity\": 0.0, \"magnitude\": 12.691587320718332, \"score\": -0.06166686754248524, \"rnk\": 2.0}, {\"eventTime\": \"2021-04-30T00:00:00\", \"stringVal\": \"California\", \"numMentions\": 2541635094.0, \"polarity\": 0.0, \"magnitude\": 15.138709677419353, \"score\": -0.037790935075541036, \"rnk\": 13.0}, {\"eventTime\": \"2021-04-30T00:00:00\", \"stringVal\": \"Canada\", \"numMentions\": 2042326531.0, \"polarity\": 0.0, \"magnitude\": 16.370548500405295, \"score\": -0.030478249121858956, \"rnk\": 14.0}, {\"eventTime\": \"2021-04-30T00:00:00\", \"stringVal\": \"China\", \"numMentions\": 5990670711.0, \"polarity\": 0.0, \"magnitude\": 15.928157665505228, \"score\": -0.0804006968641115, \"rnk\": 5.0}, {\"eventTime\": \"2021-04-30T00:00:00\", \"stringVal\": \"Democrats\", \"numMentions\": 2805857412.0, \"polarity\": 0.0, \"magnitude\": 18.74845055241175, \"score\": -0.1933980059283212, \"rnk\": 11.0}, {\"eventTime\": \"2021-04-30T00:00:00\", \"stringVal\": \"Donald Trump\", \"numMentions\": 4889943920.0, \"polarity\": 0.0, \"magnitude\": 16.990309734513275, \"score\": -0.2093141592920354, \"rnk\": 6.0}, {\"eventTime\": \"2021-04-30T00:00:00\", \"stringVal\": \"Europe\", \"numMentions\": 2835991200.0, \"polarity\": 0.0, \"magnitude\": 19.206831896551723, \"score\": -0.0603448275862069, \"rnk\": 10.0}, {\"eventTime\": \"2021-04-30T00:00:00\", \"stringVal\": \"European Union\", \"numMentions\": 1339183010.0, \"polarity\": 0.0, \"magnitude\": 17.40702840495266, \"score\": -0.1697742170429716, \"rnk\": 19.0}, {\"eventTime\": \"2021-04-30T00:00:00\", \"stringVal\": \"Facebook\", \"numMentions\": 1938900782.0, \"polarity\": 0.0, \"magnitude\": 15.844768275203057, \"score\": -0.05573339703774487, \"rnk\": 15.0}, {\"eventTime\": \"2021-04-30T00:00:00\", \"stringVal\": \"Florida\", \"numMentions\": 1251240548.0, \"polarity\": 0.0, \"magnitude\": 12.894284143995604, \"score\": -0.0660071448200055, \"rnk\": 20.0}, {\"eventTime\": \"2021-04-30T00:00:00\", \"stringVal\": \"Germany\", \"numMentions\": 994501332.0, \"polarity\": 0.0, \"magnitude\": 17.25451467268623, \"score\": -0.046802106847253576, \"rnk\": 25.0}, {\"eventTime\": \"2021-04-30T00:00:00\", \"stringVal\": \"India\", \"numMentions\": 4739355772.0, \"polarity\": 0.0, \"magnitude\": 13.62383314957736, \"score\": -0.0828555678059537, \"rnk\": 7.0}, {\"eventTime\": \"2021-04-30T00:00:00\", \"stringVal\": \"Israel\", \"numMentions\": 1076411622.0, \"polarity\": 0.0, \"magnitude\": 18.856060606060606, \"score\": -0.18226863226863227, \"rnk\": 23.0}, {\"eventTime\": \"2021-04-30T00:00:00\", \"stringVal\": \"Joe Biden\", \"numMentions\": 13242456000.0, \"polarity\": 0.0, \"magnitude\": 13.875885304659498, \"score\": -0.16503225806451613, \"rnk\": 3.0}, {\"eventTime\": \"2021-04-30T00:00:00\", \"stringVal\": \"London\", \"numMentions\": 1212752603.0, \"polarity\": 0.0, \"magnitude\": 16.73874269005848, \"score\": -0.0834795321637427, \"rnk\": 22.0}, {\"eventTime\": \"2021-04-30T00:00:00\", \"stringVal\": \"New York\", \"numMentions\": 2706003336.0, \"polarity\": 0.0, \"magnitude\": 15.815708182714426, \"score\": -0.04311534669733816, \"rnk\": 12.0}, {\"eventTime\": \"2021-04-30T00:00:00\", \"stringVal\": \"Republican\", \"numMentions\": 4562141452.0, \"polarity\": 0.0, \"magnitude\": 17.342034834324554, \"score\": -0.2033984706881903, \"rnk\": 8.0}, {\"eventTime\": \"2021-04-30T00:00:00\", \"stringVal\": \"Russia\", \"numMentions\": 1582185249.0, \"polarity\": 0.0, \"magnitude\": 21.2777423214998, \"score\": -0.16485839648982847, \"rnk\": 16.0}, {\"eventTime\": \"2021-04-30T00:00:00\", \"stringVal\": \"Senate\", \"numMentions\": 1365382847.0, \"polarity\": 0.0, \"magnitude\": 17.0294342242277, \"score\": -0.17226657410621313, \"rnk\": 18.0}, {\"eventTime\": \"2021-04-30T00:00:00\", \"stringVal\": \"Texas\", \"numMentions\": 1245798398.0, \"polarity\": 0.0, \"magnitude\": 16.734963406520293, \"score\": -0.08100465735196274, \"rnk\": 21.0}, {\"eventTime\": \"2021-04-30T00:00:00\", \"stringVal\": \"U.S.\", \"numMentions\": 92436310698.0, \"polarity\": 0.0, \"magnitude\": 14.26787159935988, \"score\": -0.09145721547585428, \"rnk\": 1.0}, {\"eventTime\": \"2021-04-30T00:00:00\", \"stringVal\": \"UK\", \"numMentions\": 11755002720.0, \"polarity\": 0.0, \"magnitude\": 14.513300970873788, \"score\": -0.07845873786407767, \"rnk\": 4.0}, {\"eventTime\": \"2021-04-30T00:00:00\", \"stringVal\": \"White House\", \"numMentions\": 1010059805.0, \"polarity\": 0.0, \"magnitude\": 18.737630779615255, \"score\": -0.15183935200809992, \"rnk\": 24.0}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.VConcatChart(...)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alt.data_transformers.disable_max_rows()\n",
