statistics/patsy_example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d41337db",
   "metadata": {},
   "source": [
    "# Getting Started"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "450e610a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-13T23:53:12.815167Z",
     "start_time": "2021-08-13T23:53:10.556410Z"
    }
   },
   "outputs": [],
   "source": [
    "pip install openpyxl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c42d7eb8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-13T23:53:13.392806Z",
     "start_time": "2021-08-13T23:53:12.821032Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "980dfaee",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-13T23:53:14.887709Z",
     "start_time": "2021-08-13T23:53:13.401762Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_excel(\"Life Expectancy at Birth.xlsx\", engine=\"openpyxl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7853382f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-13T23:53:14.902826Z",
     "start_time": "2021-08-13T23:53:14.890765Z"
    }
   },
   "outputs": [],
   "source": [
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "be2a87cb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-13T23:53:14.909120Z",
     "start_time": "2021-08-13T23:53:14.905102Z"
    }
   },
   "outputs": [],
   "source": [
    "df.rename(\n",
    "    columns={\n",
    "        \"Country\": \"country\",\n",
    "        \"Country Code\": \"country_code\",\n",
    "        \"Region\": \"region\",\n",
    "        \"Income Group\": \"income_group\",\n",
    "        \"Year\": \"year\",\n",
    "        \"Life Expectancy\": \"life_expectancy\",\n",
    "    },\n",
    "    inplace=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "406dbf67",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-13T23:53:14.926562Z",
     "start_time": "2021-08-13T23:53:14.912438Z"
    }
   },
   "outputs": [],
   "source": [
    "sample = df.groupby(\"region\").sample(n=20).sort_values(by=\"year\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "51759797",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-13T23:53:14.957348Z",
     "start_time": "2021-08-13T23:53:14.931429Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>country_code</th>\n",
       "      <th>region</th>\n",
       "      <th>income_group</th>\n",
       "      <th>year</th>\n",
       "      <th>life_expectancy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>United States</td>\n",
       "      <td>USA</td>\n",
       "      <td>North America</td>\n",
       "      <td>High income</td>\n",
       "      <td>1960</td>\n",
       "      <td>69.770732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>Ghana</td>\n",
       "      <td>GHA</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>1961</td>\n",
       "      <td>46.266707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>Tunisia</td>\n",
       "      <td>TUN</td>\n",
       "      <td>Middle East &amp; North Africa</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>1961</td>\n",
       "      <td>42.645512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>664</th>\n",
       "      <td>Lesotho</td>\n",
       "      <td>LSO</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>1962</td>\n",
       "      <td>47.402244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>Guinea</td>\n",
       "      <td>GIN</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Low income</td>\n",
       "      <td>1963</td>\n",
       "      <td>35.425171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>964</th>\n",
       "      <td>Nepal</td>\n",
       "      <td>NPL</td>\n",
       "      <td>South Asia</td>\n",
       "      <td>Low income</td>\n",
       "      <td>1963</td>\n",
       "      <td>36.425171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1106</th>\n",
       "      <td>Djibouti</td>\n",
       "      <td>DJI</td>\n",
       "      <td>Middle East &amp; North Africa</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>1964</td>\n",
       "      <td>45.708634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1565</th>\n",
       "      <td>St. Vincent and the Grenadines</td>\n",
       "      <td>VCT</td>\n",
       "      <td>Latin America &amp; Caribbean</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>1965</td>\n",
       "      <td>62.347463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1472</th>\n",
       "      <td>Myanmar</td>\n",
       "      <td>MMR</td>\n",
       "      <td>East Asia &amp; Pacific</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>1965</td>\n",
       "      <td>46.966390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1366</th>\n",
       "      <td>Cyprus</td>\n",
       "      <td>CYP</td>\n",
       "      <td>Europe &amp; Central Asia</td>\n",
       "      <td>High income</td>\n",
       "      <td>1965</td>\n",
       "      <td>71.165073</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             country country_code                      region  \\\n",
       "248                    United States          USA               North America   \n",
       "344                            Ghana          GHA          Sub-Saharan Africa   \n",
       "503                          Tunisia          TUN  Middle East & North Africa   \n",
       "664                          Lesotho          LSO          Sub-Saharan Africa   \n",
       "872                           Guinea          GIN          Sub-Saharan Africa   \n",
       "964                            Nepal          NPL                  South Asia   \n",
       "1106                        Djibouti          DJI  Middle East & North Africa   \n",
       "1565  St. Vincent and the Grenadines          VCT   Latin America & Caribbean   \n",
       "1472                         Myanmar          MMR         East Asia & Pacific   \n",
       "1366                          Cyprus          CYP       Europe & Central Asia   \n",
       "\n",
       "             income_group  year  life_expectancy  \n",
       "248           High income  1960        69.770732  \n",
       "344   Lower middle income  1961        46.266707  \n",
       "503   Lower middle income  1961        42.645512  \n",
       "664   Lower middle income  1962        47.402244  \n",
       "872            Low income  1963        35.425171  \n",
       "964            Low income  1963        36.425171  \n",
       "1106  Lower middle income  1964        45.708634  \n",
       "1565  Upper middle income  1965        62.347463  \n",
       "1472  Lower middle income  1965        46.966390  \n",
       "1366          High income  1965        71.165073  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a88cea62",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-13T23:53:15.013105Z",
     "start_time": "2021-08-13T23:53:14.960447Z"
    }
   },
   "outputs": [],
   "source": [
    "from patsy import dmatrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d2062357",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-13T23:53:15.026378Z",
     "start_time": "2021-08-13T23:53:15.015251Z"
    }
   },
   "outputs": [],
   "source": [
    "outcome, predictors = dmatrices(\n",
    "    \"life_expectancy ~ income_group + region + year\", data=sample\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "61610b80",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-13T23:53:15.041750Z",
     "start_time": "2021-08-13T23:53:15.028836Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DesignMatrix with shape (140, 1)\n",
       "  life_expectancy\n",
       "         69.77073\n",
       "         46.26671\n",
       "         42.64551\n",
       "         47.40224\n",
       "         35.42517\n",
       "         36.42517\n",
       "         45.70863\n",
       "         62.34746\n",
       "         46.96639\n",
       "         71.16507\n",
       "         63.63863\n",
       "         68.82088\n",
       "         70.50732\n",
       "         69.00537\n",
       "         60.24490\n",
       "         70.80439\n",
       "         46.54956\n",
       "         68.65046\n",
       "         70.70732\n",
       "         54.42012\n",
       "         67.42244\n",
       "         69.29000\n",
       "         47.73302\n",
       "         67.72390\n",
       "         73.93041\n",
       "         72.95610\n",
       "         73.22439\n",
       "         68.47815\n",
       "         42.60083\n",
       "         72.95854\n",
       "  [110 rows omitted]\n",
       "  Terms:\n",
       "    'life_expectancy' (column 0)\n",
       "  (to view full data, use np.asarray(this_obj))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "86f67319",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-13T23:53:15.048529Z",
     "start_time": "2021-08-13T23:53:15.043731Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DesignMatrix with shape (140, 11)\n",
       "  Columns:\n",
       "    ['Intercept',\n",
       "     'income_group[T.Low income]',\n",
       "     'income_group[T.Lower middle income]',\n",
       "     'income_group[T.Upper middle income]',\n",
       "     'region[T.Europe & Central Asia]',\n",
       "     'region[T.Latin America & Caribbean]',\n",
       "     'region[T.Middle East & North Africa]',\n",
       "     'region[T.North America]',\n",
       "     'region[T.South Asia]',\n",
       "     'region[T.Sub-Saharan Africa]',\n",
       "     'year']\n",
       "  Terms:\n",
       "    'Intercept' (column 0)\n",
       "    'income_group' (columns 1:4)\n",
       "    'region' (columns 4:10)\n",
       "    'year' (column 10)\n",
       "  (to view full data, use np.asarray(this_obj))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "457a3359",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-13T23:53:37.136542Z",
     "start_time": "2021-08-13T23:53:37.133943Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6638bf5f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-13T23:53:37.620911Z",
     "start_time": "2021-08-13T23:53:37.614722Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1.])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.asarray(predictors)[:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "92a3e0e2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-13T23:53:38.167976Z",
     "start_time": "2021-08-13T23:53:38.162393Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [1., 0., 0.],\n",
       "       [1., 0., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 0., 1.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 0., 0.]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.asarray(predictors)[:10, 1:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1a22e7c8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-13T23:53:38.623800Z",
     "start_time": "2021-08-13T23:53:38.618869Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 1., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 1.],\n",
