spearman_multithreaded.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HI\n",
      "   Year  Population (1000 persons)  \\\n",
      "0  1961                3090305.279   \n",
      "1  1962                3149244.245   \n",
      "2  1963                3210271.352   \n",
      "3  1964                3273670.772   \n",
      "4  1965                3339592.688   \n",
      "\n",
      "   Area with Permanent Snow/Glaciers (CCI-LC, 1000 ha)  \\\n",
      "0                                                0.0     \n",
      "1                                                0.0     \n",
      "2                                                0.0     \n",
      "3                                                0.0     \n",
      "4                                                0.0     \n",
      "\n",
      "   Major livestock density in agricultural areas (LSU/ha)  \\\n",
      "0                                             125.44        \n",
      "1                                             131.79        \n",
      "2                                             135.30        \n",
      "3                                             129.70        \n",
      "4                                             135.18        \n",
      "\n",
      "   Total Emissions of CO2eq (gigagrams)  \\\n",
      "0                                   0.0   \n",
      "1                                   0.0   \n",
      "2                                   0.0   \n",
      "3                                   0.0   \n",
      "4                                   0.0   \n",
      "\n",
      "   Emissions of CO2eq from Agriculture (gigagrams)  \\\n",
      "0                                              0.0   \n",
      "1                                              0.0   \n",
      "2                                              0.0   \n",
      "3                                              0.0   \n",
      "4                                              0.0   \n",
      "\n",
      "   Emissions of CO2eq from Energy Production (gigagrams)  \\\n",
      "0                                                0.0       \n",
      "1                                                0.0       \n",
      "2                                                0.0       \n",
      "3                                                0.0       \n",
      "4                                                0.0       \n",
      "\n",
      "   Emissions of CO2eq from Land Use Sources (gigagrams)  \\\n",
      "0                                                0.0      \n",
      "1                                                0.0      \n",
      "2                                                0.0      \n",
      "3                                                0.0      \n",
      "4                                                0.0      \n",
      "\n",
      "   Emissions of CO2eq from Forests (gigagrams)  \\\n",
      "0                                          0.0   \n",
      "1                                          0.0   \n",
      "2                                          0.0   \n",
      "3                                          0.0   \n",
      "4                                          0.0   \n",
      "\n",
      "   Emissions of CO2eq from Waste (gigagrams)  \\\n",
      "0                                        0.0   \n",
      "1                                        0.0   \n",
      "2                                        0.0   \n",
      "3                                        0.0   \n",
      "4                                        0.0   \n",
      "\n",
      "                       ...                       Total (Eco. Footprint, gha)  \\\n",
      "0                      ...                                      7.051328e+09   \n",
      "1                      ...                                      7.285129e+09   \n",
      "2                      ...                                      7.568390e+09   \n",
