PPD_NER.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# data overview \n",
    "run again? but \n",
    "- without the, photo's by China\n",
    "- making sure spacing is fixed!!!!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/kf/vzjyh1g17x3gw7hgly31fnc80000gn/T/ipykernel_68233/3534879354.py:5: DtypeWarning: Columns (40) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  df = pd.read_csv('/Users/jasperhewitt/Desktop/text mining/homework /module 2/team_project/peoples_daily.csv')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "91954\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Index(['id', 'Page Name', 'User Name', 'Facebook Id', 'Page Category',\n",
       "       'Page Admin Top Country', 'Page Description', 'Page Created',\n",
       "       'Likes at Posting', 'Followers at Posting', 'Post Created',\n",
       "       'Post Created Date', 'Post Created Time', 'Type', 'Total Interactions',\n",
       "       'Likes', 'Comments', 'Shares', 'Love', 'Wow', 'Haha', 'Sad', 'Angry',\n",
       "       'Care', 'Video Share Status', 'Is Video Owner?', 'Post Views',\n",
       "       'Total Views', 'Total Views For All Crossposts', 'Video Length', 'URL',\n",
       "       'Message', 'Link', 'Final Link', 'Image Text', 'Link Text',\n",
       "       'Description', 'Sponsor Id', 'Sponsor Name', 'Sponsor Category',\n",
       "       'Overperforming Score (weighted  —  Likes 1x Shares 1x Comments 1x Love 1x Wow 1x Haha 1x Sad 1x Angry 1x Care 1x )'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read this into df /Users/jasperhewitt/Desktop/peoples_daily.csv\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "df = pd.read_csv('/Users/jasperhewitt/Desktop/text mining/homework /module 2/team_project/peoples_daily.csv')\n",
    "print(len(df))\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Post Created Date'] = pd.to_datetime(df['Post Created Date'])\n",
    "df_yearly = df.groupby(df['Post Created Date'].dt.year).size()\n",
    "df_cumulative = df_yearly.cumsum()\n",
    "\n",
    "# Plot\n",
    "plt.figure(figsize=(10,6))\n",
    "df_cumulative.plot(kind='line')\n",
    "plt.title('Cumulative number of articles per year')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Cumulative number of articles')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# preprocessing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Symbol !: 18934\n",
      "Symbol \": 42811\n",
      "Symbol #: 73516\n",
      "Symbol $: 3126\n",
      "Symbol %: 2606\n",
      "Symbol &: 2214\n",
      "Symbol ': 203438\n",
      "Symbol (: 35747\n",
      "Symbol ): 35763\n",
      "Symbol *: 59\n",
      "Symbol +: 333\n",
      "Symbol ,: 484651\n",
      "Symbol -: 126560\n",
      "Symbol .: 380468\n",
      "Symbol /: 64527\n",
      "Symbol :: 34858\n",
      "Symbol ;: 2080\n",
      "Symbol <: 11\n",
      "Symbol =: 3816\n",
      "Symbol >: 13\n",
      "Symbol ?: 7793\n",
      "Symbol @: 371\n",
      "Symbol [: 359\n",
      "Symbol \\: 9\n",
      "Symbol ]: 359\n",
      "Symbol ^: 5\n",
      "Symbol _: 877\n",
      "Symbol `: 5\n",
      "Symbol {: 0\n",
      "Symbol |: 30\n",
      "Symbol }: 0\n",
      "Symbol ~: 113\n",
      "link: 10064\n",
      "nan: 211243\n"
     ]
    }
   ],
   "source": [
    "import string\n",
    "#create combined_text column\n",
    "df['combined_text'] = df['Message'].astype(str) + ' ' + df['Link Text'].astype(str) + ' ' + df['Image Text'].astype(str) + ' ' + df['Description'].astype(str)\n",
    "\n",
    "#most important symbols that should be deleted '#', '@', '-'\n",
    "# Get all ASCII special symbols\n",
    "symbols = string.punctuation\n",
    "\n",
    "for symbol in symbols:\n",
    "    print('Symbol ' + symbol + ': ' + str(df['combined_text'].str.count('\\\\' + symbol).sum()))\n",
    "link_pattern = r'https?://[^\\s]+'\n",
    "print(\"link: \" + str(df['combined_text'].str.contains(link_pattern).sum()))\n",
    "print(\"nan: \" + str(df['combined_text'].str.count('nan').sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Symbol #: 0\n",
      "Symbol @: 0\n",
      "Symbol -: 0\n",
      "Symbol nan: 0\n",
      "link: 0\n"
     ]
    }
   ],
   "source": [
    "#delete special symbols and link pattern\n",
    "df['combined_text'] = df['combined_text'].str.replace(link_pattern, '', regex=True)\n",
    "df['combined_text'] = df['combined_text'].str.replace('#', '')\n",
    "df['combined_text'] = df['combined_text'].str.replace('@', '')\n",
    "df['combined_text'] = df['combined_text'].str.replace('-', '')\n",
    "#since nan is a string, we can just replace it\n",
    "df['combined_text'] = df['combined_text'].str.replace(\"nan\", '')\n",
    "\n",
    "#output results\n",
    "symbols = ['#', '@', '-', 'nan']\n",
    "\n",
    "for symbol in symbols:\n",
    "    print('Symbol ' + symbol + ': ' + str(df['combined_text'].str.count(symbol).sum()))\n",
