Pearson Correlation Graph.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy import stats\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Dataset-1\n",
    "x_raw = [0.526,0.636,0.585,0.586,0.661,0.536,0.523,0.607 ]\n",
    "y_woe = [0.669,0.649,0.789,0.79,0.8,0.71,0.673,0.786,]\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(x_raw, y_woe, color='dodgerblue')\n",
    "plt.xlabel(\"RAW based CCP models\", labelpad=13)\n",
    "plt.ylabel(\"WOE based CCP models\", labelpad=13)\n",
    "plt.title(\"Pearson Correlations for dataset-1\", y=1.015);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4600017697130547"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy.stats\n",
    "scipy.stats.pearsonr(x_raw, y_woe)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Dataset-2\n",
    "x_raw = [0.364,0.447,0.028,0.238,0.117,0.06,0.693,0.78 ]\n",
    "y_woe = [0.68,0.684,0.713,0.75,0.732,0.492,0.629,0.738]\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(x_raw, y_woe, color='dodgerblue')\n",
    "plt.xlabel(\"RAW based CCP models\", labelpad=13)\n",
    "plt.ylabel(\"WOE based CCP models\", labelpad=13)\n",
    "plt.title(\"Pearson Correlations for dataset-2\", y=1.015);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.18773996696358825"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy.stats\n",
    "scipy.stats.pearsonr(x_raw, y_woe)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Dataset-3\n",
    "x_raw = [0.379,0.534,0.041,0.278,0.131,0.004,0.727,0.809 ]\n",
    "y_woe = [0.673,0.688,0.716,0.757,0.757,0.507,0.674,0.747]\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(x_raw, y_woe, color='dodgerblue')\n",
    "plt.xlabel(\"RAW based CCP models\", labelpad=13)\n",
    "plt.ylabel(\"WOE based CCP models\", labelpad=13)\n",
    "plt.title(\"Pearson Correlations for dataset-3\", y=1.015);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.31417822780003146"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy.stats\n",