      "3                      ...                                      7.886046e+09   \n",
      "4                      ...                                      8.175833e+09   \n",
      "\n",
      "   Built-up Land (Biocapacity, gha)  Carbon (Biocapacity, gha)  \\\n",
      "0                      8.185577e+07                          0   \n",
      "1                      8.675759e+07                          0   \n",
      "2                      8.878870e+07                          0   \n",
      "3                      9.326747e+07                          0   \n",
      "4                      9.629964e+07                          0   \n",
      "\n",
      "   Cropland (Biocapacity, gha)  Fishing Grounds (Biocapacity, gha)  \\\n",
      "0                 1.444871e+09                        1.127685e+09   \n",
      "1                 1.507910e+09                        1.125044e+09   \n",
      "2                 1.524017e+09                        1.121237e+09   \n",
      "3                 1.573895e+09                        1.118825e+09   \n",
      "4                 1.599579e+09                        1.114830e+09   \n",
      "\n",
      "   Forest Products (Biocapacity, gha)  Grazing Land (Biocapacity, gha)  \\\n",
      "0                        5.496960e+09                     1.477614e+09   \n",
      "1                        5.491694e+09                     1.477223e+09   \n",
      "2                        5.478200e+09                     1.473314e+09   \n",
      "3                        5.477081e+09                     1.472889e+09   \n",
      "4                        5.476387e+09                     1.472158e+09   \n",
      "\n",
      "   Total (Biocapacity, gha)  Birth Rate, Crude (annually per 1000 people)  \\\n",
      "0              9.628986e+09                                     31.085604   \n",
      "1              9.688629e+09                                     34.937833   \n",
      "2              9.685557e+09                                     36.110627   \n",
      "3              9.735957e+09                                     35.002338   \n",
      "4              9.759252e+09                                     34.386962   \n",
      "\n",
      "   Death Rate, Crude (annually per 1000 people)  \n",
      "0                                     15.023582  \n",
      "1                                     13.972581  \n",
      "2                                     13.749269  \n",
      "3                                     13.797060  \n",
      "4                                     13.205036  \n",
      "\n",
      "[5 rows x 37 columns]\n",
      "Use of Nitrogen per area of cropland (kg/ha)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from scipy.stats import spearmanr as sp\n",
    "\n",
    "print(\"HI\")\n",
    "data = open('World_Data.csv', 'r', encoding=\"ISO-8859-1\");\n",
    "# Read Bryan's file\n",
    "df = pd.read_csv(data)\n",
    "a = [column for column in df]\n",
    "del df[a[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_col = 1\n",
    "y_col = 2\n",
    "\n",
    "k = df[a[y_col]]\n",
    "\n",
    "fin = []\n",
    "sec = []\n",
    "count = 0\n",
    "for val in k:\n",
    "    if(int(val)!=0):\n",
    "        fin.append(val)\n",
    "        sec.append(df[a[x_col]][count])\n",
    "        \n",
    "    count+=1\n",
    "    \n",
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "plt.plot(sec, fin)\n",
    "\n",
    "plt.xlabel(a[x_col])\n",
    "plt.ylabel(a[y_col])\n",
    "\n",
    "plt.legend()\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14,)\n",
      "16\n",
      "Started\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/numpy/lib/function_base.py:2400: RuntimeWarning: invalid value encountered in true_divide\n",
      "  c /= stddev[:, None]\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/numpy/lib/function_base.py:2400: RuntimeWarning: invalid value encountered in true_divide\n",