       "       [0., 0., 1., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 1.],\n",
       "       [0., 0., 0., 0., 0., 1.]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.asarray(predictors)[:5, 4:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b82735cd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-13T23:53:40.029942Z",
     "start_time": "2021-08-13T23:53:38.912876Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    predictors, outcome, test_size=0.2, random_state=42\n",
    ")\n",
    "y_train = y_train.squeeze()\n",
    "y_test = y_test.squeeze()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bef65101",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-13T23:53:40.094463Z",
     "start_time": "2021-08-13T23:53:40.031952Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "model = LinearRegression().fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fc9364b9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-13T23:53:41.012663Z",
     "start_time": "2021-08-13T23:53:40.096963Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from yellowbrick.regressor import prediction_error\n",
    "from IPython.core.pylabtools import figsize\n",
    "\n",
    "figsize(6, 6)\n",
    "model = LinearRegression()\n",
    "visualizer = prediction_error(model, X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5e466c7e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-13T23:53:41.019187Z",
     "start_time": "2021-08-13T23:53:41.014857Z"
    }
   },
   "outputs": [],
   "source": [
    "def split_data_into_train_test(X, y):\n",
    "    X_train, X_test, y_train, y_test = train_test_split(\n",
    "        X, y, test_size=0.2, random_state=42\n",
    "    )\n",
    "    y_train = y_train.squeeze()\n",
    "    y_test = y_test.squeeze()\n",
    "    return X_train, X_test, y_train, y_test\n",
    "\n",
    "\n",
    "def plot_equation(data: pd.DataFrame, equation: str):\n",
    "\n",
    "    # Create X and y\n",
    "    outcome, predictors = dmatrices(equation, data=data)\n",
    "\n",
    "    # Split data into train and test sets\n",
    "    X_train, X_test, y_train, y_test = split_data_into_train_test(predictors, outcome)\n",
    "\n",
    "    # Instantiate a model\n",
    "    model = LinearRegression()\n",
    "\n",
    "    # Visualize model\n",
    "    visualizer = prediction_error(model, X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1c0fbde7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-13T23:53:41.214638Z",
     "start_time": "2021-08-13T23:53:41.020906Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_equation(sample, \"life_expectancy ~ region\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a1a4a8f6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-13T23:53:41.885885Z",
     "start_time": "2021-08-13T23:53:41.683467Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YPnw4aWlpzJw5E51Ox7Rp0xg1ahRKKXx8fHj77bdJTU1l8uTJLFu2DIvFQmRkJJB9F+6kSZPuOqqnZs2anDhxgi+++MJmf5UIIZzDlqNubscln0FrNBqtU4Pm/AbMmSbBmXewpqam4uPj47T9O0JBz7PBYLDLF9ZVyfm4Qc7FDQaDAcAmQZ9fLubm8Ja9yWRiypQpXLhwAa1Wy6xZs9Dr9UyZMgWNRkO9evV4/fXX833SihBClCanTp0iNjYWsF+LPofDw37r1q1kZWWxatUqduzYwXvvvYfJZGLChAm0adOGGTNmsGnTJrp27ero0oQQwqGqVKnC1atXad26td3/2nF42AcHB2M2m7FYLKSkpKDX6zlw4AD3338/AB07dmTHjh0FCvtDhw7d9LpOnTqYTCa71F1QqampTt2/vZlMJmJiYgr03pw/UUU2OR83yLnI5uvrS/PmzQH7nxOHh723tzcXLlygR48eXL16leXLl7Nv3z7rnCs+Pj4FnkZX+uwdLzMzk6ZNm0qffSHJ+bjB3c+FwWAgIyOD9u3bYzAYaNu2rU22m9NnfzsOD/svvviCsLAwXn75ZS5dusSIESNuao2npqbi5+fn6LKEEMLuco+6CQ4Odui+HX4V1M/Pz/rkc39/f7KysmjcuDF79uwBICoqilatWjm6LCGEsKu8wyurVq3q0P07POxHjhzJ4cOHGTp0KCNGjGDixInMmDGDpUuXMmjQIEwmEw8//LCjy7KJHTt23DJXzeOPP05cXFyhtmM0GlmzZg0A69atY9OmTUD+M2IKIUo+R4yjvxuHd+P4+PiwePHiW5aXhiBr3769Tfrs//77b9asWcPAgQPp37+/dfmyZct48skni719IYTjlISgh1J+B+28efNuu6579+7Wq+AHDhzgl19+ue17p0yZUqD9bdiwgQsXLqDT6di2bZt1WBVkPx932rRp1tevvfYaDRo0oFu3brRo0YLY2FgCAwNZunQpy5cv59SpU7z//vsopahYsSLXrl2zzoiZnJxMnz59CA8PJyYmhvnz5/Pxxx8X8KwIIRzFZDLxxx9/AM4NepBZL23u0KFD7Nu3j7Vr11qnRQBYvnw5bdu2ZcWKFcyaNcs6s+X58+cZP3483377LYmJiURHRzN69Gjq1q3LmDFjrNvNPSPmwIEDWb9+PQBr165lwIABDj9OIcTdeXh4MGTIEJtNU1wcpbplX9AWefPmza2t/OK6dOkSnTp1QqvV4uvrS/369QE4ceIEu3fv5ueffwYgKSkJgAoVKlifJ3nPPfdgNBrvuo82bdowe/ZsEhMT2bFjBy+99JJNahdC2MbFixetF2D9/PxsNk1xcZTqsHeG6tWrc/DgQSwWCxkZGZw6dQqAkJAQ+vbtS58+fUhISLBegM3vma63m9EyZxojjUZD3759mT17Nu3bt8fDw8OORySEKIycPvqwsDDCwsKcXY6VhL2NNWrUCG9vbwYMGEDlypUJDAwEsh82Mm3aNFavXk1KSspNXTR5BQYGYjKZWLBgAWXKlLEuzz0jZv/+/QkPD+eHH36w+zEJIQom98XYsmXLOrmam0nY21Dfvn2to3FeeOGFW9Z/+OGHtyzbsWOH9d+LFi2y/ju/EF+xYoX132azmZYtW1KnTp1i1SyEsI2SMurmduQCrQv69ddfiYiIYNy4cc4uRQhByQ96kJa9S+rWrRvdunVzdhlCCLJH4JX0oAcJeyGEKJaaNWsSEBBAy5YtS2zQg4S9EEIUi5+fH0899VSJHxUnffZCCFFIBoOBbdu2WYdDl/SgB2nZCyFEoeS+GBsSEkK1atWcXFHBSMteCCEKKO+oG1cJepCwF0KIAnGF4ZV3It04DvTbb7+xZcsWUlJSGDBgQIm6lVoIcXuuHvQgYW8Xq1atYunSpQQGBpKWlsaYMWN45JFHeOihh3jooYdISkpi/vz5RQ77qKgo5syZg8ViYeDAgYwaNSrf912/fp3XXnuNEydOoNFoeOutt7jvvvuA7DtwH3vsMYKCgvjoo4+sn+ncuTM+Pj5otVp0Oh3r1q0rUo1ClBYmk8n6MPCSGvTXr1+3Po/7diTs7eDEiROMGTOGIUOGcPDgQZ599lkeeeQR6/ply5bxxBNPFGnbZrOZmTNn8vnnnxMUFMSAAQPo3LkzdevWveW9c+bMoUOHDixZsoTMzEwyMjKs67788kvq1KlDSkrKLZ/717/+RUBAQJHqE6K08fDwYPDgwZw7d47Q0FBnl3MTi8XCZ599xqxZs1i1ahWenp63fa/b99kbs8xcTErDmGW22TaPHz9ufZhw9erVrcOylFIsWLCAjh070qRJkyJt++DBg9SqVYsaNWrg6elJr169rI8tzC05OZl9+/ZZ57r39PS0Psj98uXLbNmyRebBF+IO4uLirEMr/fz8SlzQnzp1ij59+vDqq6+SnJzMli1b7vh+t23Zmy0WlkQdZUtMPAmpRgJ9vAivE8S4jo3QaYv3O/DEiRMEBwejlGLlypVMnDgRyJ7IbNeuXSQnJ3P27FmGDBly0+eGDh1qfdhJbpMnT+aBBx4AID4+nipVqljXBQUFcfDgwVs+ExcXR0BAAJGRkRw7dowmTZowbdo0vL29eeutt3jllVfy3RfAM888g0ajYdCgQQwaNKjI50EIV5XTR9+uXTs6derk7HJukpWVxYcffsi8efPIyMigcuXKLFiwgG7dunHo0KHbfs5tw35J1FE2HI5Dq9HgpdeRYsxiw+HsB4NPDC9aqxuyH16SmprKqFGjiI+Pp0GDBowdOxaA4cOHM3z48Nt+9uuvvy7yfvPKysriyJEjTJ8+nWbNmjF79mw+/vhjmjVrRkBAAKGhoezZs+eWz33zzTcEBQWRkJDAU089RUhICK1bt7ZZXUKUdLkvxvr6+jq5mpvFxsYSERHB/v37ARgyZAizZ8+mQoUKd33wkVuGvTHLzJaYeLR5Hhyi1WjYEhPPC2EN8dLrirTtEydO0KpVK7788kuSkpLo3bs3+/fvp0WLFnf9bEFa9kFBQVy+fNm6Lj4+nqCgoFs+U6VKFapUqUKzZs2A7Gfufvzxx5jNZjZv3kxUVBRGo5GUlBTrHPk524fsOfW7du3KwYMHJeyF2yjpo27KlSvHuXPnqFatGosWLeKhhx4q8GfdMuwTUo0kpBrzDfTEtOx1Vf29i7Tt48eP07hxYwD8/f3p3bs3W7duLVDYF6Rl37RpU86cOcP58+cJCgrip59+4p133rnlfZUqVaJKlSqcPn2akJAQdu3aRZ06dXj55Zd5+eWXAdizZw+fffaZNejT0tKwWCz4+vqSlpbGjh078p2XX4jSqKQGfXR0NA0aNMDT05OKFSuyatUq6tWrZ70GV1BueYE20MeLQJ/8hykFeN9+XUEcP36cRo0aWV937tyZrVu3Fnl7een1embMmEFERAQ9e/akR48e1KtXz7r+2WefJT4+HoDp06czadIk+vTpw9GjRxk9evQdt52QkMDQoUPp27cvAwcOpFOnTnTs2NFmtQtRUpXEaYrT09N54403ePDBB1m8eLF1ecuWLQsd9OCmLXsvvY7wOkHWPvscFqUIrxNU5C4c4JZWduvWrfn++++LvL38dOrU6bYXjT755BPrvxs1anTHcfJt2rShTZs21tc1atRgw4YNtitUCBdRq1YtAgMDadGiRYkI+t27dzNu3DhOnTqFVqslPT292Nt0y7AHGNcxu/W9JSaexDQjAd43RuMIIdxLuXLlGDlypNNnr0xJSWHWrFl8+umnKKVo0KABS5cupVWrVsXettuGvU6rZWJ4E14Ia2gdelmcFr0QwrUYDAaSk5Pp1KkTGo3G6UF/4cIFevbsyfnz59Hr9UyYMIGXX375rnfGFpTbhn0OL72uyBdj3ZFSCk2eUUzCtRmzzEVu8DjzsxeT0kADVf2y//8tzLZyX4ytW7cu1atXL3ZNBak5v23nLPcNqEjVGjUpX6ECH7z/PvUaNsp+v86Ml153yzEXtr5SE/ZarZbMzMw73i4sis9sNss5LiWKc2Nh8T6rWLTlcJH3+97WI3y+L4ZLSekoBWX0WgJ8vPAv60nFAmwr76ib6tWr2/Umy9tt+8WwBsz99r8kbTxGTFZZ0k1ZeN37GE1qVuG1PVew7L7yvy5mTzRATEIKl66nAxqq+pVhZJu6TChEfaUm7PV6Penp6aSlpaHT6ZzS+jSZTGRmZjp8v46glMJsNmM2m9HrS83Xxq0V58bC4nx21fFE9iepIu932c4TJKYa0Wg0GLPMJGeaSDKaqObvTRm97o7but3wSnvdZHm7bX+35zDL35zM33/txKNGA0y9xqLRaDHixd64a2RZFJV8y1CjvA/H4q8Tl5QKaCjroQMUF66ns3zHcbSFqK9UDb0sV64cnp6eTutmiImJccp+HUGj0eDp6Um5cuWcXYqwgbvdWHinuaKK+1lDfGqRP7vp5GWSMkxoNBqUUmQpC1qNhiyL4mp6Jhalbrut2wV9cY7nbvJuWylF4l/bOb50En//tRONhyfmWvfe9Jnrxiw0Gg3X0jPJslhITDdiVgqzUqj/vUcDJGWY2HTycoHrK3VNNGe3OqWLQ7iC4txYWNzPJmVmEVj21nUF+eyV5HRMZoVOAwpQCjT/+7fJbMFkVnjpNbdsKysriz///BO4dRy9PW+yzL3tzOuJnN/wf1w/lj1dsqrWgErdh3PFowLa//2eUYDFolAKTBZFeqaZTHP2azQ3jhey1/+dklHg+kpd2Ash7i7nxsIUY9Yt6+52Y2FxP+vvmX/sFOSzlcuVJfZqKhaLQsON4NMAHjotHjpNvtvS6/UMHjyYs2fP3jJ7ZXGO525ytn09OZXjH0SSlZqE1qssVbs/yZUaLfDx1uGRasZsUdbj0Go1aDSg12oo66nDU6chp7Tcf3x4aDVU8i1T4PpKVTeOEKJgcm4stCh10/KC3FhY3M+2DPIp8me71KuCfxkP66gwvUaLRSn0Wg0Vynqi1Whu2lbuaYrLlSuX7zTFxTmeu8nZNh6eVGrXHb8GLWg0/h0CW3Wmba1KaIHyZT3JvWc/Lz1KKcqX9USv1RJQ1gudRoNOoyEn6xXgX8aDLvWqFLg+adkL4aaKc2NhcT47uEEANVLLFnm/FqX4fF8Ml6+nU8ZDh7/OgwAfL8qX9cTXS2/dVk4ffZs2bXjwwQftdjz5sVgsfPrpp1SqVIlx/foB8LvnYySmZxLgU8Y6GmfKqs2cytBz+PI10kxZeHvoaBzkj06rxaLgarqRhkF+NAryIyYhhcvJ/xuNUy57NE5h6tMolefXmQswGo0cOnSI0NBQm91wYAsGg6FE3GpdEsi5uFlJPh+OHiufcy7sOc6+qJOa2WKc/cmTJxk/fjy7d+8mICCAP//8Ez8/v3y3bTAYCG3WPHucvZeeFGOWdX3e999tnP3dclFa9kK4ueLcWOjMzwYH3jwyLGdbxZm9sjg1ZWVl8cEHHzBv3jyMRiNBQUEsXLjQOmnZ7bade7lfGc98l+e8znvMhSFhL4QoNZw1TfHhw4cZO3YsBw4cALKfTTF79mzKly/vkP0XhIS9EKJUOHz4sFOC3mKxMGrUKI4ePUqNGjVYtGgRnTt3dsi+C0PCXghRKtSqVYuKFSty3333OSToc0YEabVa3nnnHdatW8f06dNL7I2HEvZCiFLB19eXkSNH2v3GyrS0NObOnUtKSgqLFi0CoG3btrRt29au+y0uGWcvhHBZBoOBzZs3W8fS2zvod+zYQceOHfnggw9YuXIlsbGxdt2fLUnYCyFcUs7F2L1793LhwgW77uv69evWR3yePn2aRo0a8d///pfg4GC77teWpBtHCOFy8pum2F5+++03Jk6cyIULF9Dr9bz88stMnDjR5ebBkrAXQrgURw+v/P7777lw4QL33XcfS5cupXHjxnbdn71I2AshXIajgj4pKQl/f38AZs+eTWhoKBEREU6fVbc4pM9eCOESsrKyrDct2Svor1y5wlNPPUW3bt3IyMgAoHz58owePdqlgx6kZS+EcBE50xSfOXOGJk2K9/SovJRSrFmzhsjISK5evYqPjw8HDx7k/vvvt+l+nEla9kKIEi33NMU+Pj42D/oLFy4wePBgRo8ezdWrVwkPD2fHjh2lKuhBwl4IUYIZDAZWrlzJ5s2b7bL91atX065dOzZu3Ii/vz9Lly7lu+++o2bNmnbZnzNJN44QokTKfTHWnhOKpaSk0KNHDxYuXMg999xjt/04m4S9EKLEsdeoG7PZzF9//UWLFi0AGDhwIPfccw9hYWFo8jxwvLSRbhwhRIlir6A/fvw4PXr0oFevXpw4cQIAjUZDhw4dSn3Qg4S9EKIEscc0xSaTiXfeeYdOnTrxxx9/EBAQwD///FPs7boa6cYRQpQYtWvXpnLlyjRr1swmQX/w4EHGjh1LdHQ0AMOGDWPmzJnWG6bciYS9EKLE8PHxYfjw4Ta5genrr79m/PjxmM1matasyXvvvUd4eHjxi3RR0o0jhHAqg8HAb7/9ZvNpitu2bUuZMmUYNWoU27dvd+ugB2nZCyGcKPfF2AYNGlCjRo0ibys1NZVVq1bx9NNPo9FoCAkJwWAwULlyZVuV69Ik7IUQTpF31E1xgn7btm2MHz+eM2fO4OnpybBhwwAk6HORsBdCOJythldev36d119/nX/9618ANG7cmKZNm9qsztLE4WG/bt061q9fD4DRaOTo0aOsWLGCOXPmoNPpCAsLY8yYMY4uSwjhIMeOHeP8+fNA8YJ+48aNTJgwgUuXLuHh4cGkSZMYP368yz1UxFEcHvb9+/enf//+ALz55ps89thjvP766yxdupQaNWowatQojhw54rIPCBBC3F5WVhYnT56kTJkyxQr6f//73wwfPhyAFi1asGTJEsmMu3DaaJzo6GhOnTpFr169yMzMpGbNmmg0GsLCwti5c6ezyhJC2JFer+ehhx6ib9++xRpH361bN1q2bMnMmTP573//K0FfABqVM97JwcaMGcOTTz5J7dq1GTt2LGvWrAFg7dq1nD9/nokTJ972s0ajkUOHDjmqVCFEMV2+fJmgoKAiT0uQmJjI559/TkREhPWGKIvFglYro8fzCg0NxcvL65blTrlAe/36dWJjY2nbti0pKSmkpqZa16WmpuLn51eg7dzuoJzFYDDY/XmYrkLOxc3c+XwYDAaio6Px9PSka9euhToXSim+/fZbpk6dyrVr1wgMDGTp0qV2rthxbPm9uFsj2Cm/Fvft20e7du0A8PX1xcPDg3PnzqGUYvv27bRq1coZZQkhbCz3qJuAgIBCfTYuLo7HH3+cF154gWvXrtGlSxcmT55sjzLdglNa9rGxsVSvXt36+s0332TSpEmYzWbCwsJo1qyZM8oSQthQUYdXWiwWvvjiC9544w1SUlIoX748c+bMYfDgwW4xO6W9OCXsIyIibnrdvHlzVq9e7YxShBB2UJxx9AcPHmTSpEkA9O7dm7fffpsqVarYpU53IjdVCSFs6siRI4UOeqWUtdXevHlzXn75ZZo0aUK/fv2kNW8jcilbCGFTtWvXJigoqMBBf/ToUbp168b27duty6ZNm8YjjzwiQW9DEvZCCJvy9vZm+PDhdw16k8nEggULCA8Px2AwMH/+fAdV6J4k7IUQxWYwGPj111+t0xTrdLo7vv/AgQN07tyZuXPnYjKZGDFiBF999ZUjSnVb0mcvhCiW3BdjGzZsSM2aNW/73oyMDP7v//6PNWvWYDabqV27NosXL6ZDhw6OKtdtScteCBszZpm5mJSGMcvs7FKKrKDHkBP0ZovivnYdKF+5yh0/l5aWxi+//ILFYmHY0xFs+n2L3YK+NPwcbEla9kLYiNliYUnUUbbExJOQaiTQx4vwOkGM69jI2aUV2J2OQZdnaoKcrps9Z/8hqXJ9/m9bPOmbv6esXkfoPeV5sG4VxnVsRHpaGh4eHnh5eeFfvjythzzPFa0vhkq1Gbn2j9tu3xHH4E4k7IWwkSVRR9lwOA6tRoOXXkeKMYsNh+MA6FjOycUV0J2OYWJ4E+v7clr0e87+w6UKIVwtU5lrqUY0gNFk5lj8dZKNWcQe/IOozxbx+OOPM3XqVJZEHSWmXDD+fn54wW2374hjcDfu+2tOCBsyZpnZEhOPNs9QQa1Gw5aYeDLNFidVVnB3O4ac7hCz2czBgwcxWxTXg+rjXb0u19IzyfmURqMhMeka57//mJVvTOTcuXNs3ryZ1AxjgbbviGNwRxL2QthAQqqRhFRjvusS04wkGUt+yNztGHLW6XQ6Bg0aRPsu3cgMrInJrDCZb0yeq84cxPz1TBINv6PR6Rn78qv8/PPPJBnNBdq+I47BHUnYC2EDgT5eBPrkPwNrgLcX/l53HopYEtztGNIS4q1DK729vWnXsjmBPl546DR46DSorEzUxs/g5+Vo0pLwrlGP+yYuYOrkV/Dw8Ljr9m+3zpbHYIt9uCoJeyFswEuvI7xOEJY8j4ewKEV4nSA8dSX/f7U7HUM9SwJrV3/Lxo0brYGf836A8mU9QecBxjTQe+ATPpi6EW/Q64FWeOl1d91+eJ0g6/vsdQy22oerkgu0QthIzqibLTHxJKYZCfC+MQrkwP79Tq6uYPI7hnqWBAL/OQUaDYGBgTdNYfB4vfJcu3KJ/V7+HLYoUrs8SRmt4t6G9a2jcfJu/3zcBWKM+lvOkT2PwdVGRdmDhL0QNqLTapkY3oQXwhpah/y5Wksy7zGcO36YLZv3gkZz01w3Sim++uorXnvtNRo3bsx33//A1XQTvl56UoxZtz12nVbLE40CCW3W3G7nqDT8HOxBwl4IG/PS66jq7+3sMorFS6/j0qmjbNm8Cbh59srz588zfvx4tmzZAkC5cuUwZaRT1T/7CXN+ZTwLtH17n6PS8HOwpZLfkSiEcLj8pim2WCx8+umntG/fni1btlChQgWWL1/OqlWrCvwoUeE80rIXQtwiODiYKlWq0LRpU1q2bIlSioEDB/L7778D0LdvX95++20qV67s5EpFQUnYCyGsch4iUrZsWYYNG2advVKj0dCxY0cOHz7MggUL6NOnj5MrFYUl3ThCCODWaYqPHz/Or7/+al3/4osvsmvXLgl6FyUteyHETdMUh4SEsG7dOt599128vb3ZtWsXVapUQa/XU6FCBSdXKopKwl4IN5c76GvWrMlTTz3FkSNHAOjfvz/e3jKipTSQsBfCjeUEfVZWFufPn2fhwoVYLBaCg4NZvHgxYWFhzi5R2IiEvRBuKneLfvfu3ezcuROtVsuLL75IZGSktOhLGQl7IdyQ2WwmOjoayB5H/9BDDzFhwgTee+89WrVq5eTqhD1I2AvhhrZu3crp06cZPHgwjRs3BiAqKgqtGz/JqbSTsBfCjURHR7N8+XK++eYbALp162ZdJ0FfuknYC+EmlixZwoIFC0hNTcXT05PIyEjatm3r7LKEg0jYC1HK/fPPPzz77LNs3boVgMaNG/P5559Tr149J1cmHEn+bhOilJs8eTJbt27Fw8ODF198kaioKAl6NyQteyFKoZw5bgwGAzVr1qRx48ZERkbSq1cvZ5cmnERa9kKUIkopvvzyS7p27cr+/fvZuHEjXl5eLF68WILezUnLXohS4uzZs0yYMMHaN3/gwAHuueceQkNDrQ8eEe5Lwl4IF2exWPjkk0+YNWsWaWlpVKhQgbfffpv+/ftjsVis0xQL9yZhL4QLO3nyJOPGjWPPnj0AhIeH8/jjj9O/f380Go0EvbCSsBfChR08eJA9e/ZQpUoVnn/+eVJTUzlz5gxnz56ldu3azi5PlCAS9kK4mMTERAICAoDsKYgTEhJo0KABu3btArLnupGgF3nJaBwhXITRaOStt97i3nvvtc43r9FoaNmy5U1BLxdjRX4k7IVwAX/88Qfh4eEsXLiQtLQ064ib3NMUS9CLO5FuHCFKsLS0NObOncuyZcuwWCzUqVOHJUuW0K5dO8xmM4cPHwYk6MXdSdgLUUIdOHCAiIgITp8+jVarZezYsUyZMoWyZcsCoNPpePzxxzl9+rR1mmIhbkfCXogSqkKFCly+fJlGjRqxdOlSWrRoAcCZM2eoWbMmWq2WMmXKSNCLApE+eyFKkD179mCxWACoVasW33//Pb///rs16A0GA6tWreLnn39GKeXMUoWLkbAXogS4evUqL774Ij169ODLL7+0Lm/VqhWenp7AzRdjq1SpgkajcUqtwjVJN44QTvbjjz/yyiuvcOXKFcqUKYPJZLrlPTLqRhSXhL0QTnLlyhVeffVVNmzYAEC7du1YvHgxdevWvel9EvTCFiTshXCCI0eO0KdPH65evYqvry+vv/46Tz311C3PgT169KgEvbAJCXshnKBevXpUr16d5s2b895771GjRo183xccHEzVqlVp0qSJBL0oFgl7IRzAYrHw5Zdf0qNHDypVqoSHhwfr1q0jICAg3wutOU+aKlOmDE8++eQtLX4hCku+QULYWWxsLK+++ioTJkxg8uTJ1uWBgYH5Br3BYOCnn36yDsGUoBe2IN8iIezEbDbz4YcfEhYWxl9//UXFihXp3bv3HT+TczH20KFDnD171kGVCncg3ThC2MGxY8cYN24cf/zxBwCdO3fmo48+IjAw8LafyTvqJjg42CG1CvcgYS+EjcXHx9O5c2cyMjK45557eOedd6hUqVKhgl4uxgpbk7AXwsaCgoIYMWIEaWlpzJw5E39/fwwGw23fL0EvHEHCXohiysjIYOHChbRv354HH3wQgDlz5hTowqrFYrE+iESCXtiThL0QxbB3717Gjh3LyZMn+e6779i7dy8eHh4FHkGj1Wqt0xQ3atTIztUKdyajcYQogtTUVKZOnUqPHj04efIk9erVY9myZXh4eBTo87GxsdahlV5eXhL0wu4k7IUopG3bttGhQweWL1+OVqtlwoQJbN26lbZt2xbo8waDgW+//ZaffvpJpikWDiPdOEIUQkZGBqNHj+bSpUs0adKEpUuX0rx58wJ/PvfF2KpVq8o0xcJhJOyFKACLxWJ9MtQ777xDdHQ048ePt841XxAy6kY4k1PC/qOPPmLz5s2YTCaGDBnC/fffz5QpU9BoNNSrV4/XX39dbhEXJUJiYiKRkZFUr16d6dOnA9C9e3e6d+9eqO0cO3aM8+fPAxL0wjkcnqh79uxh//79fPPNN6xYsYLLly8zd+5cJkyYwNdff41Sik2bNjm6LCFuopTi+++/p127dqxZs4ZPPvmEq1evFmlbx44dY+/evYAEvXAeh4f99u3bqV+/Pi+++CKjR48mPDycw4cPc//99wPQsWNHdu7c6eiyhLCKj49nxIgRPP300/z999+0b9+eLVu2UKFChSJtLzg4mMqVK0vQC6dyeDfO1atXuXjxIsuXLycuLo7nn3/eOp0rgI+PD8nJyQXa1qFDh+xZapHc6U5Jd+Nq50IpxcaNG1m+fDnJycl4e3sTERFBr169uHr1aqGPJ/f3ulu3boDrnRN7kfNwg6POhcPDvnz58oSEhODp6UlISAheXl5cvnzZuj41NRU/P78CbSs0NBQvLy97lVpoBoNBWm7/44rnQinFggULSE5OpkuXLixatIjq1asXaVsGg4FLly7Rq1cvtFqtS54Pe5FzcYMtz4XRaLxjA9jh3TgtW7Zk27ZtKKWIj48nPT2ddu3asWfPHgCioqJo1aqVo8sSpYAxy8zFpDSMWeYCf8ZisZCYmAiARqPh3Xff5cMPP2T16tXFCvqNGzdy+PBhzpw5c0uNsQnJxCYm37XOgh5PUY67qBy5L1twtXrtyeEt+wcffJB9+/YxYMAAlFLMmDHDOtLh3XffJSQkhIcfftjRZQkXZrZYWBJ1lC0x8SSkGgn08SK8ThDjOjZCd4dRXadPn2b8+PEYjUZ+/vlndDod1apVY/DgwUWuJe/wypCQkP/VqHjn90N8vi+GS0npgIaqfmUY2aYuE/LUWdDjKepxF4Uj92ULrlavIzhl6OWrr756y7KVK1c6oRJRGiyJOsqGw3FoNRq89DpSjFlsOBwHwMTwJre832w2s2zZMubOnUt6ejqVKlXizJkz1KlTp1h13Gkc/arjiWw4m0JiqvF//fiKC9fTWb7jONo8dRb0eAp73MXhyH3ZgqvV6wju+StOlBrGLDNbYuLR5rkTVavRsCUm/pY/348ePcrDDz/MjBkzSE9PZ9CgQezatcuuQW/MMrPvcgpJGaab7pjVAEkZJjadvGyts6DHU9jjLg5H7ssWXK1eR5GwFy4tIdVIQqox33WJaTevW7p0KeHh4fz5559UrVqVb7/9lmXLlhEQEFCsGiwWC8eOHQPyH0efkGokMSMLk/nWeXBMFsXfKRnWOgt6PIU57uJy5L5swdXqdZRCh/2vv/5qjzqEKJJAHy8CffIfkRXgffM6rVaLyWRi5MiR7Ny5k65du9qkBq1Wy8CBA3nkkUfyHVkR6ONFQBk9Hrpb58Hx0Gqo5FvGWmdBj6cwx11cjtyXLbhavY5y17BPTk5mxowZ1tdr1qzhueee4+LFi3YtTIiC8NLrCK8ThCXP7JEWpQirUZ7DB/+yLhs9ejT//e9/effddws8vPdOTp8+bZ2m2NPTk4YNG962xtZVfPEv43HTLJcK8C/jQZd6VfDS6+56POF1ggr9Pltw5L5swdXqdZS7hv2QIUMYOnSo9fUnn3xCv379GDlyJB9//DFms3v2f4mSY1zHRvRtUh1fLz2ZZjO+Xnru0yay5rXR9O/f39ow0el0tG7d2ib7NBgMrF69mh9//LFA0xQPbhDA8w/Up2p5bzSa7GGe1fzKMrp9A8Z1vHku+/yOp2+T6kV+ny04cl+24Gr1OoS6iw8++EBNmTLlluXJyclq1qxZqk+fPmrfvn1324xNZWRkqD/++ENlZGQ4dL9388cffzi7hBLDGeciw5SlTl64oia98ooKCAhQFSpUUG3atFGHDx+26X7++OMPNXfuXDV37twCH2fO+zJMWer0P9fV6YTrKsOUdcfPZJiy1IVrqTZ7ny3YYl+O/G448twUhS3Pxd1y8a5DL1944QViYmJuWnbixAn2799PSkoK8fHxjBo1il69ejF16lTKli1rt19MQtzJru3bmDBhAufOnUOn0zF+/HheeeUVm95lXdxpir30OoIDyxX4vVX9vW32Pltw5L5swdXqtacCXaDNPSytVatWjB8/nujoaNq2bcvatWv5448/CAkJYdy4cXYrVIg7effdd+nfvz/nzp2jadOmbNq0iddee61EBb0QzlTom6p+/fXXfIeqPfXUU6xZs8YmRQlRWA8++CDvvvsuL730EmPHji3ws2AL6tixYxL0wqUVOuzvNCb5/fffL1YxQhTUP//8w4YNG3j66acBuO+++zh48GCxx8zfTkhICDVr1qRBgwYS9MIl2XS6hJx5QISwF6UU69atY8qUKSQkJFCtWjXrXEr2CHr1v2mKPT09GTx4sDxBTbgs+eYKl3Hp0iWGDRvGs88+S0JCAh07dqR+/fp225/BYOCHH36wDi+WoBeuTL69osRTSrFy5UratWvHf/7zH3x9fVm0aBHr168nODjYLvvMuRh77NixW6YpFsIVOWXWSyEK45NPPmHKlClA9sXRd999l2rVqtltf3lH3RR3kjQhSgJp2YsSb8iQIdx777189NFHrFq1yqFBLxdjRWkhYS9KnFOnTjFq1ChSU1MBKFeuHL///jsDBw68aYpgW5OgF6WZhL0oMbKysliyZAkdOnRg7dq1LFq0yLrOniEP2dMUHz9+HJCgF6WT9NmLEuHw4cOMHTuWAwcOANldNy+++KLD9q/VahkwYACxsbE0aNDAYfsVwlGkZS+cKjMzk3nz5vHggw9y4MABqlWrxurVq/nggw+oUKGC3fcfExNjHVrp6ekpQS9KLQl74VRbt27l7bffJisri2eeeYadO3fy0EMPOWTfBoOBNWvWsGHDhgJNUyyEK5NuHOFwFovFeoNS165deeGFF+jRowft27d3WA25L8bWrFnT7tcEhHA2adkLh9q1axft27fn0KFD1mWzZ892WtDLxVjhLiTshUMkJyfz6quv0qtXL44fP86SJUucUocEvXBX0o0j7G7z5s1MmDCBuLg49Ho9EydO5KWXXnJ4HcePH5egF25Lwl7YzbVr15g2bRrffPMNAM2aNWPp0qWEhoY6pZ6QkBBq1apF/fr1JeiF25GwF3aTnJzMjz/+iJeXF1OmTOHFF19Er3f8Vy5nmmIPDw8GDRoks1cKtyRhL2wqISHBOj6+Ro0aLFu2jPr161OvXj2n1GMwGDh79iz9+vVDp9NJ0Au3Jd98YRNKKdauXUubNm347LPPrMt79erl1KDfuHEjJ06ckGmKhduTsBfFdvHiRYYOHcqoUaNITExk69atTr9JSaYpFuJmEvaiyJRS/Otf/6Jdu3b897//xc/Pj8WLF/Pll1869SYlGV4pxK2kz14USWJiIs888wxbt24FoHv37ixcuJCqVas6tS4JeiHyJ2EvisTPz4+kpCQCAwOZN28e/fv3d/qUAxaLhZMnTwIS9ELkJWEvCuzkyZP4+fkRFBSEXq/n008/xc/Pj4oVKzq7NCB7muLHHnuM06dPy+yVQuQhffbirrKysnjvvffo2LEjr7zyivXia0hISIkI+lOnTlmnKfbw8JCgFyIf0rIXd3To0CHGjh3LX3/9BYC/vz8mkwlPT08nV5Ytp4++fv36PProo07vShKipJKwF/kyGo0sXLiQxYsXk5WVRY0aNVi0aBGdO3d2dmlWuS/G1qpVS4JeiDuQsBe3MBqNdOnShSNHjgAQERHB9OnTKVeunJMru0FG3QhROBL24hZeXl6Eh4djNBpZsmQJ7dq1c3ZJN5GgF6Lw5AKtAGDHjh1s27bN+nratGlERUWVuKA/ceKEBL0QRSAtezd3/fp1Zs6cyWeffUa1atXYsWMHfn5+lC1b1tml5Ss4OJjg4GDq1q0rQS9EIUjYu7HffvuNiRMncuHCBTw8PHjyyScpU6aMs8vKV+5pih9//HG5GCtEIUnYu6GrV68ybdo0Vq1aBUCLFi1YsmQJjRs3dnJl+TMYDJw5c4Z+/fqh1+sl6IUoAgl7N6OUYsCAAezfv58yZcoQGRnJ888/75SHihRE7ouxsbGxTpsuWQhXJxdo3YxGo2Hy5Mm0a9eObdu2MXbsWJcI+q5du0rQC1EMJfP/cmEzSilWr17N+fPnmTRpEgDdunWja9euJbo7RIZXCmFbEvalWFxcHC+99BK//fYbWq2WXr160ahRIwAJeiHcjHTjlEIWi4UvvviCBx54gN9++w1/f38WL15Mw4YNnV3aXSmliImJASTohbAladmXMrGxsYwfP57t27cD0LNnTxYuXEiVKlWcXFnBaDQa+vfvz+nTp6lfv76zyxGi1JCWfSkze/Zstm/fTsWKFfm///s/VqxY4RJBf/LkSbKysgDQ6/US9ELYmLTsSwGLxYJWm/17e86cOfj6+jJjxgwCAwOdXFnB5PTR161bl8cee6xEX08QwlVJy96FmUwm3nnnHXr37m1tFVepUoXFixe7XNBD9lQIEvRC2Ie07F3UwYMHGTt2LNHR0QBs3bqVLl26OLmqwpFRN0I4jrTsXUxGRgazZ8+mS5cuREdHU6tWLdavXy9BL4S4I2nZu5C9e/cyduxYTp48iUaj4bnnnuO1117Dx8fH2aUVikxTLITjSdi7kP3793Py5Enq1avH4sWLadu2rbNLKpKQkBDq1KlDSEiIBL0QDiJhX8JduXKFypUrA/Dss8+i0+lK9FTEd5IzTbFer2fAgAFyMVYIB5I++xLq+vXrTJgwgdatWxMXFweAVqslIiLCJYPeYDCwdu1a66ghCXohHEvCvgT69ddfadeuHV9++SVGo5F9+/Y5u6RiybkYGxMTQ2xsrLPLEcItSTdOCZKQkMDUqVNZs2YNkP1QkaVLl1onL3NFMk2xECWDU8L+0UcfxdfXF4Dq1aszaNAg5syZg06nIywsjDFjxjijLKf6/fffGT16NH///Tdly5Zl6tSpjB49Gp1O5+zSikyGVwpRcjg87I1GI0opVqxYYV3Wr18/li5dSo0aNRg1ahRHjhwpsY/Is5eKFSty9epVwsLCeO+99wgJCXF2ScVy7Ngxzp8/D0jQC1ESOLzP/tixY6Snp/P0008zfPhw9u3bR2ZmJjVr1kSj0RAWFsbOnTsdXZbDKaXYtm2b9XXTpk355Zdf+P77710+6JVSXLhwAZCgF6Kk0CillCN3ePz4cf766y8GDhzImTNnePbZZ/Hz82PdunUArF27lvPnzzNx4sTbbsNoNHLo0CFHlWxzV65c4b333mPfvn3MmDGDDh06OLskmzObzVy8eJEaNWo4uxQh3EpoaCheXl63LHd4N05wcDC1atVCo9EQHBxMuXLluHbtmnV9amoqfn5+BdrW7Q7KWQwGwx1bsRaLhc8//5w333yTlJQUypcvT3BwcKlp+Z44cYKQkBD0ej0Gg4FHHnnE2SWVGHf7brgTORc32PJc3K0R7PBunLVr1zJv3jwA4uPjSU9Px9vbm3PnzqGUYvv27bRq1crRZdldTEwMffv25ZVXXiElJYU+ffqwa9cu+vXr5+zSbMJgMLBu3TrWr1+Pg/9YFEIUgMNb9gMGDCAyMpIhQ4ag0Wh466230Gq1TJo0CbPZTFhYGM2aNXN0WXa1bds2Bg0aREZGBpUqVWLBggX07dvX2WXZTO5RNyEhIXLDlBAlkMPD3tPTk3feeeeW5atXr3Z0KQ7TokULgoKCaNu2LXPmzCEgIMDZJdmMDK8UwjXITVV2kJmZySeffMKIESPw9fXFx8eHLVu24O/v7+zSbEqCXgjXIWFvYwcOHGDs2LEcPnyYc+fOMX/+fIBSF/QnT56UoBfChZSqsDdmmUlINRLo44WX3rF3nqanp/Ppp5+ydu1azGYztWvXpnfv3g6twZGCg4OpV68etWvXtlvQO/PnKURpUyrC3myxsCTqKFti4q3hEF4niHEdG6HT2n/A0e7duxk/frz1oSLPP/88U6dOdbmHihRE7mmK+/fvb5eLsc7+eQpRGpWKsF8SdZQNh+PQajR46XWkGLPYcDh7WuCJ4U3suu8jR47Qq1cvlFLUrFmTTz75hNatW9t1n85iMBiIiYmhf//+6PV6u426cebPU4jSyuWbScYsM1ti4tHmCR6tRsOWmHiMWWa77r9x48YMHDiQl19+mWXLlpXqoN+4cSOnT5/m9OnTdtuPs3+eQpRWLh/2CalGElKN+a5LTLv9uqJKSkpiwoQJHDhwwLps2bJlTJs2DU9PT5vuq6TIO+qmfv36dtuXo3+eQrgLlw/7QB8vAn3ynzIhwPv264ri559/5oEHHuDLL79k0qRJ1jtFS/NNRI4eXunIn6cQ7sTlw95LryO8ThCWPLfoW5QivE6QTUZx/PPPP0RERPDEE09w6dIlWrduzQcffFCqQx6cM47eET9PIdxRqbhAO65j9pOctsTEk5hmJMD7xuiN4lBKsW7dOqZMmUJCQgLe3t5MmzaNUaNGufRDRQpCKcWZM2cAx4+jt9fPUwh3VirCXqfVMjG8CS+ENbTpuOzExEReeuklkpOT6dixI++99x61a9cufsEuQKPR8MgjjxAbG0vdunUdum97/TyFcGelIuxzeOl1VPX3LtY2lFIopdBqtQQGBvL222+TkZHB8OHDS323DWQ/byAkJAQPDw90Op3Dgz43W/w8hRDZXL7P3pbOnTvHY489xkcffWRdNmjQIEaMGOEWQW8wGFi/fj3fffcdFovF2eUIIWxIwp7sh4p88skntG/fni1btvDhhx9iNLrXEL/cF2Pr1auHVu5UFaJUcfv/o0+dOkXv3r2ZPHkyqamp9OvXj02bNpWoJ2DZm8xeKUTpV6r67AvDbDbzwQcfMHfuXIxGI0FBQSxYsKBUT16WHwl6IdyDW7fsf/zxR4xGI0OGDGHXrl1uF/SnTp2SoBfCTbhVyz4zM5PU1FQqVKiATqfj/fffJy4uji5duji7NKcIDg6mfv361KpVS4JeiFLObcLeYDAwduxYateuzVdffYVGo6FBgwY0aNDA2aU5nMViQavVotPpePTRR91ipJEQ7q7Ud+Okp6czY8YMHn74YY4dO8aJEydISEhwdllOYzAYWL16NSaTCSjd8/oIIW4o1WG/c+dOOnTowPvvvw/AmDFjiIqKomLFik6uzDlyLsaeOXPGrtMUCyFKnlLZjaOUIjIyko8//hiAhg0bsnTpUrful8476sYdu6+EcGelsmWv0WgoW7Yser2eV155hd9//12CXkbdCOHWSk3L/tq1a8TGxnLfffcB8OqrrzJw4EAaN27s5MqcS4JeCAGlpGX/008/0a5dO5544gmSkpIAKFu2rNsHvVKKs2fPAhL0Qrg7l27ZJyYmMm3aNL7//nsA7r//fpKTk/H393duYSWERqOhX79+nDlzhjp16ji7HCGEE7l02Pfv35/jx4/j4+PD9OnTiYiIkAm8yJ6mODg4GE9PT3Q6nQS9EMK1u3GuXbtGp06d2LFjB6NGjZKgR6YpFkLkz6Vb9m+88QaPP/643Bj0P7kvxtavX19++QkhrFw6DR555BEJ+v+RUTdCiDtx6bAX2STohRB3I2Hv4mJiYiTohRB35dJ99gJq165NgwYNqFmzpgS9EOK2JOxdVO5piuXahRDibqQbxwUZDAZWrVpFZmYmINMUCyHuTsLexeRcjD137hyxsbHOLkcI4SIk7F2ITFMshCgqCXsXIcMrhRDFIWHvAiTohRDFJWFfwimliIuLAyTohRBFJ0MvSziNRkOfPn1o2rQpISEhzi5HCOGipGVfQh07dsw6tFKr1UrQCyGKRcK+BDIYDHz//fesXbtWpikWQtiEhH0Jk/tibIMGDWSaYiGETUiSlCAy6kYIYS8S9iWEBL0Qwp4k7EuA06dPS9ALIexKhl6WALVr16Zx48ZUq1ZNgl4IYRcS9k6UM02xVqulT58+MnulEMJupBvHSQwGA19//TVGoxGQaYqFEPYlYe8EORdj4+LiZJpiIYRDSNg7WN5RNw0bNnRyRUIIdyBh70AyvFII4SwS