    "scipy.stats.pearsonr(x_raw, y_woe)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JNp4N7bZ37m1ckiR1waxJQFUdN4T9Hw1srqqbAZJcDJwC3DDL+qfRXPgBTgQuq6o72m0vA04CPjavEUuStEgMcp+AH0vyliQXtvNHJHnhHO1/JXBrz/yWtmymOA6l6Zz4md3dVpIkPdggQwT/BPghMDWoZQvwu3O0/5mGGtYs654KXFJV9+7OtklemWQyyeS2bdv2MExJkhafQZKAw6vqfOAegKrawdzdJ2ALcHDP/EHA1lnWPZUHVvUPtG1VXVhVE1U1sWLFir0MV5KkxWOQJOCHSZbR/spOcjhw9xzt/2rgiCSHJdmH5kK/fvpKSZ4A7A9c2VN8KXBCkv2T7A+c0JZJkqQBDDLU763AJ4GDk/w5cAxwxlzsvKp2JjmT5uK9BLioqjYmOReYrKqphOA04OKqqp5t70jyNppEAuDcqU6CkiRp19JzXZ19peYGQc+maQa4qqq+Pd+BzYeJiYmanJwcdRiSJA1Nkg1VNTHTsllrAtobA/X6Rvt+SJJDqupLcxWgJEkavkFuFrQfzQ15ph4c9FTgn4DnzG9okiRpPs3aMbCqjmtvGHQL8Iy2h/0zae7Yt3lYAUqSpPkxSMfA1VV1/dRMVX0lyVH9NpAkwbpNPkFQ422QJODGJB8A/oxmmOBLgBvnNSpJWuDWbYKzL4cdO5v527Y382AioPExyH0CXgpsBF4DvJbmvv4+SliS+jj/ivsTgCk7djbl0rjYZU1AVf0gyQXAp2lqAm6qqnvmPTJJmsFCqWLfun33yqVR2GUSkORY4EPA12hGBxyc5PSq+vz8hiZJD7SQqtgPXN7EN1O5NC4GaQ54B3BCVT23qv4TzSN8/2B+w5KkB1tIVexnrYFl035mLVvalEvjYpCOgQ+tqpumZqrqX5I8dB5jkqQZLaQq9qmaiYXQdKHuGiQJmEzyQeAj7fyLgQ3zF5KkURn39vaFVsW+dvV4HT9pukGaA36DZnTAq2lGCNwA/Lf5DErS8E21t9+2vekBPNXevm7TqCO7n1Xs0twaZHTA3cDvty9Ji1S/9vZx+TVrFbs0twYZHfBC4G3Aoe36AaqqHjHPsUkaooXS3m4VuzR3BmkOeCdwOvCYqnpEVS03AZAWn9na1ce1vV3S3hskCbgV+EpV1XwHI2l0bG/XYrNuE6y5CFa9q3kfp/4t42KQ0QFnAZ9I8g/A3VOFVWUfAWkR2VV7+7iPHBglj834WUg3lhqlQZKA/wV8D9gP2Gd+w5E0SrO1t/sPdXYem/G0EDq6joNBkoBHV9UJ8x6JpLHlP9TZeWzG00Lp6Dpqg/QJ+HQSkwCpw/yHOjuPzXiyo+tgBkkCfgv4ZJIdSb6bZHuS7853YJLGh/9QZzfbMXhI7Ig2SnZ0Hcwuk4B2SOBDqmqZQwSlbvIf6uxmOjYA99b43XGxS9auhvOOh5XLm5vbrFzezNtE80CD9AmQ1HHeqW92U8fg9Z9qLvy97BswWt5YatdMAiQNxH+os1u7Gl576czL7BugcTZInwBJ0i7Yb0IL0axJQJL9krw2yXuS/Nck1hpI0izsN6GFqN+F/UPAPcAXgP8MPInmUcKSpGnsN6GFqF8S8KSqegpAkg8C/zyckCRpYbLfhBaafn0C7pmaqKqdfdaTJEkLUL+agKf13BQowLJ2PkB5rwBJkha2WZOAqloyzEAkSdJw9Rsd8Kwk/3mG8l9I8sz5DUuSJM23fn0Cfg+4cYbyG9tlkiRpAeuXBDymqr42vbCqNgOPmbeIJEnSUPRLApb1WfawuQ5EkiQNV78k4NNJ/leS9BYm+R3gM/MbliRJmm/9hgi+AfgAsDnJtW3Z04BJ4NfnOzBJkjS/+g0R/A/gtCSPA57cFm+sqpvnMoAkJwHvApYAH6iq82ZY51eAc4ACvlxVL2rL7wWub1f7elWdPJexSZK0mM2aBCQ5EVheVZcAN/eUvxj4VlVdtrc7T7IEuAB4PrAFuDrJ+qq6oWedI4A3A8dU1Z1JHtvzETuq6qi9jUOSNLt1m3wmwmLVr0/A7wD/MEP55cC5c7T/o4HNVXVzVf0QuBg4Zdo6rwAuqKo7AarqW3O0b0nSLqzbBGdfDrdtb6pib9vezK/bNOrINBf6JQE/VlXbphdW1b8zd6MDVgK39sxvact6PR54fJIvJrmqbT6Ysl+SybZ87Uw7SPLKdp3Jbdse9HUkSX2cfwXsmPb0mB07m3ItfP06Bu6XZOn0hwcleSj9hw/ujsxQVtPmlwJHAMcCBwFfSHJkVd0FHFJVW9t+C59Jcn1V/dsDPqzqQuBCgImJiemfLUnqY+v23SvXwtKvJuCvgfcn+dGv/nb6ve2yubAFOLhn/iBg6wzr/G1V3VNVXwVuokkKqKqt7fvNwOeAp89RXJIkmj4Au1OuhaVfEvAW4JvALUk2JNkAfA3Y1i6bC1cDRyQ5LMk+wKnA+mnrrAOOA0hyAE3zwM1J9k+yb0/5McANSJLmzFlrYNm0OuNlS5tyLXz9hgjuBM5ubw70U23x5qraMVc7r6qdSc4ELqUZInhRVW1Mci4wWVXr22UnJLkBuBd4U1XdnmQN8L4k99EkM+f1jiqQJO29qVEAjg5YnFLVnWbyiYmJmpycHHUYkiQNTZINVTUx07J+zQGSJGkRMwmQJKmj+t0x8Bn9NqyqL819OJIkaVj63SfgHe37fsAE8GWacf1PBf4JeM78hiZJkubTrM0BVXVcVR0H3AI8o6omquqZNGPxNw8rQEmSND8G6ROwuqqmntRHVX0F8KE9kiQtcP2aA6bcmOQDwJ/R3NL3JcCN8xqVJEmad4MkAS8FfgN4TTv/eeCP5y0iSZI0FLtMAqrqB0neC3yiqm4aQkySJGkIdtknIMnJwLXAJ9v5o5JMv7+/JElaYAbpGPhW4GjgLoCquhZYNY8xSZI6bt0mWHMRrHpX875u06gjWpwG6ROws6q+k2Teg5Ekad0mOPty2LGzmb9tezMPPrhorg1SE/CVJC8CliQ5IskfAlfMc1ySpI46/4r7E4ApO3Y25ZpbgyQBrwKeDNwNfAz4LvDa+QxKktRdW7fvXrn23CCjA74P/Dbw20mWAA+rqh/Me2SSpE46cHnTBDBTuebWIKMDPprkEUkeBmwEbkrypvkPTZLURWetgWXTfqIuW9qUa24N0hzwpKr6LrAW+ARwCPCr8xqVJKmz1q6G846Hlcubp9atXN7M2ylw7g0yOuChSR5KkwS8p6ruSVLzHJckqcPWrvaiPwyD1AS8D/ga8DDg80kOpekcKEmSFrBBOga+G3h3T9EtSY6bv5AkSdIwDNIcQJKfpxkmuF9P8bnzEpEkSRqKQUYHvBf4LzT3Cwjwy8Ch8xyXJEmaZ4P0CVhTVb8G3FlVvwP8DHDw/IYlSZLm2yBJwI72/ftJDgTuAQ6bv5AkSdIwDNIn4O+TPAr4PeBLQAHvn9eoJEnSvBtkdMDb2sm/SvL3wH5V9Z35DUuSJM23XSYBSfYDfhN4Dk0twD8m+WOfHyBJ0sI2SHPAh4HtwB+286cBH6EZJSBJkhaoQZKAJ1TV03rmP5vky/MVkCRJGo5BRgdck+TZUzNJfhr44vyFJEmShmHWmoAk19P0AXgo8GtJvt7OHwrcMJzwJEnSfOnXHPDCoUUhSZKGbtYkoKpuGWYgkiRpuAbpEyBJkhYhkwBJkjpq1iQgyeqe6X2nLXv2g7eQJEkLSb+agI/2TF85bdkfzVUASU5KclOSzUnOnmWdX0lyQ5KNST7aU356kn9tX6fPVUySJHVBv9EBmWV6pvk9kmQJcAHwfGALcHWS9VV1Q886RwBvBo6pqjuTPLYtfzTwVmCCZujihnbbO+ciNkmSFrt+NQE1y/RM83vqaGBzVd1cVT8ELgZOmbbOK4ALpi7uVfWttvxE4LKquqNddhlw0hzFJUnSotevJuCgJO+m+dU/NU07v3KO9r8SuLVnfgvw09PWeTxAki8CS4BzquqTs2w7V3FJkrTo9UsC3tQzPTlt2fT5PTVTs8L0WoalwBHAscBBwBeSHDngtiR5JfBKgEMOOWRvYpUkaVHpd7OgD01NJ3l4U1T/Mcf73wIc3DN/ELB1hnWuqqp7gK8muYkmKdhCkxj0bvu56TuoqguBCwEmJibmqhlDkqQFr+99ApL8RvvMgFuArye5JclvzuH+rwaOSHJYkn2AU4H109ZZBxzXxnMATfPAzcClwAlJ9k+yP3BCWyZJkgbQ7wFCbwHWAMdW1c1t2eOAdyV5dFX97t7uvKp2JjmT5uK9BLioqjYmOReYrKr13H+xvwG4F3hTVd3exvM2mkQC4NyqumNvY5IkqStSNXMNeVvt/rSq+sG08mXAl6vq8UOIb05NTEzU5ORcdWeQJGn8JdlQVRMzLevbHDA9AWjLdgD3zVFskiRpRPolAVuSHD+9MMnPAd+Yv5AkSdIw9Bsi+Grgb5P8I7CBZvjds4BjePANfSRJ0gIza01AVW0EjgQ+D6wCHtdOH9kukyRJC1i/0QGvBf4R+HBV7RxeSJIkaRj63jYYeDewOsl1wBXAF4ErHYonSdLC1++OgW8EaG/iM0Fzz4CXAe9PcldVPWk4IUqSpPnQryZgyjLgEcAj29dW4Pr5DEqSJM2/fn0CLgSeDGwH/ommOeD3px7pK0mSFrZ+9wk4BNgX+HfgNpoH9tw1jKAkSdL869cn4KQkoakNWAO8ATgyyR00nQPfOqQYJUnSPOjbJ6CaBwt8JcldwHfa1wuBowGTAEmSFrB+fQJeTVMDcAxwD+3wQOAi7BgoSdKC168mYBVwCfC6qvJZAZIkLTL9+gS8fpiBSJKk4er7KGFJkrR4mQRIktRRJgGSJHWUSYAkSR1lEiBJUkeZBEiS1FEmAZIkdZRJgCRJHWUSIElSR5kESJLUUSYBkiR1lEmAJEkdZRIgSVJHmQRIktRRJgGSJHWUSYAkSR1lEiBJUkeZBEiS1FEmAZIkdZRJgCRJHWUSIElSR408CUhyUpKbkmxOcvYMy89Isi3Jte3r13uW3dtTvn64kUuStLAtHeXOkywBLgCeD2wBrk6yvqpumLbqX1TVmTN8xI6qOmq+45QkaTEadU3A0cDmqrq5qn4IXAycMuKYJEnqhFEnASuBW3vmt7Rl0/1ikuuSXJLk4J7y/ZJMJrkqydp5jVSSpEVm1ElAZiirafN/B6yqqqcCnwY+1LPskKqaAF4EvDPJ4Q/aQfLKNlGY3LZt21zFLUnSgjfqJGAL0PvL/iBga+8KVXV7Vd3dzr4feGbPsq3t+83A54CnT99BVV1YVRNVNbFixYq5jV6SpAVs1EnA1cARSQ5Lsg9wKvCAXv5JfrJn9mTgxrZ8/yT7ttMHAMcA0zsUSpKkWYx0dEBV7UxyJnApsAS4qKo2JjkXmKyq9cCrk5wM7ATuAM5oN38i8L4k99EkM+fNMKpAkiTNIlXTm+AXr4mJiZqcnBx1GJIkDU2SDW3/uQcZdXOAJEkaEZMASZI6yiRAkqSOMgmQJKmjTAIkSeookwBJkjrKJECSpI4yCZAkqaNMAiRJ6iiTAEmSOsokQJKkjjIJkCSpo0wCJEnqKJMASZI6yiRAkqSOMgmQJKmjTAIkSeookwBJkjrKJECSpI4yCZAkqaNMAiRJ6iiTAEmSOsokQJKkjjIJkCSpo0wCJEnqKJMASZI6yiRAkqSOMgmQJKmjTAIkSeookwBJkjrKJECSpI5aOuoAFoN1m+D8K2DrdjhwOZy1BtauHnVUkiT1ZxKwl9ZtgrMvhx07m/nbtjfzYCIgSRpvNgfspfOvuD8BmLJjZ1MuSdI4MwnYS1u37165JEnjwiRgLx24fPfKJUkaFyYBe+msNbBsWs+KZUubckmSxtnIk4AkJyW5KcnmJGfPsPyMJNuSXNu+fr1n2elJ/rV9nT7cyBtrV8N