      "  c /= stddev[:, None]\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/numpy/lib/function_base.py:2401: RuntimeWarning: invalid value encountered in true_divide\n",
      "  c /= stddev[None, :]\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater\n",
      "  return (self.a < x) & (x < self.b)\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/numpy/lib/function_base.py:2400: RuntimeWarning: invalid value encountered in true_divide\n",
      "  c /= stddev[:, None]\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/numpy/lib/function_base.py:2401: RuntimeWarning: invalid value encountered in true_divide\n",
      "  c /= stddev[None, :]\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less\n",
      "  return (self.a < x) & (x < self.b)\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater\n",
      "  return (self.a < x) & (x < self.b)\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less\n",
      "  return (self.a < x) & (x < self.b)\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/numpy/lib/function_base.py:2401: RuntimeWarning: invalid value encountered in true_divide\n",
      "  c /= stddev[None, :]\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/numpy/lib/function_base.py:2400: RuntimeWarning: invalid value encountered in true_divide\n",
      "  c /= stddev[:, None]\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater\n",
      "  return (self.a < x) & (x < self.b)\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/numpy/lib/function_base.py:2401: RuntimeWarning: invalid value encountered in true_divide\n",
      "  c /= stddev[None, :]\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal\n",
      "  cond2 = cond0 & (x <= self.a)\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal\n",
      "  cond2 = cond0 & (x <= self.a)\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less\n",
      "  return (self.a < x) & (x < self.b)\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/numpy/lib/function_base.py:2400: RuntimeWarning: invalid value encountered in true_divide\n",
      "  c /= stddev[:, None]\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/numpy/lib/function_base.py:2401: RuntimeWarning: invalid value encountered in true_divide\n",
      "  c /= stddev[None, :]\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal\n",
      "  cond2 = cond0 & (x <= self.a)\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater\n",
      "  return (self.a < x) & (x < self.b)\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal\n",
      "  cond2 = cond0 & (x <= self.a)\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less\n",
      "  return (self.a < x) & (x < self.b)\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater\n",
      "  return (self.a < x) & (x < self.b)\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/numpy/lib/function_base.py:2400: RuntimeWarning: invalid value encountered in true_divide\n",
      "  c /= stddev[:, None]\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/numpy/lib/function_base.py:2401: RuntimeWarning: invalid value encountered in true_divide\n",
      "  c /= stddev[None, :]\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater\n",
      "  return (self.a < x) & (x < self.b)\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less\n",
      "  return (self.a < x) & (x < self.b)\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal\n",
      "  cond2 = cond0 & (x <= self.a)\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less\n",
      "  return (self.a < x) & (x < self.b)\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal\n",
      "  cond2 = cond0 & (x <= self.a)\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/numpy/lib/function_base.py:2400: RuntimeWarning: invalid value encountered in true_divide\n",
      "  c /= stddev[:, None]\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/numpy/lib/function_base.py:2401: RuntimeWarning: invalid value encountered in true_divide\n",
