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ZSI_Reconnect_China/WOS/wos_extract/wos_search_kw_analysis.ipynb

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"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": 2,
"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": 3,
"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": 4,
"outputs": [],
"source": [
"agg_df = agg_df[~agg_df[\"Record Count\"].isna()]"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 5,
"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.. ... ...\n384 TS=(\"word embedding*\") AND PY=(2011-2022) 7068.0\n385 TS=(\"word vector*\") AND PY=(2011-2022) 1747.0\n386 TS=((\"face recognition\" NOT \"brain\")) AND PY=(... 19690.0\n387 TS=((\"object detection\" NOT \"brain\")) AND PY=(... 28989.0\n388 TS=((\"speech recognition\" NOT \"brain\")) AND PY... 19912.0\n\n[389 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>384</th>\n <td>TS=(\"word embedding*\") AND PY=(2011-2022)</td>\n <td>7068.0</td>\n </tr>\n <tr>\n <th>385</th>\n <td>TS=(\"word vector*\") AND PY=(2011-2022)</td>\n <td>1747.0</td>\n </tr>\n <tr>\n <th>386</th>\n <td>TS=((\"face recognition\" NOT \"brain\")) AND PY=(...</td>\n <td>19690.0</td>\n </tr>\n <tr>\n <th>387</th>\n <td>TS=((\"object detection\" NOT \"brain\")) AND PY=(...</td>\n <td>28989.0</td>\n </tr>\n <tr>\n <th>388</th>\n <td>TS=((\"speech recognition\" NOT \"brain\")) AND PY...</td>\n <td>19912.0</td>\n </tr>\n </tbody>\n</table>\n<p>389 rows × 2 columns</p>\n</div>"
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agg_df.groupby(\"query\",as_index=False)[\"Record Count\"].sum()"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 6,
"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": 7,
"outputs": [],
"source": [
"# agg_df[\"Publication Years\"].value_counts()"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 8,
"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
}
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
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"nbformat": 4,
"nbformat_minor": 0
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