5x8PK5RCa9/OOt1OgJGn8jXR0QJIlwAXA84EtwNVJ1lfVDdNW/YuqOnPato8G3gpMAAVsaLe9cwihP8Da1V70JUkLz6hrAo4GNlfVzVX1Q+Bi4JQBtz0RuKyq7mgv/JcBJ81TnJIkLTqjTgJWArf2zG9py6b7xSTXJbkkycG7s22SVyaZTDK5bdu2uYpbkqQFb9RJQGYoq2nzfwesqqqnAp8GPrQb209UoDMAAAowSURBVFJVF1bVRFVNrFixYq+ClSRpMRl1ErAFOLhn/iBga+8KVXV7Vd3dzr4feOag20qSpNmNOgm4GjgiyWFJ9gFOBdb3rpDkJ3tmTwZubKcvBU5Isn+S/YET2jJJkjSAkY4OqKqdSc6kuXgvAS6qqo1JzgUmq2o98OokJwM7gTuAM9pt70jyNppEAuDcqrpj6F9CkqQFKlUPakZftCYmJmpycnLUYew1n1ooSRpUkg1VNTHTMp8iuMD41EJJ0lwZdZ8A7SafWihJmismAQuMTy2UJM0Vk4AFxqcWSpLmiknAAuNTCyVJc8WOgQvMVOc/RwdIkvaWScAC5FMLJUlzweYASZI6yiRAkqSOMgmQJKmjTAIkSeookwBJkjrKJECSpI4yCZAkqaNMAiRJ6iiTAEmSOsokQJKkjjIJkCSpo0wCJEnqKJMASZI6yiRAkqSOMgmQJKmjTAIkSeookwBJkjrKJECSpI5KVY06hqFJsg24ZR4++gDg2/Pwudp9novx4bkYL56P8THsc3FoVa2YaUGnkoD5kmSyqiZGHYc8F+PEczFePB/jY5zOhc0BkiR1lEmAJEkdZRIwNy4cdQD6Ec/F+PBcjBfPx/gYm3NhnwBJkjrKmgBJkjrKJGA3JDkpyU1JNic5e4bl+yb5i3b5PyVZNfwou2GAc/H6JDckuS7J5UkOHUWcXbCrc9Gz3i8lqSRj0St6MRrkXCT5lfZvY2OSjw47xi4Z4P/UIUk+m+Sa9n/VC4YeZFX5GuAFLAH+DXgcsA/wZeBJ09b5TeC97fSpwF+MOu7F+BrwXBwH/Fg7/Ruei9Gdi3a95cDngauAiVHHvRhfA/5dHAFcA+zfzj921HEv1teA5+NC4Dfa6ScBXxt2nNYEDO5oYHNV3VxVPwQuBk6Zts4pwIfa6UuA45NkiDF2xS7PRVV9tqq+385eBRw05Bi7YpC/C4C3AecDPxhmcB0zyLl4BXBBVd0JUFXfGnKMXTLI+SjgEe30I4GtQ4wPsDlgd6wEbu2Z39KWzbhOVe0EvgM8ZijRdcsg56LXy4H/O68Rddcuz0WSpwMHV9XfDzOwDhrk7+LxwOOTfDHJVUlOGlp03TPI+TgHeEmSLcAngFcNJ7T7LR32DhewmX7RTx9aMcg62nsDH+ckLwEmgOfOa0Td1fdcJHkI8AfAGcMKqMMG+btYStMkcCxN7dgXkhxZVXfNc2xdNMj5OA3406p6R5KfAT7Sno/75j+8hjUBg9sCHNwzfxAPrrr50TpJltJU79wxlOi6ZZBzQZLnAb8NnFxVdw8ptq7Z1blYDhwJfC7J14BnA+vtHDgvBv0f9bdVdU9VfRW4iSYp0Nwb5Hy8HPg4QFVdCexH81yBoTEJGNzVwBFJDkuyD03Hv/XT1lkPnN5O/xLwmWp7fGhO7fJctFXQ76NJAGz3nD99z0VVfaeqDqiqVVW1iqZ/xslVNTmacBe1Qf5HraPpNEuSA2iaB24eapTdMcj5+DpwPECSJ9IkAduGGaRJwIDaNv4zgUuBG4GPV9XGJOcmObld7YPAY5JsBl4PzDpcSntuwHPxe8DDgb9Mcm2S6X98mgMDngsNwYDn4lLg9iQ3AJ8F3lRVt48m4sVtwPPxBuAVSb4MfAw4Y9g/HL1joCRJHWVNgCRJHWUSIElSR5kESJLUUSYBkiR1lEmAJEkdZRIgSVJHmQRIktRRJgGSJHWUSYAkSR1lEiBJUkeZBEiS1FEmAZIkdZRJgCRJHWUSIElSR5kESCOU5N