    "\n",
    "avg_magnitude = alt.Chart(ts_data_top50).mark_line().encode(\n",
    "    x={\"field\": \"eventTime\", 'type': 'temporal'},\n",
    "    y={'field': 'magnitude', 'type': 'quantitative'},\n",
    "    tooltip=['eventTime','magnitude', 'stringVal'],\n",
    "    color='stringVal:N',\n",
    ").properties(\n",
    "        title=f\"Average Magnitude Score by Entity\",\n",
    "        height=HEIGHT,\n",
    "        width=WIDTH\n",
    "    ).interactive()\n",
    "\n",
    "num_mentions = alt.Chart(ts_data_top50).mark_line().encode(\n",
    "    x={\"field\": \"eventTime\", 'type': 'temporal'},\n",
    "    y={'field': 'numMentions', 'type': 'quantitative'},\n",
    "    tooltip=['eventTime','numMentions', 'stringVal'],\n",
    "    color='stringVal:N',\n",
    ").properties(\n",
    "        title=f\"Number of Mentions by Entity\",\n",
    "        height=HEIGHT,\n",
    "        width=WIDTH\n",
    "    ).interactive()\n",
    "\n",
    "avg_polarity = alt.Chart(ts_data_top50).mark_line().encode(\n",
    "    x={\"field\": \"eventTime\", 'type': 'temporal'},\n",
    "    y={'field': 'polarity', 'type': 'quantitative'},\n",
    "    tooltip=['eventTime','polarity', 'stringVal'],\n",
    "    color='stringVal:N',\n",
    ").properties(\n",
    "        title=f\"Average Polarity by Entity\",\n",
    "        height=HEIGHT,\n",
    "        width=WIDTH\n",
    "    ).interactive()\n",
    "\n",
    "avg_score = alt.Chart(ts_data_top50).mark_line().encode(\n",
    "    x={\"field\": \"eventTime\", 'type': 'temporal'},\n",
    "    y={'field': 'score', 'type': 'quantitative'},\n",
    "    tooltip=['eventTime','score', 'stringVal'],\n",
    "    color='stringVal:N',\n",
    ").properties(\n",
    "        title=f\"Avgerage Score by Entity\",\n",
    "        height=HEIGHT,\n",
    "        width=WIDTH\n",
    "    ).interactive()\n",
    "\n",
    "alt.vconcat(\n",
    "    alt.hconcat(num_mentions, avg_magnitude),\n",
    "    alt.hconcat(avg_polarity, avg_score),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "737f981f-ed20-4753-b15a-91be731709d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  import sys\n"
     ]
    }
   ],
   "source": [
    "ts_data = create_ts_chart_data(april_events)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "86ad5bed-5aea-44fc-b489-7b3e22777ca2",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Object of type date is not JSON serializable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m~/.local/lib/python3.7/site-packages/altair/vegalite/v4/api.py\u001b[0m in \u001b[0;36mto_dict\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    372\u001b[0m         \u001b[0mcopy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdeep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    373\u001b[0m         \u001b[0moriginal_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"data\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mUndefined\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 374\u001b[0;31m         \u001b[0mcopy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_prepare_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moriginal_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    375\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    376\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0moriginal_data\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mUndefined\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.7/site-packages/altair/vegalite/v4/api.py\u001b[0m in \u001b[0;36m_prepare_data\u001b[0;34m(data, context)\u001b[0m\n\u001b[1;32m     95\u001b[0m     \u001b[0;31m# consolidate inline data to top-level datasets\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     