      "  c /= stddev[None, :]\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater\n",
      "  return (self.a < x) & (x < self.b)\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less\n",
      "  return (self.a < x) & (x < self.b)\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal\n",
      "  cond2 = cond0 & (x <= self.a)\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/numpy/lib/function_base.py:2400: RuntimeWarning: invalid value encountered in true_divide\n",
      "  c /= stddev[:, None]\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/numpy/lib/function_base.py:2401: RuntimeWarning: invalid value encountered in true_divide\n",
      "  c /= stddev[None, :]\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater\n",
      "  return (self.a < x) & (x < self.b)\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/numpy/lib/function_base.py:2400: RuntimeWarning: invalid value encountered in true_divide\n",
      "  c /= stddev[:, None]\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less\n",
      "  return (self.a < x) & (x < self.b)\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/numpy/lib/function_base.py:2400: RuntimeWarning: invalid value encountered in true_divide\n",
      "  c /= stddev[:, None]\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/numpy/lib/function_base.py:2401: RuntimeWarning: invalid value encountered in true_divide\n",
      "  c /= stddev[None, :]\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/numpy/lib/function_base.py:2401: RuntimeWarning: invalid value encountered in true_divide\n",
      "  c /= stddev[None, :]\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/numpy/lib/function_base.py:2400: RuntimeWarning: invalid value encountered in true_divide\n",
      "  c /= stddev[:, None]\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal\n",
      "  cond2 = cond0 & (x <= self.a)\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/numpy/lib/function_base.py:2401: RuntimeWarning: invalid value encountered in true_divide\n",
      "  c /= stddev[None, :]\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater\n",
      "  return (self.a < x) & (x < self.b)\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less\n",
      "  return (self.a < x) & (x < self.b)\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater\n",
      "  return (self.a < x) & (x < self.b)\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater\n",
      "  return (self.a < x) & (x < self.b)\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal\n",
      "  cond2 = cond0 & (x <= self.a)\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/numpy/lib/function_base.py:2400: RuntimeWarning: invalid value encountered in true_divide\n",
      "  c /= stddev[:, None]\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less\n",
      "  return (self.a < x) & (x < self.b)\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal\n",
      "  cond2 = cond0 & (x <= self.a)\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater\n",
      "  return (self.a < x) & (x < self.b)\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less\n",
      "  return (self.a < x) & (x < self.b)\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less\n",
      "  return (self.a < x) & (x < self.b)\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal\n",
      "  cond2 = cond0 & (x <= self.a)\n",
      "/home/gautom/anaconda3/envs/pyenv35/lib/python3.5/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal\n",
      "  cond2 = cond0 & (x <= self.a)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finishing\n",
      "Multiprocessing Time: 0.32311582565307617\n"
     ]
    }
   ],
   "source": [
    "import multiprocessing as mp\n",
    "import random\n",
    "import time\n",
    "import numpy as np\n",
    "from scipy.stats import spearmanr as sp\n",
    "\n",
    "# Make all combinations for Spearman\n",
    "fins = []\n",
    "for col in df:\n",
    "    for sec_col in df:\n",
    "        tot = [col, sec_col]\n",