    "print(\"link: \" + str(df['combined_text'].str.contains(link_pattern).sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least 25 people have been killed since typhoon Usagi made landfall in south China's Guangdong Province on Sunday evening. Around 5.5 million people have been affected and 310,000 displaced by the storm. Some 8,490 houses have been destroyed and 50,800 hectares of cropland damaged. The typhoon has incurred direct economic losses of 7.1 billion yuan (1.16 billion U.S. dollars) in the province.   \n"
     ]
    }
   ],
   "source": [
    "#print combined_text at loc 1\n",
    "print(df['combined_text'].loc[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NER and save results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ITS ALREADY SAVED IN THE FOLDER!!!!!!!!\n",
    "import pandas as pd\n",
    "import spacy\n",
    "from tqdm.auto import tqdm  # Import tqdm for the progress bar functionality\n",
    "\n",
    "# Assuming 'df' is your DataFrame and it already exists\n",
    "\n",
    "# Load spaCy's NER model\n",
    "nlp = spacy.load(\"en_core_web_sm\")  \n",
    "\n",
    "# Define a function to apply NER on a piece of text\n",
    "def apply_ner_spacy(text):\n",
    "    doc = nlp(text)\n",
    "    ner_results = [(ent.text, ent.label_) for ent in doc.ents]\n",
    "    return ner_results\n",
    "\n",
    "# Make sure you've called this before using progress_apply\n",
    "tqdm.pandas(desc=\"Processing posts\")\n",
    "\n",
    "# Apply the NER function to the 'combined_text' column with a progress bar\n",
    "df['NER_output_spacy'] = df['combined_text'].progress_apply(apply_ner_spacy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to filter for GPE and LOC entities from the list of entities in each row\n",
    "def filter_gpe_loc(entities):\n",
    "    return [text for text, label in entities if label in ['GPE', 'LOC']]\n",
    "\n",
    "# Apply the filter function to the 'NER_output_spacy' column to create a new 'GPE_LOC_only' column\n",
    "df['GPE_LOC_only'] = df['NER_output_spacy'].apply(filter_gpe_loc)\n",
    "\n",
    "#save to a csv file called spacy_full_output.csv\n",
    "#df.to_csv('spacy_full_output.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #create eval dataset \n",
    "# df_eval = df[['id', 'combined_text', 'GPE_LOC_only']]\n",
    "\n",
    "# # # give 20 random rows that contain Xinjiang\n",
    "# df_xinjiang = df_eval[df_eval['combined_text'].str.contains('Xinjiang')].sample(n=20)\n",
    "\n",
    "# #give 50 random rows in general\n",
    "# df_eval= df_eval.sample(n=50) \n",
    "\n",
    "# #combine the two together\n",
    "# df_eval_com = pd.concat([df_eval, df_xinjiang])\n",
    "\n",
    "# #_________\n",
    "# df_eval_com \n",
    "# #save to an excel file called Spacy_eval.xlsx\n",
    "# #df_eval_com.to_excel('Spacy_eval_complete.xlsx', index=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/kf/vzjyh1g17x3gw7hgly31fnc80000gn/T/ipykernel_68233/2183636168.py:2: DtypeWarning: Columns (40) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  df_ner_full = pd.read_csv('/Users/jasperhewitt/Desktop/text mining/homework /module 2/team_project/spacy_full_output.csv')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "91954"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read this csv file to df_ner_full /Users/jasperhewitt/Desktop/text mining/homework /module 2/team_project/peoples_daily_ner_spacy_full.csv\n",
    "df_ner_full = pd.read_csv('/Users/jasperhewitt/Desktop/text mining/homework /module 2/team_project/spacy_full_output.csv')\n",
    "len(df_ner_full)\n",
    "\n",
    "#only keep the columns 'serial_number' 'combined_text', 'NER_output_spacy' and 'GPE_only'\n",
    "df_ner_full = df_ner_full[['id', 'Post Created Date', 'combined_text', 'NER_output_spacy', 'GPE_LOC_only']]\n",
    "len(df_ner_full)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import ast\n",
    "\n",
    "def get_unique_values(text):\n",
    "    # Convert string representation of list to actual list\n",
    "    list_values = ast.literal_eval(text)\n",
    "    # Get unique values\n",
    "    unique_values = list(set(list_values))\n",
    "    return unique_values\n",
    "\n",
    "df_ner_full['GPE_LOC_unique'] = df_ner_full['GPE_LOC_only'].apply(get_unique_values)\n",
    "#convert back to string\n",
    "type(df_ner_full['GPE_LOC_unique'][0])\n",
    "#df_ner_full['GPE_only_unique'] = df_ner_full['GPE_only_unique'].apply(lambda x: ' '.join(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create new column 'GPE_LOC_unique_count' that counts the number of values in 'GPE_LOC_unique'(it is a list)\n",
    "df_ner_full['GPE_LOC_unique_count'] = df_ner_full['GPE_LOC_unique'].apply(lambda x: len(x))\n",
    "#convert to string\n",
    "df_ner_full['GPE_LOC_unique_count'] = df_ner_full['GPE_LOC_unique_count'].astype(str)\n",
    "\n",
    "\n",