96\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcontext\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mdata_transformers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconsolidate_datasets\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 97\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_consolidate_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     98\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     99\u001b[0m     \u001b[0;31m# if data is still not a recognized type, then return\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.7/site-packages/altair/vegalite/v4/api.py\u001b[0m in \u001b[0;36m_consolidate_data\u001b[0;34m(data, context)\u001b[0m\n\u001b[1;32m     62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mvalues\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mUndefined\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m         \u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_dataset_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNamedData\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m         \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"datasets\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.7/site-packages/altair/vegalite/v4/api.py\u001b[0m in \u001b[0;36m_dataset_name\u001b[0;34m(values)\u001b[0m\n\u001b[1;32m     35\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mvalues\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m\"empty\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 37\u001b[0;31m     \u001b[0mvalues_json\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdumps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msort_keys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     38\u001b[0m     \u001b[0mhsh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhashlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmd5\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues_json\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhexdigest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0;34m\"data-\"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mhsh\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/json/__init__.py\u001b[0m in \u001b[0;36mdumps\u001b[0;34m(obj, skipkeys, ensure_ascii, check_circular, allow_nan, cls, indent, separators, default, sort_keys, **kw)\u001b[0m\n\u001b[1;32m    236\u001b[0m         \u001b[0mcheck_circular\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcheck_circular\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_nan\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mallow_nan\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindent\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    237\u001b[0m         \u001b[0mseparators\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mseparators\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdefault\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdefault\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msort_keys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msort_keys\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 238\u001b[0;31m         **kw).encode(obj)\n\u001b[0m\u001b[1;32m    239\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    240\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/json/encoder.py\u001b[0m in \u001b[0;36mencode\u001b[0;34m(self, o)\u001b[0m\n\u001b[1;32m    197\u001b[0m         \u001b[0;31m# exceptions aren't as detailed.  The list call should be roughly\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m         \u001b[0;31m# equivalent to the PySequence_Fast that ''.join() would do.