    "        tot.sort()\n",
    "        \n",
    "        fins.append(tot)\n",
    "\n",
    "# Remove duplicates\n",
    "proper = list(set(map(tuple,fins)))\n",
    "\n",
    "split_ways = 14\n",
    "final = [[] for i in range(0, split_ways)]\n",
    "\n",
    "for i in range(0, len(proper)):\n",
    "    final[i%split_ways].append(proper[i])\n",
    "\n",
    "print(np.asarray(final).shape)\n",
    "\n",
    "\n",
    "print(mp.cpu_count())\n",
    "\n",
    "global alls\n",
    "alls = []\n",
    "\n",
    "# spearman function\n",
    "def spearman(L, df, arra):\n",
    "    for group in arra:\n",
    "        row = []\n",
    "        row.append(group[0])\n",
    "        row.append(group[1])\n",
    "        rho, p = sp(df[group[0]], df[group[1]])\n",
    "        row.append(rho)\n",
    "        L.append(row)\n",
    "\n",
    "procc = []\n",
    "\n",
    "start = time.time()\n",
    "\n",
    "print(\"Started\")\n",
    "with mp.Manager() as manager:\n",
    "    L = manager.list()\n",
    "    for i in range(0, 14):\n",
    "        procc.append(mp.Process(target=spearman, args=(L, df, final[i])))\n",
    "        procc[i].start()\n",
    "\n",
    "    for i in range(0, 14):\n",
    "        procc[i].join()\n",
    "    \n",
    "    alls = list(L)\n",
    "\n",
    "print(\"Finishing\")\n",
    "    \n",
    "end = time.time()\n",
    "print(\"Multiprocessing Time: \"+str(end-start)) #fast!\n",
    "\n",
    "pd.DataFrame(np.asarray(alls)).to_csv(\"spearm.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "x2n=np.random.randn(100,2)\n",
    "y2n=np.random.randn(100,2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0      8\n",
      "1     19\n",
      "2     29\n",
      "3     17\n",
      "4     25\n",
      "5     22\n",
      "6      6\n",
      "7      0\n",
      "8     24\n",
      "9     11\n",
      "10    38\n",
      "11     7\n",
      "12    32\n",
      "13    15\n",
      "14     9\n",
      "15    10\n",
      "16    14\n",
      "17    33\n",
      "18    39\n",
      "19    18\n",
      "20     2\n",
      "21    36\n",
      "22     1\n",
      "23    20\n",
      "24    12\n",
      "25    27\n",
      "26    13\n",
      "27    23\n",
      "28     3\n",
      "29    37\n",
      "30    26\n",
      "31    31\n",
      "32    30\n",
      "33    35\n",
      "34    21\n",
      "35     5\n",
      "36     4\n",
      "37    34\n",
      "38    28\n",
      "39    16\n",
      "Name: name, dtype: int64, 0      9\n",
      "1     31\n",
      "2     26\n",
      "3     22\n",
      "4      3\n",
      "5     13\n",
      "6     28\n",
      "7     15\n",
      "8     20\n",
      "9     12\n",
      "10    14\n",
      "11    24\n",
      "12    10\n",
      "13    27\n",
      "14    39\n",
      "15     6\n",
      "16    30\n",
      "17    16\n",
      "18    29\n",
      "19    19\n",
      "20    33\n",
      "21     5\n",
      "22    37\n",
      "23    35\n",
      "24    11\n",
      "25    23\n",
      "26    36\n",
      "27     1\n",
      "28    21\n",
      "29     4\n",
      "30     2\n",
      "31    17\n",
      "32    34\n",
      "33     8\n",
      "34    32\n",
      "35     7\n",
      "36    25\n",
      "37    38\n",
      "38    18\n",
      "39     0\n",
      "Name: type, dtype: int64]\n",
      "[[ 1.  1. -1. ...  1. -1. -1.]\n",
      " [ 1.  1. -1. ...  1. -1. -1.]\n",
      " [-1. -1.  1. ... -1.  1.  1.]\n",
      " ...\n",
      " [ 1.  1. -1. ...  1. -1. -1.]\n",
      " [-1. -1.  1. ... -1.  1.  1.]\n",
      " [-1. -1.  1. ... -1.  1.  1.]]\n",
      "[[nan nan nan ... nan nan nan]\n",
      " [nan nan nan ... nan nan nan]\n",
      " [nan nan nan ... nan nan nan]\n",
      " ...\n",
      " [nan nan nan ... nan nan nan]\n",
      " [nan nan nan ... nan nan nan]\n",
      " [nan nan nan ... nan nan nan]]\n"
     ]
    }
   ],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Industrial Production (% Relative to 2015)', 'Total (Eco. Footprint, gha)', 0.9835142226493659]\n",
