pdp-notebook-2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pickle\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Load model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[18:42:11] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
     ]
    }
   ],
   "source": [
    "filename = 'model.sav'\n",
    "model = pickle.load(open(filename, 'rb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"2017.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### PDP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Country', 'Happiness.Rank', 'Happiness.Score', 'Whisker.high',\n",
       "       'Whisker.low', 'Economy..GDP.per.Capita.', 'Family',\n",
       "       'Health..Life.Expectancy.', 'Freedom', 'Generosity',\n",
       "       'Trust..Government.Corruption.', 'Dystopia.Residual'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pdp(feature, df, x_labels, y_label):\n",
    "    df_copy = df.copy()\n",
    "    unique_vals = np.unique(df_copy[feature].values)\n",
    "    y = []\n",
    "    for val in unique_vals:\n",
    "        df_copy[feature] = val\n",
    "        X = df_copy[x_labels]\n",
    "        y.append(np.average(model.predict(X)))\n",
    "    plt.ylim(2, 8)\n",
    "    g = sns.lineplot(x=unique_vals, y=y)\n",
    "    g.set(xticks=unique_vals)\n",
    "    g.set(xticklabels=[])\n",
    "    g.set(xlabel = feature)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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S1mRGT5jZzpOsaZakQyc571hTaF/Nyryf7H4s5jPI9o3OM1RduX3jrne49eTOG7ffcPVOyQy3FGjXEMs7R1Iyz7pn6eOmFFiHMsvJbctOi7YV2kfR8SmZmrLTorUviYxn38+JtCcjdS6RtEMf7aPJklozw+MldWemNRfYpmhN0f0U3a5kpn12ZNnRdUf7RD+LfNub/RyTOdOjw9F9FpXbPpRTya8FBVvcPdZL6Z2+SdIX485zMi9JTeVcfiW9Cu0rSU2nsh+LmTfbNzrPUHXFaT+Z9eTOG7dfjP3YLqm9mP0cqbU937qz8+W88q4ju5xC06JthfaRpP7oeqLTcpY50C+6nsirKbLe/px95JHh/uy0OP/Govspul2Rmr3AuqN9+iP159ve9pz+HxvO/VwLtZfqv51