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 199\u001b[0;31m         \u001b[0mchunks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miterencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_one_shot\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    200\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchunks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    201\u001b[0m             \u001b[0mchunks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchunks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/json/encoder.py\u001b[0m in \u001b[0;36miterencode\u001b[0;34m(self, o, _one_shot)\u001b[0m\n\u001b[1;32m    255\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkey_separator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem_separator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_keys\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    256\u001b[0m                 self.skipkeys, _one_shot)\n\u001b[0;32m--> 257\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_iterencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    258\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    259\u001b[0m def _make_iterencode(markers, _default, _encoder, _indent, _floatstr,\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/json/encoder.py\u001b[0m in \u001b[0;36mdefault\u001b[0;34m(self, o)\u001b[0m\n\u001b[1;32m    177\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    178\u001b[0m         \"\"\"\n\u001b[0;32m--> 179\u001b[0;31m         raise TypeError(f'Object of type {o.__class__.__name__} '\n\u001b[0m\u001b[1;32m    180\u001b[0m                         f'is not JSON serializable')\n\u001b[1;32m    181\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: Object of type date is not JSON serializable"
     ]
    },
    {
     "data": {
      "text/plain": [
       "alt.VConcatChart(...)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "avg_magnitude = alt.Chart(ts_data).mark_line().encode(\n",
    "    x={\"field\": \"eventTime\", 'type': 'temporal'},\n",
    "    y={'field': 'magnitude', 'type': 'quantitative'},\n",
    "    tooltip=['eventTime','magnitude', 'stringVal'],\n",
    "    color='stringVal:N',\n",
    ").properties(\n",
    "        title=f\"Average Magnitude Score by Entity\",\n",
    "        height=HEIGHT,\n",
    "        width=WIDTH\n",
    "    )\n",
    "\n",
    "num_mentions = alt.Chart(ts_data).mark_line().encode(\n",
    "    x={\"field\": \"eventTime\", 'type': 'temporal'},\n",
    "    y={'field': 'numMentions', 'type': 'quantitative'},\n",
    "    tooltip=['eventTime','numMentions', 'stringVal'],\n",
    "    color='stringVal:N',\n",
    ").properties(\n",
    "        title=f\"Number of Mentions by Entity\",\n",
    "        height=HEIGHT,\n",
    "        width=WIDTH\n",
    "    )\n",
    "\n",
    "avg_polarity = alt.Chart(ts_data).mark_line().encode(\n",
    "    x={\"field\": \"eventTime\", 'type': 'temporal'},\n",
    "    y={'field': 'polarity', 'type': 'quantitative'},\n",
    "    tooltip=['eventTime','polarity', 'stringVal'],\n",
    "    color='stringVal:N',\n",
    ").properties(\n",
    "        title=f\"Average Polarity by Entity\",\n",
    "        height=HEIGHT,\n",
    "        width=WIDTH\n",
    "    )\n",
    "\n",
    "avg_score = alt.Chart(ts_data).mark_line().encode(\n",
    "    x={\"field\": \"eventTime\", 'type': 'temporal'},\n",
    "    y={'field': 'score', 'type': 'quantitative'},\n",
    "    tooltip=['eventTime','score', 'stringVal'],\n",
    "    color='stringVal:N',\n",
    ").properties(\n",
    "        title=f\"Avgerage Score by Entity\",\n",
    "        height=HEIGHT,\n",
    "        width=WIDTH\n",
    "    )\n",
    "\n",
    "alt.vconcat(\n",
    "    alt.hconcat(num_mentions, avg_magnitude),\n",
    "    alt.hconcat(avg_polarity, avg_score),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "97c4e50c-e7e4-436c-af30-7690c8552662",
   "metadata": {},
   "outputs": [],
   "source": [
    "zz=april_events[april_events['stringVal'].str.contains(\"z\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "43073b7a-2978-418f-ad41-954cb7829487",
   "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>groupId</th>\n",
       "      <th>name</th>\n",
       "      <th>stringVal</th>\n",
       "      <th>numMentions</th>\n",
       "      <th>avgSalience</th>\n",
       "      <th>eventTime</th>\n",
       "      <th>polarity</th>\n",
       "      <th>magnitude</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>-3105079244623794583</td>\n",
       "      <td>EntityPERSON</td>\n",
       "      <td>Queen Elizabeth</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.880214</td>\n",
       "      <td>2021-03-01T07:16:47+00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>6107597291347116114</td>\n",
       "      <td>EntityPERSON</td>\n",