    "#run Keyword Search for Xinjiang, Urumqi, Qiemo, Taklamakan, Tianshan Mountains, Ili Kazak, Kizilsu Kirgiz, Kashgar, Hotan, Aksu, Turpan, Karamay, Tacheng, Tarbagatay, Altay, Korla, Yining\n",
    "#create a new column called Xinjiang_related_hits that counts the number of times these keywords appear in the GPE_LOC_unique column per row\n",
    "keywords = ['Xinjiang', 'Urumqi', 'Qiemo', 'Taklamakan', 'Tianshan Mountains', 'Ili Kazak', 'Kizilsu Kirgiz', 'Kashgar', 'Hotan', 'Aksu', 'Turpan', 'Karamay', 'Tacheng', 'Tarbagatay', 'Altay', 'Korla', 'Yining', 'Kizilsu Kyrgyz', 'Dushanzi', 'Moyu', 'Tashkurgan', 'Shufu']\n",
    "\n",
    "def keyword_search(text):\n",
    "    hits = 0\n",
    "    for keyword in keywords:\n",
    "        if keyword in text:\n",
    "            hits += 1\n",
    "    return hits\n",
    "\n",
    "df_ner_full['Xinjiang_related_hits'] = df_ner_full['GPE_LOC_unique'].apply(keyword_search)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "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>id</th>\n",
       "      <th>Post Created Date</th>\n",
       "      <th>combined_text</th>\n",
       "      <th>NER_output_spacy</th>\n",
       "      <th>GPE_LOC_only</th>\n",
       "      <th>GPE_LOC_unique</th>\n",
       "      <th>GPE_LOC_unique_count</th>\n",
       "      <th>Xinjiang_related_hits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>38798</th>\n",
       "      <td>PD_38799</td>\n",
       "      <td>2018-09-25</td>\n",
       "      <td>Road trips reveal Xinjiang splendor The divers...</td>\n",
       "      <td>[('Xinjiang', 'GPE'), ('Xinjiang Uygur', 'GPE'...</td>\n",
       "      <td>['Xinjiang', 'Xinjiang Uygur', 'China', 'Xinji...</td>\n",
       "      <td>[Xinjiang, Kazak, China, Aksu, Urumqi, the Tak...</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61336</th>\n",
       "      <td>PD_61337</td>\n",
       "      <td>2020-10-21</td>\n",
       "      <td>No 'forced labor' in Xinjiang, report finds Pe...</td>\n",
       "      <td>[('Xinjiang', 'GPE'), ('Xinjiang Uygur', 'GPE'...</td>\n",
       "      <td>['Xinjiang', 'Xinjiang Uygur', 'Xinjiang', 'Xi...</td>\n",
       "      <td>[Xinjiang, Kizilsu Kirgiz, West, Hotan, Beijin...</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45752</th>\n",
       "      <td>PD_45753</td>\n",
       "      <td>2019-06-26</td>\n",
       "      <td>Diplomats and representatives based in Geneva ...</td>\n",
       "      <td>[('Geneva', 'GPE'), ('14', 'CARDINAL'), ('Xinj...</td>\n",
       "      <td>['Geneva', 'Xinjiang Uygur Autonomous Region',...</td>\n",
       "      <td>[Hotan Prefecture, Nigeria, Kashgar, Congo, La...</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66597</th>\n",
       "      <td>PD_66598</td>\n",
       "      <td>2021-04-04</td>\n",
       "      <td>Witnessing the real Xinjiang, foreign diplomat...</td>\n",
       "      <td>[('Xinjiang', 'GPE'), ('the International Gran...</td>\n",
       "      <td>['Xinjiang', 'Urumqi', 'China', 'Xinjiang Uygu...</td>\n",
       "      <td>[Xinjiang Uygur Autonomous Region, Xinjiang, A...</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65808</th>\n",
       "      <td>PD_65809</td>\n",
       "      <td>2021-03-10</td>\n",
       "      <td>Departed hero deputy missed at NPC session, bu...</td>\n",
       "      <td>[('NPC', 'ORG'), ('Proposals', 'GPE'), ('Laqin...</td>\n",
       "      <td>['Proposals', 'Beijing', 'Xinjiang', 'Xinjiang...</td>\n",
       "      <td>[the Pamir Plateau, Xinjiang, Shufu, Beijing, ...</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47632</th>\n",
       "      <td>PD_47633</td>\n",
       "      <td>2019-08-24</td>\n",
       "      <td>Diplomats from 7 countries visit Xinjiang Amba...</td>\n",
       "      <td>[('7', 'CARDINAL'), ('Xinjiang Ambassadors', '...</td>\n",
       "      <td>['Xinjiang Uyghur Autonomous Region', 'Laos', ...</td>\n",
       "      <td>[Xinjiang, Philippines, Turpan, Cambodia, Sri ...</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33782</th>\n",
       "      <td>PD_33783</td>\n",
       "      <td>2018-03-26</td>\n",
       "      <td>Xinjiang police deal with heavy workload prote...</td>\n",
       "      <td>[('Xinjiang', 'GPE'), ('Deep', 'GPE'), ('the n...</td>\n",
       "      <td>['Xinjiang', 'Deep', 'Liudaowan', 'Urumqi', 'X...</td>\n",
       "      <td>[Xinjiang, China, Urumqi, Xinjiang Uyghur Auto...</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64793</th>\n",
       "      <td>PD_64794</td>\n",
       "      <td>2021-02-05</td>\n",
       "      <td>Xinjiang officials and residents use personal ...</td>\n",
       "      <td>[('Xinjiang', 'GPE'), ('China', 'GPE'), ('Xinj...</td>\n",
       "      <td>['Xinjiang', 'China', 'Xinjiang Uygur', 'Xinji...</td>\n",
       "      <td>[Xinjiang, Beijing, Hotan, China, Kuqa, Aksu, ...</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2192</th>\n",
       "      <td>PD_2193</td>\n",
       "      <td>2014-03-19</td>\n",
