Contact/Export Utilization Histogram

test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sqlqueries as queries\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "contact_df = pd.read_csv('contact.csv')\n",
    "contact_array = np.genfromtxt('contact.csv', delimiter=',')\n",
    "\n",
    "export_df = pd.read_csv('export.csv')\n",
    "export_array = np.genfromtxt('export.csv', delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "percent = contact_array[:,2]\n",
    "percent=percent[percent<1.0]\n",
    "len(percent[percent>0.0])\n",
    "percent_remove_outliers = percent[percent>0.02]\n",
    "percent_remove_outliers\n",
    "\n",
    "e_percent = export_array[:,2]\n",
    "e_percent_no_zero = e_percent[e_percent<1.0]\n",
    "e_percent_no_zero = e_percent_no_zero[e_percent_no_zero>0.02]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Contact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bf92e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1,1,1)\n",
    "plt.hist(percent,50, color = \"orange\")\n",
    "plt.title(\"Contact Utilization (with zeros)\")\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GRKyLiJGIGBkdHZ1hGZKkqQz6ZGz0aMteM2bmBZm5KjNXLVu2bMBlSJLGzDTod44NyZS/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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bf92860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1,1,1)\n",
    "plt.hist(percent_remove_outliers,50)\n",
    "plt.title(\"Contact Utilization (without zeros)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1208e82b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(e_percent[~np.isnan(e_percent)],50,color = \"orange\")\n",
    "plt.title(\"Export (with zeroes)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/TpLpJH8JUFW3An8GfLu//Wm/TZK0RLYdpVJVHTWk6Llz1F0D/MHA+inAKfPqnSRp4jxz\nV5IaY/BLUmMMfklqjMEvSY0x+CWpMSP9qkdSZ+XxZ8+5fe0Jhy5yT6T584hfkhpj8EtSYwx+SWqM\nwS9JjTH4JakxBr8kNcbgl6TGGPyS1BiDX5IaY/BLUmO8ZMMQnpovaaEsdb54xC9JjZl38CfZJ8ll\nA7c7k7xpVp0Dk9wxUOePx++yJGkc857qqaprgP0AkmwD/Ag4Y46qX6+qw+bbjiRpsiY11fNc4AdV\n9cMJ7U+StEAmFfxHAqcNKXtaksuTfDHJE4btIMnqJGuSrNmwYcOEuiVJmm3s4E+yPXA48Lk5ii8F\n9qyqJwEfAM4ctp+qOrmqpqtqempqatxuSZKGmMQR/yHApVX1k9kFVXVnVd3dL58DbJdkxQTalCTN\n0ySC/yiGTPMkeUyS9MsH9O3dMoE2JUnzNNYJXEkeAjwfeM3AttcCVNVJwIuA1yXZCPwMOLKqapw2\nJUnjGSv4q+oe4JGztp00sHwicOI4bUiSJstLNmwFlvr0b0lzG/Zvc6l5yQZJaozBL0mNMfglqTEG\nvyQ1xuCXpMYY/JLUGINfkhpj8EtSYwx+SWpM82fubumZdVta37NnlwfPbpZ+wyN+SWqMwS9JjTH4\nJakxBr8kNcbgl6TGGPyS1BiDX5IaM3bwJ1mb5DtJLkuyZo7yJPmLJNcluSLJ/uO2KUmav0mdwPWc\nqrp5SNkhwF797SnAh/q/kqQlsBhTPUcAH6/ORcBOSR67CO1KkuYwiSP+As5NUsCHq+rkWeW7AjcM\nrK/rt904WCnJamA1wB577DGBbm19luo/bl6qy1Rsqt2FbmOpLuWw3PqjrdMkjvifUVX7003pHJfk\nWbPKM8d96j4bqk6uqumqmp6amppAtyRJcxk7+Ktqff/3JuAM4IBZVdYBuw+s7wasH7ddSdL8jBX8\nSR6aZIeZZeAg4MpZ1c4CXtn/uuepwB1VdSOSpCUx7hz/o4Ezkszs69NV9aUkrwWoqpOAc4BVwHXA\nPcDvj9mmJGkMYwV/VV0PPGmO7ScNLBdw3DjtSJImxzN3JakxBr8kNcbgl6TGGPyS1BiDX5IaM6mL\ntGkIT8Fv20I//5O8rMVS9XUp+9nqv0+P+CWpMQa/JDXG4Jekxhj8ktQYg1+SGmPwS1JjDH5JaozB\nL0mNMfglqTEGvyQ1xks2LJFNnWp/f2pjS7R6erw2bbm9TmHrf616xC9JjZl38CfZPclXk3wvyVVJ\n3jhHnQOT3JHksv72x+N1V5I0rnGmejYCb66qS5PsAFyS5Lyq+u6sel+vqsPGaEeSNEHzPuKvqhur\n6tJ++S7ge8Cuk+qYJGlhTGSOP8lK4MnAt+YoflqSy5N8MckTJtGeJGn+xv5VT5KHAf8beFNV3Tmr\n+FJgz6q6O8kq4ExgryH7WQ2sBthjjz3G7ZYkaYixjviTbEcX+p+qqv8zu7yq7qyqu/vlc4DtkqyY\na19VdXJVTVfV9NTU1DjdkiRtwji/6gnwUeB7VfW+IXUe09cjyQF9e7fMt01J0vjGmep5BvAK4DtJ\nLuu3vQ3YA6CqTgJeBLwuyUbgZ8CRVVVjtClJGtO8g7+qLgSymTonAifOtw1J0uR5yQZN3Jaegj/J\nU/aX4+n/W6vl9lgvt/4sZ16yQZIaY/BLUmMMfklqjMEvSY0x+CWpMQa/JDXG4Jekxhj8ktQYg1+S\nGmPwS1JjshyvmTY9PV1r1qyZ1309bVtbo7UnHDrndl/vW5dhz/MoklxSVdOj1PWIX5IaY/BLUmMM\nfklqjMEvSY0x+CWpMQa/JDXG4JekxowV/EkOTnJNkuuSHD9H+QOTnN6XfyvJynHakySNb97Bn2Qb\n4IPAIcC+wFFJ9p1V7Vjgtqp6HPB+4D3zbU+SNBnjHPEfAFxXVddX1S+BzwBHzKpzBPCxfvnzwHOT\nZIw2JUlj2naM++4K3DCwvg54yrA6VbUxyR3AI4GbZ+8syWpgdb96d5JrNtH2irn20QDH3Z4VwM1p\n77Nyk8953jPWuPccteI4wT/XkfvsC/+MUqfbWHUycPJIDSdrRr0mxdbEcben1bE77oU1zlTPOmD3\ngfXdgPXD6iTZFtgRuHWMNiVJYxon+L8N7JXkd5JsDxwJnDWrzlnA0f3yi4C/reV4OVBJasi8p3r6\nOfvXA18GtgFOqaqrkvwpsKaqzgI+CnwiyXV0R/pHTqLTjDgltBVy3O1pdeyOewEty+vxS5IWjmfu\nSlJjDH5JasyyDf6WLwcxwtj/MMl3k1yR5CtJRv797nK2uXEP1HtRkkqyVfzcb5RxJ3lx/5xfleTT\ni93HhTLCa32PJF9N8vf9633VUvRz0pKckuSmJFcOKU+Sv+gflyuS7D/RDlTVsrvRfVn8A+CfANsD\nlwP7zqrz74CT+uUjgdOXut+LOPbnAA/pl1+3NYx9lHH39XYALgAuAqaXut+L9HzvBfw9sHO//qil\n7vcijv1k4HX98r7A2qXu94TG/ixgf+DKIeWrgC/SnQv1VOBbk2x/uR7xt3w5iM2Ovaq+WlX39KsX\n0Z1DcX83ynMO8GfAfwd+vpidW0CjjPvVwAer6jaAqrppkfu4UEYZewEP75d35L7nCt0vVdUFbPqc\npiOAj1fnImCnJI+dVPvLNfjnuhzErsPqVNVGYOZyEPd3o4x90LF0Rwb3d5sdd5InA7tX1V8vZscW\n2CjP997A3kn+LslFSQ5etN4trFHG/i7g5UnWAecAb1icri25Lc2BLTLOJRsW0kQvB3E/M/K4krwc\nmAaevaA9WhybHHeSB9Bd4fWYxerQIhnl+d6WbrrnQLpPd19P8sSqun2B+7bQRhn7UcCpVfXeJE+j\nOy/oiVX164Xv3pJa0Hxbrkf8LV8OYpSxk+R5wNuBw6vqF4vUt4W0uXHvADwROD/JWrp5z7O2gi94\nR32tf6GqflVV/wBcQ/dGcH83ytiPBT4LUFXfBB5EdwG3rd1IOTBfyzX4W74cxGbH3k95fJgu9LeW\n+d5Njruq7qiqFVW1sqpW0n23cXhVrVma7k7MKK/1M+m+0CfJCrqpn+sXtZcLY5Sx/yPwXIAkj6cL\n/g2L2sulcRbwyv7XPU8F7qiqGye182U51VNLezmIJTXi2P8ceBjwuf777H+sqsOXrNMTMOK4tzoj\njvvLwEFJvgvcC/ynqrpl6Xo9GSOO/c3AR5L8B7qpjmO2hgO8JKfRTd2t6L+/eCewHUBVnUT3fcYq\n4DrgHuD3J9r+VvAYSpK2wHKd6pEkLRCDX5IaY/BLUmMMfklqjMEvSY0x+CWpMQa/JDXm/wE7851D\nPqjsUQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x120cf4470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(e_percent_no_zero,50)\n",
    "plt.title('Exports (without zeroes)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ------------------------------------------------------------------------"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}