      "['Major livestock density in agricultural areas (LSU/ha)', 'Year', 0.9886534518113466]\n",
      "['Cropland (Eco. Footprint, gha)', 'Total (Eco. Footprint, gha)', 0.9954887218045115]\n",
      "['Built-up Land (Eco. Footprint, gha)', 'Major livestock density in agricultural areas (LSU/ha)', 0.9883116883116884]\n",
      "['Area with Permanent Snow/Glaciers (CCI-LC, 1000 ha)', 'Area with Permanent Snow/Glaciers (CCI-LC, 1000 ha)', 0.9999999999999999]\n",
      "['Emissions of CO2eq from Waste (gigagrams)', 'Total Emissions of CO2eq (gigagrams)', 0.9983725135623869]\n",
      "['Population (1000 persons)', 'Total (Biocapacity, gha)', 0.9974709501025291]\n",
      "['Built-up Land (Biocapacity, gha)', 'Year', 0.9995898838004101]\n",
      "['Use of Nitrogen per area of cropland (kg/ha)', 'Use of Nitrogen per area of cropland (kg/ha)', 0.9999999999999999]\n",
      "['Fishing Grounds (Eco. Footprint, gha)', 'Population (1000 persons)', 0.9847573479152427]\n",
      "['Carbon (Eco. Footprint, gha)', 'Population (1000 persons)', 0.9905673274094328]\n",
      "['Built-up Land (Biocapacity, gha)', 'Total (Eco. Footprint, gha)', 0.9965823650034177]\n",
      "['Cropland (Biocapacity, gha)', 'Total (Biocapacity, gha)', 0.9989747095010254]\n",
      "['Industrial Production (% Relative to 2015)', 'Major livestock density in agricultural areas (LSU/ha)', 0.9711828356949839]\n",
      "['Use of Phosphate per area of cropland (kg/ha)', 'Use of Potash per area of cropland (kg/ha)', 0.9925759280089989]\n",
      "['Cropland (Biocapacity, gha)', 'Fishing Grounds (Eco. Footprint, gha)', 0.9813397129186603]\n",
      "['Carbon (Eco. Footprint, gha)', 'Total (Eco. Footprint, gha)', 0.996719070403281]\n",
      "['Cropland (Eco. Footprint, gha)', 'Fishing Grounds (Eco. Footprint, gha)', 0.9813397129186603]\n",
      "['Fishing Grounds (Eco. Footprint, gha)', 'Major livestock density in agricultural areas (LSU/ha)', 0.9712235133287764]\n",
      "['Emissions of CO2eq from Agriculture (gigagrams)', 'Emissions of CO2eq from Waste (gigagrams)', 0.9990054249547919]\n",
      "['Industrial Production (% Relative to 2015)', 'Year', 0.9834453322194533]\n",
      "['Built-up Land (Biocapacity, gha)', 'Major livestock density in agricultural areas (LSU/ha)', 0.9883116883116884]\n",
      "['Built-up Land (Biocapacity, gha)', 'Total (Biocapacity, gha)', 0.9982228298017771]\n",
      "['Population (1000 persons)', 'Total (Eco. Footprint, gha)', 0.9965823650034177]\n",
      "['Cropland (Biocapacity, gha)', 'Major livestock density in agricultural areas (LSU/ha)', 0.9875598086124403]\n",
      "['Total (Biocapacity, gha)', 'Year', 0.9974709501025291]\n",
      "['Carbon (Eco. Footprint, gha)', 'Cropland (Biocapacity, gha)', 0.9883800410116199]\n",
      "['Emissions of CO2eq from Agriculture (gigagrams)', 'Emissions of CO2eq from Energy Production (gigagrams)', 0.9989150090415914]\n",
      "['Use of Potash per area of cropland (kg/ha)', 'Use of Potash per area of cropland (kg/ha)', 0.9999999999999999]\n",
      "['Built-up Land (Biocapacity, gha)', 'Carbon (Eco. Footprint, gha)', 0.990498974709501]\n",
      "['Built-up Land (Eco. Footprint, gha)', 'Total (Biocapacity, gha)', 0.9982228298017771]\n",
      "['Built-up Land (Biocapacity, gha)', 'Fishing Grounds (Eco. Footprint, gha)', 0.9838687628161312]\n",
      "['Built-up Land (Eco. Footprint, gha)', 'Cropland (Biocapacity, gha)', 0.9982228298017771]\n",
      "['Fishing Grounds (Eco. Footprint, gha)', 'Industrial Production (% Relative to 2015)', 0.9676349785544773]\n",
      "['Use of Phosphate per area of cropland (kg/ha)', 'Use of Phosphate per area of cropland (kg/ha)', 0.9999999999999999]\n",
      "['Built-up Land (Eco. Footprint, gha)', 'Cropland (Eco. Footprint, gha)', 0.9982228298017771]\n",
      "['Built-up Land (Eco. Footprint, gha)', 'Total (Eco. Footprint, gha)', 0.9965823650034177]\n",
      "['Carbon (Eco. Footprint, gha)', 'Total (Biocapacity, gha)', 0.9887901572112098]\n",