       "      <td>A Uyghur man stabbed a police officer to death...</td>\n",
       "      <td>[('Uyghur', 'ORG'), ('Urumqi', 'GPE'), ('Xinji...</td>\n",
       "      <td>['Urumqi', 'Xinjiang', 'Xinjiang', 'Aksu']</td>\n",
       "      <td>[Urumqi, Xinjiang, Aksu]</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44677</th>\n",
       "      <td>PD_44678</td>\n",
       "      <td>2019-05-16</td>\n",
       "      <td>Dailyrunning 10,000tonne freight trains spur e...</td>\n",
       "      <td>[('10,000tonne', 'CARDINAL'), ('China', 'GPE')...</td>\n",
       "      <td>['China', 'Xinjiang', 'China', 'Xinjiang Uygur...</td>\n",
       "      <td>[Xinjiang Uygur Autonomous Region, Xinjiang, K...</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             id Post Created Date   \n",
       "38798  PD_38799        2018-09-25  \\\n",
       "61336  PD_61337        2020-10-21   \n",
       "45752  PD_45753        2019-06-26   \n",
       "66597  PD_66598        2021-04-04   \n",
       "65808  PD_65809        2021-03-10   \n",
       "47632  PD_47633        2019-08-24   \n",
       "33782  PD_33783        2018-03-26   \n",
       "64793  PD_64794        2021-02-05   \n",
       "2192    PD_2193        2014-03-19   \n",
       "44677  PD_44678        2019-05-16   \n",
       "\n",
       "                                           combined_text   \n",
       "38798  Road trips reveal Xinjiang splendor The divers...  \\\n",
       "61336  No 'forced labor' in Xinjiang, report finds Pe...   \n",
       "45752  Diplomats and representatives based in Geneva ...   \n",
       "66597  Witnessing the real Xinjiang, foreign diplomat...   \n",
       "65808  Departed hero deputy missed at NPC session, bu...   \n",
       "47632  Diplomats from 7 countries visit Xinjiang Amba...   \n",
       "33782  Xinjiang police deal with heavy workload prote...   \n",
       "64793  Xinjiang officials and residents use personal ...   \n",
       "2192   A Uyghur man stabbed a police officer to death...   \n",
       "44677  Dailyrunning 10,000tonne freight trains spur e...   \n",
       "\n",
       "                                        NER_output_spacy   \n",
       "38798  [('Xinjiang', 'GPE'), ('Xinjiang Uygur', 'GPE'...  \\\n",
       "61336  [('Xinjiang', 'GPE'), ('Xinjiang Uygur', 'GPE'...   \n",
       "45752  [('Geneva', 'GPE'), ('14', 'CARDINAL'), ('Xinj...   \n",
       "66597  [('Xinjiang', 'GPE'), ('the International Gran...   \n",
       "65808  [('NPC', 'ORG'), ('Proposals', 'GPE'), ('Laqin...   \n",
       "47632  [('7', 'CARDINAL'), ('Xinjiang Ambassadors', '...   \n",
       "33782  [('Xinjiang', 'GPE'), ('Deep', 'GPE'), ('the n...   \n",
       "64793  [('Xinjiang', 'GPE'), ('China', 'GPE'), ('Xinj...   \n",
       "2192   [('Uyghur', 'ORG'), ('Urumqi', 'GPE'), ('Xinji...   \n",
       "44677  [('10,000tonne', 'CARDINAL'), ('China', 'GPE')...   \n",
       "\n",
       "                                            GPE_LOC_only   \n",
       "38798  ['Xinjiang', 'Xinjiang Uygur', 'China', 'Xinji...  \\\n",
       "61336  ['Xinjiang', 'Xinjiang Uygur', 'Xinjiang', 'Xi...   \n",
       "45752  ['Geneva', 'Xinjiang Uygur Autonomous Region',...   \n",
       "66597  ['Xinjiang', 'Urumqi', 'China', 'Xinjiang Uygu...   \n",
       "65808  ['Proposals', 'Beijing', 'Xinjiang', 'Xinjiang...   \n",
       "47632  ['Xinjiang Uyghur Autonomous Region', 'Laos', ...   \n",
       "33782  ['Xinjiang', 'Deep', 'Liudaowan', 'Urumqi', 'X...   \n",
       "64793  ['Xinjiang', 'China', 'Xinjiang Uygur', 'Xinji...   \n",
       "2192          ['Urumqi', 'Xinjiang', 'Xinjiang', 'Aksu']   \n",
       "44677  ['China', 'Xinjiang', 'China', 'Xinjiang Uygur...   \n",
       "\n",
       "                                          GPE_LOC_unique   \n",
       "38798  [Xinjiang, Kazak, China, Aksu, Urumqi, the Tak...  \\\n",
       "61336  [Xinjiang, Kizilsu Kirgiz, West, Hotan, Beijin...   \n",
       "45752  [Hotan Prefecture, Nigeria, Kashgar, Congo, La...   \n",
       "66597  [Xinjiang Uygur Autonomous Region, Xinjiang, A...   \n",
       "65808  [the Pamir Plateau, Xinjiang, Shufu, Beijing, ...   \n",
       "47632  [Xinjiang, Philippines, Turpan, Cambodia, Sri ...   \n",
       "33782  [Xinjiang, China, Urumqi, Xinjiang Uyghur Auto...   \n",
       "64793  [Xinjiang, Beijing, Hotan, China, Kuqa, Aksu, ...   \n",
       "2192                            [Urumqi, Xinjiang, Aksu]   \n",
       "44677  [Xinjiang Uygur Autonomous Region, Xinjiang, K...   \n",
       "\n",
       "       GPE_LOC_unique_count  Xinjiang_related_hits  \n",
       "38798                    16                      6  \n",
       "61336                    11                      5  \n",
       "45752                    19                      4  \n",
       "66597                     9                      4  \n",
       "65808                     8                      4  \n",