       "      <td>Queen Elizabeth</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.880214</td>\n",
       "      <td>2021-03-01T14:33:18+00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>-1480449247246516428</td>\n",
       "      <td>EntityPERSON</td>\n",
       "      <td>Queen Elizabeth</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.880214</td>\n",
       "      <td>2021-03-01T03:32:30+00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>8228111562446977735</td>\n",
       "      <td>EntityPERSON</td>\n",
       "      <td>Queen Elizabeth</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.880214</td>\n",
       "      <td>2021-03-01T12:32:36+00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>-5462915138951645714</td>\n",
       "      <td>EntityPERSON</td>\n",
       "      <td>Queen Elizabeth</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.880214</td>\n",
       "      <td>2021-03-01T08:02:55+00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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>4328367</th>\n",
       "      <td>-5085596850111108760</td>\n",
       "      <td>EntityORGANIZATION</td>\n",
       "      <td>World Health Organization</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.851978</td>\n",
       "      <td>2021-03-18T08:19:06+00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4328368</th>\n",
       "      <td>-35215210535195339</td>\n",
       "      <td>EntityORGANIZATION</td>\n",
       "      <td>World Health Organization</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.851978</td>\n",
       "      <td>2021-03-18T01:17:51+00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>-0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4328369</th>\n",
       "      <td>2322684724690910539</td>\n",
       "      <td>EntityORGANIZATION</td>\n",
       "      <td>World Health Organization</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.851978</td>\n",
       "      <td>2021-03-18T22:32:15+00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4328370</th>\n",
       "      <td>8993312145740526654</td>\n",
       "      <td>EntityORGANIZATION</td>\n",
       "      <td>World Health Organization</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.851978</td>\n",
       "      <td>2021-03-18T17:01:52+00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>-0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4328371</th>\n",
       "      <td>1561300274510195713</td>\n",
       "      <td>EntityORGANIZATION</td>\n",
       "      <td>World Health Organization</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.851978</td>\n",
       "      <td>2021-03-18T20:17:52+00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>-0.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>141077 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     groupId                name                  stringVal  \\\n",
       "31      -3105079244623794583        EntityPERSON            Queen Elizabeth   \n",
       "33       6107597291347116114        EntityPERSON            Queen Elizabeth   \n",
       "35      -1480449247246516428        EntityPERSON            Queen Elizabeth   \n",
       "37       8228111562446977735        EntityPERSON            Queen Elizabeth   \n",
       "39      -5462915138951645714        EntityPERSON            Queen Elizabeth   \n",
       "...                      ...                 ...                        ...   \n",
       "4328367 -5085596850111108760  EntityORGANIZATION  World Health Organization   \n",
       "4328368   -35215210535195339  EntityORGANIZATION  World Health Organization   \n",
       "4328369  2322684724690910539  EntityORGANIZATION  World Health Organization   \n",
       "4328370  8993312145740526654  EntityORGANIZATION  World Health Organization   \n",
       "4328371  1561300274510195713  EntityORGANIZATION  World Health Organization   \n",
       "\n",
       "         numMentions  avgSalience                  eventTime  polarity  \\\n",
       "31               6.0     0.880214  2021-03-01T07:16:47+00:00       0.0   \n",
       "33               6.0     0.880214  2021-03-01T14:33:18+00:00       0.0   \n",