      "['Built-up Land (Eco. Footprint, gha)', 'Industrial Production (% Relative to 2015)', 0.9832386609297151]\n",
      "['Industrial Production (% Relative to 2015)', 'Population (1000 persons)', 0.9834453322194533]\n",
      "['Use of Nitrogen per area of cropland (kg/ha)', 'Use of Potash per area of cropland (kg/ha)', 0.9883014623172103]\n",
      "['Emissions of CO2eq from Transportation (gigagrams)', 'Total Emissions of CO2eq (gigagrams)', 0.9968354430379747]\n",
      "['Carbon (Eco. Footprint, gha)', 'Industrial Production (% Relative to 2015)', 0.9775896456768697]\n",
      "['Industrial Production (% Relative to 2015)', 'Total (Biocapacity, gha)', 0.9807241602378997]\n",
      "['Use of Nitrogen per area of cropland (kg/ha)', 'Use of Phosphate per area of cropland (kg/ha)', 0.9896512935883015]\n",
      "['Emissions of CO2eq from Agriculture (gigagrams)', 'Emissions of CO2eq from Transportation (gigagrams)', 0.9971971066907775]\n",
      "['Cropland (Eco. Footprint, gha)', 'Year', 0.9973342447026657]\n",
      "['Major livestock density in agricultural areas (LSU/ha)', 'Total (Eco. Footprint, gha)', 0.9849624060150376]\n",
      "['Major livestock density in agricultural areas (LSU/ha)', 'Total (Biocapacity, gha)', 0.987218045112782]\n",
      "['Carbon (Eco. Footprint, gha)', 'Fishing Grounds (Eco. Footprint, gha)', 0.9717703349282298]\n",
      "['Built-up Land (Biocapacity, gha)', 'Population (1000 persons)', 0.9995898838004101]\n",
      "['Fishing Grounds (Eco. Footprint, gha)', 'Total (Biocapacity, gha)', 0.9818181818181817]\n",
      "['Cropland (Eco. Footprint, gha)', 'Industrial Production (% Relative to 2015)', 0.9817575166865912]\n",
      "['Carbon (Eco. Footprint, gha)', 'Year', 0.9905673274094328]\n",
      "['Cropland (Biocapacity, gha)', 'Population (1000 persons)', 0.9973342447026657]\n",
      "['Emissions of CO2eq from Other Sources (gigagrams)', 'Total Emissions of CO2eq (gigagrams)', 0.9531645569620252]\n",
      "['Total (Biocapacity, gha)', 'Total (Eco. Footprint, gha)', 0.9955570745044431]\n",
      "['Major livestock density in agricultural areas (LSU/ha)', 'Population (1000 persons)', 0.9886534518113466]\n",
      "['Emissions of CO2eq from Energy Production (gigagrams)', 'Emissions of CO2eq from Waste (gigagrams)', 0.9999095840867992]\n",
      "['Cropland (Biocapacity, gha)', 'Total (Eco. Footprint, gha)', 0.9954887218045115]\n",
      "['Birth Rate, Crude (annually per 1000 people)', 'Death Rate, Crude (annually per 1000 people)', 0.9921394395078605]\n",
      "['Built-up Land (Eco. Footprint, gha)', 'Population (1000 persons)', 0.9995898838004101]\n",
      "['Built-up Land (Biocapacity, gha)', 'Industrial Production (% Relative to 2015)', 0.9832386609297151]\n",
      "['Emissions of CO2eq from Energy Production (gigagrams)', 'Emissions of CO2eq from Transportation (gigagrams)', 0.9983725135623869]\n",
      "['Emissions of CO2eq from Transportation (gigagrams)', 'Emissions of CO2eq from Waste (gigagrams)', 0.9981916817359854]\n",
      "['Built-up Land (Eco. Footprint, gha)', 'Fishing Grounds (Eco. Footprint, gha)', 0.9838687628161312]\n",
      "['Emissions of CO2eq from Agriculture (gigagrams)', 'Total Emissions of CO2eq (gigagrams)', 0.9969258589511754]\n",
      "['Built-up Land (Eco. Footprint, gha)', 'Carbon (Eco. Footprint, gha)', 0.990498974709501]\n",
      "['Cropland (Eco. Footprint, gha)', 'Population (1000 persons)', 0.9973342447026657]\n",
      "['Cropland (Eco. Footprint, gha)', 'Major livestock density in agricultural areas (LSU/ha)', 0.9875598086124403]\n",
      "['Fishing Grounds (Eco. Footprint, gha)', 'Year', 0.9847573479152427]\n",
      "['Carbon (Eco. Footprint, gha)', 'Cropland (Eco. Footprint, gha)', 0.9883800410116199]\n",
      "['Fishing Grounds (Eco. Footprint, gha)', 'Total (Eco. Footprint, gha)', 0.9810663021189336]\n",
      "['Emissions of CO2eq from Energy Production (gigagrams)', 'Total Emissions of CO2eq (gigagrams)', 0.9984629294755877]\n",