       "47632                    13                      3  \n",
       "33782                     7                      3  \n",
       "64793                    10                      3  \n",
       "2192                      3                      3  \n",
       "44677                     6                      3  "
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#overview of the rows with most Xinjiang_related_hits\n",
    "#sort by Xinjiang_related_hits\n",
    "df_ner_full['GPE_LOC_unique_count'] = df_ner_full['GPE_LOC_unique_count'].astype(int)\n",
    "sort_by_xinjiang = df_ner_full.sort_values(by='Xinjiang_related_hits', ascending=False)\n",
    "#print top 20 rows\n",
    "sort_by_xinjiang.head(10)\n",
    "#convert GPE_LOC_unique to int\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1473\n",
      "252810\n"
     ]
    }
   ],
   "source": [
    "#sum of Xinjiang_related_hits, and sum of GPE_LOC_unique_count\n",
    "#sum df_ner_full['Xinjiang_related_hits']\n",
    "print(df_ner_full['Xinjiang_related_hits'].sum())\n",
    "#sum df_ner_full['GPE_LOC_unique_count']\n",
    "print(df_ner_full['GPE_LOC_unique_count'].sum())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #rough code \n",
    "\n",
    "# import pandas as pd\n",
    "# import matplotlib.pyplot as plt\n",
    "\n",
    "# # Convert 'Post_created_date' to datetime and set it as the index\n",
    "# df_ner_full['Post Created Date'] = pd.to_datetime(df_ner_full['Post Created Date'])\n",
    "# df_ner_full.set_index('Post Created Date', inplace=True)\n",
    "\n",
    "# # Resample the DataFrame to semi-annual frequency\n",
    "# df_resampled = df_ner_full.resample('6M')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# # Calculate the percentage of 'Xinjiang' mentions\n",
    "# percentage_xinjiang = (Xinjiang_related_hits / GPE_LOC_unique_count) * 100\n",
    "\n",
    "# # Plot the percentages over time\n",
    "# percentage_xinjiang.plot()\n",
    "# plt.title('Percentage of Xinjiang Mentions Over Time')\n",
    "# plt.ylabel('Percentage')\n",
    "# plt.show()\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Convert 'Post Created Date' to datetime and set it as the index\n",
    "df_ner_full['Post Created Date'] = pd.to_datetime(df_ner_full['Post Created Date'])\n",
    "df_ner_full.set_index('Post Created Date', inplace=True)\n",
    "\n",
    "# Resample the DataFrame to quarterly frequency\n",
    "df_resampled = df_ner_full.resample('3M')\n",
    "\n",
    "# Calculate the sum of 'Xinjiang_related_hits' and 'GPE_LOC_unique_count' for each period\n",
    "xinjiang_hits_resampled = df_resampled['Xinjiang_related_hits'].sum()\n",
    "gpe_loc_count_resampled = df_resampled['GPE_LOC_unique_count'].sum()\n",
    "\n",
    "# Calculate the percentage of 'Xinjiang' mentions for each period\n",
    "percentage_xinjiang = (xinjiang_hits_resampled / gpe_loc_count_resampled) * 100\n",
    "\n",
    "# Plot the percentages over time\n",
    "percentage_xinjiang.plot()\n",
    "plt.title('Percentage of Xinjiang Mentions Over Time')\n",
    "plt.ylabel('Percentage')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1473\n",
      "252810\n"
     ]
    }
   ],
   "source": [
    "#sum of Xinjiang_related_hits, and sum of GPE_LOC_unique_count\n",
    "#sum df_ner_full['Xinjiang_related_hits']\n",
    "print(df_ner_full['Xinjiang_related_hits'].sum())\n",
    "#sum df_ner_full['GPE_LOC_unique_count']\n",
    "print(df_ner_full['GPE_LOC_unique_count'].sum())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2013-09-24 00:00:00\n",
      "2023-09-22 00:00:00\n"
     ]
    }
   ],
   "source": [
    "#print min and max date of df_ner_full. the date is on index\n",
    "print(df_ner_full.index.min())\n",
    "print(df_ner_full.index.max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# T-test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23979\n",
      "27348\n"
     ]
    }
   ],
   "source": [
    "#prepare a new dataframe from df_ner_full that contains only the rows from 2016-01-01 until 2018-06-01 \n",
    "df_ner_full_2016_2018 = df_ner_full.loc['2016-01-01':'2018-06-30']\n",
    "print(len(df_ner_full_2016_2018))\n",
    "#now for 2018-06-01 until 2021-01-01\n",
    "df_ner_full_2018_2021 = df_ner_full.loc['2018-06-30':'2021-01-01']\n",
    "print(len(df_ner_full_2018_2021))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "keywords = ['Xinjiang', 'Urumqi', 'Qiemo', 'Taklamakan', 'Tianshan Mountains', 'Ili Kazak', 'Kizilsu Kirgiz', 'Kashgar', 'Hotan', 'Aksu', 'Turpan', 'Karamay', 'Tacheng', 'Tarbagatay', 'Altay', 'Korla', 'Yining', 'Kizilsu Kyrgyz', 'Dushanzi', 'Moyu', 'Tashkurgan', 'Shufu']\n",
    "\n",
    "# # Initialize a dictionary to store the counts\n",
    "# counts = {}\n",
    "\n",
    "# # Iterate over each keyword and count its occurrences\n",
    "# for keyword in keywords:\n",
    "#     counts[keyword] = df_ner_full_2018_2021['GPE_LOC_unique'].apply(lambda x: x.count(keyword)).sum()\n",
