construct_csv.ipynb

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    "<h3>CSV construction</h3>\n",
    "\n",
    "Constructs the basic `csv` files that hold the raw data for the analysis"
   ]
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    "user = 'tew207'  # tew207 or rweldzius\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import itertools as it\n",
    "\n",
    "years = np.arange(1981, 2015)\n",
    "industries = ['15', '17', '20', '21', '23', '26', '27', '28', '36']\n",
    "countries = ['can', 'mex', 'usa']\n",
    "industry_variables = ['firms', 'imports', 'exports', 'employment', 'markup', 'openness', 'ppi', 'productivity', 'wage', 'fxrate']\n",
    "country_variables = ['gdp', 'cpi', 'fxrate']\n",
    "\n",
    "year = list(it.chain.from_iterable([years]*9))\n",
    "industry = np.repeat(industries,len(years))"
   ]
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    "for var in industry_variables:\n",
    "    for country in countries:\n",
    "        df = pd.DataFrame({'year':year, 'industry':industry, var+'_'+country:np.zeros(len(year))})\n",
    "        df = df[['year', 'industry', var+'_'+country]]\n",
    "        df.to_csv('/Users/'+user+'/Dropbox/Trade Paper/Data/Raw/'+str(var)+'_'+str(country)+'.csv', index=False)\n",
    "        \n",
    "for var in country_variables:\n",
    "    for country in countries:\n",
    "        df = pd.DataFrame({'year':years, var+'_'+country:np.zeros(len(years))})\n",
    "        df.to_csv('/Users/'+user+'/Dropbox/Trade Paper/Data/Raw/'+str(var)+'_'+str(country)+'.csv', index=False)"
   ]
  }
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