      "['Built-up Land (Biocapacity, gha)', 'Cropland (Biocapacity, gha)', 0.9982228298017771]\n",
      "['Built-up Land (Eco. Footprint, gha)', 'Year', 0.9995898838004101]\n",
      "['Cropland (Eco. Footprint, gha)', 'Total (Biocapacity, gha)', 0.9989747095010254]\n",
      "['Total (Eco. Footprint, gha)', 'Year', 0.9965823650034177]\n",
      "['Built-up Land (Biocapacity, gha)', 'Cropland (Eco. Footprint, gha)', 0.9982228298017771]\n",
      "['Cropland (Biocapacity, gha)', 'Industrial Production (% Relative to 2015)', 0.9817575166865912]\n",
      "['Cropland (Biocapacity, gha)', 'Year', 0.9973342447026657]\n",
      "['Carbon (Eco. Footprint, gha)', 'Major livestock density in agricultural areas (LSU/ha)', 0.9785372522214627]\n"
     ]
    }
   ],
   "source": [
    "filter_val = 0.95\n",
    "\n",
    "for val in alls:\n",
    "    if(val[2]>=filter_val and val[2]<1):\n",
    "        print(val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "56\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_col = 'Year'\n",
    "y_col = 'Food supply (kcal/capita/day)'\n",
    "#x_col = 'Population (1000 persons)'\n",
    "\n",
    "k = df[y_col]\n",
    "\n",
    "print(len(k))\n",
    "\n",
    "fin = []\n",
    "sec = []\n",
    "count = 0\n",
    "for val in k:\n",
    "    if(int(val)!=0):\n",
    "        fin.append(val)\n",
    "        sec.append(df[x_col][count])\n",
    "        \n",
    "    count+=1\n",
    "    \n",
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "plt.plot(sec, fin)\n",
    "\n",
    "plt.xlabel(x_col)\n",
    "plt.ylabel(y_col)\n",
    "plt.title(y_col)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Unnamed: 0',\n",
       " 'Year',\n",
       " 'Population (1000 persons)',\n",
       " 'Area with Permanent Snow/Glaciers (CCI-LC, 1000 ha)',\n",
       " 'Major livestock density in agricultural areas (LSU/ha)',\n",
       " 'Total Emissions of CO2eq (gigagrams)',\n",
       " 'Emissions of CO2eq from Agriculture (gigagrams)',\n",
       " 'Emissions of CO2eq from Energy Production (gigagrams)',\n",
       " 'Emissions of CO2eq from Land Use Sources (gigagrams)',\n",
       " 'Emissions of CO2eq from Forests (gigagrams)',\n",
       " 'Emissions of CO2eq from Waste (gigagrams)',\n",
       " 'Emissions of CO2eq from Other Sources (gigagrams)',\n",
       " 'Emissions of CO2eq from Transportation (gigagrams)',\n",
       " 'Use of Nitrogen per area of cropland (kg/ha)',\n",
       " 'Use of Phosphate per area of cropland (kg/ha)',\n",
       " 'Use of Potash per area of cropland (kg/ha)',\n",
       " 'Use of Pesticides per area of cropland (kg/ha)',\n",
       " 'Food supply (kcal/capita/day)',\n",
       " 'Industrial Production (% Relative to 2015)',\n",
       " 'Average Annual Precipitation (mm/year)',\n",
       " 'Total Renewable Water Resources (10^9 m^3/year)',\n",
       " 'Total Renewable Water Resources Per Capita (m^3/capita/year)',\n",
       " 'Built-up Land (Eco. Footprint, gha)',\n",
       " 'Carbon (Eco. Footprint, gha)',\n",
       " 'Cropland (Eco. Footprint, gha)',\n",
       " 'Fishing Grounds (Eco. Footprint, gha)',\n",
       " 'Forest Products (Eco. Footprint, gha)',\n",
       " 'Grazing Land (Eco. Footprint, gha)',\n",
       " 'Total (Eco. Footprint, gha)',\n",
       " 'Built-up Land (Biocapacity, gha)',\n",
       " 'Carbon (Biocapacity, gha)',\n",
       " 'Cropland (Biocapacity, gha)',\n",
       " 'Fishing Grounds (Biocapacity, gha)',\n",
       " 'Forest Products (Biocapacity, gha)',\n",
       " 'Grazing Land (Biocapacity, gha)',\n",
       " 'Total (Biocapacity, gha)',\n",
       " 'Birth Rate, Crude (annually per 1000 people)',\n",
       " 'Death Rate, Crude (annually per 1000 people)']"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[val for val in a]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "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": "3.5.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}