{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6c9cf5e0",
   "metadata": {},
   "source": [
    "# Example of the Box-Cox power transformation\n",
    "\n",
    "The Box-Cox power transformation is given by *lambda* parameter as:\n",
    "\n",
    "* `y = (x**lambda - 1) / lambda,  for lambda != 0`\n",
    "* `y = log(x),                    for lambda == 0`\n",
    "\n",
    "There is the *boxcox* function in the *scipy* package, see https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.boxcox.html.\n",
    "\n",
    "We introduce an example of this transformation applied on a sample from the exponential distribution with various *lambda*s."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c64493d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "c_lambdas = [1.5, 1, 0.5, 0.25, 0, -0.5] # various lambdas\n",
    "\n",
    "D = pd.DataFrame({'x': stats.expon.rvs(loc=0, scale=1, size=5000)}) # data frame, x = sample from the exponential distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0d24b1c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal lambda:  0.27391805825323995\n"
     ]
    }
   ],
   "source": [
    "# note that numerator is decreased by 1 after powering\n",
    "D[\"x0\"] = stats.boxcox(D[\"x\"], c_lambdas[0]) # powered to 1.5\n",
    "D[\"x1\"] = stats.boxcox(D[\"x\"], c_lambdas[1]) # identity\n",
    "D[\"x2\"] = stats.boxcox(D[\"x\"], c_lambdas[2]) # sqrt\n",
    "D[\"x3\"] = stats.boxcox(D[\"x\"], c_lambdas[3]) # 4th root\n",
    "D[\"x4\"] = stats.boxcox(D[\"x\"], c_lambdas[4]) # logarithm\n",
    "D[\"x5\"] = stats.boxcox(D[\"x\"], c_lambdas[5]) # reciprocal sqrt\n",
    "D[\"xopt\"], lopt = stats.boxcox(D[\"x\"]) # let lambda optimized\n",
    "print(\"Optimal lambda: \", lopt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "aa71d4ff",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jhucin\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\jhucin\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:118: UserWarning: The figure layout has changed to tight\n",
      "  self._figure.tight_layout(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.displot(data=D, x=\"x\", kind=\"hist\").set(title=\"Original data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "38725cf5",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jhucin\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\jhucin\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:118: UserWarning: The figure layout has changed to tight\n",
      "  self._figure.tight_layout(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.displot(data=D, x=\"x0\", kind=\"hist\").set(title=\"Powered by 1.5 - even skewer than original\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "04c13562",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jhucin\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\jhucin\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:118: UserWarning: The figure layout has changed to tight\n",
      "  self._figure.tight_layout(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.displot(data=D, x=\"x2\", kind=\"hist\").set(title=\"Square rooted - a good step but still skewed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f8e418a0",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jhucin\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\jhucin\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:118: UserWarning: The figure layout has changed to tight\n",
      "  self._figure.tight_layout(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.displot(data=D, x=\"x3\", kind=\"hist\").set(title=\"4th rooted - looks very well\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b55cc014",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jhucin\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\jhucin\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:118: UserWarning: The figure layout has changed to tight\n",
      "  self._figure.tight_layout(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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JSkpKKrdPq1atJJ3eXmduG6ns9ipP+/bttWXLFoe2P+9JnMnNzU3jxo3TuHHjdPToUa1Zs0aPPvqo+vbtq/379zscHagJw4cP17vvvqv09HQ1adJEW7Zs0axZs+zTAwIC7HtaFV1oEx0d7TBe0e/J4MGD9fDDD2vmzJnq3LmzsrOzL+rinXnz5qlHjx4O9UqnL0osT1X9/p45n9JwrOi96eLiooCAgCpZ9sW4kM+QUtXxnp83b57uuusupaSkOLQfPny4zFcOL9Zll12mhIQEzZw5UwkJCVq2bJmefPJJubq6VulyqhMhXUs0aNBAnTp10uLFi/X888/L29tb0umrhefNm6fIyEi1bNlSktS9e3etWLFChw8ftr/pTp06VeYK2tI34JlfH3nttdcuut4//wXvDK1atVKLFi30zTfflPkwOFNcXJyWLVumX375xX60oaSkRIsWLTrncnx9fdWhQ4dK1XjJJZfolltu0cGDBzV27Fjt27dPMTExFR5VqA7x8fFq3Lix0tLS1KRJE3l5eemvf/2rfbqPj4/i4uL09ddfq02bNvY9o8rw8vLSPffco5dfflkbN25U27Ztde2111Z6fjabrczv7rfffqtNmzYpKiqq0vO9UK1atVLjxo21YMECTZgwwf6+OnHihN5//337Fd/OdiGfIdWpvJ/b8uXLdfDgQV122WUXPL9zfdY8+OCDio+P19ChQ+Xq6qqRI0de8DKciZC2mLVr15Z7uLR///5KTU1Vnz59FBcXpwkTJsjDw0OvvPKKduzYobffftv+4TBp0iR9+OGH6tWrlyZNmiRvb2+9+uqr9vMypefNunbtqoCAAN13332aPHmy3N3dNX/+fH3zzTcXvR6tW7fW+vXr9eGHHyo8PFy+vr72Pdea8tprrykhIUF9+/bVsGHD1LhxY/3222/auXOnvvrqK/sfLY899piWLVumnj176oknnpCPj49mzpxZ5mtoVeGGG25QbGysOnTooEaNGunnn3/W9OnT1bRpU7Vo0ULS6W0nSS+++KKGDh0qd3d3tWrVyuF8Y1VxdXXVXXfdpalTp8rPz09JSUny9/d36PPiiy+qW7duuu6663T//ferWbNmOnbsmPbs2aMPP/xQa9euPe/ljRo1SlOmTFFGRoZef/31i6o9MTFRTz/9tCZPnqzu3btr165deuqppxQdHX3OawOqkouLi6ZMmaI77rhDiYmJuvfee1VQUKDnnntOR48e1bPPPltjtZzL+X6GVKfExETNmTNHl19+udq0aaOMjAw999xzioyMrNT8WrdurcWLF2vWrFlq3769XFxcHP5w7tOnj2JiYrRu3ToNGTJEISEhVbUqNYJz0hbz8MMP69Zbby0z5OTkqHv37lq7dq0aNGigYcOG6fbbb1deXp6WLVvmcJHTVVddpfT0dHl7e+uuu+7SPffcoyuvvNJ+Lrb0QzgoKEjLly+Xj4+PhgwZorvvvlsNGzY8rz3Ic3nxxRfVokUL3X777erYsaPuvffei57nhYqLi9N///tfXXLJJRo7dqx69+6t+++/X2vWrFHv3r3t/WJjY7VmzRr5+flp6NChuueee9SmTRs9/vjj1VLTp59+qvvuu099+vTRY489pl69etnPbUunL7KaOHGiPvzwQ3Xr1k0dO3ZURkZGlddSavjw4SooKNCvv/5qv2Dsz2JiYvTVV18pNjZWjz32mOLj4zVixAi999579u/Kn6/GjRurW7duCgwM1ODBgy+q7kmTJmn8+PGaPXu2rr/+er3++ut69dVX1a1bt4uab2UMHjxYS5cu1ZEjR3Tbbbdp+PDh8vPz07p165xST0XO9zOkOr344osaMmSIUlNTdcMNN2jZsmVavHhxpW+K9OCDD+qWW27Ro48+qs6dO6tjx45l+pReNFubLhgrZTPGGGcXgZoRHx+vffv26YcffnB2KaincnJy1LRpU40ZM+a8b9QBXKwOHTrIZrOVuX6kNuBwdx01btw4tWvXTlFRUfrtt980f/58paenl3uHKaC6HThwQD/99JOee+45ubi4lLnXO1DV8vPztWPHDn300UfKyMjQkiVLnF1SpRDSdVRJSYmeeOIJZWdny2azKSYmRnPnztWQIUOcXRrqoddff11PPfWUmjVrpvnz5zt89x2oDl999ZXi4uIUFBSkyZMn19qH/XC4GwAAi+LCMQAALIqQBgDAoghpAAAsipDW6Yck5Ofni9PzAAArIaR1+l6//v7+Fd7zFwAAZyCkAQCwKEIaAACLIqQBALAop4b0rFmz1KZNG/n5+cnPz09dunTRxx9/bJ8+bNgw2Ww2h6Fz584O8ygoKNCYMWMUHBysBg0aaMCAATpw4EBNrwoAAFXOqSEdGRmpZ599Vlu3btXWrVvVs2dP3XjjjcrMzLT36devn7KysuzDihUrHOYxduxYLVmyRAsXLtTnn3+u48ePKzExUSUlJTW9OgAAVCnL3RY0MDBQzz33nEaMGKFhw4bp6NGjWrp0abl98/Ly1KhRI82dO9f+mLVDhw4pKipKK1asUN++fc9rmfn5+fL391deXp78/PyqalUAALgoljknXVJSooULF+rEiRPq0qWLvX39+vUKCQlRy5YtNXLkSOXk5NinZWRkqKioSPHx8fa2iIgIxcbGauPGjRUuq6CgQPn5+Q4DAABW4/SQ3r59uxo2bChPT0/dd999WrJkiWJiYiRJCQkJmj9/vtauXasXXnhBW7ZsUc+ePVVQUCBJys7OloeHhwICAhzmGRoaquzs7AqXmZqaKn9/f/sQFRVVfSsIAEAlOf1Rla1atdK2bdt09OhRvf/++xo6dKg2bNigmJgY+yFsSYqNjVWHDh3UtGlTLV++XElJSRXO0xgjm81W4fSJEydq3Lhx9vH8/HyCGgBgOU4PaQ8PD1122WWSpA4dOmjLli168cUX9dprr5XpGx4erqZNm2r37t2SpLCwMBUWFio3N9dhbzonJ0ddu3atcJmenp7y9PSs4jUBAKBqOf1w95mMMfbD2Wc6cuSI9u/fr/DwcElS+/bt5e7urvT0dHufrKws7dix46whDQBAbeDUPelHH31UCQkJioqK0rFjx7Rw4UKtX79eK1eu1PHjx5WcnKybb75Z4eHh2rdvnx599FEFBwfrpptukiT5+/trxIgRGj9+vIKCghQYGKgJEyaodevW6t27tzNXDQCAi+bUkP7ll1905513KisrS/7+/mrTpo1WrlypPn366OTJk9q+fbveeustHT16VOHh4YqLi9OiRYvk6+trn8e0adPk5uamQYMG6eTJk+rVq5fmzJkjV1dXJ64ZAAAXz3Lfk3YGvicNALAiy52TBgAApxHSAABYFCENAIBFEdIAAFgUIQ0AgEU5/Y5jAIDaZdCQ4Tp0OLfcaRHBAXpnXloNV1R3EdIAgAty6HCuIpMeLnfagcX/quFq6jYOdwMAYFGENAAAFkVIAwBgUYQ0AAAWRUgDAGBRhDQAABZFSAMAYFGENAAAFkVIAwBgUYQ0AAAWxW1BAaAWquj+2dw7u24hpAGgFqro/tncO7tu4XA3AAAWRUgDAGBRhDQAABbFOWkAqCe42Kz2IaQBoJ7gYrPah8PdAABYFCENAIBFEdIAAFgUIQ0AgEUR0gAAWBQhDQCARfEVLABAuSr6XvXuPT8q0gn11EeENACgXBV9rzoz5W4nVFM/EdIAUM/9sGunuvUbWKadPWbnI6QBoJ4rMi7sMVsUF44BAGBRhDQAABZFSAMAYFGENAAAFkVIAwBgUYQ0AAAWRUgDAGBRhDQAABZFSAMAYFGENAAAFkVIAwBgUYQ0AAAWRUgDAGBRhDQAABZFSAMAYFFODelZs2apTZs28vPzk5+fn7p06aKPP/7YPt0Yo+TkZEVERMjb21s9evRQZmamwzwKCgo0ZswYBQcHq0GDBhowYIAOHDhQ06sCAECVc2pIR0ZG6tlnn9XWrVu1detW9ezZUzfeeKM9iKdMmaKpU6fq5Zdf1pYtWxQWFqY+ffro2LFj9nmMHTtWS5Ys0cKFC/X555/r+PHjSkxMVElJibNWCwCAKuHUkL7hhhvUv39/tWzZUi1bttQ///lPNWzYUJs3b5YxRtOnT9ekSZOUlJSk2NhYvfnmm/r999+1YMECSVJeXp5mz56tF154Qb1791a7du00b948bd++XWvWrHHmqgEAcNEsc066pKRECxcu1IkTJ9SlSxft3btX2dnZio+Pt/fx9PRU9+7dtXHjRklSRkaGioqKHPpEREQoNjbW3qc8BQUFys/PdxgAALAap4f09u3b1bBhQ3l6euq+++7TkiVLFBMTo+zsbElSaGioQ//Q0FD7tOzsbHl4eCggIKDCPuVJTU2Vv7+/fYiKiqritQIA4OI5PaRbtWqlbdu2afPmzbr//vs1dOhQfffdd/bpNpvNob8xpkzbmc7VZ+LEicrLy7MP+/fvv7iVAACgGjg9pD08PHTZZZepQ4cOSk1N1VVXXaUXX3xRYWFhklRmjzgnJ8e+dx0WFqbCwkLl5uZW2Kc8np6e9ivKSwcAAKzG6SF9JmOMCgoKFB0drbCwMKWnp9unFRYWasOGDerataskqX379nJ3d3fok5WVpR07dtj7AABQW7k5c+GPPvqoEhISFBUVpWPHjmnhwoVav369Vq5cKZvNprFjxyolJUUtWrRQixYtlJKSIh8fHw0ePFiS5O/vrxEjRmj8+PEKCgpSYGCgJkyYoNatW6t3797OXDUAAC6aU0P6l19+0Z133qmsrCz5+/urTZs2Wrlypfr06SNJeuihh3Ty5EmNGjVKubm56tSpk1avXi1fX1/7PKZNmyY3NzcNGjRIJ0+eVK9evTRnzhy5uro6a7UAAKgSTg3p2bNnn3W6zWZTcnKykpOTK+zj5eWlGTNmaMaMGVVcHQDUPj/s2qlu/QaWO233nh8VWbPl4CI5NaQBAFWryLgoMunhcqdlptxdw9XgYlnuwjEAAHAaIQ0AgEUR0gAAWBQhDQCARRHSAABYFCENAIBFEdIAAFgUIQ0AgEUR0gAAWBQhDQCARRHSAABYFCENAIBFEdIAAFgUIQ0AgEUR0gAAWBQhDQCARRHSAABYFCENAIBFEdIAAFgUIQ0AgEUR0gAAWJSbswsAANQdP+zaqW79BpZpjwgO0Dvz0mq+oFqOkAYAVJki46LIpIfLtB9Y/C8nVFP7cbgbAACLIqQBALAoQhoAAIsipAEAsChCGgAAiyKkAQCwKEIaAACL4nvSAGBhg4YM16HDuWXad+/5UZFOqAc1i5AGAAs7dDi33JuDZKbc7YRqUNMIaQCwAPaYUR5CGgAsgD1mlIcLxwAAsChCGgAAiyKkAQCwKEIaAACLIqQBALAoQhoAAIsipAEAsChCGgAAiyKkAQCwKEIaAACLIqQBALAoQhoAAIsipAEAsChCGgAAi3JqSKempqpjx47y9fVVSEiIBg4cqF27djn0GTZsmGw2m8PQuXNnhz4FBQUaM2aMgoOD1aBBAw0YMEAHDhyoyVUBAKDKOTWkN2zYoAceeECbN29Wenq6iouLFR8frxMnTjj069evn7KysuzDihUrHKaPHTtWS5Ys0cKFC/X555/r+PHjSkxMVElJSU2uDgAAVcrNmQtfuXKlw3haWppCQkKUkZGhv/zlL/Z2T09PhYWFlTuPvLw8zZ49W3PnzlXv3r0lSfPmzVNUVJTWrFmjvn37Vt8KAABQjSx1TjovL0+SFBgY6NC+fv16hYSEqGXLlho5cqRycnLs0zIyMlRUVKT4+Hh7W0REhGJjY7Vx48Zyl1NQUKD8/HyHAQAAq7FMSBtjNG7cOHXr1k2xsbH29oSEBM2fP19r167VCy+8oC1btqhnz54qKCiQJGVnZ8vDw0MBAQEO8wsNDVV2dna5y0pNTZW/v799iIqKqr4VAwCgkpx6uPvPRo8erW+//Vaff/65Q/ttt91m/39sbKw6dOigpk2bavny5UpKSqpwfsYY2Wy2cqdNnDhR48aNs4/n5+cT1AAAy7HEnvSYMWO0bNkyrVu3TpGRkWftGx4erqZNm2r37t2SpLCwMBUWFio3N9ehX05OjkJDQ8udh6enp/z8/BwGAACsxqkhbYzR6NGjtXjxYq1du1bR0dHnfM2RI0e0f/9+hYeHS5Lat28vd3d3paen2/tkZWVpx44d6tq1a7XVDgBAdXPq4e4HHnhACxYs0AcffCBfX1/7OWR/f395e3vr+PHjSk5O1s0336zw8HDt27dPjz76qIKDg3XTTTfZ+44YMULjx49XUFCQAgMDNWHCBLVu3dp+tTcAALWRU0N61qxZkqQePXo4tKelpWnYsGFydXXV9u3b9dZbb+no0aMKDw9XXFycFi1aJF9fX3v/adOmyc3NTYMGDdLJkyfVq1cvzZkzR66urjW5OgCACvywa6e69RtYpj0iOEDvzEur+YJqCaeGtDHmrNO9vb21atWqc87Hy8tLM2bM0IwZM6qqNABAFSoyLopMerhM+4HF/3JCNbWHJS4cAwAAZRHSAABYFCENAIBFEdIAAFgUIQ0AgEUR0gAAWBQhDQCARRHSAABYFCENAIBFEdIAAFgUIQ0AgEUR0gAAWBQhDQCARRHSAABYFCENAIBFEdIAAFgUIQ0AgEUR0gAAWBQhDQCARRHSAABYFCENAIBFEdIAAFgUIQ0AgEUR0gAAWBQhDQCARRHSAABYFCENAIBFEdIAAFgUIQ0AgEUR0gAAWJSbswsAgPpi0JDhOnQ4t9xpu/f8qMgargfWR0gDQA05dDhXkUkPlzstM+XuGq4GtQGHuwEAsChCGgAAiyKkAQCwKEIaAACLIqQBALAoQhoAAIsipAEAsChCGgAAiyKkAQCwKEIaAACLIqQBALAoQhoAAIsipAEAsChCGgAAiyKkAQCwKEIaAACLcmpIp6amqmPHjvL19VVISIgGDhyoXbt2OfQxxig5OVkRERHy9vZWjx49lJmZ6dCnoKBAY8aMUXBwsBo0aKABAwbowIEDNbkqAABUOaeG9IYNG/TAAw9o8+bNSk9PV3FxseLj43XixAl7nylTpmjq1Kl6+eWXtWXLFoWFhalPnz46duyYvc/YsWO1ZMkSLVy4UJ9//rmOHz+uxMRElZSUOGO1AACoEm7OXPjKlSsdxtPS0hQSEqKMjAz95S9/kTFG06dP16RJk5SUlCRJevPNNxUaGqoFCxbo3nvvVV5enmbPnq25c+eqd+/ekqR58+YpKipKa9asUd++fWt8vQAAqAqWOiedl5cnSQoMDJQk7d27V9nZ2YqPj7f38fT0VPfu3bVx40ZJUkZGhoqKihz6REREKDY21t4HAIDayKl70n9mjNG4cePUrVs3xcbGSpKys7MlSaGhoQ59Q0ND9fPPP9v7eHh4KCAgoEyf0tefqaCgQAUFBfbx/Pz8KlsPAACqimX2pEePHq1vv/1Wb7/9dplpNpvNYdwYU6btTGfrk5qaKn9/f/sQFRVV+cIBAKgmlgjpMWPGaNmyZVq3bp0iIyPt7WFhYZJUZo84JyfHvncdFhamwsJC5ebmVtjnTBMnTlReXp592L9/f1WuDgAAVcKpIW2M0ejRo7V48WKtXbtW0dHRDtOjo6MVFham9PR0e1thYaE2bNigrl27SpLat28vd3d3hz5ZWVnasWOHvc+ZPD095efn5zAAAGA1Tj0n/cADD2jBggX64IMP5Ovra99j9vf3l7e3t2w2m8aOHauUlBS1aNFCLVq0UEpKinx8fDR48GB73xEjRmj8+PEKCgpSYGCgJkyYoNatW9uv9gYAoDZyakjPmjVLktSjRw+H9rS0NA0bNkyS9NBDD+nkyZMaNWqUcnNz1alTJ61evVq+vr72/tOmTZObm5sGDRqkkydPqlevXpozZ45cXV1ralUAAKhyTg1pY8w5+9hsNiUnJys5ObnCPl5eXpoxY4ZmzJhRhdUBAOBclrhwDAAAlFWpkL700kt15MiRMu1Hjx7VpZdeetFFAQCASh7u3rdvX7n3xS4oKNDBgwcvuigAQP3ww66d6tZvYLnTIoID9M68tJotyGIuKKSXLVtm//+qVavk7+9vHy8pKdEnn3yiZs2aVVlxAIC6rci4KDLp4XKnHVj8rxquxnouKKQHDhwo6fTFXEOHDnWY5u7urmbNmumFF16osuIAAKjPLiikT506Jen0TUa2bNmi4ODgaikKAABU8pz03r17q7oOAABwhkp/T/qTTz7RJ598opycHPsedqk33njjogsDAKC+q1RIP/nkk3rqqafUoUMHhYeHn/OJVAAA4MJVKqRfffVVzZkzR3feeWdV1wMAAP5/lbqZSWFhYYVPmAIAAFWjUiH9t7/9TQsWLKjqWgAAwJ9U6nD3H3/8oX//+99as2aN2rRpI3d3d4fpU6dOrZLiAACozyoV0t9++63atm0rSdqxY4fDNC4iAwCgalQqpNetW1fVdQAAgDPwqEoAACyqUnvScXFxZz2svXbt2koXBAAATqtUSJeejy5VVFSkbdu2aceOHWUevAEAACqnUiE9bdq0ctuTk5N1/PjxiyoIAACcVqXnpIcMGcJ9uwEAqCJVGtKbNm2Sl5dXVc4SAIB6q1KHu5OSkhzGjTHKysrS1q1b9fjjj1dJYQAA1HeVCml/f3+HcRcXF7Vq1UpPPfWU4uPjq6QwAADqu0qFdFpaWlXXAQAAzlCpkC6VkZGhnTt3ymazKSYmRu3atauqugAAqPcqFdI5OTm6/fbbtX79el1yySUyxigvL09xcXFauHChGjVqVNV1AgBQ71Tq6u4xY8YoPz9fmZmZ+u2335Sbm6sdO3YoPz9ff//736u6RgAA6qVK7UmvXLlSa9as0RVXXGFvi4mJ0cyZM7lwDACAKlKpkD516lSZZ0hLkru7u06dOnXRRQFAbTZoyHAdOpxbpn33nh8V6YR6UHtVKqR79uypBx98UG+//bYiIiIkSQcPHtQ//vEP9erVq0oLBIDa5tDhXEUmPVymPTPlbidUg9qsUuekX375ZR07dkzNmjVT8+bNddlllyk6OlrHjh3TjBkzqrpGAADqpUrtSUdFRemrr75Senq6vv/+exljFBMTo969e1d1fQAA1FsXtCe9du1axcTEKD8/X5LUp08fjRkzRn//+9/VsWNHXXnllfrss8+qpVAAAOqbCwrp6dOna+TIkfLz8yszzd/fX/fee6+mTp1aZcUBAFCfXVBIf/PNN+rXr1+F0+Pj45WRkXHRRQEAgAsM6V9++aXcr16VcnNz06+//nrRRQEAgAsM6caNG2v79u0VTv/2228VHh5+0UUBAIALDOn+/fvriSee0B9//FFm2smTJzV58mQlJiZWWXEAANRnF/QVrMcee0yLFy9Wy5YtNXr0aLVq1Uo2m007d+7UzJkzVVJSokmTJlVXrQAA1CsXFNKhoaHauHGj7r//fk2cOFHGGEmSzWZT37599corryg0NLRaCgUAoL654JuZNG3aVCtWrFBubq727NkjY4xatGihgICA6qgPAIB6q1J3HJOkgIAAdezYsSprAQAAf1Kpe3cDAIDqR0gDAGBRlT7cDQD1QUXPhpakiOAAvTMvrYYrQn1CSAPAWVT0bGhJOrD4XzVcDeobDncDAGBRhDQAABZFSAMAYFFODelPP/1UN9xwgyIiImSz2bR06VKH6cOGDZPNZnMYOnfu7NCnoKBAY8aMUXBwsBo0aKABAwbowIEDNbgWAABUD6deOHbixAldddVVGj58uG6++eZy+/Tr109paf939aSHh4fD9LFjx+rDDz/UwoULFRQUpPHjxysxMVEZGRlydXWt1voB1G8/7Nqpbv0GlmnfvedHRdZ8OaiDnBrSCQkJSkhIOGsfT09PhYWFlTstLy9Ps2fP1ty5c9W7d29J0rx58xQVFaU1a9aob9++VV4zAJQqMi7lXvmdmXK3E6pBXWT5c9Lr169XSEiIWrZsqZEjRyonJ8c+LSMjQ0VFRYqPj7e3RUREKDY2Vhs3bqxwngUFBcrPz3cYAACwGkuHdEJCgubPn6+1a9fqhRde0JYtW9SzZ08VFBRIkrKzs+Xh4VHm4R6hoaHKzs6ucL6pqany9/e3D1FRUdW6HgAAVIalb2Zy22232f8fGxurDh06qGnTplq+fLmSkpIqfJ0xRjabrcLpEydO1Lhx4+zj+fn5BDUAwHIsvSd9pvDwcDVt2lS7d++WJIWFhamwsFC5uY637MvJyTnrc609PT3l5+fnMAAAYDW1KqSPHDmi/fv3Kzw8XJLUvn17ubu7Kz093d4nKytLO3bsUNeuXZ1VJgAAVcKph7uPHz+uPXv22Mf37t2rbdu2KTAwUIGBgUpOTtbNN9+s8PBw7du3T48++qiCg4N10003SZL8/f01YsQIjR8/XkFBQQoMDNSECRPUunVr+9XeAADUVk4N6a1btyouLs4+XnqeeOjQoZo1a5a2b9+ut956S0ePHlV4eLji4uK0aNEi+fr62l8zbdo0ubm5adCgQTp58qR69eqlOXPm8B1pAECt59SQ7tGjh4wxFU5ftWrVOefh5eWlGTNmaMaMGVVZGgAATlerzkkDAFCfENIAAFgUIQ0AgEUR0gAAWBQhDQCARRHSAABYFCENAIBFEdIAAFgUIQ0AgEVZ+lGVAID664ddO9Wt38Ay7RHBAXpnXlrNF+QEhDQAwJKKjIsikx4u035g8b+cUI1zcLgbAACLIqQBALAoQhoAAIsipAEAsChCGgAAiyKkAQCwKEIaAACLIqQBALAoQhoAAIsipAEAsChCGgAAiyKkAQCwKEIaAACLIqQBALAoQhoAAIsipAEAsChCGgAAi3JzdgEAYAWDhgzXocO5Zdp37/lRkU6oB5AIaQCQJB06nKvIpIfLtGem3O2EaoDTONwNAIBFEdIAAFgUIQ0AgEUR0gAAWBQhDQCARRHSAABYFCENAIBFEdIAAFgUIQ0AgEUR0gAAWBQhDQCARRHSAABYFCENAIBFEdIAAFgUIQ0AgEUR0gAAWBQhDQCARRHSAABYFCENAIBFOTWkP/30U91www2KiIiQzWbT0qVLHaYbY5ScnKyIiAh5e3urR48eyszMdOhTUFCgMWPGKDg4WA0aNNCAAQN04MCBGlwLAACqh1ND+sSJE7rqqqv08ssvlzt9ypQpmjp1ql5++WVt2bJFYWFh6tOnj44dO2bvM3bsWC1ZskQLFy7U559/ruPHjysxMVElJSU1tRoAAFQLN2cuPCEhQQkJCeVOM8Zo+vTpmjRpkpKSkiRJb775pkJDQ7VgwQLde++9ysvL0+zZszV37lz17t1bkjRv3jxFRUVpzZo16tu3b42tCwAAVc2y56T37t2r7OxsxcfH29s8PT3VvXt3bdy4UZKUkZGhoqIihz4RERGKjY219ylPQUGB8vPzHQYAAKzGsiGdnZ0tSQoNDXVoDw0NtU/Lzs6Wh4eHAgICKuxTntTUVPn7+9uHqKioKq4eAICLZ9mQLmWz2RzGjTFl2s50rj4TJ05UXl6efdi/f3+V1AoAQFWybEiHhYVJUpk94pycHPvedVhYmAoLC5Wbm1thn/J4enrKz8/PYQAAwGosG9LR0dEKCwtTenq6va2wsFAbNmxQ165dJUnt27eXu7u7Q5+srCzt2LHD3gcAgNrKqVd3Hz9+XHv27LGP7927V9u2bVNgYKCaNGmisWPHKiUlRS1atFCLFi2UkpIiHx8fDR48WJLk7++vESNGaPz48QoKClJgYKAmTJig1q1b26/2BgCgtnJqSG/dulVxcXH28XHjxkmShg4dqjlz5uihhx7SyZMnNWrUKOXm5qpTp05avXq1fH197a+ZNm2a3NzcNGjQIJ08eVK9evXSnDlz5OrqWuPrAwBAVXJqSPfo0UPGmAqn22w2JScnKzk5ucI+Xl5emjFjhmbMmFENFQIA4DyWPScNAEB9R0gDAGBRhDQAABZFSAMAYFGENAAAFuXUq7sB4GIMGjJchw7nljstIjhA78xLq+GKgKpFSAOotQ4dzlVk0sPlTjuw+F81XA1Q9TjcDQCARbEnDQCoVX7YtVPd+g0s014XT3EQ0gCAWqXIuJR7mqMunuLgcDcAABbFnjSAOqk+HRJF3UVIA6iT6tMhUdRdHO4GAMCiCGkAACyKkAYAwKIIaQAALIqQBgDAoghpAAAsipAGAMCiCGkAACyKkAYAwKIIaQAALIqQBgDAoghpAAAsigdsAKhXKno61u49Pyqy5ssBzoqQBlCvVPR0rMyUu51QDXB2HO4GAMCiCGkAACyKkAYAwKIIaQAALIqQBgDAoghpAAAsipAGAMCiCGkAACyKkAYAwKK44xgAyxs0ZLgOHc4t086tPFHXEdIALONsYRz30L/LtHMrT9R1hDQAyzh0OJf7agN/wjlpAAAsipAGAMCiCGkAACyKkAYAwKIIaQAALIqQBgDAoghpAAAsipAGAMCiCGkAACyKO44BqFEV3fpT4l7cwJksvSednJwsm83mMISFhdmnG2OUnJysiIgIeXt7q0ePHsrMzHRixQDOpfTWn+UNhcXFzi4PsBRLh7QkXXnllcrKyrIP27dvt0+bMmWKpk6dqpdffllbtmxRWFiY+vTpo2PHjjmxYgAAqoblQ9rNzU1hYWH2oVGjRpJO70VPnz5dkyZNUlJSkmJjY/Xmm2/q999/14IFC5xcNQAAF8/yIb17925FREQoOjpat99+u3766SdJ0t69e5Wdna34+Hh7X09PT3Xv3l0bN2486zwLCgqUn5/vMAAAYDWWDulOnTrprbfe0qpVq/Sf//xH2dnZ6tq1q44cOaLs7GxJUmhoqMNrQkND7dMqkpqaKn9/f/sQFRVVbesAAEBlWfrq7oSEBPv/W7durS5duqh58+Z688031blzZ0mSzWZzeI0xpkzbmSZOnKhx48bZx/Pz8wlqAKjlfti1U936DSzTHhEcoHfmpdV8QVXA0iF9pgYNGqh169bavXu3Bg4cKEnKzs5WeHi4vU9OTk6ZveszeXp6ytPTszpLBQDUsCLjosikh8u0H1j8LydUUzUsfbj7TAUFBdq5c6fCw8MVHR2tsLAwpaen26cXFhZqw4YN6tq1qxOrBACgalh6T3rChAm64YYb1KRJE+Xk5OiZZ55Rfn6+hg4dKpvNprFjxyolJUUtWrRQixYtlJKSIh8fHw0ePNjZpQMAcNEsHdIHDhzQX//6Vx0+fFiNGjVS586dtXnzZjVt2lSS9NBDD+nkyZMaNWqUcnNz1alTJ61evVq+vr5OrhwAgItn6ZBeuHDhWafbbDYlJycrOTm5ZgoCAKAGWTqkAVhfRffirs1X1AJWQUgDuCil9+I+U22+ohawilp1dTcAAPUJIQ0AgEUR0gAAWBTnpAFUi4pu0bh7z4+KrPlygFqJkAZQLSq6RWNmyt1OqAaonTjcDQCARRHSAABYFCENAIBFEdIAAFgUIQ0AgEUR0gAAWBRfwQIA1GkVfWdfsv6DYAhpAECdVtF39iXrPwiGw90AAFgUIQ0AgEUR0gAAWBQhDQCARRHSAABYFCENAIBFEdIAAFgUIQ0AgEUR0gAAWBR3HANgN2jIcB06nFvuNKvfPhGoiwhpAHaHDudWePvEtanDyr3/8e49PyqymusC6itCGsB5qej+x5kpdzuhGqB+4Jw0AAAWRUgDAGBRhDQAABZFSAMAYFGENAAAFkVIAwBgUYQ0AAAWxfekgTqgojuFVXSXsIr6c2MSwFoIaaAOqOhOYQcW/+uC+nNjEsBaONwNAIBFEdIAAFgUh7uBOuyHXTt5KAZQixHSQB3GQzGA2o2QBmqJsz3rmT1joHIqOtpkleenE9JALXG2Zz2zZwxUTkVHmyr6ZkRNI6QBi+E7zABKEdJAFagoWP+390c1iW5e7msqOpzGd5gB57PKYXBCGqgCZwvWig5RW+VwGoCyrHIYnO9JAwBgUYQ0AAAWxeFu1HkX+vCJyr4GAKoaIY1a50ID9EIfPnG216xNHcYdvADUmDoT0q+88oqee+45ZWVl6corr9T06dN13XXXOaUW9sKqV2VCt6pU5R28uGUngHOpEyG9aNEijR07Vq+88oquvfZavfbaa0pISNB3332nJk2a1Hg9zgyRmlCVf4RU5qtLdSXEuGUngHOpEyE9depUjRgxQn/7298kSdOnT9eqVas0a9YspaamOrm6uqcq/wipzFeXCDEA9UWtD+nCwkJlZGTokUcecWiPj4/Xxo0by31NQUGBCgoK7ON5eXmSpPz8/Cqpqbi4SEUnT5TbfqHLGDryfmUdOVqmPTzoEr35n1mVLfGilv3jT3sVWs76fb9zhzr3vr7ceR3Yt1eRzaLPe17mVEm52/Bs0yravpX5eVT0moqWfaHtVp1XXVkG9db+ZVi13sp8jp+Nr6+vbDZbxR1MLXfw4EEjyXzxxRcO7f/85z9Ny5Yty33N5MmTjSQGBgYGBganDnl5eWfNuFq/J13qzL9EjDEV/nUyceJEjRs3zj5+6tQp/fbbbwoKCjr7XzQWk5+fr6ioKO3fv19+fn7OLscS2CZlsU3KYpuUj+1SVnVvE19f37NOr/UhHRwcLFdXV2VnZzu05+TkKDQ0tNzXeHp6ytPT06Htkksuqa4Sq52fnx9vqDOwTcpim5TFNikf26UsZ22TWn/HMQ8PD7Vv317p6ekO7enp6eratauTqgIA4OLV+j1pSRo3bpzuvPNOdejQQV26dNG///1v/e9//9N9993n7NIAAKi0OhHSt912m44cOaKnnnpKWVlZio2N1YoVK9S0aVNnl1atPD09NXny5DKH7usztklZbJOy2CblY7uU5extYjPGGKcsGQAAnFWtPycNAEBdRUgDAGBRhDQAABZFSAMAYFGEdB3xww8/6MYbb1RwcLD8/Px07bXXat26dc4uy+mWL1+uTp06ydvbW8HBwUpKSnJ2SZZQUFCgtm3bymazadu2bc4ux6n27dunESNGKDo6Wt7e3mrevLkmT56swsJCZ5dWo1555RVFR0fLy8tL7du312effebskpwqNTVVHTt2lK+vr0JCQjRw4EDt2rWrxusgpOuI66+/XsXFxVq7dq0yMjLUtm1bJSYmlrkTW33y/vvv684779Tw4cP1zTff6IsvvtDgwYOdXZYlPPTQQ4qIiHB2GZbw/fff69SpU3rttdeUmZmpadOm6dVXX9Wjjz7q7NJqTOnjfidNmqSvv/5a1113nRISEvS///3P2aU5zYYNG/TAAw9o8+bNSk9PV3FxseLj43XiRPkP6qg2VfOYCzjTr7/+aiSZTz/91N6Wn59vJJk1a9Y4sTLnKSoqMo0bNzavv/66s0uxnBUrVpjLL7/cZGZmGknm66+/dnZJljNlyhQTHR3t7DJqzDXXXGPuu+8+h7bLL7/cPPLII06qyHpycnKMJLNhw4YaXS570nVAUFCQrrjiCr311ls6ceKEiouL9dprryk0NFTt27d3dnlO8dVXX+ngwYNycXFRu3btFB4eroSEBGVmZjq7NKf65ZdfNHLkSM2dO1c+Pj7OLsey8vLyFBgY6OwyakTp437j4+Md2s/2uN/6qPSRxjX9e0FI1wE2m03p6en