9g4iQS+EcCYJewdQSnHx4kVAgl4I4Rwy9NIBNBoNvXr1okmTJjJ7pRDCKaRlb0dHjx61Dq2UaYqFEM4kYW8nBoOBH374gTVr1sg0xUIIp5Owt4PcF2MbNWok0xQLIZzOaSmUkJBAp06diImJ4ezZswwZMoShQ4fy+uuvu3RLWEbdCCFKIqeEvclkYsaMGZQpUwaAuXPnMmHCBL7++muUUmzatMkZZRXbsWPHJOiFECWSU8J+/vz5DB48mMqVKwNw+PBh7r//fgA6duzIzp07nVFWscTGxrJ3715Agl4IUfI4fOjlunXrCAgIoEOHDnz88cdA9jj0nInAfHx8SE5OLtC2Dh06ZLc6C8tisRAcHEylSpWA7O4cdyfn4GZyPm6Qc3GDo86Fw8P+u+++Q6PRsGvXLo4ePcrkyZNJTEy0rk9NTcXPz69A2woNDcXLy8tepRZIzjTFkD2evlWrVk6tp6QwGAzy100ucj5ukHNxgy3PhdFovGMD2OHdOF999RUrV65kxYoVNGrUiPnz59OxY0f27NkDQFRUlMsEpsFg4KuvvpJpioUQJV6JGBM4efJkli5dyqBBgzCZTDz88MPOLumuckbdXLhwgdOnTzu7HCGEuCOnTpewYsUK679XrlzpxEoKJ+/wykaNGjm5IiGEuLMS0bJ3JTKOXgjhiiTsC0GCXgjhqiTsC0gpxaVLlwAJeiGE65EpjgtIo9HQs2dPmjRpQnBwsLPLEUKIQpGW/V0cPXqUjIwMIHuaYgl6IYQrkrC/g5xpilevXu3Sk7MJIYSE/W3kvhjbpEkTmaZYCOHSJMHyIaNuhBCljYR9HhL0QojSSMI+lzNnzkjQCyFKJRl6mUutWrW49957CQoKkqAXQpQqEvaA2WxGp9Oh0Wjo0aOHzF4phCh13L4bJ2ea4pyx9BL0QojSyK3DPudi7MWLF2WaYiFEqea2YZ931E3jxo2dXJEQQtiPW4a9DK8UQrgbtwt7CXohhDtyq7BXSnHlyhVAgl4I4V7cauilRqOhe/fuNGrUiNq1azu7HCGEcBi3aNkfOXKE9PR0IDvwJeiFEO6m1Ie9wWBgw4YNfPvtt5jNZmeXI4QQTlGqwz73xdimTZui0+mcXJEQQjhHqQ17GXUjhBA3lMqwl6AXQoiblbqwP3v2rAS9EELkUeqGXtasWZPmzZtTqVIlCXohhPifUhP2uacpfvjhh2X2SiGEyKVUdOMYDAZWrFhx01h6IYQQN7h82OdcjL18+TKxsbHOLkcIIUoklw77Q4cOyTTFQghRAC4d9tu2bQNk1I0QQtyNS4c9SNALIURBuORoHKUUAB06dCA0NBSj0ejkim4oSbU4m5yLm8n5uEHOxQ22OheZmZnAjXzMS6Nut6YES05O5sSJE84uQwghSpz69etTrly5W5a7ZNhbLBZSU1Px8PCQYZZCCEF2i95kMuHj44NWe2sPvUuGvRBCiMJx+Qu0Qggh7k7CXggh3ICEvRBCuAEJeyGEcAMuOc6+JElISKB///589tln6PV6pkyZgkajoV69erz++uv5XhUvjR599FF8fX0BqF69OoMGDWLOnDnodDrCwsIYM2aMkyt0nI8++ojNmzdjMpkYMmQI999/v9t+L9atW8f69euB7PHkR48eZcWKFW753TCZTEyZMoULFy6g1WqZNWuWYzNDiSLLzMxUL7zwgurWrZs6deqUeu6559Tu3buVUkpNnz5d/frrr06u0DEyMjJUv379blrWt29fdfbsWWWxWFRERIQ6fPiwc4pzsN27d6vnnntOmc1mlZKSopYsWeK234u83njjDbVq1Sq3/W5s3LhRjRs3Timl1Pbt29WYMWMc+t1wj+aFncyfP5/BgwdTuXJlAA4fPsz9998PQMeOHdm5c6czy3OYY8eOkZ6eztNPP83w4cPZt28fmZmZ1KxZE41GQ1hYmNuci+3bt1O/fn1efPFFRo8eTXh4uNt+L3KLjo7m1KlT9OrVy22/G8HBwZjNZiwWCykpKej1eod+N6Qbp4jWrVtHQEAAHTp04OOPPwayb2rIucnLx8eH5ORkZ5boMGXKlOGZZ55h4MCBnDlzhmeffRY/Pz/reh8fH86fP+/ECh3n6tWrXLx4keXLlxMXF8fzzz/vtt+L3D766CNefPFFUlJSrN194F7fDW9vby5cuECPHj24evUqy5cvZ9++fQ77bkjYF9F3332HRqNh165dHD16lMmTJ5OYmGhdn5qaelPglWbBwcHUqlULjUZDcHAw5cqV49q1a9b17nQuypcvT0hICJ6enoSEhODl5cXly5et693pXOS4fv06sbGxtG3blpSUFFJTU63r3Ol8fPHFF4SFhfHyyy9z6dIlRowYgclksq6397mQbpwi+uqrr1i5ciUrVqygUaNGzJ8/n44dO7Jnzx4AoqKiaNWqlZOrdIy1a9cyb948AOLj40lPT8fb25tz586hlGL79u1ucy5atmzJtm3bUEpZz0W7du3c8nuRY9++fbRr1w4AX19fPDw83PK74efnZ52zxt/fn6ysLBo3buyw74ZMl2ADw4YN44033kCr1TJ9+nRMJhMhISHMnj0bnU7n7PLsLjMzk8jISC5evIhGo2HSpElotVreeustzGYzYWFhTJw40dllOszbb7/Nnj17UEoxceJEqlev7pbfixyffvoper2ekSNHAnDgwAG3/G6kpqYydepU/v77b0wmE8OHDyc0NNRh3w0JeyGEcAPSjSOEEG5Awl4IIdyAhL0QQrgBCXshhHADEvZCCOEGJOyFEMINSNgLIYQbkLAXopASEhJo2bIlFovFuiwiIoJffvnFiVUJcWcS9kIUUmBgIBUrVuTEiRMA/Oc//0Gj0dC9e3cnVybE7clEaEIUQatWrdi/fz/Vq1dn0aJFfPbZZ84uSYg7krAXoghatWrF7t27OXXqFI899hg1atRwdklC3JHMjSNEEZw/f54BAwZQuXJl1q1bh4eHh7NLEuKOpM9eiCKoWrUqmZmZTJ8+XYJeuAQJeyGK4Msvv6Rnz57WR8oJUdJJn70QhRATE8OYMWOoWrUqS5YscXY5QhSY9NkLIYQbkG4cIYRwAxL2QgjhBiTshRDCDUjYCyGEG5CwF0IINyBhL4QQbkDCXggh3ICEvRBCuIH/B9Gbf9co154DAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_equation(sample, \"life_expectancy ~ income_group\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ae7066b5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-13T23:53:42.520709Z",
     "start_time": "2021-08-13T23:53:42.322060Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_equation(sample, \"life_expectancy ~ income_group + region\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "773a9028",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-13T23:53:42.994023Z",
     "start_time": "2021-08-13T23:53:42.770817Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_equation(sample, \"life_expectancy ~ year\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "059cbfcd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-13T23:53:43.456892Z",
     "start_time": "2021-08-13T23:53:43.250594Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_equation(sample, \"life_expectancy ~ income_group + year\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b6c3ed21",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-13T23:53:43.634319Z",
     "start_time": "2021-08-13T23:53:43.617951Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>country_code</th>\n",
       "      <th>region</th>\n",
       "      <th>income_group</th>\n",
       "      <th>year</th>\n",
       "      <th>life_expectancy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>United States</td>\n",
       "      <td>USA</td>\n",
       "      <td>North America</td>\n",
       "      <td>High income</td>\n",
       "      <td>1960</td>\n",
       "      <td>69.770732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>Ghana</td>\n",
       "      <td>GHA</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>1961</td>\n",
       "      <td>46.266707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>Tunisia</td>\n",
       "      <td>TUN</td>\n",
       "      <td>Middle East &amp; North Africa</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>1961</td>\n",
       "      <td>42.645512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>664</th>\n",
       "      <td>Lesotho</td>\n",
       "      <td>LSO</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>1962</td>\n",
       "      <td>47.402244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>Guinea</td>\n",
       "      <td>GIN</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Low income</td>\n",
       "      <td>1963</td>\n",
       "      <td>35.425171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14337</th>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>LKA</td>\n",
       "      <td>South Asia</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>2014</td>\n",
       "      <td>74.794805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14227</th>\n",
       "      <td>Bermuda</td>\n",
       "      <td>BMU</td>\n",
       "      <td>North America</td>\n",
       "      <td>High income</td>\n",
       "      <td>2014</td>\n",
       "      <td>80.797317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14483</th>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>BGD</td>\n",
       "      <td>South Asia</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>2015</td>\n",
       "      <td>72.001195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14621</th>\n",
       "      <td>Malta</td>\n",
       "      <td>MLT</td>\n",
       "      <td>Middle East &amp; North Africa</td>\n",
       "      <td>High income</td>\n",
       "      <td>2015</td>\n",
       "      <td>81.946341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14498</th>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>North America</td>\n",
       "      <td>High income</td>\n",
       "      <td>2015</td>\n",
       "      <td>82.137634</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>140 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             country country_code                      region  \\\n",
       "248    United States          USA               North America   \n",
       "344            Ghana          GHA          Sub-Saharan Africa   \n",
       "503          Tunisia          TUN  Middle East & North Africa   \n",
       "664          Lesotho          LSO          Sub-Saharan Africa   \n",
       "872           Guinea          GIN          Sub-Saharan Africa   \n",
       "...              ...          ...                         ...   \n",
       "14337      Sri Lanka          LKA                  South Asia   \n",
       "14227        Bermuda          BMU               North America   \n",
       "14483     Bangladesh          BGD                  South Asia   \n",
       "14621          Malta          MLT  Middle East & North Africa   \n",
       "14498         Canada          CAN               North America   \n",
       "\n",
       "              income_group  year  life_expectancy  \n",
       "248            High income  1960        69.770732  \n",
       "344    Lower middle income  1961        46.266707  \n",
       "503    Lower middle income  1961        42.645512  \n",
       "664    Lower middle income  1962        47.402244  \n",
       "872             Low income  1963        35.425171  \n",
       "...                    ...   ...              ...  \n",
       "14337  Lower middle income  2014        74.794805  \n",
       "14227          High income  2014        80.797317  \n",
       "14483  Lower middle income  2015        72.001195  \n",
       "14621          High income  2015        81.946341  \n",
       "14498          High income  2015        82.137634  \n",
       "\n",
       "[140 rows x 6 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aaf84152",
   "metadata": {},
   "source": [
    "# Builtin Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0cfd05d2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-13T23:53:44.210291Z",
     "start_time": "2021-08-13T23:53:44.165636Z"
    }
   },
   "outputs": [],
   "source": [
    "from yellowbrick.datasets import load_bikeshare\n",
    "\n",
    "df = load_bikeshare(return_dataset=True).to_dataframe()\n",
    "sample = df.sample(2000, random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c1fbfffb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-13T23:53:44.404614Z",
     "start_time": "2021-08-13T23:53:44.382332Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>season</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>hour</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>feelslike</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>riders</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5943</th>\n",
       "      <td>2011-09-10</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.5455</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.0896</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15739</th>\n",
       "      <td>2012-10-23</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.4545</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.1642</td>\n",
       "      <td>5</td>\n",
       "      <td>32</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2906</th>\n",
       "      <td>2011-05-06</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.4394</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.1642</td>\n",
       "      <td>13</td>\n",
       "      <td>43</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12842</th>\n",
       "      <td>2012-06-24</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.6061</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.1045</td>\n",
       "      <td>23</td>\n",
       "      <td>48</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9053</th>\n",
       "      <td>2012-01-18</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.4394</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.4925</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11372</th>\n",