iXrGu+jCzWklPSnrM3dfFmQcAUBpxrvowST+WtN3dv1v+kgAAUXHOUV8l6Q5JzWa2JTPtm+6+vkw1rS3TcitRoX11qvuwmPnX5rwPNX++vnHXO9x6cueN22+4ej4Vo6ZC09dJeiXP9HzzDbWefP8Xuy5PW6F9dJekhyPreSVnWtbreaZFl/9KpM7XI8v/lNI5kR0+X9I7mWmFRGuM7qd1eabdp8Gfxet5+tyVeX9Y+bc3O+8rOdNzh3Nry9c+lLLkl2XOqwAAAsWdiQAQOIIaAAJHUANA4AhqAAgcQQ0AgSOoASBwBDVGHTPrN7MtkdfCU1zeLWZ2X2b4H83sGyUpFCiRoh7KBASi092Xl2ph7v4rSb8q1fKAUuOIGqOemU02sxfMbLOZNZvZFzLTF5rZDjP7dzN728weM7MbzOxVM3vHzC7L9LvTzH6Qs8xFZrY5Mn5+dBw4nQhqjEYTIqc9npLUJekP3H2FpGslPZB5Ro0knSfpAaUfjblE0pclXS3pG5K+WWgF7v6upGNmtjwzabWkR8qxMcBwOPWB0WjQqY/M0x3/xcyukZRS+octGjLNLe7enOm3VdIL7u5m1ixp4TDreUjSajP7uqQvSbqspFsBxMQRNSrBbZLqJa3MBPgBSXWZtu5Iv1RkPKXhD1SelLRK0s2SNrn74VIVDBSDoEYlmCbpoLv3mtm1GuqXMorg7l2SnpX0oDjtgRFEUKMSPCapMXM64ytK/0xTKZedkvRcCZcJFIXHnAJDyFxTPc3dvzXStWDs4stEoIDMFSWLJF030rVgbOOIGgACxzlqAAgcQQ0AgSOoASBwBDUABI6gBoDA/T9hs+3DSywqugAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWoAAAEACAYAAACatzzfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAXAklEQVR4nO3de5ScdX3H8fd3Zu/XJJvdJCSEEIGEEiDRFREFJYJKsajIQSutoPXk2CMq1ktt1eNBW7W2UqFU2xzwdkBpuWlVREBAQSGwm0ACBAzkTkiyu7ntJXub+faPeTaZDDM7s8lM9rebz+ucOfPMPL/nN99nZvazv332uZi7IyIi4YqNdwEiIjI6BbWISOAU1CIigVNQi4gETkEtIhI4BbWISOAKCmoz+7SZPWtmz5jZT82sqtSFiYhISt6gNrPZwCeBVndfBMSBD5S6MBERSSl000cZUG1mZUANsK10JYmISLqyfA3c/WUz+zdgM7AfuM/d78tsZ2bLgGUAtbW1r1u4cGGxaxURmbTa29s73b052zzLdwi5mU0F7gTeD+wBbgfucPdbci3T2trqbW1th12wiMixxsza3b0127xCNn1cAGxw9w53HwLuAs4pZoEiIpJbIUG9GTjbzGrMzIC3AWtLW5aIiIzIG9TuvgK4A1gJrImWWV7iukREJJL3n4kA7v4V4CslrkVERLLQkYkiIoFTUIuIBE5BLSISOAW1iEjgFNQiIoFTUIuIBE5BLSISOAW1iEjgFNQiIoFTUIuIBE5BLSISOAW1iEjgFNQiIoFTUIuIBE5BLSISOAW1iEjgFNQiIoFTUIuIBE5BLSISOAW1iEjgFNQiIoFTUIuIBE5BLSISOAW1iEjgFNQiIoFTUIuIBE5BLSISuLxBbWYLzOyptNs+M7vmKNQmIiJAWb4G7v4CsBjAzOLAy8DdpS1LRERGjHXTx9uAl9x9UymKERGRVxtrUH8A+GkpChERkewKDmozqwAuAW7PMX+ZmbWZWVtHR0ex6hMROeaNZUR9EbDS3Xdkm+nuy9291d1bm5ubi1OdiIiMKaj/Em32EBE56goKajOrBS4E7iptOSIikinv7nkA7t4LNJW4FhERyUJHJoqIBE5BLSISOAW1iEjgFNQiIoFTUIuIBE5BLSISOAW1iEjgFNQiIoFTUIuIBE5BLSISOAW1iEjgFNQiIoFTUIuIBE5BLSISOAW1iEjgFNQiIoFTUIuIBE5BLSISOAW1iEjgFNQiIoFTUIuIBE5BLSISOAW1iEjgFNQiIoFTUIuIBE5BLSISOAW1iEjgFNQiIoErKKjNbIqZ3WFmz5vZWjN7Y6kLExGRlLIC210P3Ovul5lZBVBTwppERA7oH0qwdXcfGzv72NjVy7Y9/STdc7ZPJJ3hpDOcSB6cTiYZTji5lyqOhqpyvn35mUXvN29Qm1kjcB5wFYC7DwKDRa9ERCatRNJZsaGLR9Z1sm3Pfvb0DeVdZmA4wZZd+9m2dz/puVxbEacsnntjQMygLB6jLGaUxY2yWIx4zCiLGWZWjNXJaWrNcEn6LWREfSLQAfzAzM4E2oFPuXtveiMzWwYsA5g7d26x6xSRCWY4kWTFhl3cs+YVfvPsdjp7BimPG7Maq5laUw55QrM8ZrzhxGmc0FTLvOk1qfumGqbUVBylNQiH+Sh/QgCYWSvwOPAmd19hZtcD+9z9y7mWaW1t9ba2tuJWKiLBGRxOsr6zhx37Btixr58de/vZ0d3Pjn0DrNy0m67eQarL4yw9tYWLT5/FWxc0U1NR6BbXY4uZtbt7a7Z5hbxjW4Gt7r4ienwH8IViFSciE4+788DanXz1l8+yZdf+Q+ZNqSlnZkMV55w0nT9fNJO3LmihuiI+TpVODnmD2t23m9kWM1vg7i8AbwOeK31pIhKiDZ29XPuLZ3n4hQ5ObqnjusvPZO60GmY0VNFcX0lVuUK52Ar9G+QTwK3RHh/rgQ+XriQRCVHvwDA3PvQiNz+ygYqyGF+6+FSuPGce5aP8Y0+Ko6CgdvengKzbTkQkPMmk82JHD+s7esjzb6iCdPUOcuODL7J9Xz+XvnY2X7hoIS31VUfesRREW/VFJpmO7gHev/wx1nf05m88Bqcd18B/XrGE150wraj9Sn4KapFJZGA4wcduaWfbnv1849LTOX12I2XxI993OG7G/OY64rHS7ocs2SmoRSYJd+dLdz9D+6bd3PjBJbzrjOPGuyQpEv0XQGSSuPnRDdzevpVPLj1JIT3JKKhFJoHf/amDr9+zlnecNoNrLjhlvMuRIlNQi0xwL3X0cPVPVnLKjHquu3wxMW1HnnS0jVpkgkgmnQfW7mDbnkOPBPzxY5uoiMe46cpWaiv1Iz0Z6VMVmQB+tfoV/v2BP/Hizp5XzaupiPPDD5/FnKk6+/BkpaAWCdzj67v41G2rmF5XybcuO4MLTp1B+saN6oq4Dtue5BTUIgHb3NXH397SzglNNdz98TfRUFU+3iXJOFBQi5SQu3PFTStY39EbncTeopPYxzCD4aSPerWSrp7UNTpuuvL1CuljmIJapIQ6ugf440tdvH7eVI6fVnPg0lCJhJNwpzxuxEY5gX48Zlx5zjxOnF57FKuW0CioRUpoQ2fqfBtXLz2Zt5zSPM7VyESl/ahFSmhjVyqo52tELEdAQS1SIpu7+vj7O9cQMzhuSvV4lyMTmIJapER+sXobAJ95+wKddU6OiIJapEQefH4np89u5OPnnzTepcgEp6AWKYFdvYOs3LybpQtbxrsUmQS014dIJJG2T/Ou3kEeXdfJUCLJUNIZTiR5esse1nf24g6Op+4dnNT+0pB6nHSnbzCBO7ztVAW1HDkFtRzTrrvvBe57bgc9A8Ps7B5gcDiZs211eZzWeVMpixlmhgGpXaANMw48tujxOxfNZNFxjUdpTWQyU1DLpNM/lOBXq19hY1cva17ey3Ai+5F/g8NJnti4i2m1Fbz1lGam1FQwrTZ19J+Zcfb8aRw3pZp4zCiPxXRODRk3CmqZcLr7h9g/mCDhTiJ58JZ0J5GER9Z18E+/WgvAqbMaqKnIHa5nz5/GtZcsYsHM+qNVvsiYKahlQln7yj4uvuERkrlPj3HAI58/n+On6dSfMvEpqCU4z27by8rNe7LOa9+4i6TDly4+lbrKMmIxI26pEx2N3GJmtDRUKqRl0lBQS1C27Orjsu89xv6hRM4286fX8jdvPhEb5WRGIpOJglqCMZRI8sWfPUPM4N5rzqWptjJru4bqMoW0HFMU1HLUbe7qY9WW3azv6OXeZ7YzlEjtEtfVO8je/UNce8lpLJzZMM5VioSjoKA2s41AN5AAht29tZRFSdj6hxI8sHYH7Zt2HzjAYzg6KGQ42gNjOOEMJ5PRfeq5oUSS7fv62dTVd6CvN85vYnp9auRcUx7nHYtmcP4CHSQikm4sI+rz3b2zZJVIybg73QPD7Nw3wM7ufjq6Bw6ZHm17cKahhLNifRe9gwlqKuKUx1NXKimLxQ5cvaQ8fvAqJiNXNSmLx4jHjFNm1PPhc+bxhvlNNNVV0FJfVcI1F5kctOljkrvht+v47sMv0j/06iPuKstitDRUUlsxtq/BxWfM4j2LZ/OG+U06K5zIUVDoT6gD95mZA//t7sszG5jZMmAZwNy5c4tXoRyRO9q3Mq+plktfO5uW+ipa6itpaaikub6Khir9U05kIig0qN/s7i+bWQtwv5k97+6/T28QhfdygNbW1gIOR5BS6RscZn1HL+t2drN5Vx9fuvhUPnru/PEuS0QOU0FB7e4vR/c7zexu4Czg96MvJePhpY4e3ve9P7KnbwiAsphxnq7VJzKh5Q1qM6sFYu7eHU2/HfhqySuTw3LbE5vp7h/mO+9fzEktdZzQVEN9Vfl4lyUiR6CQEfUM4O5oW2YZ8BN3v7ekVclhGUokWbV5D4uOa+A9S2aPdzkiUiR5g9rd1wNnHoVa5DBt6urltie3cHvbVjp7Brhal34SmVS0e94E1dUzwKMvdnJ721YefbGTeMxYurCFD541V9ukRSYZBXWA3J29+4d4ZW8/2/f1s31v2m1fP1t39/FSRy8As6dU83cXnsLlrcczs1EHj4hMRgrqoyyRdDp7BlIhvLef7Xv3s33fANv37ueVvf3s2JcK48wDVMxgel0lsxqrmN9cx6WvncPZ85tYfPwUHXQiMskpqIuob3CYju6BAyPf7Xv7D4TvyP3O7gESGWe9r4jHmNFYycyGKk6fM4ULGyqZ2VjNzIYqZjambi31lZTHddF4kWORgnoU/UMJunoH6eoZiO5T07t6B+nsGWRXb9rzvQNZD9OuqyxLhW1DFSedNP1g+Eb3sxqrmFpTQUyjYhHJYdIGdXf/EO2bdr9q9DrCHboHhqKQPTSAu3oH2NUzSO9g9pMVVZTFmF5bQVNdJdNqKzippY6m6HFTbQWzGquZ2VjJjIYq7cMsIkds0gR1/1CCNS/vZdXm3bRt3M3v/tTBwPCrR7jZlMeNabUVNNVW0lRXwbymGqZF09PrKg5Mj4RxbUVc58gQkaNmQga1u7N5Vx+rNu9h1ebdrNqyh+e27WM4Gj3PmVrN5a3Hc9GimdRV5V7FusoymuoqdXIiEQnahAjqnoFhVm/Zw6otUTBv3kNX7yAANRVxzpwzhWXnzWfJ3KksPn4KzfXZL+EkIjIRBRXUH1j+2Ks2V/T0D/NiRw8ebWp+TXMtSxe2sGTuVJbMncIpM+q1e5qITGpBBXVdZRnl8UODuqm2kovPmJUaLc+ZQmON/jknIseWoIL6pitfP94liIgER0dQiIgETkEtIhI4BbWISOAU1CIigVNQi4gETkEtIhI4BbWISOAU1CIigVNQi4gETkEtIhI4BbWISOAU1CIigVNQi4gETkEtIhI4BbWISOAKDmozi5vZKjP7ZSkLEhGRQ41lRP0pYG2pChERkewKCmozmwNcDNxU2nJERCRToSPq7wCfB5K5GpjZMjNrM7O2jo6OYtQmIiIUENRm9i5gp7u3j9bO3Ze7e6u7tzY3NxetQBGRY10hI+o3AZeY2UbgNmCpmd1S0qpEROSAvEHt7v/g7nPcfR7wAeBBd/+rklcmIiKA9qMWEQle2Vgau/vDwMMlqURERLLSiFpEJHAKahGRwCmoRUQCp6AWEQmcglpEJHAKahGRwCmoRUQCp6AWEQmcglpEJHAKahGRwCmoRUQCp6AWEQmcglpEJHAKahGRwCmoRUQCp6AWEQmcglpEJHAKahGRwCmoRUQCp6AWEQmcglpEJHAKahGRwCmoRUQCp6AWEQmcglpEJHAKahGRwCmoRUQClzeozazKzJ4ws6fN7Fkzu/ZoFCYiIillBbQZAJa6e4+ZlQOPmtmv3f3xEtcmIiIUENTu7kBP9LA8unkpixIRkYMK2kZtZnEzewrYCdzv7itKWpWIiBxQUFC7e8LdFwNzgLPMbFFmGzNbZmZtZtbW0dFR5DJFRI5dY9rrw933AA8B78wyb7m7t7p7a3Nzc5HKExGRQvb6aDazKdF0NXAh8HyJ6xIRkUghe33MAn5kZnFSwf6/7v7L0pYlIiIjCtnrYzWw5CjUIiIiWejIRBGRwCmoRUQCp6AWEQmcglpEJHAKahGRwCmoRUQCp6AWEQmcglpEJHAKahGRwCmoRUQCp6AWEQmcglpEJHAKahGRwCmoRUQCp6AWEQmcglpEJHAKahGRwCmoRUQCp6AWEQmcglpEJHAKahGRwCmoRUQCp6AWEQmcglpEJHAKahGRwCmoRUQCp6AWEQmcglpEJHB5g9rMjjezh8zsOTN71sw+dTQKExGRlLIC2gwDn3H3lWZWD7Sb2f3u/lyJaxMREQoYUbv7K+6+MpruBtYCs0tdmIiIpBQyoj7AzOYBS4AVWeYtA5ZFD3vM7IXDrGk60DnK47H0Q55lD7fvfK9bzD5z9TfyfLb5JwLdafPJU9NYal4YtU1/7ROBDaMskz4/vW6A+mje9Oj2fHRfn2UdRtbjRKAyS9v09QEYiPpeGE13Z/TRndY+ff3r06bTa5gd9fN8xmtlfg7p9ab3tyFj3izglYyaRt6nzM8w1/vQzcH3MFP6OmbKrDXX55+5Ltna5fr8c303N2SZl61tvu9+Zn2dWeZnLkeOecVyJD//J+Sc4+4F3YA6oB24tNBlDucGtI32eCz95Fv2cPs+mn3m6m/k+Wzzgd70+cV8H4BE5msDvXmW6c18rZG6RuZF04m06VetQ/rrZWubdktEt960mnuz9JHePn353rRbeg0+8rqjfQ4ZfR3oL8s8z6wp12c4yvvQRo73P8v7csjyhXw/ci2X6/PN971Kfx8KaDvqdz/be5JvudHWtRi3UvVf0F4fZlYO3Anc6u53FbKMiIgURyF7fRhwM7DW3a8rfUkiIpKukG3UbwL+GlhjZk9Fz/2ju99TopqW53l8uP0cbptSvG4x+ls+yvy7gEfGUM9Yan4S+H7Gcvn+ykqfn1n3uWmPP5I2fS651+Eu4OQcbUnrZ11azesy2qS/Rx/h4Dql10RGDf8MvJRRS7bPIbPec3PM+0LG47sypjPXP9v78EhG/9nmjybfZ1/IdyPX55/ru5ltXra2+b77R7JcqZTkNSzariIiIoHSkYkiIoFTUIuIBE5BLSISOAW1iEjgFNQiIoFTUIuIBE5BLUfEzBJm9lR0CtynzewzZjbm75WZzTOzDx5hLX8cY/uHzeyFqO4nzWxxMV/XzH5oZpcdZp9vNbNfHs6yMvkoqOVI7Xf3xe5+GnAhcBHwlcPoZx5wREHt7uccxmJXuPuZwHeBfz2KrytSMAW1FI277yR1BsWrLeX36aNUM3vUzM40s7dEo/CnzGxVdJ7zbwLnRs992syqzOwHZrYmanN+1MdVZvbzaDS8zsy+ktZ/T3RfZ2a/NbOV0fLvLqD8x4hO32tmtWb2fTN7Inrtd0fPnxY995SZrTazkzNe18zsxmiU/gDQklbbRjObHk23mtnD0fRZZvZY9Dp/NLMFh/n2yyQ2ptOciuTj7uvNLE4qpG4GrgKuMbNTgCp3f9rMfgF83N3/YGZ1QD+pw6k/6+7vAjCzz6S689PNbCFwX9QHwFnAIqAPeNLMfuXubWll9APvdfd9UTg+bmb/56MfhvtO4GfR9BeBB939I2Y2BXgiCt6PAde7+61mVgHEM/p4L7AA+DNgBvAchx6ans3zwLnuPmxmFwBfB96XZxk5xiiopZRuB75sZp8jdT6NH0bP/wG4zsxuBe5y962pc38d4s3AfwC4+/NmtgkYCer73b0LwMzuitqmB7UBXzez84AkqZHyDGB7lhpHQrcOWBw993bgEjP7bPS4CphLatT9RTObE9W9LqOv84CfunsC2GZmD4725kQagR9Fo3MHygtYRo4x2vQhRWVm80md33mnu/cB9wPvBi4HbgVw928CHwWqgT9EI+axyBwZZz6+AmgGXufui4EdpMI2myuA+cCPiH4xkAr690Xb3he7+1x3X+vuPwEuAfYD95jZ0jHUPMzBn7f0Wr4GPOTui4C/GKVOOYYpqKVozKwZ+C/gxrTNDDcBNwBPuvvuqN1r3H2Nu/8LqTPbLeTglUpGPEIqRIk2ecwFRq4adKGZTTOzauA9pEbo6RpJ/aIYirZt575yBqntK8CXgbOjXxq/AT4RneIXM1sS3c8H1rv7DcDPgTMyuvo98H4zi5vZLOD8tHkbgddF0+mbNhqBl6Ppq0arU45dCmo5UtUju+cBDwD3AdeOzHT3dmAf8IO0Za4xs2fMbDUwBPwaWA0kol3lPk1qL4yYma0B/ge4yt0HouWfIHUhi9XAnRnbpyE1cm+Nlv0QaZfOMrN7zOy4zJVw9/3At4HPkRrllgOro/X6WtTscuAZS53udxHw44xu7iZ1OtXnonmPpc27FrjezEauKDPiW8A3zGwV2hQpOeg0p1JSUSg+DCx092QR+rsKaHX3q4+0L5GJQiNqKRkz+xCpCyF/sRghLXKs0ohaRCRwGlGLiAROQS0iEjgFtYhI4BTUIiKBU1CLiATu/wG52vtZehGS/gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for v in ['Economy..GDP.per.Capita.', 'Family',\n",
    "       'Health..Life.Expectancy.', 'Freedom', 'Generosity',\n",
    "       'Trust..Government.Corruption.', 'Dystopia.Residual']:\n",
    "    pdp(v, df, ['Economy..GDP.per.Capita.', 'Family',\n",
    "       'Health..Life.Expectancy.', 'Freedom', 'Generosity',\n",
    "       'Trust..Government.Corruption.', 'Dystopia.Residual'], 'Happiness.Score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "analysis flow",
   "language": "python",
   "name": "analysis"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}