07_individual_product.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Trade network analysis\n",
    "**Brian Dew (brianwdew@gmail.com)**\n",
    "\n",
    "`07_individual_product.ipynb`\n",
    "\n",
    "This notebook summarizes results for an individual product and year. \n",
    "\n",
    "METODO:\n",
    "\n",
    "1. Annotate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# uses panadas, networkx, powerlaw, and os packages\n",
    "import pandas as pd\n",
    "import networkx as nx\n",
    "import powerlaw\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from scipy import optimize\n",
    "\n",
    "os.chdir('C:/Working/trade_network/data/')        # Change to working directory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Build network and fit to distributions\n",
    "\n",
    "The work of deg_dist is repeated here for the product code identified in `prod` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Values less than or equal to 0 in data. Throwing out 0 or negative values\n",
      "Calculating best minimal value for power law fit\n"
     ]
    }
   ],
   "source": [
    "pl_ = {}\n",
    "prod = 90111\n",
    "df = pd.read_csv('clean/baci07_2014_clean.csv', index_col='hs6').sort_index()\n",
    "G = nx.from_pandas_dataframe(df.loc[prod], 'i', 'j', 'v', nx.DiGraph())  #build network\n",
    "deg = G.out_degree(weight='v').values()         # calc weighted outdeg for each country\n",
    "fit = powerlaw.Fit(deg)         # est. distrib. and save alpha value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Print the estimated values\n",
    "\n",
    "Print estimated alpha value, std dev, and estimated minimum value/cutoff for the distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Alpha:  1.34316809034\n",
      "Sigma:  0.0374427083355\n",
      "X-Min:  970.3526\n"
     ]
    }
   ],
   "source": [
    "print 'Alpha: ', fit.power_law.alpha\n",
    "print 'Sigma: ', fit.power_law.sigma\n",
    "print 'X-Min: ', fit.power_law.xmin"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compare fit\n",
    "\n",
    "Compare the how well the data fit a power law compared to exponential distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(130.95966288145141, 3.8383554839378416e-07)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit.distribution_compare('power_law', 'exponential')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot fit\n",
    "\n",
    "How well do the data (solid line) fit the distribution (dashed line)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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YEF591T7/44/zO+aNsS2T9evt1+hom3x0/op3ZOWW1wQgHhif+ks/bXtx4F0R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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x151b4fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2 = fit.plot_ccdf(label='fitted values', color='b', linewidth=2)\n",
    "fit.power_law.plot_ccdf(label='power law distribution', color='b', linestyle='--', ax=fig2)\n",
    "plt.legend(handlelength=3, fancybox=True, shadow=True, loc=3, prop={'size':10})\n",
    "fig2.text(0.05, 0.95, 'Coffee \\n \\n$\\\\alpha = 1.34$ \\n$\\\\sigma = 0.04$', transform=fig2.transAxes, fontsize=12,\n",
    "        verticalalignment='top')\n",
    "fig2.set_xlabel('$x$')\n",
    "fig2.set_ylabel('P$(X)>x$')\n",
    "plt.savefig('prod/coffee_ccdf.pdf', bbox_inches='tight')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}