       "      <td>2012-04-24</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.3333</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.1343</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6968</th>\n",
       "      <td>2011-10-22</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.3939</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>27</td>\n",
       "      <td>81</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4262</th>\n",
       "      <td>2011-07-01</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.6970</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>83</td>\n",
       "      <td>180</td>\n",
       "      <td>263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5243</th>\n",
       "      <td>2011-08-11</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.6515</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.2239</td>\n",
       "      <td>31</td>\n",
       "      <td>166</td>\n",
       "      <td>197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11643</th>\n",
       "      <td>2012-05-05</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.5455</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.0896</td>\n",
       "      <td>42</td>\n",
       "      <td>155</td>\n",
       "      <td>197</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             date  season  year  month  hour  holiday  weekday  workingday  \\\n",
       "5943   2011-09-10       3     0      9     4        0        6           0   \n",
       "15739  2012-10-23       4     1     10     0        0        2           1   \n",
       "2906   2011-05-06       2     0      5     0        0        5           1   \n",
       "12842  2012-06-24       3     1      6     7        0        0           0   \n",
       "9053   2012-01-18       1     1      1     3        0        3           1   \n",
       "...           ...     ...   ...    ...   ...      ...      ...         ...   \n",
       "11372  2012-04-24       2     1      4     1        0        2           1   \n",
       "6968   2011-10-22       4     0     10    23        0        6           0   \n",
       "4262   2011-07-01       3     0      7    12        0        5           1   \n",
       "5243   2011-08-11       3     0      8     9        0        4           1   \n",
       "11643  2012-05-05       2     1      5     8        0        6           0   \n",
       "\n",
       "       weather  temp  feelslike  humidity  windspeed  casual  registered  \\\n",
       "5943         1  0.58     0.5455      0.94     0.0896       3           4   \n",
       "15739        1  0.46     0.4545      0.88     0.1642       5          32   \n",
       "2906         1  0.44     0.4394      0.44     0.1642      13          43   \n",
       "12842        1  0.64     0.6061      0.69     0.1045      23          48   \n",
       "9053         2  0.44     0.4394      0.51     0.4925       1           3   \n",
       "...        ...   ...        ...       ...        ...     ...         ...   \n",
       "11372        2  0.32     0.3333      0.81     0.1343       1           6   \n",
       "6968         1  0.38     0.3939      0.82     0.0000      27          81   \n",
       "4262         1  0.80     0.6970      0.26     0.0000      83         180   \n",
       "5243         1  0.72     0.6515      0.39     0.2239      31         166   \n",
       "11643        1  0.58     0.5455      0.83     0.0896      42         155   \n",
       "\n",
       "       riders  \n",
       "5943        7  \n",
       "15739      37  \n",
       "2906       56  \n",
       "12842      71  \n",
       "9053        4  \n",
       "...       ...  \n",
       "11372       7  \n",
       "6968      108  \n",
       "4262      263  \n",
       "5243      197  \n",
       "11643     197  \n",
       "\n",
       "[2000 rows x 16 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdf096c2",
   "metadata": {},
   "source": [
    "## Categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ba0ce536",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-13T23:53:44.754809Z",
     "start_time": "2021-08-13T23:53:44.740530Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DesignMatrix with shape (2000, 4)\n",
       "  Intercept  year  hour  temp\n",
       "          1     0     4  0.58\n",
       "          1     1     0  0.46\n",
       "          1     0     0  0.44\n",
       "          1     1     7  0.64\n",
       "          1     1     3  0.44\n",
       "          1     1     6  0.30\n",
       "          1     0     6  0.72\n",
       "          1     1    21  0.70\n",
       "          1     1    21  0.54\n",
       "          1     0    16  0.52\n",
       "          1     0    22  0.76\n",
       "          1     1     5  0.40\n",
       "          1     1    22  0.62\n",
       "          1     1     8  0.42\n",
       "          1     0    20  0.64\n",
       "          1     1     1  0.28\n",
       "          1     1    19  0.62\n",
       "          1     1     6  0.66\n",
       "          1     1     2  0.54\n",
       "          1     1    22  0.50\n",
       "          1     1    23  0.70\n",
       "          1     1     7  0.26\n",
       "          1     1     2  0.66\n",
       "          1     1    22  0.18\n",
       "          1     1    21  0.72\n",
       "          1     1    11  0.76\n",
       "          1     1    15  0.80\n",
       "          1     1    19  0.78\n",
       "          1     1     2  0.50\n",
       "          1     0    14  0.54\n",
       "  [1970 rows omitted]\n",
       "  Terms:\n",
       "    'Intercept' (column 0)\n",
       "    'year' (column 1)\n",
       "    'hour' (column 2)\n",
       "    'temp' (column 3)\n",
       "  (to view full data, use np.asarray(this_obj))"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome, predictors = dmatrices(\"riders ~ year + hour + temp\", data=sample)\n",
    "predictors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ac6d0399",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-13T23:53:45.395507Z",
     "start_time": "2021-08-13T23:53:45.212780Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_equation(sample, \"riders ~ year + hour + temp \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8b9d90c4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-13T23:53:45.460764Z",
     "start_time": "2021-08-13T23:53:45.434101Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DesignMatrix with shape (2000, 26)\n",
       "  Columns:\n",
       "    ['Intercept',\n",
       "     'C(year)[T.1]',\n",
       "     'C(hour)[T.1]',\n",
       "     'C(hour)[T.2]',\n",
       "     'C(hour)[T.3]',\n",
       "     'C(hour)[T.4]',\n",
       "     'C(hour)[T.5]',\n",
       "     'C(hour)[T.6]',\n",
       "     'C(hour)[T.7]',\n",
       "     'C(hour)[T.8]',\n",
       "     'C(hour)[T.9]',\n",
       "     'C(hour)[T.10]',\n",
       "     'C(hour)[T.11]',\n",
       "     'C(hour)[T.12]',\n",
       "     'C(hour)[T.13]',\n",
       "     'C(hour)[T.14]',\n",
       "     'C(hour)[T.15]',\n",
       "     'C(hour)[T.16]',\n",
       "     'C(hour)[T.17]',\n",
       "     'C(hour)[T.18]',\n",
       "     'C(hour)[T.19]',\n",
       "     'C(hour)[T.20]',\n",
       "     'C(hour)[T.21]',\n",
       "     'C(hour)[T.22]',\n",
       "     'C(hour)[T.23]',\n",
       "     'temp']\n",
       "  Terms:\n",
       "    'Intercept' (column 0)\n",
       "    'C(year)' (column 1)\n",
       "    'C(hour)' (columns 2:25)\n",
       "    'temp' (column 25)\n",
       "  (to view full data, use np.asarray(this_obj))"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome, predictors = dmatrices(\"riders ~ C(year) + C(hour) + temp\", data=sample)\n",
    "predictors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "530b72af",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-13T23:53:45.860141Z",
     "start_time": "2021-08-13T23:53:45.621129Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_equation(sample, \"riders ~ C(year) + C(hour) + temp \")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9b7ef43",
   "metadata": {},
   "source": [
    "## Standardize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0bf0b204",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-13T23:53:46.002362Z",
     "start_time": "2021-08-13T23:53:45.966479Z"
    }
   },
   "outputs": [],
   "source": [
    "outcome, predictors = dmatrices(\n",
    "    \"riders ~ C(year) + C(hour) + C(workingday) + humidity\", data=sample\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "108c3b84",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-13T23:53:46.170341Z",
     "start_time": "2021-08-13T23:53:46.164920Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DesignMatrix with shape (2000, 27)\n",
       "  Columns:\n",
       "    ['Intercept',\n",
       "     'C(year)[T.1]',\n",
       "     'C(hour)[T.1]',\n",
       "     'C(hour)[T.2]',\n",
       "     'C(hour)[T.3]',\n",
       "     'C(hour)[T.4]',\n",
       "     'C(hour)[T.5]',\n",
       "     'C(hour)[T.6]',\n",
       "     'C(hour)[T.7]',\n",
       "     'C(hour)[T.8]',\n",
       "     'C(hour)[T.9]',\n",
       "     'C(hour)[T.10]',\n",
       "     'C(hour)[T.11]',\n",
       "     'C(hour)[T.12]',\n",
       "     'C(hour)[T.13]',\n",
       "     'C(hour)[T.14]',\n",
       "     'C(hour)[T.15]',\n",
       "     'C(hour)[T.16]',\n",
       "     'C(hour)[T.17]',\n",
       "     'C(hour)[T.18]',\n",
       "     'C(hour)[T.19]',\n",
       "     'C(hour)[T.20]',\n",
       "     'C(hour)[T.21]',\n",
       "     'C(hour)[T.22]',\n",
       "     'C(hour)[T.23]',\n",
       "     'C(workingday)[T.1]',\n",
       "     'humidity']\n",
       "  Terms:\n",
       "    'Intercept' (column 0)\n",
       "    'C(year)' (column 1)\n",
       "    'C(hour)' (columns 2:25)\n",
       "    'C(workingday)' (column 25)\n",
       "    'humidity' (column 26)\n",
       "  (to view full data, use np.asarray(this_obj))"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2deb7528",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-13T23:53:46.532759Z",
     "start_time": "2021-08-13T23:53:46.399690Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(np.asarray(predictors[:, 26]))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "20897343",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-13T23:53:46.606749Z",
     "start_time": "2021-08-13T23:53:46.543963Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DesignMatrix with shape (2000, 27)\n",
       "  Columns:\n",
       "    ['Intercept',\n",
       "     'C(year)[T.1]',\n",
       "     'C(hour)[T.1]',\n",
       "     'C(hour)[T.2]',\n",
       "     'C(hour)[T.3]',\n",
       "     'C(hour)[T.4]',\n",
       "     'C(hour)[T.5]',\n",
       "     'C(hour)[T.6]',\n",
       "     'C(hour)[T.7]',\n",