4k1yb5SpK/S/Koactfl+QHSR7ZU3ZNkqPa6aVJ/iPJS3qWb0jyjGmfc0aS98z39+nZ3/dmKf+JJBcn+bckNyT5RJLHt8se385vTnJjko8n+fEkxyb5Tvu9b0zy1lHFv7vrSOPOJEAarR1VdVRVHQncAfzWtOWnAVcD/09P2RXAmnb6acBNU/NJHgY8DvjyfAa9J5IE+Bvgc1V1eFU9CfgfwI8n2Q/4P8AfV9VPVdUTgT8GVrSbf6Gqng5MAC9J8swRfAVp0TEJkMbHlcDKqZkkhwMPB95CkwxM+SL3JwFrgPcCR7XzRwNfqqp7Z/j8g5N8MslNvb+mk6xraw82JnllW7YkyZ+2NRTXJ3ndVEztZ2xI8oUkq9vyw5JcmeTqJG+b5fsdB9xTVe+dKqiqa6vqC8CLgCur6u96ln22qr7S+wFV9R/ABuDw3vK2tuAf2tqDf0lyXpIXJ/nnNv7D2/UOTXJ5kuva90N2FX+SN7Xl1yX5nelfKslPJvl8T43Oz87y/aWxYxIgjYEkS4DjgfU9xacBHwO+ADwhyWPb8t6agDXA54G7kyxv5784y26OBl5MkzD8cpKJtvxlVfVMml/Zr07ymHadlVV1ZFU9BfiTdt0LgVe1678R+KO2/F00v+KfBfz7LPs/kuYCvrvLfqSN7dnAxhkWPw14DfAU4FeBx1fV0cAHgFe167wH+HBVPRX4c+Dd/eJPcgJwBM2xOwp4ZpL/NG2/LwIuraqj2hiu3dX3kMaFSYA0WsuSXAvcDjwauKxn2anAxVV1H/DXwC8DVNXXgH2S/ASwmqY54Grgp2mSgCtm2ddlVXV7Ve1oP+85bfmrk3wZuAo4mOaidzPwuCR/mOQk4LtJHt5+/l+2Mb8P+Mn2M46hSVgAPrKnB6OPn01yDfAp4LyqmikJuLqqvlFVdwP/1q4LcD2wqp3+GeCjPXFOHYPZ4j+hfV0DfInmeB8xfb/AS5OcAzylqrbv9reTRmTpqAOQOm5HVR3Vdvz7e5o+Ae9O8lSai81lTVM6+9BcmC9ot7sS+CXgG1VVSa6iuZAdTXMxn0lNn09yLPA84Geq6vtJPgfsV1V3JnkacGIb068ArwXuan/xDvL5021sY55t2XP7bPuFqnrhLj7/7p7p+3rm72P2/3U1y/SUAG+vqvfNttOq+nxbO/DzwEeS/F5VfXgXsUpjwZoAaQxU1XeAVwNvTPJQmqaAc6pqVfs6EFiZ5NB2ky8Cr6NJBmjffw3496q6a5bdPD/Jo5MsA9a2n/FI4M42AVhNU9VOkgOAh1TVXwH/E3hGVX0X+GqSX27XSZsoTMVzajv94ln2/xlg3ySvmCpI8qwkz6X5db4myc/3LDspyVP6H7nddsW0OP+xnZ4t/kuBl7W1ICRZ2dMsMxXnocC3qur9wAeBB4zMkMaZSYA0JqrqGppe/ae2r7+ZtsrfcP+F6os0owCubLf9BrCE2ZsCoLngfYSmzfqvqmoS+CSwNMl1wNu4vxZhJfC5ttr/T4E3t+UvBl7eNh9sBE5py18D/FaSq2kSi5m+X9GMcnh+O0RwI3AOsLVtongh8Kok/5rkBuAM4Ft9vs+eeDVN1f11NP0GXtMv/qr6FE2CcmWS64FLgOXTPvNY4Nq2ueIXafoXSAtCmr9LSZLUNdYESJLUUSYBkiR1lEmAJEkdZRIgSVJHmQRIktRRJgGSJHWUSYAkSR1lEiBJUkf9/8QwBAvHLsqCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Dataset-1 , 2 and 3\n",
    "x_raw = [0.526,0.636,0.585,0.586,0.661,0.536,0.523,0.607,0.364,0.447,0.028,0.238,0.117,0.06,0.693,0.78,0.379,0.534,0.041,0.278,0.131,0.004,0.727,0.809 ]\n",