       "35               6.0     0.880214  2021-03-01T03:32:30+00:00       0.0   \n",
       "37               6.0     0.880214  2021-03-01T12:32:36+00:00       0.0   \n",
       "39               6.0     0.880214  2021-03-01T08:02:55+00:00       0.0   \n",
       "...              ...          ...                        ...       ...   \n",
       "4328367          4.0     0.851978  2021-03-18T08:19:06+00:00       0.0   \n",
       "4328368          4.0     0.851978  2021-03-18T01:17:51+00:00       0.0   \n",
       "4328369          4.0     0.851978  2021-03-18T22:32:15+00:00       0.0   \n",
       "4328370          4.0     0.851978  2021-03-18T17:01:52+00:00       0.0   \n",
       "4328371          4.0     0.851978  2021-03-18T20:17:52+00:00       0.0   \n",
       "\n",
       "         magnitude  score  \n",
       "31             2.0    0.2  \n",
       "33             2.0    0.2  \n",
       "35             2.0    0.2  \n",
       "37             2.0    0.2  \n",
       "39             2.0    0.2  \n",
       "...            ...    ...  \n",
       "4328367        1.8    0.0  \n",
       "4328368        1.8   -0.2  \n",
       "4328369        1.8    0.0  \n",
       "4328370        1.8   -0.3  \n",
       "4328371        1.8   -0.3  \n",
       "\n",
       "[141077 rows x 9 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "42cc2298-4986-4075-bb15-f750bee75a1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Get the top counts for stuff\n",
    "\n",
    "entity_count = zz.groupby(\"stringVal\").count()\n",
    "top_n = entity_count.sort_values('stringVal', ascending=False)[0:20]\n",
    "other_count = entity_count.sort_values('groupId', ascending=False)['groupId'][21::].sum()\n",
    "top_n = top_n.reset_index()\n",
    "top_n = top_n.append({'stringVal': 'Other', 'groupId': other_count}, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "220a6ae3-cb5c-42ed-b575-f48f13915b4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#debug\n",
    "data = zz\n",
    "### Get the top counts for stuff\n",
    "entity_count = data.groupby('stringVal').count()\n",
    "top_n = entity_count.sort_values('groupId', ascending=False)[0:20]\n",
    "other_count = entity_count.sort_values('groupId', ascending=False)['groupId'][20+1::].sum()\n",
    "top_n = top_n.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "9e6f4206-ea15-4b3f-928a-1c5e247c1da6",
   "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>stringVal</th>\n",
       "      <th>groupId</th>\n",
       "      <th>name</th>\n",
       "      <th>numMentions</th>\n",
       "      <th>avgSalience</th>\n",
       "      <th>eventTime</th>\n",
       "      <th>polarity</th>\n",
       "      <th>magnitude</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>World Health Organization</td>\n",
       "      <td>43845</td>\n",
       "      <td>43845</td>\n",
       "      <td>43845</td>\n",
       "      <td>43845</td>\n",
       "      <td>43845</td>\n",
       "      <td>43845</td>\n",
       "      <td>43845</td>\n",
       "      <td>43845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Amazon</td>\n",
       "      <td>38094</td>\n",
       "      <td>38094</td>\n",
       "      <td>38094</td>\n",
       "      <td>38094</td>\n",
       "      <td>38094</td>\n",
       "      <td>38094</td>\n",
       "      <td>38094</td>\n",
       "      <td>38094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Queen Elizabeth</td>\n",
       "      <td>12228</td>\n",
       "      <td>12228</td>\n",
       "      <td>12228</td>\n",
       "      <td>12228</td>\n",
       "      <td>12228</td>\n",
       "      <td>12228</td>\n",
       "      <td>12228</td>\n",
       "      <td>12228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ted Cruz</td>\n",
       "      <td>5963</td>\n",
       "      <td>5963</td>\n",
       "      <td>5963</td>\n",
       "      <td>5963</td>\n",
       "      <td>5963</td>\n",
       "      <td>5963</td>\n",
       "      <td>5963</td>\n",
       "      <td>5963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Southern Azerbaijan</td>\n",
       "      <td>4478</td>\n",
       "      <td>4478</td>\n",
       "      <td>4478</td>\n",
       "      <td>4478</td>\n",