       "     'C(hour)[T.8]',\n",
       "     'C(hour)[T.9]',\n",
       "     'C(hour)[T.10]',\n",
       "     'C(hour)[T.11]',\n",
       "     'C(hour)[T.12]',\n",
       "     'C(hour)[T.13]',\n",
       "     'C(hour)[T.14]',\n",
       "     'C(hour)[T.15]',\n",
       "     'C(hour)[T.16]',\n",
       "     'C(hour)[T.17]',\n",
       "     'C(hour)[T.18]',\n",
       "     'C(hour)[T.19]',\n",
       "     'C(hour)[T.20]',\n",
       "     'C(hour)[T.21]',\n",
       "     'C(hour)[T.22]',\n",
       "     'C(hour)[T.23]',\n",
       "     'C(workingday)[T.1]',\n",
       "     'standardize(humidity)']\n",
       "  Terms:\n",
       "    'Intercept' (column 0)\n",
       "    'C(year)' (column 1)\n",
       "    'C(hour)' (columns 2:25)\n",
       "    'C(workingday)' (column 25)\n",
       "    'standardize(humidity)' (column 26)\n",
       "  (to view full data, use np.asarray(this_obj))"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome, predictors = dmatrices(\n",
    "    \"riders ~ C(year) + C(hour) + C(workingday) + standardize(humidity)\", data=sample\n",
    ")\n",
    "predictors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c87b45a1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-13T23:53:46.936027Z",
     "start_time": "2021-08-13T23:53:46.801057Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(np.asarray(predictors[:, 26]))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b0c5b3e",
   "metadata": {},
   "source": [
    "# Custom functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "3e7dfea8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-13T23:53:47.860080Z",
     "start_time": "2021-08-13T23:53:47.847660Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DesignMatrix with shape (2000, 2)\n",
       "  Intercept  temp\n",
       "          1  0.58\n",
       "          1  0.46\n",
       "          1  0.44\n",
       "          1  0.64\n",
       "          1  0.44\n",
       "          1  0.30\n",
       "          1  0.72\n",
       "          1  0.70\n",
       "          1  0.54\n",
       "          1  0.52\n",
       "          1  0.76\n",
       "          1  0.40\n",
       "          1  0.62\n",
       "          1  0.42\n",
       "          1  0.64\n",
       "          1  0.28\n",
       "          1  0.62\n",
       "          1  0.66\n",
       "          1  0.54\n",
       "          1  0.50\n",
       "          1  0.70\n",
       "          1  0.26\n",
       "          1  0.66\n",
       "          1  0.18\n",
       "          1  0.72\n",
       "          1  0.76\n",
       "          1  0.80\n",
       "          1  0.78\n",
       "          1  0.50\n",
       "          1  0.54\n",
       "  [1970 rows omitted]\n",
       "  Terms:\n",
       "    'Intercept' (column 0)\n",
       "    'temp' (column 1)\n",
       "  (to view full data, use np.asarray(this_obj))"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome, predictors = dmatrices(\"riders ~ temp\", data=sample)\n",
    "predictors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "efee9e20",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-13T23:53:48.291423Z",
     "start_time": "2021-08-13T23:53:48.279495Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DesignMatrix with shape (2000, 2)\n",
       "  Intercept  multiply_by_two(temp)\n",
       "          1                   1.16\n",
       "          1                   0.92\n",
       "          1                   0.88\n",
       "          1                   1.28\n",
       "          1                   0.88\n",
       "          1                   0.60\n",
       "          1                   1.44\n",
       "          1                   1.40\n",
       "          1                   1.08\n",
       "          1                   1.04\n",
       "          1                   1.52\n",
       "          1                   0.80\n",
       "          1                   1.24\n",
       "          1                   0.84\n",
       "          1                   1.28\n",
       "          1                   0.56\n",
       "          1                   1.24\n",
       "          1                   1.32\n",
       "          1                   1.08\n",
       "          1                   1.00\n",
       "          1                   1.40\n",
       "          1                   0.52\n",
       "          1                   1.32\n",
       "          1                   0.36\n",
       "          1                   1.44\n",
       "          1                   1.52\n",
       "          1                   1.60\n",
       "          1                   1.56\n",
       "          1                   1.00\n",
       "          1                   1.08\n",
       "  [1970 rows omitted]\n",
       "  Terms:\n",
       "    'Intercept' (column 0)\n",
       "    'multiply_by_two(temp)' (column 1)\n",
       "  (to view full data, use np.asarray(this_obj))"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def multiply_by_two(col):\n",
    "    return col * 2\n",
    "\n",
    "\n",
    "equation = \"riders ~ multiply_by_two(temp)\"\n",
    "\n",
    "outcome, predictors = dmatrices(equation, data=sample)\n",
    "predictors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6666a822",
   "metadata": {},
   "source": [
    "# Operators"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0380b88f",
   "metadata": {},
   "source": [
    "## `+` Operator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "cee9df42",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-13T23:53:49.723395Z",
     "start_time": "2021-08-13T23:53:49.709973Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DesignMatrix with shape (2000, 3)\n",
       "  Intercept  month  temp\n",
       "          1      9  0.58\n",
       "          1     10  0.46\n",
       "          1      5  0.44\n",
       "          1      6  0.64\n",
       "          1      1  0.44\n",
       "          1      4  0.30\n",
       "          1      7  0.72\n",
       "          1      8  0.70\n",
       "          1     10  0.54\n",
       "          1      4  0.52\n",
       "          1      7  0.76\n",
       "          1      3  0.40\n",
       "          1      8  0.62\n",
       "          1     10  0.42\n",
       "          1      6  0.64\n",
       "          1     12  0.28\n",
       "          1      6  0.62\n",
       "          1      7  0.66\n",
       "          1     10  0.54\n",
       "          1      2  0.50\n",
       "          1      8  0.70\n",
       "          1      1  0.26\n",
       "          1      7  0.66\n",
       "          1      1  0.18\n",
       "          1      8  0.72\n",
       "          1      8  0.76\n",
       "          1      7  0.80\n",
       "          1      5  0.78\n",
       "          1      6  0.50\n",
       "          1      4  0.54\n",
       "  [1970 rows omitted]\n",
       "  Terms:\n",
       "    'Intercept' (column 0)\n",
       "    'month' (column 1)\n",
       "    'temp' (column 2)\n",
       "  (to view full data, use np.asarray(this_obj))"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome, predictors = dmatrices(\"riders ~ month + temp\", data=sample)\n",
    "predictors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "201b005f",
   "metadata": {},
   "source": [
    "## `-` Operator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "702b4e77",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-13T23:53:50.582751Z",
     "start_time": "2021-08-13T23:53:50.569682Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DesignMatrix with shape (2000, 2)\n",
       "  month  temp\n",
       "      9  0.58\n",
       "     10  0.46\n",
       "      5  0.44\n",
       "      6  0.64\n",
       "      1  0.44\n",
       "      4  0.30\n",
       "      7  0.72\n",
       "      8  0.70\n",
       "     10  0.54\n",
       "      4  0.52\n",
       "      7  0.76\n",
       "      3  0.40\n",
       "      8  0.62\n",
       "     10  0.42\n",
       "      6  0.64\n",
       "     12  0.28\n",
       "      6  0.62\n",
       "      7  0.66\n",
       "     10  0.54\n",
       "      2  0.50\n",
       "      8  0.70\n",
       "      1  0.26\n",
       "      7  0.66\n",
       "      1  0.18\n",
       "      8  0.72\n",
       "      8  0.76\n",
       "      7  0.80\n",
       "      5  0.78\n",
       "      6  0.50\n",
       "      4  0.54\n",
       "  [1970 rows omitted]\n",
       "  Terms:\n",
       "    'month' (column 0)\n",
       "    'temp' (column 1)\n",
       "  (to view full data, use np.asarray(this_obj))"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome, predictors = dmatrices(\"riders ~ month + temp - 1\", data=sample)\n",
    "predictors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49795dda",
   "metadata": {},
   "source": [
    "## `:` Operator"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8507fef4",
   "metadata": {},
   "source": [
    "### Basic example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c7905fc4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-13T23:53:51.686058Z",
     "start_time": "2021-08-13T23:53:51.674452Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DesignMatrix with shape (2000, 2)\n",
       "  Intercept  month:temp\n",
       "          1        5.22\n",
       "          1        4.60\n",
       "          1        2.20\n",
       "          1        3.84\n",
       "          1        0.44\n",
       "          1        1.20\n",
       "          1        5.04\n",
       "          1        5.60\n",
       "          1        5.40\n",
       "          1        2.08\n",
       "          1        5.32\n",
       "          1        1.20\n",
       "          1        4.96\n",
       "          1        4.20\n",
       "          1        3.84\n",
       "          1        3.36\n",
       "          1        3.72\n",
       "          1        4.62\n",
       "          1        5.40\n",
       "          1        1.00\n",
       "          1        5.60\n",
       "          1        0.26\n",
       "          1        4.62\n",
       "          1        0.18\n",
       "          1        5.76\n",
       "          1        6.08\n",
       "          1        5.60\n",
       "          1        3.90\n",
       "          1        3.00\n",
       "          1        2.16\n",
       "  [1970 rows omitted]\n",
       "  Terms:\n",
       "    'Intercept' (column 0)\n",
       "    'month:temp' (column 1)\n",
       "  (to view full data, use np.asarray(this_obj))"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome, predictors = dmatrices(\"riders ~ month:temp\", data=sample)\n",
    "predictors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "481926d0",
   "metadata": {},
   "source": [
    "### More complicated equation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "d126f99f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-13T23:53:52.327876Z",