    "y_woe = [0.669,0.649,0.789,0.79,0.8,0.71,0.673,0.786,0.68,0.684,0.713,0.75,0.732,0.492,0.629,0.738,0.673,0.688,0.716,0.757,0.757,0.507,0.674,0.747]\n",
    "\n",
    " \n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(x_raw, y_woe, color='dodgerblue')\n",
    "plt.xlabel(\"RAW based CCP models\", labelpad=13)\n",
    "plt.ylabel(\"WOE based CCP models\", labelpad=13)\n",
    "plt.title(\"Pearson Correlations\", y=1.015);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3456906872850647"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy.stats\n",
    "scipy.stats.pearsonr(x_raw, y_woe)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 1 - Make a scatter plot with square markers, set column names as labels\n",
    "\n",
    "def heatmap(x, y, size):\n",
    "    fig, ax = plt.subplots()\n",
    "    \n",
    "    # Mapping from column names to integer coordinates\n",
    "    x_labels = [v for v in sorted(x.unique())]\n",
    "    y_labels = [v for v in sorted(y.unique())]\n",
    "    x_to_num = {p[1]:p[0] for p in enumerate(x_labels)} \n",
    "    y_to_num = {p[1]:p[0] for p in enumerate(y_labels)} \n",
    "    \n",
    "    size_scale = 500\n",
    "    ax.scatter(\n",
    "        x=x.map(x_to_num), # Use mapping for x\n",
    "        y=y.map(y_to_num), # Use mapping for y\n",
    "        s=size * size_scale, # Vector of square sizes, proportional to size parameter\n",
    "        marker='s' # Use square as scatterplot marker\n",
    "    )\n",
    "    \n",
    "    # Show column labels on the axes\n",
    "    ax.set_xticks([x_to_num[v] for v in x_labels])\n",
    "    ax.set_xticklabels(x_labels, rotation=45, horizontalalignment='right')\n",
    "    ax.set_yticks([y_to_num[v] for v in y_labels])\n",
    "    ax.set_yticklabels(y_labels)\n",
    "    \n",
    "data = pd.read_csv('https://raw.githubusercontent.com/drazenz/heatmap/master/autos.clean.csv')\n",
    "columns = ['bore', 'stroke', 'compression-ratio', 'horsepower', 'city-mpg', 'price'] \n",