       "      <td>4478</td>\n",
       "      <td>4478</td>\n",
       "      <td>4478</td>\n",
       "      <td>4478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Jennifer Lopez</td>\n",
       "      <td>3578</td>\n",
       "      <td>3578</td>\n",
       "      <td>3578</td>\n",
       "      <td>3578</td>\n",
       "      <td>3578</td>\n",
       "      <td>3578</td>\n",
       "      <td>3578</td>\n",
       "      <td>3578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Alexandria Ocasio-Cortez</td>\n",
       "      <td>3462</td>\n",
       "      <td>3462</td>\n",
       "      <td>3462</td>\n",
       "      <td>3462</td>\n",
       "      <td>3462</td>\n",
       "      <td>3462</td>\n",
       "      <td>3462</td>\n",
       "      <td>3462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Riz Ahmed</td>\n",
       "      <td>2720</td>\n",
       "      <td>2720</td>\n",
       "      <td>2720</td>\n",
       "      <td>2720</td>\n",
       "      <td>2720</td>\n",
       "      <td>2720</td>\n",
       "      <td>2720</td>\n",
       "      <td>2720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Nazanin Zaghari-Ratcliffe</td>\n",
       "      <td>1808</td>\n",
       "      <td>1808</td>\n",
       "      <td>1808</td>\n",
       "      <td>1808</td>\n",
       "      <td>1808</td>\n",
       "      <td>1808</td>\n",
       "      <td>1808</td>\n",
       "      <td>1808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Arnold Schwarzenegger</td>\n",
       "      <td>1690</td>\n",
       "      <td>1690</td>\n",
       "      <td>1690</td>\n",
       "      <td>1690</td>\n",
       "      <td>1690</td>\n",
       "      <td>1690</td>\n",
       "      <td>1690</td>\n",
       "      <td>1690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Selena Gomez</td>\n",
       "      <td>1553</td>\n",
       "      <td>1553</td>\n",
       "      <td>1553</td>\n",
       "      <td>1553</td>\n",
       "      <td>1553</td>\n",
       "      <td>1553</td>\n",
       "      <td>1553</td>\n",
       "      <td>1553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Matt Gaetz</td>\n",
       "      <td>1524</td>\n",
       "      <td>1524</td>\n",
       "      <td>1524</td>\n",
       "      <td>1524</td>\n",
       "      <td>1524</td>\n",
       "      <td>1524</td>\n",
       "      <td>1524</td>\n",
       "      <td>1524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Benny Gantz</td>\n",
       "      <td>1477</td>\n",
       "      <td>1477</td>\n",
       "      <td>1477</td>\n",
       "      <td>1477</td>\n",
       "      <td>1477</td>\n",
       "      <td>1477</td>\n",
       "      <td>1477</td>\n",
       "      <td>1477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Lizzo</td>\n",
       "      <td>1303</td>\n",
       "      <td>1303</td>\n",
       "      <td>1303</td>\n",
       "      <td>1303</td>\n",
       "      <td>1303</td>\n",
       "      <td>1303</td>\n",
       "      <td>1303</td>\n",
       "      <td>1303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Luiz Inácio Lula da Silva</td>\n",
       "      <td>1036</td>\n",
       "      <td>1036</td>\n",
       "      <td>1036</td>\n",
       "      <td>1036</td>\n",
       "      <td>1036</td>\n",
       "      <td>1036</td>\n",
       "      <td>1036</td>\n",
       "      <td>1036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Ngozi Okonjo-Iweala</td>\n",
       "      <td>1019</td>\n",
       "      <td>1019</td>\n",
       "      <td>1019</td>\n",
       "      <td>1019</td>\n",
       "      <td>1019</td>\n",
       "      <td>1019</td>\n",
       "      <td>1019</td>\n",
       "      <td>1019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Nicolas Sarkozy</td>\n",
       "      <td>998</td>\n",
       "      <td>998</td>\n",
       "      <td>998</td>\n",
       "      <td>998</td>\n",
       "      <td>998</td>\n",
       "      <td>998</td>\n",
       "      <td>998</td>\n",
       "      <td>998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Wolfgang Amadeus Mozart</td>\n",
       "      <td>902</td>\n",
       "      <td>902</td>\n",
       "      <td>902</td>\n",
       "      <td>902</td>\n",
       "      <td>902</td>\n",