6+uuv5evrKy8vL02bNk0rV66s1Q8OuRg//fSTJCk5OVmPPfaYPvroIwUEBKh79+767bffnFydcxhjNGzYMN13333q0KGDs8uxrB9//FEzZsyoN7cVPnz4sEpKSso8kCg0NLReny77M2OMxo0bp27duik2NrZGl01IW1hycrJsNttZh61bt8oYo1GjRikkJESfffaZ/vvf/+rGG29UYmKisrKynL0aVep8t8mpU6ckSZMmTdLNN9+s9u3bKy0tTTabTe+++66T16Jqne82mTFjhvLz8zVx4kRnl1wjzne7/NmhQ4fUr18/3Xrrrfrb3/7mpMqd40Ie91vfjB49Wt9++63efvvtGl82twW1sMOHD+vw4cNn7dOsWTN98cUXio+PV25ursOj1Fq0aKERI0bokUceqe5Sa8z5bpNNmzapZ8+e+uyzz9StWzf7tE6dOql379765z//Wd2l1pjz3Sa33367PvzwQ4cP3pKSErm6uuqOO+7Qm2++Wd2l1qjz3S5eXl6STgd0XFycOnXqpDlz5sjFpX7swxQWFsrHx0fvvvuubrrpJnv7gw8+qG3btmnDhg1OrM75xowZo6VLl+rTTz9VdHR0jS+/Tjxgo64KDg5WcHDwOfv9/vvvklTmQ8XFxcW+R1lXnO82ad++vTw9PbVr1y57SBcVFWnfvn117sEr57tNXnrpJT3zzDP28UOHDqlv375atGiROnXqVJ0lOsX5bhdJOnjwoOLi4uxHXOpLQEuOj/v9c0inp6frxhtvdGJlzmWM0ZgxY7RkyRKtX7/eKQEtEdJ1QpcuXRQQEKChQ4fqiSeekLe3t/7zn/9o7969uv76651dnlP4+fnpvvvu0+TJkxUVFaWmTZvqueeekyTdeuutTq7OOZo0aeIw3rBhQ0lS8+bNFRkZ6YySLOHQoUPq0aOHmjRpoueff16//vqrfVpYWJgTK6s5PO63rAceeEALFizQBx98IF9fX/v5eX9/f3l7e9dcITV6LTmqzZYtW0x8fLwJDAw0vr6+pnPnzmbFihXOLsupCgsLzfjx401ISIjx9fU1vXv3Njt27HB2WZaxd+9evoJljElLSzOSyh3qk5kzZ5qmTZsaDw8Pc/XVV9f4V42spqLfibS0tBqtg3PSAABYVP058QIAQC1DSAMAYFGENAAAFkVIAwBgUYQ0AAAWRUgDAGBRhDQAABZFSAMAYFGENIAq88UXX8jNzU1t27Z1dilAncAdxwBUiby8PF199dW67LLL9Msvv2jbtm3OLgmo9diTBnBWv/76q8LCwpSSkmJv+/LLL+Xh4aHVq1fb2+69914NHjxYXbp0cUaZQJ1ESAM4q0aNGumNN95QcnKytm7dquPHj2vIkCEaNWqU4uPjJUlpaWn68ccfNXnyZCdXC9QtPKoSwDn1799fI0eO1B133KGOHTvKy8tLzz77rCRp9+7deuSRR/TZZ5/JzY2PFKAqsScN4Lw8//zzKi4u1jvvvKP58+fLy8tLJSUlGjx4sJ588km1bNnS2SUCdQ4XjgE4L5mZmerQoYOKioq0ZMkS3XDDDTp69KgCAgLk6upq73fq1CkZY+Tq6qrVq1erZ8+eTqwaqN0IaQDnVFhYqGuuuUZt27bV5ZdfrqlTp2r79u1q1KiRvvvuO4e+r7zyitauXav33ntP0dHRatCggZOqBmo/TiABOKdJkyYpLy9PL730kho2bKiPP/5YI0aM0EcffaTY2FiHviEhIfLy8irTDuDCcU4awFmtX79e06dP19y5c+Xn5ycXFxfNnTtXn3/+uWbNmuXs8oA6jcPdAABYFHvSAABYFCENAIBFEdIAAFgUIQ0AgEUR0gAAWBQhDQCARRHSAABYFCENAIBFEdIAAFgUIQ0AgEUR0gAAWBQhDQCARf1/flRIzqTtEpAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.displot(data=D, x=\"x4\", kind=\"hist\").set(title=\"Logarithmed - oops, skewed in opposite direction\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "82ccbb69",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jhucin\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\jhucin\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:118: UserWarning: The figure layout has changed to tight\n",
      "  self._figure.tight_layout(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.displot(data=D, x=\"x5\", kind=\"hist\").set(title=\"Reciprocal sqrt - just for a complete view, very skewed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0da492a2",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jhucin\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\jhucin\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:118: UserWarning: The figure layout has changed to tight\n",
      "  self._figure.tight_layout(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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lJiZq0aJF6ty5s0JCQrR48WINHDhQ48aN8+ahAQDgEV4N6i+++EIzZ85UYWGhgoODNWjQIO3cuVPjx4/XpUuXdPToUb344os6d+6cwsPDNXr0aG3ZskWBgYGufaxcuVK+vr6aPn26Ll26pLFjxyo9PV0+Pj5ePDIAADzDdM9RewPPUQMAzMp096gBAMC/ENQAAJgYQQ0AgIl5fcATAM3L6XSqIV1TLBaLrFb+lge8jZ9CoA1xOp3qFhklX1/fepdukVFyOp3eLhlo8zijBtoQwzDkKMjX1DVZstRxtmw4nfr7/JENOvMG0LQIaqANslitslprH2uA82jAPLj0DQCAiRHUAACYGEENAICJEdQAAJgYQQ0AgIkR1AAAmBhBDQCAiRHUAACYGEENAICJEdQAAJgYQQ0AgIkR1AAAmBhBDQCAiRHUAACYGEENAICJEdQAAJgYQQ0AgIkR1AAAmBhBDQCAiRHUAACYGEENAICJEdQAAJgYQQ0AgIkR1AAAmBhBDQCAiRHUAACYGEENAICJEdQAAJiYr7cLANCyOZ1OGYZRbzuLxSKrlXMD4GrxUwOg0ZxOp7pFRsnX17fepVtklJxOp7dLBloczqgBNJphGHIU5GvqmixZ6jhbNpxO/X3+yAadeQNwR1ADuGYWq1VWq0+t2zmPBhqPS98AAJgYQQ0AgIkR1AAAmBhBDQCAiRHUAACYGEENAICJEdQAAJgYQQ0AgIkR1AAAmBhBDQCAiXk1qNetW6dBgwYpKChIQUFBGjFihN544w3XdsMwlJqaqoiICLVr106jRo3S8ePH3fZRVlampKQkhYaGqkOHDpoyZYry8/Ob+1AAAGgSXg3q7t2766mnnlJ2drays7M1ZswY3XHHHa4wXrFihZ555hmtXr1ahw4dkt1u1/jx43X+/HnXPpKTk7V9+3ZlZGRo//79unDhgiZPnqyqqipvHRYAAB7j1aC+/fbbNWnSJPXp00d9+vTRk08+qY4dO+rgwYMyDEOrVq3SkiVLlJCQoJiYGG3YsEHffPONNm/eLEkqKSnRCy+8oKefflrjxo3T4MGDtXHjRh09elS7d+/25qEBAOARprlHXVVVpYyMDF28eFEjRoxQbm6uHA6H4uLiXG1sNptGjhypAwcOSJJycnJUUVHh1iYiIkIxMTGuNjUpKytTaWmp2wIAgBl5PaiPHj2qjh07ymazae7cudq+fbv69+8vh8MhSQoLC3NrHxYW5trmcDjk7++vTp061dqmJsuXL1dwcLBriYyM9PBRAZ7jdDpVVVVV7+J0Mpkk0Bp5Paj79u2rw4cP6+DBg3rooYc0a9YsffTRR67tFovFrb1hGNXWXam+NikpKSopKXEteXl513YQQBNxOp3qFhklX1/fepdukVGENdAK+Xq7AH9/f/Xq1UuSNHToUB06dEh/+tOf9Mgjj0j67qw5PDzc1b6oqMh1lm2321VeXq7i4mK3s+qioiLFxsbW+jVtNptsNltTHA7gUYZhyFGQr6lrsmSx1v53teF06u/zR8owjGasDkBz8PoZ9ZUMw1BZWZmio6Nlt9uVmZnp2lZeXq6srCxXCA8ZMkR+fn5ubQoLC3Xs2LE6gxpoaSxWq6xWn1qXukIcQMvm1TPqRx99VPHx8YqMjNT58+eVkZGhffv2aefOnbJYLEpOTtayZcvUu3dv9e7dW8uWLVP79u117733SpKCg4OVmJioRYsWqXPnzgoJCdHixYs1cOBAjRs3zpuHBgCAR3g1qL/44gvNnDlThYWFCg4O1qBBg7Rz506NHz9ekvTwww/r0qVLmjdvnoqLizVs2DDt2rVLgYGBrn2sXLlSvr6+mj59ui5duqSxY8cqPT1dPj4+3josAAA8xqtB/cILL9S53WKxKDU1VampqbW2CQgIUFpamtLS0jxcHQAA3seNLQAATIygBgDAxAhqAABMjKAGAMDECGoAAEyMoAYAwMS8PoQoADSG0+ls0JCpFotFVkZuQwvGv14ALQ6TlaAt4YwaaEWqqqquaXtLwWQlaEsIaqAVMJxOyerT8FnhWkluXZ6spDacR6M1IKiBVsGQnFW6e3WWrD61n2E6Kyu0NWlMa8lpoE0gqIFWpL4zTMN6dZe+28qldMDMCGoA1bTVS+mAGRHUAGrApXTALAhqALXy9KV0AFeP56gBADAxghoAABMjqAEAMDHuUQNoNg15nIuxuQF3BDWAJnc1j3vZI7rrTN5pwhr4fwQ1gGbQsMe9GJsbqI6gBtBsGJsbuHpcWwIAwMQIagAATIygBgDAxAhqAABMjKAGAMDECGoAAEyMoAYAwMQIagAATIygBgDAxAhqAABMjKAGAMDEGOsbQKtX3/SaTK0JMyOoAbRaDZ1ek6k1YWYENYBWrP7pNZlaE2ZHUANo9eqaXpOpNWF2BDUA06nvnnJ924HWhKAGYBoNvaf8rzc0bT2AGRDUAEyk/nvKkuSsrNDWpDHkNNoEghqA6dR1T1mSDCuXvtF28CwCAAAmRlADAGBiBDUAACZGUAMAYGIENQAAJkZQAwBgYgQ1AAAmRlADAGBiXg3q5cuX6+abb1ZgYKC6du2qO++8UydOnHBrM3v2bFksFrdl+PDhbm3KysqUlJSk0NBQdejQQVOmTFF+fn5zHgoAAE3Cq0GdlZWl+fPn6+DBg8rMzFRlZaXi4uJ08eJFt3YTJ05UYWGha9mxY4fb9uTkZG3fvl0ZGRnav3+/Lly4oMmTJzNwPwCgxfPqEKI7d+50e71+/Xp17dpVOTk5+vGPf+xab7PZZLfba9xHSUmJXnjhBb300ksaN26cJGnjxo2KjIzU7t27NWHChKY7AAAAmpip7lGXlJRIkkJCQtzW79u3T127dlWfPn00Z84cFRUVubbl5OSooqJCcXFxrnURERGKiYnRgQMHavw6ZWVlKi0tdVsAADAj0wS1YRhauHChbr31VsXExLjWx8fHa9OmTdqzZ4+efvppHTp0SGPGjFFZWZkkyeFwyN/fX506dXLbX1hYmBwOR41fa/ny5QoODnYtkZGRTXdgAABcA9PMnrVgwQIdOXJE+/fvd1s/Y8YM1//HxMRo6NChioqK0uuvv66EhIRa92cYhiwWS43bUlJStHDhQtfr0tJSwhoAYEqmOKNOSkrSa6+9pr1796p79+51tg0PD1dUVJROnjwpSbLb7SovL1dxcbFbu6KiIoWFhdW4D5vNpqCgILcFAAAz8mpQG4ahBQsWaNu2bdqzZ4+io6Prfc/Zs2eVl5en8PBwSdKQIUPk5+enzMxMV5vCwkIdO3ZMsbGxTVY7AADNwauXvufPn6/Nmzfr1VdfVWBgoOuecnBwsNq1a6cLFy4oNTVVd999t8LDw3Xq1Ck9+uijCg0N1V133eVqm5iYqEWLFqlz584KCQnR4sWLNXDgQFcvcAAAWiqvBvW6deskSaNGjXJbv379es2ePVs+Pj46evSoXnzxRZ07d07h4eEaPXq0tmzZosDAQFf7lStXytfXV9OnT9elS5c0duxYpaeny8fHpzkPBwAAj/NqUBuGUef2du3a6c0336x3PwEBAUpLS1NaWpqnSgMAwBRM0ZkMAADUjKAGAMDECGoAAEyMoAYAwMQIagAATMw0Q4gCbY3T6az3yQemagVAUANe4HQ61S0ySo6C/Ia9oe48B9CKEdSAFxiGIUdBvqauyZLFWvsdKGdlhbYmjSGngTaMoAa8yGK1ymqtfQQ9w8qlb6CtozMZAAAmRlADAGBiBDUAACZGUAMAYGIENQAAJkZQAwBgYgQ1AAAmRlADAGBiBDUAACbGyGRAE6hvwg0m2wDQUAQ14GFXNeEGg3gDqAdBDXhYQybcYLINAA1FUANNpK4JN5hsA0BD0ZkMAAATI6gBADAxghoAABMjqAEAMDGCGgAAEyOoAQAwMYIaAAATI6gBADAxghoAABMjqAEAMDGCGgAAEyOoAQAwMYIaAAATI6gBADAxghoAABMjqAEAMDGCGgAAEyOoAQAwMYIaAAATI6gBADAxghoAABPz9XYBQEvidDplGEadbaqqqpqpGgBtAUENNJDT6VS3yCg5CvIb9oa68xwAGoSgBhrIMAw5CvI1dU2WLNba7xo5Kyu0NWkMOQ3AIwhq4CpZrFZZrT61bjesXPoG4Dl0JgMAwMS8GtTLly/XzTffrMDAQHXt2lV33nmnTpw44dbGMAylpqYqIiJC7dq106hRo3T8+HG3NmVlZUpKSlJoaKg6dOigKVOmKD+/gfcRAQAwMa8GdVZWlubPn6+DBw8qMzNTlZWViouL08WLF11tVqxYoWeeeUarV6/WoUOHZLfbNX78eJ0/f97VJjk5Wdu3b1dGRob279+vCxcuaPLkyfS+BQC0eF69R71z50631+vXr1fXrl2Vk5OjH//4xzIMQ6tWrdKSJUuUkJAgSdqwYYPCwsK0efNmPfjggyopKdELL7ygl156SePGjZMkbdy4UZGRkdq9e7cmTJjQ7McFoOVpyB/2FotF1jo6EgJNwVT/4kpKSiRJISEhkqTc3Fw5HA7FxcW52thsNo0cOVIHDhyQJOXk5KiiosKtTUREhGJiYlxtrlRWVqbS0lK3BUDbZDidktVHNptNvr6+dS7dIqPkdDq9XTLaGNP0+jYMQwsXLtStt96qmJgYSZLD4ZAkhYWFubUNCwvT6dOnXW38/f3VqVOnam0uv/9Ky5cv19KlSz19CABaJENyVunu1Vmy+tR+7mI4nfr7/JH1DngDeJppzqgXLFigI0eO6K9//Wu1bRaLxe21YRjV1l2prjYpKSkqKSlxLXl5eY0vHECrcPmxu9qWup6dB5qSKf7lJSUl6bXXXtPevXvVvXt313q73S5J1c6Mi4qKXGfZdrtd5eXlKi4urrXNlWw2m4KCgtwWAADMyKtBbRiGFixYoG3btmnPnj2Kjo522x4dHS273a7MzEzXuvLycmVlZSk2NlaSNGTIEPn5+bm1KSws1LFjx1xtAABoqbx6j3r+/PnavHmzXn31VQUGBrrOnIODg9WuXTtZLBYlJydr2bJl6t27t3r37q1ly5apffv2uvfee11tExMTtWjRInXu3FkhISFavHixBg4c6OoFDgBAS+XVoF63bp0kadSoUW7r169fr9mzZ0uSHn74YV26dEnz5s1TcXGxhg0bpl27dikwMNDVfuXKlfL19dX06dN16dIljR07Vunp6fLxqX2YRwAAWgKvBnVDek9aLBalpqYqNTW11jYBAQFKS0tTWlqaB6sDAMD7GnWP+vrrr9fZs2errT937pyuv/76ay4KAAB8p1FBferUqRpH8SkrK9OZM2euuSgAAPCdq7r0/dprr7n+/80331RwcLDrdVVVld566y317NnTY8UBANDWXVVQ33nnnZK+u288a9Yst21+fn7q2bOnnn76aY8VBwBAW3dVQX15jNvo6GgdOnRIoaGhTVIUAAD4TqN6fefm5nq6DgAAUINGP5711ltv6a233lJRUVG12WT+8pe/XHNhAACgkUG9dOlS/f73v9fQoUMVHh5e7wQZAACgcRoV1M8++6zS09M1c+ZMT9cDAAC+p1HPUZeXlzPhBQAAzaBRQf3AAw9o8+bNnq4FAABcoVGXvr/99ls9//zz2r17twYNGiQ/Pz+37c8884xHigMAoK1rVFAfOXJEN910kyTp2LFjbtvoWAYAgOc0Kqj37t3r6ToAAEANGnWPGgAANI9GnVGPHj26zkvce/bsaXRBAADgXxoV1JfvT19WUVGhw4cP69ixY9Um6wAAAI3XqKBeuXJljetTU1N14cKFayoIAAD8i0fvUd93332M8w0AgAd5NKjfffddBQQEeHKXAAC0aY269J2QkOD22jAMFRYWKjs7W4899phHCgMAAI0M6uDgYLfXVqtVffv21e9//3vFxcV5pDAAANDIoF6/fr2n6wAAADVoVFBflpOTo48//lgWi0X9+/fX4MGDPVUXAABQI4O6qKhI99xzj/bt26frrrtOhmGopKREo0ePVkZGhrp06eLpOgEAaJMa1es7KSlJpaWlOn78uL7++msVFxfr2LFjKi0t1b/92795ukYAANqsRp1R79y5U7t371a/fv1c6/r37681a9bQmQwAAA9q1Bm10+msNge1JPn5+cnpdF5zUQAA4DuNCuoxY8bol7/8pQoKClzrzpw5o1/96lcaO3asx4oDAKCta1RQr169WufPn1fPnj31gx/8QL169VJ0dLTOnz+vtLQ0T9cIAKZRVVVV78KVRXhSo+5RR0ZG6oMPPlBmZqY++eQTGYah/v37a9y4cZ6uDwBMwXA6JauPbDZbvW3tEd11Ju+0rFaPjtKMNuqqgnrPnj1asGCBDh48qKCgII0fP17jx4+XJJWUlGjAgAF69tlnddtttzVJsQDgPYbkrNLdq7Nk9ak9gA2nU3+fP1KGYTRjbWjNrurPvVWrVmnOnDkKCgqqti04OFgPPvignnnmGY8VBwBmY7FaZbX61LpYOIuGh13Vv6gPP/xQEydOrHV7XFyccnJyrrkoAADwnasK6i+++KLGx7Iu8/X11ZdffnnNRQEAgO9cVVB369ZNR48erXX7kSNHFB4efs1FAQCA71xVUE+aNEm/+93v9O2331bbdunSJT3++OOaPHmyx4oDAKCtu6pe37/97W+1bds29enTRwsWLFDfvn1lsVj08ccfa82aNaqqqtKSJUuaqlYAANqcqwrqsLAwHThwQA899JBSUlJcjx9YLBZNmDBBa9euVVhYWJMUCgBAW3TVA55ERUVpx44dKi4u1qeffirDMNS7d2916tSpKeoDAKBNa9TIZJLUqVMn3XzzzZ6sBQAAXIEn8wEAMLFGn1EDAGpXVVVVbxuLxcJ44KgXQQ0AHsTkHfA0ghoAPIrJO+BZBDUANIHLk3fUhhmr0VBcbwEAwMQIagAATIygBgDAxLwa1G+//bZuv/12RUREyGKx6JVXXnHbPnv2bFksFrdl+PDhbm3KysqUlJSk0NBQdejQQVOmTFF+fn4zHgUAAE3Hq0F98eJF3XjjjVq9enWtbSZOnKjCwkLXsmPHDrftycnJ2r59uzIyMrR//35duHBBkydPbtAzjAAAmJ1Xe33Hx8crPj6+zjY2m012u73GbSUlJXrhhRf00ksvady4cZKkjRs3KjIyUrt379aECRM8XjMAAM3J9Peo9+3bp65du6pPnz6aM2eOioqKXNtycnJUUVGhuLg417qIiAjFxMTowIEDte6zrKxMpaWlbgsAAGZk6qCOj4/Xpk2btGfPHj399NM6dOiQxowZo7KyMkmSw+GQv79/tZm7wsLC5HA4at3v8uXLFRwc7FoiIyOb9DgAAGgsUw94MmPGDNf/x8TEaOjQoYqKitLrr7+uhISEWt9nGIYsFkut21NSUrRw4ULX69LSUsK6jXM6nfWOEEW/BwDeYOqgvlJ4eLiioqJ08uRJSZLdbld5ebmKi4vdzqqLiooUGxtb635sNluDxuFF2+B0OtUtMkqOggY+LcCIjwCaUYsK6rNnzyovL0/h4eGSpCFDhsjPz0+ZmZmaPn26JKmwsFDHjh3TihUrvFkqWhDDMOQoyNfUNVmy1DE5grOyQluTxpDTAJqVV4P6woUL+vTTT12vc3NzdfjwYYWEhCgkJESpqam6++67FR4erlOnTunRRx9VaGio7rrrLklScHCwEhMTtWjRInXu3FkhISFavHixBg4c6OoFDjRUfWMzG1YufQNofl4N6uzsbI0ePdr1+vJ941mzZmndunU6evSoXnzxRZ07d07h4eEaPXq0tmzZosDAQNd7Vq5cKV9fX02fPl2XLl3S2LFjlZ6eLh+f2n/hAgDQUng1qEeNGlVnB54333yz3n0EBAQoLS1NaWlpniwNAABTMPXjWQAAtHUENQAAJkZQAwBgYgQ1AAAmRlADAGBiBDUAACZGUAMAYGIENQAAJkZQAwBgYgQ1AAAm1qJmzwKuFvNMA2jpCGq0WswzDaA1IKjRajHPNIDWgKBGq8c80wBaMjqTAQBgYgQ1AAAmRlADAGBi3KNGi8RjVwDaCoIaLQ6PXQFoSwhqtDg8dgWgLSGo0WLx2BWAtoDOZAAAmBhBDQCAiRHUAACYGEENAICJEdQAAJgYQQ0AgIkR1AAAmBhBDQCAiRHUAACYGEENAICJEdQAAJgYQQ0AgIkR1AAAmBhBDQCAiRHUAACYGPNRw1ScTqcMw6izTVUV80wDaDsIapiG0+lUt8goOQryG/aGuvMcAFoFghqmYRiGHAX5mromSxZr7XdlnJUV2po0hpxGq9CQK0QWi0XWOn4m0LoR1DAdi9Uqq9Wn1u2GlUvfaPkMp1Oy+shms9Xb1h7RXWfyThPWbRRBDQBeYUjOKt29OktWn9oD2HA69ff5I+vtu4HWi6AGAC+q7wqSsxlrgTlxHQUAABMjqAEAMDGCGgAAEyOoAQAwMYIaAAATI6gBADAxrwb122+/rdtvv10RERGyWCx65ZVX3LYbhqHU1FRFRESoXbt2GjVqlI4fP+7WpqysTElJSQoNDVWHDh00ZcoU5ec3cAhKAABMzqtBffHiRd14441avXp1jdtXrFihZ555RqtXr9ahQ4dkt9s1fvx4nT9/3tUmOTlZ27dvV0ZGhvbv368LFy5o8uTJTNwAAGgVvDrgSXx8vOLj42vcZhiGVq1apSVLlighIUGStGHDBoWFhWnz5s168MEHVVJSohdeeEEvvfSSxo0bJ0nauHGjIiMjtXv3bk2YMKHZjgUAgKZg2nvUubm5cjgciouLc62z2WwaOXKkDhw4IEnKyclRRUWFW5uIiAjFxMS42gAA0JKZdghRh8MhSQoLC3NbHxYWptOnT7va+Pv7q1OnTtXaXH5/TcrKylRWVuZ6XVpa6qmyAQDwKNOeUV9msVjcXhuGUW3dleprs3z5cgUHB7uWyMhIj9QKAICnmTao7Xa7JFU7My4qKnKdZdvtdpWXl6u4uLjWNjVJSUlRSUmJa8nLy/Nw9QAAeIZpgzo6Olp2u12ZmZmudeXl5crKylJsbKwkaciQIfLz83NrU1hYqGPHjrna1MRmsykoKMhtAQDAjLx6j/rChQv69NNPXa9zc3N1+PBhhYSEqEePHkpOTtayZcvUu3dv9e7dW8uWLVP79u117733SpKCg4OVmJioRYsWqXPnzgoJCdHixYs1cOBAVy9wAABaMq8GdXZ2tkaPHu16vXDhQknSrFmzlJ6erocffliXLl3SvHnzVFxcrGHDhmnXrl0KDAx0vWflypXy9fXV9OnTdenSJY0dO1bp6eny8al9flcAAFoKrwb1qFGjZBhGrdstFotSU1OVmppaa5uAgAClpaUpLS2tCSoEAMC7THuPGgAAENQAAJgaQQ0AgImZdmQytC5Op7PO/giSmEgFAGpAUKPJOZ1OdYuMkqOggdOP1p3nANCmENRocoZhyFGQr6lrsmSx1n63xVlZoa1JY8hpAPgeghrNxmK1ymqt/fl2w8qlbwC4Ep3JAAAwMYIaAAATI6gBADAx7lEDQAvQkMcXLRaLrHV02ETLRFADgIkZTqdk9ZHNZqu3rT2iu87knSasWxmCGgBMzZCcVbp7dZasPrUHsOF06u/zR9Y7sBBaHoIaAFqA+h5vdDZjLWheXB8BAMDECGoAAEyMoAYAwMQIagAATIygBgDAxAhqAABMjKAGAMDECGoAAEyMoAYAwMQIagAATIygBgDAxBjrGwBaEabDbH0IagBoBZgOs/UiqAGgVWA6zNaKoAaAVoTpMFsfrnsAAGBiBDUAACZGUAMAYGIENQAAJkZQAwBgYgQ1AAAmRlADAGBiBDUAACZGUAMAYGIENQAAJkZQAwBgYgQ1AAAmRlADAGBiBDUAACZGUAMAYGIENQAAJkZQAwBgYgQ1AAAm5uvtAtCyOZ1OGYZRZ5uqqqpmqgYAWh+CGo3mdDrVLTJKjoL8hr2h7jwHANTA1EGdmpqqpUuXuq0LCwuTw+GQJBmGoaVLl+r5559XcXGxhg0bpjVr1mjAgAHeKLfNMQxDjoJ8TV2TJYu19rsozsoKbU0aQ04DQCOY/h71gAEDVFhY6FqOHj3q2rZixQo988wzWr16tQ4dOiS73a7x48fr/PnzXqy47bFYrbJafWpd6gpxAEDdTP8b1NfXV3a73bV06dJF0ndnc6tWrdKSJUuUkJCgmJgYbdiwQd988402b97s5aoBAPAM0wf1yZMnFRERoejoaN1zzz367LPPJEm5ublyOByKi4tztbXZbBo5cqQOHDhQ5z7LyspUWlrqtgAAYEamDuphw4bpxRdf1Jtvvqk///nPcjgcio2N1dmzZ133qcPCwtze8/172LVZvny5goODXUtkZGSTHQMAANfC1EEdHx+vu+++WwMHDtS4ceP0+uuvS5I2bNjgamOxWNzeYxhGtXVXSklJUUlJiWvJy8vzfPEAAHiAqYP6Sh06dNDAgQN18uRJ2e12Sap29lxUVFTtLPtKNptNQUFBbgsAAGbUooK6rKxMH3/8scLDwxUdHS273a7MzEzX9vLycmVlZSk2NtaLVQIA4Dmmfo568eLFuv3229WjRw8VFRXpiSeeUGlpqWbNmiWLxaLk5GQtW7ZMvXv3Vu/evbVs2TK1b99e9957r7dLBwDAI0wd1Pn5+frpT3+qr776Sl26dNHw4cN18OBBRUVFSZIefvhhXbp0SfPmzXMNeLJr1y4FBgZ6uXIAADzD1EGdkZFR53aLxaLU1FSlpqY2T0EAADSzFnWPGgCAtoagBgDAxAhqAABMjKAGAMDETN2ZDADQNKqqquptY7FYZGX2O68jqFEjp9Mpw6h7BumG/KADMBfD6ZSsPrLZbPW2tUd015m804S1lxHUqMbpdKpbZJQcBfkNe0PdeQ7AVAzJWaW7V2fJ6lN7ABtOp/4+f2S9f7Cj6RHUqMYwDDkK8jV1TZYsdfwl7ays0NakMeQ00AJZrFZZrT61bnc2Yy2oG0GNWtX3g2xYufQNAE2NGw8AAJgYQQ0AgIkR1AAAmBhBDQCAiRHUAACYGL2+AQC1YgQz7yOoAQDVMIKZeRDUAIAaMIKZWRDUAIBaMYKZ93GdAgAAEyOoAQAwMYIaAAATI6gBADAxghoAABOj13cr4XQ6G/R4BAMTAEDLwm/sVsDpdKpbZJR8fX3rXbpFRsnp5IEKAGgpOKNuBQzDkKMgX1PXZMlSx9kyAxMAQMtDULciDEwAAK0PQd0G1TfIfkMG4QeA72PyjqZDULchVzPI/ndvaNp6ALR8TN7R9AjqNqVhg+w7Kyu0NWkMOQ2gAZi8o6kR1G1QffeyDSuXvgFcnYb2keES+dUjqAEATY5L5I1HUAMAmgGXyBuLoAYANBseI716XFcAAMDECGoAAEyMoAYAwMQIagAATIygBgDAxAhqAABMjMezWgCn01nnM4VMogGgtanv91pbGr2MoPai+gL4cpsePaPlKDhT/w4ZHwBAC9fQEcza0uhlBLWXOJ1OdYuMkqMgv0Htp67OkqWW0XyYRANA61H/CGZtbfQygtpLDMOQoyBfU9dkyVLHX4SXQ1h1jObDJBoAWpu6RjBra6OXEdRexkxWAIC6tP6L+wAAtGCcUQMA2ryGdO6VvNPbnKBuAg35hvNIFQBcm4b8Hm1IsF5N515v9DYnqD3santz01UbAK5OQx/hkqSw8G7KO51bZ7BWVVU1qHOvt3qbt5qgXrt2rf7whz+osLBQAwYM0KpVq3Tbbbc1ex1X25ubnAaAq1X/I1yS5Kys1NZfjpO/v3+D9mqxmHOu7FYR1Fu2bFFycrLWrl2rW265Rc8995zi4+P10UcfqUePHl6pid7cANC0GvR7tkGBbu4Tp1bR6/uZZ55RYmKiHnjgAfXr10+rVq1SZGSk1q1b5+3SAABedjnQa1vquvppBi3+jLq8vFw5OTn6zW9+47Y+Li5OBw4cqPE9ZWVlKisrc70uKSmRJJWWll5zPZc7N5RfPF/vpe/L7er7S6++dp7cF+34nrW1dmaujXbm+p4Zzu8ufpeWlsrHp/Yz+asRGBgoi8VSdyOjhTtz5owhyXjnnXfc1j/55JNGnz59anzP448/bui7blwsLCwsLCxeW0pKSurNuRZ/Rn3ZlX+RGIZR618pKSkpWrhwoeu10+nU119/rc6dO9f/l00zKi0tVWRkpPLy8hQUFOTtcppNWzxujpljbs3a4nE39JgDAwPr3VeLD+rQ0FD5+PjI4XC4rS8qKlJYWFiN77HZbNW69V933XVNVeI1CwoKajP/uL+vLR43x9w2tMVjltrmcXvimM19B70B/P39NWTIEGVmZrqtz8zMVGxsrJeqAgDAM1r8GbUkLVy4UDNnztTQoUM1YsQIPf/88/r88881d+5cb5cGAMA1aRVBPWPGDJ09e1a///3vVVhYqJiYGO3YsUNRUVHeLu2a2Gw2Pf744w0afac1aYvHzTG3DW3xmKW2edyePGaLYbSRmbcBAGiBWvw9agAAWjOCGgAAEyOoAQAwMYIaAAATI6hbkClTpqhHjx4KCAhQeHi4Zs6cqYKCAm+X1WROnTqlxMRERUdHq127dvrBD36gxx9/XOXl5d4urUk9+eSTio2NVfv27U09EM+1Wrt2raKjoxUQEKAhQ4bof/7nf7xdUpN5++23dfvttysiIkIWi0WvvPKKt0tqcsuXL9fNN9+swMBAde3aVXfeeadOnDjh7bKa1Lp16zRo0CDXICcjRozQG2+8cc37JahbkNGjR+vll1/WiRMntHXrVv3zn//U1KlTvV1Wk/nkk0/kdDr13HPP6fjx41q5cqWeffZZPfroo94urUmVl5dr2rRpeuihh7xdSpO5PDXtkiVL9I9//EO33Xab4uPj9fnnn3u7tCZx8eJF3XjjjVq9erW3S2k2WVlZmj9/vg4ePKjMzExVVlYqLi5OFy9e9HZpTaZ79+566qmnlJ2drezsbI0ZM0Z33HGHjh8/fm07vvZpMeAtr776qmGxWIzy8nJvl9JsVqxYYURHR3u7jGaxfv16Izg42NtlNIkf/ehHxty5c93W3XDDDcZvfvMbL1XUfCQZ27dv93YZza6oqMiQZGRlZXm7lGbVqVMn47/+67+uaR+cUbdQX3/9tTZt2qTY2Fj5+fl5u5xmU1JSopCQEG+XgWtweWrauLg4t/V1TU2Llu/ydMJt5ee3qqpKGRkZunjxokaMGHFN+yKoW5hHHnlEHTp0UOfOnfX555/r1Vdf9XZJzeaf//yn0tLSGBq2hfvqq69UVVVVbdKcsLCwapProHUwDEMLFy7UrbfeqpiYGG+X06SOHj2qjh07ymazae7cudq+fbv69+9/TfskqL0sNTVVFoulziU7O9vV/te//rX+8Y9/aNeuXfLx8dHPf/5zGS1scLmrPWZJKigo0MSJEzVt2jQ98MADXqq88RpzzK3d1UxNi5ZtwYIFOnLkiP761796u5Qm17dvXx0+fFgHDx7UQw89pFmzZumjjz66pn22irG+W7IFCxbonnvuqbNNz549Xf8fGhqq0NBQ9enTR/369VNkZKQOHjx4zZdWmtPVHnNBQYFGjx7tmnClJbraY27NGjM1LVqupKQkvfbaa3r77bfVvXt3b5fT5Pz9/dWrVy9J0tChQ3Xo0CH96U9/0nPPPdfofRLUXnY5eBvj8pl0WVmZJ0tqcldzzGfOnNHo0aM1ZMgQrV+/XlZry7wIdC3f59bm+1PT3nXXXa71mZmZuuOOO7xYGTzJMAwlJSVp+/bt2rdvn6Kjo71dklcYhnHNv6MJ6hbi/fff1/vvv69bb71VnTp10meffabf/e53+sEPftCizqavRkFBgUaNGqUePXroj3/8o7788kvXNrvd7sXKmtbnn3+ur7/+Wp9//rmqqqp0+PBhSVKvXr3UsWNH7xbnIW1tatoLFy7o008/db3Ozc3V4cOHFRISoh49enixsqYzf/58bd68Wa+++qoCAwNdV1CCg4PVrl07L1fXNB599FHFx8crMjJS58+fV0ZGhvbt26edO3de246vue85msWRI0eM0aNHGyEhIYbNZjN69uxpzJ0718jPz/d2aU1m/fr1hqQal9Zs1qxZNR7z3r17vV2aR61Zs8aIiooy/P39jR/+8Iet+rGdvXv31vg9nTVrlrdLazK1/eyuX7/e26U1mfvvv9/1b7pLly7G2LFjjV27dl3zfpnmEgAAE2uZN/wAAGgjCGoAAEyMoAYAwMQIagAATIygBgDAxAhqAABMjKAGAMDECGoAAEyMoAbQLFJTU3XTTTd5uwygxSGoAQAwMYIagL788kvZ7XYtW7bMte69996Tv7+/du3apeLiYv385z9Xp06d1L59e8XHx+vkyZOutunp6bruuuv0yiuvqE+fPgoICND48eOVl5fn2r506VJ9+OGHrvm309PTm/swgRaJoAagLl266C9/+YtSU1OVnZ2tCxcu6L777tO8efMUFxen2bNnKzs7W6+99preffddGYahSZMmqaKiwrWPb775Rk8++aQ2bNigd955R6Wlpa45uGfMmKFFixZpwIABKiwsVGFhoWbMmOGtwwVaFKa5BCBJmjRpkubMmaOf/exnuvnmmxUQEKCnnnpKJ0+e1GuvvaZ33nlHsbGxkqRNmzYpMjJSr7zyiqZNmyZJqqio0OrVqzVs2DBJ0oYNG9SvXz+9//77+tGPfqSOHTvK19e3VU9RCjQFzqgBuPzxj39UZWWlXn75ZW3atEkBAQH6+OOP5evr6wpgSercubP69u2rjz/+2LXO19dXQ4cOdb2+4YYbdN1117m1AXD1CGoALp999pkKCgrkdDp1+vRpSVJtM+EahiGLxeK27srXta0D0HAENQBJUnl5uX72s59pxowZeuKJJ5SYmKgvvvhC/fv3V2Vlpd577z1X27Nnz+p///d/1a9fP9e6yspKZWdnu16fOHFC586d0w033CBJ8vf3V1VVVfMdENBKENQAJElLlixRSUmJ/vM//1MPP/yw+vXrp8TERPXu3Vt33HGH5syZo/379+vDDz/Ufffdp27duumOO+5wvd/Pz09JSUl677339MEHH+gXv/iFhg8frh/96EeSpJ49eyo3N1eHDx/WV199pbKyMm8dKtCiENQAtG/fPq1atUovvfSSgoKCZLVa9dJLL2n//v1at26d1q9fryFDhmjy5MkaMWKEDMPQjh075Ofn59pH+/bt9cgjj+jee+/ViBEj1K5dO2VkZLi233333Zo4caJGjx6tLl266K9//as3DhVocSxGbTegAKCB0tPTlZycrHPnznm7FKDV4YwaAAATI6gBADAxLn0DAGBinFEDAGBiBDUAACZGUAMAYGIENQAAJkZQAwBgYgQ1AAAmRlADAGBiBDUAACZGUAMAYGL/B0AIXdvqvffDAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.displot(data=D, x=\"xopt\", kind=\"hist\").set(title=\"Optimized lambda\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02ea0b05",
   "metadata": {},
   "source": [
    "# Normality test\n",
    "\n",
    "There are many statistical test of normality. The visual one is *Q-Q (quantile-quantile plot)*, see https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.probplot.html. As we see, the power transformation makes data more gaussian-like (though not perfect)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9d5262bc",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res = stats.probplot(D[\"x\"], plot=plt) # original data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "37a7d1af",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res = stats.probplot(D[\"xopt\"], plot=plt) # optimized lambda"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6005ed0",
   "metadata": {},
   "source": [
    "# Try yourself\n",
    "\n",
    "Now try to apply a power transformation either on sample from different distribution, or on some real data (e. g. on win ratios above 1, i. e. cases of winnings bigger than bets, of the *hazard* dataset)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}