    "corr = data[columns].corr()\n",
    "corr = pd.melt(corr.reset_index(), id_vars='index') # Unpivot the dataframe, so we can get pair of arrays for x and y\n",
    "corr.columns = ['x', 'y', 'value']\n",
    "heatmap(\n",
    "    x=corr['x'],\n",
    "    y=corr['y'],\n",
    "    size=corr['value'].abs()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XPQN8MDNfDVwLvCciXlNxTZv1fuCJqovogE8C/5iZvwT8MgWuKSJGgfcBtcy8CtgGvK3aqjbkCLB31dhtwIOZeSXwYPN5CY7w/LU8AFyVma8Fvg4c6HVR7TLAgcw8nZmPNR9/j+WQKLYJeURcCvwe8Omqa9mMiHgp8NvAnQCZ+cPMXKy2qrZtB4YiYjtwEXCq4nrWLTMfBs6uGr4JuKv5+C5goqdFtelCa8nM+zPzmebTR4BLe15YmwzwVSJiNzAOPFptJZvyl8CHgB9VXcgm/QKwAPxNczvo0xHx4qqL2qjMbAAfB04Ap4HvZOb91Va1aa/IzNOw/AEIuLjiejrlncCXqi5ivQzwZ4mIlwCfBz6Qmd+tup52RMSNwJnMPFZ1LR2wHfgV4K8ycxz4X8r5Uf3HmvvDNwFXALuAF0fEO6qtSqtFxEdY3k69u+pa1ssAb4qIHSyH992ZOV11PZtwHfDmiHgS+FvgDRHx2WpLattJ4GRmrvw0dC/LgV6aNwLfzMyFzDwHTAOvq7imzXoqIi4BaH49U3E9mxIR+4AbgbdnQTfHGOBARATL+6xPZOYnqq5nMzLzQGZempm7WT5R9lBmFvlpLzP/B/jviBhrDt0A/EeFJbXrBHBtRFzU/LN2AwWejF3lPmBf8/E+4AsV1rIpEbEX+DDw5sz8ftX1bIQBvuw64A9Y/rT65eZ/v1t1UQLgvcDdEfFV4BrgzyuuZ8OaP0HcCzwGHGf5710xt29HxFHg34CxiDgZEe8CDgJviohvAG9qPu97a6zlU8DPAg80/+7/daVFboC30ktSofwELkmFMsAlqVAGuCQVygCXpEIZ4JJUKANckgplgEtSof4fZkKImTM2C6QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import pearsonr\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#Create two random variable \n",
    "X = [1,2,3,4,5,6,7,8,9,10,11,12,13]\n",
    "Y = [7,8,8,11,10,11,12,15,20,14,16,15,19]\n",
    "\n",
    "#plot the variables to show linearity\n",
    "plt.scatter(X,Y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8876362751857733, 5.134724298671332e-05)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pearsonr(X,Y)\n",
    "#results are: (0.88763627518577326, 5.1347242986713319e-05) where the first value is the Pearson Correlation Coefficients and the second value is the P-value. 0.8 means that the variables are highly positively correlated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}