     "start_time": "2021-08-13T23:53:52.285753Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DesignMatrix with shape (2000, 27)\n",
       "  Columns:\n",
       "    ['Intercept',\n",
       "     'C(year)[0]:temp',\n",
       "     'C(year)[1]:temp',\n",
       "     'C(hour)[T.1]:temp',\n",
       "     'C(hour)[T.2]:temp',\n",
       "     'C(hour)[T.3]:temp',\n",
       "     'C(hour)[T.4]:temp',\n",
       "     'C(hour)[T.5]:temp',\n",
       "     'C(hour)[T.6]:temp',\n",
       "     'C(hour)[T.7]:temp',\n",
       "     'C(hour)[T.8]:temp',\n",
       "     'C(hour)[T.9]:temp',\n",
       "     'C(hour)[T.10]:temp',\n",
       "     'C(hour)[T.11]:temp',\n",
       "     'C(hour)[T.12]:temp',\n",
       "     'C(hour)[T.13]:temp',\n",
       "     'C(hour)[T.14]:temp',\n",
       "     'C(hour)[T.15]:temp',\n",
       "     'C(hour)[T.16]:temp',\n",
       "     'C(hour)[T.17]:temp',\n",
       "     'C(hour)[T.18]:temp',\n",
       "     'C(hour)[T.19]:temp',\n",
       "     'C(hour)[T.20]:temp',\n",
       "     'C(hour)[T.21]:temp',\n",
       "     'C(hour)[T.22]:temp',\n",
       "     'C(hour)[T.23]:temp',\n",
       "     'C(workingday)[T.1]:temp']\n",
       "  Terms:\n",
       "    'Intercept' (column 0)\n",
       "    'C(year):temp' (columns 1:3)\n",
       "    'C(hour):temp' (columns 3:26)\n",
       "    'C(workingday):temp' (column 26)\n",
       "  (to view full data, use np.asarray(this_obj))"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome, predictors = dmatrices(\n",
    "    \"riders ~ (C(year) + C(hour) + C(workingday)) : temp\", data=sample\n",
    ")\n",
    "predictors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "56cc451f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-13T23:53:52.759196Z",
     "start_time": "2021-08-13T23:53:52.516315Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_equation(\n",
    "    sample,\n",
    "    \"riders ~ (C(year) + C(hour) + C(workingday)) : temp\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74468463",
   "metadata": {},
   "source": [
    "## `*` Operator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "73c65cf6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-13T23:53:53.155783Z",
     "start_time": "2021-08-13T23:53:53.143030Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DesignMatrix with shape (2000, 4)\n",
       "  Intercept  month  temp  month:temp\n",
       "          1      9  0.58        5.22\n",
       "          1     10  0.46        4.60\n",
       "          1      5  0.44        2.20\n",
       "          1      6  0.64        3.84\n",
       "          1      1  0.44        0.44\n",
       "          1      4  0.30        1.20\n",
       "          1      7  0.72        5.04\n",
       "          1      8  0.70        5.60\n",
       "          1     10  0.54        5.40\n",
       "          1      4  0.52        2.08\n",
       "          1      7  0.76        5.32\n",
       "          1      3  0.40        1.20\n",
       "          1      8  0.62        4.96\n",
       "          1     10  0.42        4.20\n",
       "          1      6  0.64        3.84\n",
       "          1     12  0.28        3.36\n",
       "          1      6  0.62        3.72\n",
       "          1      7  0.66        4.62\n",
       "          1     10  0.54        5.40\n",
       "          1      2  0.50        1.00\n",
       "          1      8  0.70        5.60\n",
       "          1      1  0.26        0.26\n",
       "          1      7  0.66        4.62\n",
       "          1      1  0.18        0.18\n",
       "          1      8  0.72        5.76\n",
       "          1      8  0.76        6.08\n",
       "          1      7  0.80        5.60\n",
       "          1      5  0.78        3.90\n",
       "          1      6  0.50        3.00\n",
       "          1      4  0.54        2.16\n",
       "  [1970 rows omitted]\n",
       "  Terms:\n",
       "    'Intercept' (column 0)\n",
       "    'month' (column 1)\n",
       "    'temp' (column 2)\n",
       "    'month:temp' (column 3)\n",
       "  (to view full data, use np.asarray(this_obj))"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome, predictors = dmatrices(\"riders ~ month * temp\", data=sample)\n",
    "predictors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b1cf6b8",
   "metadata": {},
   "source": [
    "## `/` Operator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "4a65aacb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-13T23:53:54.001247Z",
     "start_time": "2021-08-13T23:53:53.987437Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DesignMatrix with shape (2000, 3)\n",
       "  Intercept  month  month:temp\n",
       "          1      9        5.22\n",
       "          1     10        4.60\n",
       "          1      5        2.20\n",
       "          1      6        3.84\n",
       "          1      1        0.44\n",
       "          1      4        1.20\n",
       "          1      7        5.04\n",
       "          1      8        5.60\n",
       "          1     10        5.40\n",
       "          1      4        2.08\n",
       "          1      7        5.32\n",
       "          1      3        1.20\n",
       "          1      8        4.96\n",
       "          1     10        4.20\n",
       "          1      6        3.84\n",
       "          1     12        3.36\n",
       "          1      6        3.72\n",
       "          1      7        4.62\n",
       "          1     10        5.40\n",
       "          1      2        1.00\n",
       "          1      8        5.60\n",
       "          1      1        0.26\n",
       "          1      7        4.62\n",
       "          1      1        0.18\n",
       "          1      8        5.76\n",
       "          1      8        6.08\n",
       "          1      7        5.60\n",
       "          1      5        3.90\n",
       "          1      6        3.00\n",
       "          1      4        2.16\n",
       "  [1970 rows omitted]\n",
       "  Terms:\n",
       "    'Intercept' (column 0)\n",
       "    'month' (column 1)\n",
       "    'month:temp' (column 2)\n",
       "  (to view full data, use np.asarray(this_obj))"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome, predictors = dmatrices(\"riders ~ month / temp\", data=sample)\n",
    "predictors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "63cad88d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-13T23:53:54.288858Z",
     "start_time": "2021-08-13T23:53:54.273931Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DesignMatrix with shape (2000, 4)\n",
       "  Intercept  month  month:temp  month:humidity\n",
       "          1      9        5.22            8.46\n",
       "          1     10        4.60            8.80\n",
       "          1      5        2.20            2.20\n",
       "          1      6        3.84            4.14\n",
       "          1      1        0.44            0.51\n",
       "          1      4        1.20            1.96\n",
       "          1      7        5.04            4.90\n",
       "          1      8        5.60            4.88\n",
       "          1     10        5.40            7.30\n",
       "          1      4        2.08            1.08\n",
       "          1      7        5.32            4.90\n",
       "          1      3        1.20            1.98\n",
       "          1      8        4.96            6.24\n",
       "          1     10        4.20            6.70\n",
       "          1      6        3.84            2.64\n",
       "          1     12        3.36            6.24\n",
       "          1      6        3.72            2.28\n",
       "          1      7        4.62            6.23\n",
       "          1     10        5.40            8.80\n",
       "          1      2        1.00            1.10\n",
       "          1      8        5.60            4.32\n",
       "          1      1        0.26            0.60\n",
       "          1      7        4.62            5.81\n",
       "          1      1        0.18            0.37\n",
       "          1      8        5.76            5.60\n",
       "          1      8        6.08            3.84\n",
       "          1      7        5.60            2.87\n",
       "          1      5        3.90            2.60\n",
       "          1      6        3.00            4.08\n",
       "          1      4        2.16            2.08\n",
       "  [1970 rows omitted]\n",
       "  Terms:\n",
       "    'Intercept' (column 0)\n",
       "    'month' (column 1)\n",
       "    'month:temp' (column 2)\n",
       "    'month:humidity' (column 3)\n",
       "  (to view full data, use np.asarray(this_obj))"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome, predictors = dmatrices(\"riders ~ month / (temp + humidity)\", data=sample)\n",
    "predictors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37aa123f",
   "metadata": {},
   "source": [
    "## `**` Operator"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c250c6d5",
   "metadata": {},
   "source": [
    "### Simple equation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "50e89b0d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-13T23:53:55.286137Z",
     "start_time": "2021-08-13T23:53:55.270917Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DesignMatrix with shape (2000, 8)\n",
       "  Columns:\n",
       "    ['Intercept',\n",
       "     'temp',\n",
       "     'humidity',\n",
       "     'windspeed',\n",
       "     'temp:humidity',\n",
       "     'temp:windspeed',\n",
       "     'humidity:windspeed',\n",
       "     'temp:humidity:windspeed']\n",
       "  Terms:\n",
       "    'Intercept' (column 0)\n",
       "    'temp' (column 1)\n",
       "    'humidity' (column 2)\n",
       "    'windspeed' (column 3)\n",
       "    'temp:humidity' (column 4)\n",
       "    'temp:windspeed' (column 5)\n",
       "    'humidity:windspeed' (column 6)\n",
       "    'temp:humidity:windspeed' (column 7)\n",
       "  (to view full data, use np.asarray(this_obj))"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome, predictors = dmatrices(\n",
    "    \"riders ~ (temp + humidity + windspeed)**3\", data=sample\n",
    ")\n",
    "predictors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "3caf675e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-13T23:53:55.704233Z",
     "start_time": "2021-08-13T23:53:55.510288Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_equation(\n",
    "    sample,\n",
    "    \"riders ~ (temp + humidity + windspeed)**3\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cb42cb2",
   "metadata": {},
   "source": [
    "### More complicated equation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "c5a71829",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-13T23:53:56.060302Z",
     "start_time": "2021-08-13T23:53:56.003937Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DesignMatrix with shape (2000, 169)\n",