    "\n",
    "# # Convert the dictionary to a pandas Series\n",
    "# counts_series = pd.Series(counts)\n",
    "\n",
    "# # Plot the Series as a bar chart\n",
    "# counts_series.plot(kind='bar')\n",
    "# plt.title('Keyword Counts in GPE_LOC_unique')\n",
    "# plt.ylabel('Count')\n",
    "# plt.show()\n",
    "\n",
    "# Plot the Series as a horizontal bar chart\n",
    "counts_series.plot(kind='barh')\n",
    "\n",
    "# Modify the y-tick labels to include the counts\n",
    "plt.yticks(range(len(counts_series)), [f'{index} ({value})' for index, value in zip(counts_series.index, counts_series.values)])\n",
    "\n",
    "plt.title('Keyword Counts in GPE_LOC_unique')\n",
    "plt.xlabel('Count')\n",
    "plt.gca().invert_yaxis()  # Invert the y-axis so the keyword with the most hits is at the top\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/kf/vzjyh1g17x3gw7hgly31fnc80000gn/T/ipykernel_68233/3756678007.py:3: 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",
      "  df_ner_full_2016_2018['xinjiang_location_share'] = (df_ner_full_2016_2018['Xinjiang_related_hits'] / df_ner_full_2016_2018['GPE_LOC_unique_count']) * 100\n",
      "/var/folders/kf/vzjyh1g17x3gw7hgly31fnc80000gn/T/ipykernel_68233/3756678007.py:8: 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",
      "  df_ner_full_2018_2021['xinjiang_location_share'] = (df_ner_full_2018_2021['Xinjiang_related_hits'] / df_ner_full_2018_2021['GPE_LOC_unique_count']) * 100\n"
     ]
    }
   ],
   "source": [
    "df_ner_full_2016_2018\n",
    "#add a column called xinjiang_location_share that contains the percentage of Xinjiang_related_hits compared to GPE_LOC_unique_count per row \n",
    "df_ner_full_2016_2018['xinjiang_location_share'] = (df_ner_full_2016_2018['Xinjiang_related_hits'] / df_ner_full_2016_2018['GPE_LOC_unique_count']) * 100\n",
    "#delete rows where xinjiang_location_share is NaN\n",
    "df_ner_full_2016_2018 = df_ner_full_2016_2018.dropna(subset=['xinjiang_location_share'])\n",
    "\n",
    "#now do the same for df_ner_full_2018_2021\n",
    "df_ner_full_2018_2021['xinjiang_location_share'] = (df_ner_full_2018_2021['Xinjiang_related_hits'] / df_ner_full_2018_2021['GPE_LOC_unique_count']) * 100\n",
    "df_ner_full_2018_2021 = df_ner_full_2018_2021.dropna(subset=['xinjiang_location_share'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "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>id</th>\n",
       "      <th>combined_text</th>\n",
       "      <th>NER_output_spacy</th>\n",
       "      <th>GPE_LOC_only</th>\n",
       "      <th>GPE_LOC_unique</th>\n",
       "      <th>GPE_LOC_unique_count</th>\n",
       "      <th>Xinjiang_related_hits</th>\n",
       "      <th>xinjiang_location_share</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Post Created Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-06-30</th>\n",
       "      <td>PD_36349</td>\n",
       "      <td>Mount Agung volcano erupts in the Kubu subdist...</td>\n",
       "      <td>[('Mount Agung', 'PERSON'), ('Kubu', 'GPE'), (...</td>\n",
       "      <td>['Kubu', 'Indonesia', 'Bali', 'China']</td>\n",
       "      <td>[Indonesia, Kubu, Bali, China]</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-30</th>\n",
       "      <td>PD_36350</td>\n",
       "      <td>Jiuzhai Valley to close following landslide UN...</td>\n",
       "      <td>[('Jiuzhai Valley', 'LOC'), ('UNESCO World Her...</td>\n",
       "      <td>['Jiuzhai Valley', 'Jiuzhai Valley', 'the Qing...</td>\n",
       "      <td>[the QinghaiTibet Plateau, Jiuzhai Valley, China]</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-30</th>\n",
       "      <td>PD_36351</td>\n",
       "      <td>An exhibition titled \"Better Life, Dream Come ...</td>\n",
       "      <td>[('Better Life, Dream Come TruePoverty Allevia...</td>\n",
       "      <td>['China', 'New York', 'China']</td>\n",
       "      <td>[New York, China]</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-30</th>\n",
       "      <td>PD_36352</td>\n",
       "      <td>Nineteen top fashion design award winners from...</td>\n",
       "      <td>[('Nineteen', 'CARDINAL'), ('China', 'GPE'), (...</td>\n",
       "      <td>['China', 'Hangzhou City', 'East China’s', 'Zh...</td>\n",
       "      <td>[East China’s, Hangzhou City, Zhejiang Provinc...</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-30</th>\n",
       "      <td>PD_36353</td>\n",
       "      <td>China is looking to open its State Preeminent ...</td>\n",
       "      <td>[('China', 'GPE'), ('State Preeminent Science ...</td>\n",
       "      <td>['China', 'South China Morning Post']</td>\n",
       "      <td>[South China Morning Post, China]</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01</th>\n",
       "      <td>PD_63692</td>\n",
       "      <td>Chinese mainland reports 9 new locally transmi...</td>\n",
