{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas.errors import EmptyDataError"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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",
    "            try:\n",
    "                chunk = pd.read_csv(path, sep='\\t')[[\"Publication Years\",\"Record Count\"]]\n",
    "            except EmptyDataError:\n",
    "                path=os.path.join(root, \"analyze.txt\")\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)\n",
    "        # elif len(files)==1:\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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].strip(\"(\"))\n",
    "agg_df[\"kw_token\"] = agg_df[\"kw_token\"].apply(lambda x: \"COMPOSITE SEARCH\" if \" OR \" in x else x)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [],
   "source": [
    "agg_df = agg_df[~agg_df[\"Record Count\"].isna()]"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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...          30.0\n3    CU=(PEOPLES R CHINA OR HONG KONG) AND CU=(AUST...          12.0\n4    CU=(PEOPLES R CHINA OR HONG KONG) AND CU=(AUST...           5.0\n..                                                 ...           ...\n543  TS=((\"face recognition\" NOT \"brain\")) AND PY=(...       19690.0\n544  TS=((\"linear regression\" NOT \"p=\")) AND PY=(20...       91493.0\n545  TS=((\"logistic regression\" NOT \"p=\")) AND PY=(...      171776.0\n546  TS=((\"object detection\" NOT \"brain\")) AND PY=(...       28989.0\n547  TS=((\"speech recognition\" NOT \"brain\")) AND PY...       19912.0\n\n[548 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>30.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>CU=(PEOPLES R CHINA OR HONG KONG) AND CU=(AUST...</td>\n      <td>12.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>CU=(PEOPLES R CHINA OR HONG KONG) AND CU=(AUST...</td>\n      <td>5.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>543</th>\n      <td>TS=((\"face recognition\" NOT \"brain\")) AND PY=(...</td>\n      <td>19690.0</td>\n    </tr>\n    <tr>\n      <th>544</th>\n      <td>TS=((\"linear regression\" NOT \"p=\")) AND PY=(20...</td>\n      <td>91493.0</td>\n    </tr>\n    <tr>\n      <th>545</th>\n      <td>TS=((\"logistic regression\" NOT \"p=\")) AND PY=(...</td>\n      <td>171776.0</td>\n    </tr>\n    <tr>\n      <th>546</th>\n      <td>TS=((\"object detection\" NOT \"brain\")) AND PY=(...</td>\n      <td>28989.0</td>\n    </tr>\n    <tr>\n      <th>547</th>\n      <td>TS=((\"speech recognition\" NOT \"brain\")) AND PY...</td>\n      <td>19912.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>548 rows × 2 columns</p>\n</div>"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_df.groupby(\"query\",as_index=False)[\"Record Count\"].sum()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [
    {
     "data": {
      "text/plain": "                        kw_token  Record Count\n0               COMPOSITE SEARCH       62205.0\n1              \"neural network*\"       10999.0\n2              \"machine* learn*\"        5765.0\n3                  \"deep learn*\"        5211.0\n4                     \"momentum\"        4974.0\n..                           ...           ...\n243       \"artificial cognition\"           1.0\n244  \"ai in disaster management\"           1.0\n245          \"vector embedding*\"           1.0\n246              \"ai in finance\"           1.0\n247    \"content based filtering\"           1.0\n\n[248 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>kw_token</th>\n      <th>Record Count</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>COMPOSITE SEARCH</td>\n      <td>62205.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>\"neural network*\"</td>\n      <td>10999.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>\"machine* learn*\"</td>\n      <td>5765.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>\"deep learn*\"</td>\n      <td>5211.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>\"momentum\"</td>\n      <td>4974.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>243</th>\n      <td>\"artificial cognition\"</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>244</th>\n      <td>\"ai in disaster management\"</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>245</th>\n      <td>\"vector embedding*\"</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>246</th>\n      <td>\"ai in finance\"</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>247</th>\n      <td>\"content based filtering\"</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>248 rows × 2 columns</p>\n</div>"
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kw_ranks = agg_df[agg_df[\"region\"]==\"EU+China\"].groupby(\"kw_token\",as_index=False)[\"Record Count\"].sum().sort_values(by=\"Record Count\", ascending=False).reset_index().drop(columns=\"index\")\n",
    "kw_ranks"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "outputs": [],
   "source": [
    "kw_ranks.to_excel(\"kw_token_ranked.xlsx\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": 22,
   "outputs": [],
   "source": [
    "# agg_df[\"Publication Years\"].value_counts()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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
   }
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
 ],
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