       "      <td>902</td>\n",
       "      <td>902</td>\n",
       "      <td>902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Elizabeth Olsen</td>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Nowruz</td>\n",
       "      <td>839</td>\n",
       "      <td>839</td>\n",
       "      <td>839</td>\n",
       "      <td>839</td>\n",
       "      <td>839</td>\n",
       "      <td>839</td>\n",
       "      <td>839</td>\n",
       "      <td>839</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    stringVal  groupId   name  numMentions  avgSalience  \\\n",
       "0   World Health Organization    43845  43845        43845        43845   \n",
       "1                      Amazon    38094  38094        38094        38094   \n",
       "2             Queen Elizabeth    12228  12228        12228        12228   \n",
       "3                    Ted Cruz     5963   5963         5963         5963   \n",
       "4         Southern Azerbaijan     4478   4478         4478         4478   \n",
       "5              Jennifer Lopez     3578   3578         3578         3578   \n",
       "6    Alexandria Ocasio-Cortez     3462   3462         3462         3462   \n",
       "7                   Riz Ahmed     2720   2720         2720         2720   \n",
       "8   Nazanin Zaghari-Ratcliffe     1808   1808         1808         1808   \n",
       "9       Arnold Schwarzenegger     1690   1690         1690         1690   \n",
       "10               Selena Gomez     1553   1553         1553         1553   \n",
       "11                 Matt Gaetz     1524   1524         1524         1524   \n",
       "12                Benny Gantz     1477   1477         1477         1477   \n",
       "13                      Lizzo     1303   1303         1303         1303   \n",
       "14  Luiz Inácio Lula da Silva     1036   1036         1036         1036   \n",
       "15        Ngozi Okonjo-Iweala     1019   1019         1019         1019   \n",
       "16            Nicolas Sarkozy      998    998          998          998   \n",
       "17    Wolfgang Amadeus Mozart      902    902          902          902   \n",
       "18            Elizabeth Olsen      891    891          891          891   \n",
       "19                     Nowruz      839    839          839          839   \n",
       "\n",
       "    eventTime  polarity  magnitude  score  \n",
       "0       43845     43845      43845  43845  \n",
       "1       38094     38094      38094  38094  \n",
       "2       12228     12228      12228  12228  \n",
       "3        5963      5963       5963   5963  \n",
       "4        4478      4478       4478   4478  \n",
       "5        3578      3578       3578   3578  \n",
       "6        3462      3462       3462   3462  \n",
       "7        2720      2720       2720   2720  \n",
       "8        1808      1808       1808   1808  \n",
       "9        1690      1690       1690   1690  \n",
       "10       1553      1553       1553   1553  \n",
       "11       1524      1524       1524   1524  \n",
       "12       1477      1477       1477   1477  \n",
       "13       1303      1303       1303   1303  \n",
       "14       1036      1036       1036   1036  \n",
       "15       1019      1019       1019   1019  \n",
       "16        998       998        998    998  \n",
       "17        902       902        902    902  \n",
       "18        891       891        891    891  \n",
       "19        839       839        839    839  "
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "f99c0c5f-67ae-4f4f-b063-62baa2232293",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'type'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_1/3482053885.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5485\u001b[0m         ):\n\u001b[1;32m   5486\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5487\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5488\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5489\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'type'"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2102c5cf-ee05-4b45-bc38-e2a58ae0f2da",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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