       "      <td>[('Chinese', 'NORP'), ('9', 'CARDINAL'), ('Chi...</td>\n",
       "      <td>['Beijing', 'China', 'Liaoning Province', 'Sha...</td>\n",
       "      <td>[Shanghai, Liaoning Province, Beijing, China, ...</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01</th>\n",
       "      <td>PD_63693</td>\n",
       "      <td>Say hi to the new year of 2021! First ray of s...</td>\n",
       "      <td>[('the new year', 'DATE'), ('2021', 'DATE'), (...</td>\n",
       "      <td>['China', 'China']</td>\n",
       "      <td>[China]</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01</th>\n",
       "      <td>PD_63694</td>\n",
       "      <td>The Forbidden City in Beijing glows with the f...</td>\n",
       "      <td>[('The Forbidden City', 'GPE'), ('Beijing', 'G...</td>\n",
       "      <td>['The Forbidden City', 'Beijing', 'The Forbidd...</td>\n",
       "      <td>[Beijing, The Forbidden City]</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01</th>\n",
       "      <td>PD_63695</td>\n",
       "      <td>A building in S China’s Guangxi has become net...</td>\n",
       "      <td>[('S China’s', 'LOC'), ('Guangxi', 'GPE'), ('t...</td>\n",
       "      <td>['S China’s', 'Guangxi', 'China']</td>\n",
       "      <td>[S China’s, Guangxi, China]</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01</th>\n",
       "      <td>PD_63696</td>\n",
       "      <td>Russian scientists have discovered a wellprese...</td>\n",
       "      <td>[('Russian', 'NORP'), ('Siberian', 'NORP'), ('...</td>\n",
       "      <td>['Yakutia', 'China']</td>\n",
       "      <td>[Yakutia, China]</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24349 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         id   \n",
       "Post Created Date             \n",
       "2018-06-30         PD_36349  \\\n",
       "2018-06-30         PD_36350   \n",
       "2018-06-30         PD_36351   \n",
       "2018-06-30         PD_36352   \n",
       "2018-06-30         PD_36353   \n",
       "...                     ...   \n",
       "2021-01-01         PD_63692   \n",
       "2021-01-01         PD_63693   \n",
       "2021-01-01         PD_63694   \n",
       "2021-01-01         PD_63695   \n",
       "2021-01-01         PD_63696   \n",
       "\n",
       "                                                       combined_text   \n",
       "Post Created Date                                                      \n",
       "2018-06-30         Mount Agung volcano erupts in the Kubu subdist...  \\\n",
       "2018-06-30         Jiuzhai Valley to close following landslide UN...   \n",
       "2018-06-30         An exhibition titled \"Better Life, Dream Come ...   \n",
       "2018-06-30         Nineteen top fashion design award winners from...   \n",
       "2018-06-30         China is looking to open its State Preeminent ...   \n",
       "...                                                              ...   \n",
       "2021-01-01         Chinese mainland reports 9 new locally transmi...   \n",
       "2021-01-01         Say hi to the new year of 2021! First ray of s...   \n",
       "2021-01-01         The Forbidden City in Beijing glows with the f...   \n",
       "2021-01-01         A building in S China’s Guangxi has become net...   \n",
       "2021-01-01         Russian scientists have discovered a wellprese...   \n",
       "\n",
       "                                                    NER_output_spacy   \n",
       "Post Created Date                                                      \n",
       "2018-06-30         [('Mount Agung', 'PERSON'), ('Kubu', 'GPE'), (...  \\\n",
       "2018-06-30         [('Jiuzhai Valley', 'LOC'), ('UNESCO World Her...   \n",
       "2018-06-30         [('Better Life, Dream Come TruePoverty Allevia...   \n",
       "2018-06-30         [('Nineteen', 'CARDINAL'), ('China', 'GPE'), (...   \n",
       "2018-06-30         [('China', 'GPE'), ('State Preeminent Science ...   \n",
       "...                                                              ...   \n",
       "2021-01-01         [('Chinese', 'NORP'), ('9', 'CARDINAL'), ('Chi...   \n",
       "2021-01-01         [('the new year', 'DATE'), ('2021', 'DATE'), (...   \n",
       "2021-01-01         [('The Forbidden City', 'GPE'), ('Beijing', 'G...   \n",
       "2021-01-01         [('S China’s', 'LOC'), ('Guangxi', 'GPE'), ('t...   \n",
       "2021-01-01         [('Russian', 'NORP'), ('Siberian', 'NORP'), ('...   \n",
       "\n",
       "                                                        GPE_LOC_only   \n",
       "Post Created Date                                                      \n",
       "2018-06-30                    ['Kubu', 'Indonesia', 'Bali', 'China']  \\\n",
       "2018-06-30         ['Jiuzhai Valley', 'Jiuzhai Valley', 'the Qing...   \n",
       "2018-06-30                            ['China', 'New York', 'China']   \n",