       "  Columns:\n",
       "    ['Intercept',\n",
       "     'C(year)[T.1]',\n",
       "     'C(hour)[T.1]',\n",
       "     'C(hour)[T.2]',\n",
       "     'C(hour)[T.3]',\n",
       "     'C(hour)[T.4]',\n",
       "     'C(hour)[T.5]',\n",
       "     'C(hour)[T.6]',\n",
       "     'C(hour)[T.7]',\n",
       "     'C(hour)[T.8]',\n",
       "     'C(hour)[T.9]',\n",
       "     'C(hour)[T.10]',\n",
       "     'C(hour)[T.11]',\n",
       "     'C(hour)[T.12]',\n",
       "     'C(hour)[T.13]',\n",
       "     'C(hour)[T.14]',\n",
       "     'C(hour)[T.15]',\n",
       "     'C(hour)[T.16]',\n",
       "     'C(hour)[T.17]',\n",
       "     'C(hour)[T.18]',\n",
       "     'C(hour)[T.19]',\n",
       "     'C(hour)[T.20]',\n",
       "     'C(hour)[T.21]',\n",
       "     'C(hour)[T.22]',\n",
       "     'C(hour)[T.23]',\n",
       "     'C(workingday)[T.1]',\n",
       "     'C(year)[T.1]:C(hour)[T.1]',\n",
       "     'C(year)[T.1]:C(hour)[T.2]',\n",
       "     'C(year)[T.1]:C(hour)[T.3]',\n",
       "     'C(year)[T.1]:C(hour)[T.4]',\n",
       "     'C(year)[T.1]:C(hour)[T.5]',\n",
       "     'C(year)[T.1]:C(hour)[T.6]',\n",
       "     'C(year)[T.1]:C(hour)[T.7]',\n",
       "     'C(year)[T.1]:C(hour)[T.8]',\n",
       "     'C(year)[T.1]:C(hour)[T.9]',\n",
       "     'C(year)[T.1]:C(hour)[T.10]',\n",
       "     'C(year)[T.1]:C(hour)[T.11]',\n",
       "     'C(year)[T.1]:C(hour)[T.12]',\n",
       "     'C(year)[T.1]:C(hour)[T.13]',\n",
       "     'C(year)[T.1]:C(hour)[T.14]',\n",
       "     'C(year)[T.1]:C(hour)[T.15]',\n",
       "     'C(year)[T.1]:C(hour)[T.16]',\n",
       "     'C(year)[T.1]:C(hour)[T.17]',\n",
       "     'C(year)[T.1]:C(hour)[T.18]',\n",
       "     'C(year)[T.1]:C(hour)[T.19]',\n",
       "     'C(year)[T.1]:C(hour)[T.20]',\n",
       "     'C(year)[T.1]:C(hour)[T.21]',\n",
       "     'C(year)[T.1]:C(hour)[T.22]',\n",
       "     'C(year)[T.1]:C(hour)[T.23]',\n",
       "     'C(year)[T.1]:C(workingday)[T.1]',\n",
       "     'C(hour)[T.1]:C(workingday)[T.1]',\n",
       "     'C(hour)[T.2]:C(workingday)[T.1]',\n",
       "     'C(hour)[T.3]:C(workingday)[T.1]',\n",
       "     'C(hour)[T.4]:C(workingday)[T.1]',\n",
       "     'C(hour)[T.5]:C(workingday)[T.1]',\n",
       "     'C(hour)[T.6]:C(workingday)[T.1]',\n",
       "     'C(hour)[T.7]:C(workingday)[T.1]',\n",
       "     'C(hour)[T.8]:C(workingday)[T.1]',\n",
       "     'C(hour)[T.9]:C(workingday)[T.1]',\n",
       "     'C(hour)[T.10]:C(workingday)[T.1]',\n",
       "     'C(hour)[T.11]:C(workingday)[T.1]',\n",
       "     'C(hour)[T.12]:C(workingday)[T.1]',\n",
       "     'C(hour)[T.13]:C(workingday)[T.1]',\n",
       "     'C(hour)[T.14]:C(workingday)[T.1]',\n",
       "     'C(hour)[T.15]:C(workingday)[T.1]',\n",
       "     'C(hour)[T.16]:C(workingday)[T.1]',\n",
       "     'C(hour)[T.17]:C(workingday)[T.1]',\n",
       "     'C(hour)[T.18]:C(workingday)[T.1]',\n",
       "     'C(hour)[T.19]:C(workingday)[T.1]',\n",
       "     'C(hour)[T.20]:C(workingday)[T.1]',\n",
       "     'C(hour)[T.21]:C(workingday)[T.1]',\n",
       "     'C(hour)[T.22]:C(workingday)[T.1]',\n",
       "     'C(hour)[T.23]:C(workingday)[T.1]',\n",
       "     'C(year)[T.1]:C(hour)[T.1]:C(workingday)[T.1]',\n",
       "     'C(year)[T.1]:C(hour)[T.2]:C(workingday)[T.1]',\n",
       "     'C(year)[T.1]:C(hour)[T.3]:C(workingday)[T.1]',\n",
       "     'C(year)[T.1]:C(hour)[T.4]:C(workingday)[T.1]',\n",
       "     'C(year)[T.1]:C(hour)[T.5]:C(workingday)[T.1]',\n",
       "     'C(year)[T.1]:C(hour)[T.6]:C(workingday)[T.1]',\n",
       "     'C(year)[T.1]:C(hour)[T.7]:C(workingday)[T.1]',\n",
       "     'C(year)[T.1]:C(hour)[T.8]:C(workingday)[T.1]',\n",
       "     'C(year)[T.1]:C(hour)[T.9]:C(workingday)[T.1]',\n",
       "     'C(year)[T.1]:C(hour)[T.10]:C(workingday)[T.1]',\n",
       "     'C(year)[T.1]:C(hour)[T.11]:C(workingday)[T.1]',\n",
       "     'C(year)[T.1]:C(hour)[T.12]:C(workingday)[T.1]',\n",
       "     'C(year)[T.1]:C(hour)[T.13]:C(workingday)[T.1]',\n",
       "     'C(year)[T.1]:C(hour)[T.14]:C(workingday)[T.1]',\n",
       "     'C(year)[T.1]:C(hour)[T.15]:C(workingday)[T.1]',\n",
       "     'C(year)[T.1]:C(hour)[T.16]:C(workingday)[T.1]',\n",
       "     'C(year)[T.1]:C(hour)[T.17]:C(workingday)[T.1]',\n",
       "     'C(year)[T.1]:C(hour)[T.18]:C(workingday)[T.1]',\n",
       "     'C(year)[T.1]:C(hour)[T.19]:C(workingday)[T.1]',\n",
       "     'C(year)[T.1]:C(hour)[T.20]:C(workingday)[T.1]',\n",
       "     'C(year)[T.1]:C(hour)[T.21]:C(workingday)[T.1]',\n",
       "     'C(year)[T.1]:C(hour)[T.22]:C(workingday)[T.1]',\n",
       "     'C(year)[T.1]:C(hour)[T.23]:C(workingday)[T.1]',\n",
       "     'temp',\n",
       "     'C(year)[T.1]:temp',\n",
       "     'C(hour)[T.1]:temp',\n",
       "     'C(hour)[T.2]:temp',\n",
       "     'C(hour)[T.3]:temp',\n",
       "     'C(hour)[T.4]:temp',\n",
       "     'C(hour)[T.5]:temp',\n",
       "     'C(hour)[T.6]:temp',\n",
       "     'C(hour)[T.7]:temp',\n",
       "     'C(hour)[T.8]:temp',\n",
       "     'C(hour)[T.9]:temp',\n",
       "     'C(hour)[T.10]:temp',\n",
       "     'C(hour)[T.11]:temp',\n",
       "     'C(hour)[T.12]:temp',\n",
       "     'C(hour)[T.13]:temp',\n",
       "     'C(hour)[T.14]:temp',\n",
       "     'C(hour)[T.15]:temp',\n",
       "     'C(hour)[T.16]:temp',\n",
       "     'C(hour)[T.17]:temp',\n",
       "     'C(hour)[T.18]:temp',\n",
       "     'C(hour)[T.19]:temp',\n",
       "     'C(hour)[T.20]:temp',\n",
       "     'C(hour)[T.21]:temp',\n",
       "     'C(hour)[T.22]:temp',\n",
       "     'C(hour)[T.23]:temp',\n",
       "     'C(workingday)[T.1]:temp',\n",
       "     'C(year)[T.1]:C(hour)[T.1]:temp',\n",
       "     'C(year)[T.1]:C(hour)[T.2]:temp',\n",
       "     'C(year)[T.1]:C(hour)[T.3]:temp',\n",
       "     'C(year)[T.1]:C(hour)[T.4]:temp',\n",
       "     'C(year)[T.1]:C(hour)[T.5]:temp',\n",
       "     'C(year)[T.1]:C(hour)[T.6]:temp',\n",
       "     'C(year)[T.1]:C(hour)[T.7]:temp',\n",
       "     'C(year)[T.1]:C(hour)[T.8]:temp',\n",
       "     'C(year)[T.1]:C(hour)[T.9]:temp',\n",
       "     'C(year)[T.1]:C(hour)[T.10]:temp',\n",
       "     'C(year)[T.1]:C(hour)[T.11]:temp',\n",
       "     'C(year)[T.1]:C(hour)[T.12]:temp',\n",
       "     'C(year)[T.1]:C(hour)[T.13]:temp',\n",
       "     'C(year)[T.1]:C(hour)[T.14]:temp',\n",
       "     'C(year)[T.1]:C(hour)[T.15]:temp',\n",
       "     'C(year)[T.1]:C(hour)[T.16]:temp',\n",
       "     'C(year)[T.1]:C(hour)[T.17]:temp',\n",
       "     'C(year)[T.1]:C(hour)[T.18]:temp',\n",
       "     'C(year)[T.1]:C(hour)[T.19]:temp',\n",
       "     'C(year)[T.1]:C(hour)[T.20]:temp',\n",
       "     'C(year)[T.1]:C(hour)[T.21]:temp',\n",
       "     'C(year)[T.1]:C(hour)[T.22]:temp',\n",
       "     'C(year)[T.1]:C(hour)[T.23]:temp',\n",
       "     'C(year)[T.1]:C(workingday)[T.1]:temp',\n",
       "     'C(hour)[T.1]:C(workingday)[T.1]:temp',\n",
       "     'C(hour)[T.2]:C(workingday)[T.1]:temp',\n",
       "     'C(hour)[T.3]:C(workingday)[T.1]:temp',\n",
       "     'C(hour)[T.4]:C(workingday)[T.1]:temp',\n",
       "     'C(hour)[T.5]:C(workingday)[T.1]:temp',\n",
       "     'C(hour)[T.6]:C(workingday)[T.1]:temp',\n",
       "     'C(hour)[T.7]:C(workingday)[T.1]:temp',\n",
       "     'C(hour)[T.8]:C(workingday)[T.1]:temp',\n",
       "     'C(hour)[T.9]:C(workingday)[T.1]:temp',\n",
       "     'C(hour)[T.10]:C(workingday)[T.1]:temp',\n",
       "     'C(hour)[T.11]:C(workingday)[T.1]:temp',\n",
       "     'C(hour)[T.12]:C(workingday)[T.1]:temp',\n",
       "     'C(hour)[T.13]:C(workingday)[T.1]:temp',\n",
       "     'C(hour)[T.14]:C(workingday)[T.1]:temp',\n",
       "     'C(hour)[T.15]:C(workingday)[T.1]:temp',\n",
       "     'C(hour)[T.16]:C(workingday)[T.1]:temp',\n",
       "     'C(hour)[T.17]:C(workingday)[T.1]:temp',\n",
       "     'C(hour)[T.18]:C(workingday)[T.1]:temp',\n",
       "     'C(hour)[T.19]:C(workingday)[T.1]:temp',\n",
       "     'C(hour)[T.20]:C(workingday)[T.1]:temp',\n",
       "     'C(hour)[T.21]:C(workingday)[T.1]:temp',\n",
       "     'C(hour)[T.22]:C(workingday)[T.1]:temp',\n",
       "     'C(hour)[T.23]:C(workingday)[T.1]:temp']\n",
       "  Terms:\n",
       "    'Intercept' (column 0)\n",
       "    'C(year)' (column 1)\n",
       "    'C(hour)' (columns 2:25)\n",
       "    'C(workingday)' (column 25)\n",
       "    'C(year):C(hour)' (columns 26:49)\n",
       "    'C(year):C(workingday)' (column 49)\n",
       "    'C(hour):C(workingday)' (columns 50:73)\n",
       "    'C(year):C(hour):C(workingday)' (columns 73:96)\n",
       "    'temp' (column 96)\n",
       "    'C(year):temp' (column 97)\n",
       "    'C(hour):temp' (columns 98:121)\n",
       "    'C(workingday):temp' (column 121)\n",
       "    'C(year):C(hour):temp' (columns 122:145)\n",
       "    'C(year):C(workingday):temp' (column 145)\n",
       "    'C(hour):C(workingday):temp' (columns 146:169)\n",
       "  (to view full data, use np.asarray(this_obj))"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome, predictors = dmatrices(\n",
    "    \"riders ~ (C(year) + C(hour) + C(workingday) + temp) ** 3\", data=sample\n",
    ")\n",
    "predictors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "0c52ec49",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-13T23:53:56.771532Z",
     "start_time": "2021-08-13T23:53:56.416537Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_equation(\n",
    "    sample,\n",
    "    \"riders ~ (C(year) + C(hour) + C(workingday) + temp) ** 3\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "397c3260",
   "metadata": {},
   "source": [
    "More feature is not alway better"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "5c4cacd6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-13T23:53:57.912181Z",
     "start_time": "2021-08-13T23:53:57.595595Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_equation(\n",
    "    sample,\n",
    "    \"riders ~ (C(year) + C(hour) + C(workingday) + temp + humidity) ** 5\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "6c4411ec",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-13T23:53:58.508945Z",
     "start_time": "2021-08-13T23:53:58.016466Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_equation(\n",
    "    sample,\n",
    "    \"riders ~ (C(year) + C(hour) + C(workingday) + temp + humidity + windspeed) ** 6\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0d27289",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "hide_input": false,
  "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": "3.8.10"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
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   "window_display": false
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 "nbformat": 4,
 "nbformat_minor": 5
}