ZSI_Reconnect_China/WOS/wos_extract/wos_search_kw_analysis.ipynb

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  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import os\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "outputs": [],
   "source": [
    "agg_df = pd.DataFrame()\n",
    "\n",
    "workdir_path = 'wos_downloads/aggregated'\n",
    "for root, dirs, files in os.walk(workdir_path):\n",
    "    for filename in files:\n",
    "        if 'analyze_' in filename:\n",
    "            path=os.path.join(root, filename)\n",
    "            with open(os.path.join(root, 'query.txt'),'r') as f:\n",
    "                query = f.readline()\n",
    "            chunk = pd.read_csv(path, sep='\\t')[[\"Publication Years\",\"Record Count\"]]\n",
    "            chunk[\"name\"] = filename.replace(\".txt\",\"\")\n",
    "            chunk[\"query\"] = query\n",
    "            agg_df = pd.concat([chunk,agg_df],ignore_index=True)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "outputs": [],
   "source": [
    "agg_df[\"region\"] = agg_df[\"query\"].apply(lambda x: \"EU+China\" if \"CU\" in x else \"Global\")\n",
    "agg_df[\"kw_token\"] = agg_df[\"query\"].apply(lambda x: x.split(\"TS=(\")[-1].split(\")\")[0])\n",
    "agg_df[\"kw_token\"] = agg_df[\"kw_token\"].apply(lambda x: \"OR COMPOSITE\" if \" OR \" in x else x)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "outputs": [],
   "source": [
    "agg_df = agg_df[~agg_df[\"Record Count\"].isna()]"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "outputs": [
    {
     "data": {
      "text/plain": "                                                 query  Record Count\n0    CU=(PEOPLES R CHINA OR HONG KONG) AND CU=(AUST...         972.0\n1    CU=(PEOPLES R CHINA OR HONG KONG) AND CU=(AUST...         451.0\n2    CU=(PEOPLES R CHINA OR HONG KONG) AND CU=(AUST...          12.0\n3    CU=(PEOPLES R CHINA OR HONG KONG) AND CU=(AUST...           5.0\n4    CU=(PEOPLES R CHINA OR HONG KONG) AND CU=(AUST...        2631.0\n..                                                 ...           ...\n275     TS=(\"ubiquitous computing\") AND PY=(2011-2022)        3655.0\n276       TS=(\"unstructured data*\") AND PY=(2011-2022)        3386.0\n277  TS=(\"unsupervised deep learning\") AND PY=(2011...         728.0\n278          TS=(\"word embedding*\") AND PY=(2011-2022)        7068.0\n279             TS=(\"word vector*\") AND PY=(2011-2022)        1747.0\n\n[280 rows x 2 columns]",
      "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>query</th>\n      <th>Record Count</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>CU=(PEOPLES R CHINA OR HONG KONG) AND CU=(AUST...</td>\n      <td>972.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>CU=(PEOPLES R CHINA OR HONG KONG) AND CU=(AUST...</td>\n      <td>451.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>CU=(PEOPLES R CHINA OR HONG KONG) AND CU=(AUST...</td>\n      <td>12.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>CU=(PEOPLES R CHINA OR HONG KONG) AND CU=(AUST...</td>\n      <td>5.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>CU=(PEOPLES R CHINA OR HONG KONG) AND CU=(AUST...</td>\n      <td>2631.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>275</th>\n      <td>TS=(\"ubiquitous computing\") AND PY=(2011-2022)</td>\n      <td>3655.0</td>\n    </tr>\n    <tr>\n      <th>276</th>\n      <td>TS=(\"unstructured data*\") AND PY=(2011-2022)</td>\n      <td>3386.0</td>\n    </tr>\n    <tr>\n      <th>277</th>\n      <td>TS=(\"unsupervised deep learning\") AND PY=(2011...</td>\n      <td>728.0</td>\n    </tr>\n    <tr>\n      <th>278</th>\n      <td>TS=(\"word embedding*\") AND PY=(2011-2022)</td>\n      <td>7068.0</td>\n    </tr>\n    <tr>\n      <th>279</th>\n      <td>TS=(\"word vector*\") AND PY=(2011-2022)</td>\n      <td>1747.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>280 rows × 2 columns</p>\n</div>"
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_df.groupby(\"query\",as_index=False)[\"Record Count\"].sum()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "outputs": [],
   "source": [
    "# agg_df = agg_df[agg_df[\"Publication Years\"].str.startswith(\"20\", na=False)].copy()\n",
    "# agg_df[\"Publication Years\"] = agg_df[\"Publication Years\"].astype(int)\n",
    "# agg_df[((agg_df[\"Publication Years\"]>2010) & (agg_df[\"Publication Years\"]<2023))]"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "outputs": [
    {
     "data": {
      "text/plain": "Publication Years\n2022    314\n2019    305\n2021    305\n2020    302\n2018    296\n2017    287\n2016    281\n2015    271\n2014    258\n2013    251\n2012    233\n2011    224\n2023     52\n2017      4\n2014      4\n2019      4\n2021      4\n2018      4\n2020      4\n2022      4\n2016      3\n2015      3\n2013      3\n2012      3\n2011      3\n2023      2\nName: count, dtype: int64"
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_df[\"Publication Years\"].value_counts()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "outputs": [],
   "source": [
    "agg_df.to_excel(r'C:\\Users\\radvanyi\\PycharmProjects\\ZSI_analytics\\WOS\\wos_processed_data\\query_yearly_agg.xlsx', index=False)"
   ],
   "metadata": {
    "collapsed": false
   }
  }
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