       "2018-06-30         ['China', 'Hangzhou City', 'East China’s', 'Zh...   \n",
       "2018-06-30                     ['China', 'South China Morning Post']   \n",
       "...                                                              ...   \n",
       "2021-01-01         ['Beijing', 'China', 'Liaoning Province', 'Sha...   \n",
       "2021-01-01                                        ['China', 'China']   \n",
       "2021-01-01         ['The Forbidden City', 'Beijing', 'The Forbidd...   \n",
       "2021-01-01                         ['S China’s', 'Guangxi', 'China']   \n",
       "2021-01-01                                      ['Yakutia', 'China']   \n",
       "\n",
       "                                                      GPE_LOC_unique   \n",
       "Post Created Date                                                      \n",
       "2018-06-30                            [Indonesia, Kubu, Bali, China]  \\\n",
       "2018-06-30         [the QinghaiTibet Plateau, Jiuzhai Valley, China]   \n",
       "2018-06-30                                         [New York, China]   \n",
       "2018-06-30         [East China’s, Hangzhou City, Zhejiang Provinc...   \n",
       "2018-06-30                         [South China Morning Post, China]   \n",
       "...                                                              ...   \n",
       "2021-01-01         [Shanghai, Liaoning Province, Beijing, China, ...   \n",
       "2021-01-01                                                   [China]   \n",
       "2021-01-01                             [Beijing, The Forbidden City]   \n",
       "2021-01-01                               [S China’s, Guangxi, China]   \n",
       "2021-01-01                                          [Yakutia, China]   \n",
       "\n",
       "                   GPE_LOC_unique_count  Xinjiang_related_hits   \n",
       "Post Created Date                                                \n",
       "2018-06-30                            4                      0  \\\n",
       "2018-06-30                            3                      0   \n",
       "2018-06-30                            2                      0   \n",
       "2018-06-30                            4                      0   \n",
       "2018-06-30                            2                      0   \n",
       "...                                 ...                    ...   \n",
       "2021-01-01                            7                      0   \n",
       "2021-01-01                            1                      0   \n",
       "2021-01-01                            2                      0   \n",
       "2021-01-01                            3                      0   \n",
       "2021-01-01                            2                      0   \n",
       "\n",
       "                   xinjiang_location_share  \n",
       "Post Created Date                           \n",
       "2018-06-30                             0.0  \n",
       "2018-06-30                             0.0  \n",
       "2018-06-30                             0.0  \n",
       "2018-06-30                             0.0  \n",
       "2018-06-30                             0.0  \n",
       "...                                    ...  \n",
       "2021-01-01                             0.0  \n",
       "2021-01-01                             0.0  \n",
       "2021-01-01                             0.0  \n",
       "2021-01-01                             0.0  \n",
       "2021-01-01                             0.0  \n",
       "\n",
       "[24349 rows x 8 columns]"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ner_full_2018_2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T-statistic: -9.05960189158543\n",
      "P-value: 1.3579135512100041e-19\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import ttest_ind\n",
    "\n",
    "# Run a t-test to compare 'xinjiang_location_share' between the two dataframes\n",
    "t_stat, p_value = ttest_ind(df_ner_full_2016_2018['xinjiang_location_share'], df_ner_full_2018_2021['xinjiang_location_share'])\n",
    "\n",
    "print(\"T-statistic:\", t_stat)\n",
    "print(\"P-value:\", p_value)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [],
   "source": [
    "#do this for the full ner dataset \n",
    "#add a column called xinjiang_location_share that contains the percentage of Xinjiang_related_hits compared to GPE_LOC_unique_count per row \n",
    "df_ner_full['xinjiang_location_share'] = (df_ner_full['Xinjiang_related_hits'] / df_ner_full['GPE_LOC_unique_count']) * 100\n",
    "#delete rows where xinjiang_location_share is NaN\n",
    "#df_ner_full = df_ner_full_2016_2018.dropna(subset=['xinjiang_location_share'])\n",
    "df_ner_full\n",
    "\n",
    "#save to csv file called xinjiang_location_share_per_post.csv\n",
    "#df_ner_full.to_csv('xinjiang_location_share_per_post.csv', index=False)"
   ]
  }
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