practice.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "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>id</th>\n",
       "      <th>full_sq</th>\n",
       "      <th>life_sq</th>\n",
       "      <th>floor</th>\n",
       "      <th>sub_area</th>\n",
       "      <th>preschool_quota</th>\n",
       "      <th>preschool_education_centers_raion</th>\n",
       "      <th>school_quota</th>\n",
       "      <th>school_education_centers_raion</th>\n",
       "      <th>school_education_centers_top_20_raion</th>\n",
       "      <th>...</th>\n",
       "      <th>office_km</th>\n",
       "      <th>additional_education_km</th>\n",
       "      <th>preschool_km</th>\n",
       "      <th>big_church_km</th>\n",
       "      <th>church_synagogue_km</th>\n",
       "      <th>theater_km</th>\n",
       "      <th>museum_km</th>\n",
       "      <th>ecology</th>\n",
       "      <th>mosque_count_1000</th>\n",
       "      <th>price_doc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>43</td>\n",
       "      <td>27.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Bibirevo</td>\n",
       "      <td>5001.0</td>\n",
       "      <td>5</td>\n",
       "      <td>11065.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.637189</td>\n",
       "      <td>0.947962</td>\n",
       "      <td>0.177975</td>\n",
       "      <td>0.625783</td>\n",
       "      <td>0.628187</td>\n",
       "      <td>14.053047</td>\n",
       "      <td>7.389498</td>\n",
       "      <td>good</td>\n",
       "      <td>0</td>\n",
       "      <td>5850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Nagatinskij Zaton</td>\n",
       "      <td>3119.0</td>\n",
       "      <td>5</td>\n",
       "      <td>6237.0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.688796</td>\n",
       "      <td>1.072315</td>\n",
       "      <td>0.273345</td>\n",
       "      <td>0.967821</td>\n",
       "      <td>0.471447</td>\n",
       "      <td>6.829889</td>\n",
       "      <td>0.709260</td>\n",
       "      <td>excellent</td>\n",
       "      <td>0</td>\n",
       "      <td>6000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>43</td>\n",
       "      <td>29.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Tekstil'shhiki</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>4</td>\n",
       "      <td>5580.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.543049</td>\n",
       "      <td>0.391957</td>\n",
       "      <td>0.158072</td>\n",
       "      <td>3.178751</td>\n",
       "      <td>0.755946</td>\n",
       "      <td>4.273200</td>\n",
       "      <td>3.156423</td>\n",
       "      <td>poor</td>\n",
       "      <td>0</td>\n",
       "      <td>5700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>89</td>\n",
       "      <td>50.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Mitino</td>\n",
       "      <td>6839.0</td>\n",
       "      <td>9</td>\n",
       "      <td>17063.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.934273</td>\n",
       "      <td>0.892674</td>\n",
       "      <td>0.236455</td>\n",
       "      <td>1.031777</td>\n",
       "      <td>1.561505</td>\n",
       "      <td>16.990677</td>\n",
       "      <td>16.041521</td>\n",
       "      <td>good</td>\n",
       "      <td>0</td>\n",
       "      <td>13100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>77</td>\n",
       "      <td>77.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Basmannoe</td>\n",
       "      <td>3240.0</td>\n",
       "      <td>7</td>\n",
       "      <td>7770.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.077901</td>\n",
       "      <td>0.810801</td>\n",
       "      <td>0.376838</td>\n",
       "      <td>0.378756</td>\n",
       "      <td>0.121681</td>\n",
       "      <td>1.112486</td>\n",
       "      <td>1.800125</td>\n",
       "      <td>excellent</td>\n",
       "      <td>0</td>\n",
       "      <td>16331452</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  full_sq  life_sq  floor           sub_area  preschool_quota  \\\n",
       "0   1       43     27.0    4.0           Bibirevo           5001.0   \n",
       "1   2       34     19.0    3.0  Nagatinskij Zaton           3119.0   \n",
       "2   3       43     29.0    2.0     Tekstil'shhiki           1463.0   \n",
       "3   4       89     50.0    9.0             Mitino           6839.0   \n",
       "4   5       77     77.0    4.0          Basmannoe           3240.0   \n",
       "\n",
       "   preschool_education_centers_raion  school_quota  \\\n",
       "0                                  5       11065.0   \n",
       "1                                  5        6237.0   \n",
       "2                                  4        5580.0   \n",
       "3                                  9       17063.0   \n",
       "4                                  7        7770.0   \n",
       "\n",
       "   school_education_centers_raion  school_education_centers_top_20_raion  ...  \\\n",
       "0                               5                                      0  ...   \n",
       "1                               8                                      0  ...   \n",
       "2                               7                                      0  ...   \n",
       "3                              10                                      0  ...   \n",
       "4                               9                                      0  ...   \n",
       "\n",
       "   office_km  additional_education_km  preschool_km  big_church_km  \\\n",
       "0   0.637189                 0.947962      0.177975       0.625783   \n",
       "1   0.688796                 1.072315      0.273345       0.967821   \n",
       "2   1.543049                 0.391957      0.158072       3.178751   \n",
       "3   0.934273                 0.892674      0.236455       1.031777   \n",
       "4   0.077901                 0.810801      0.376838       0.378756   \n",
       "\n",
       "   church_synagogue_km  theater_km  museum_km    ecology mosque_count_1000  \\\n",
       "0             0.628187   14.053047   7.389498       good                 0   \n",
       "1             0.471447    6.829889   0.709260  excellent                 0   \n",
       "2             0.755946    4.273200   3.156423       poor                 0   \n",
       "3             1.561505   16.990677  16.041521       good                 0   \n",
       "4             0.121681    1.112486   1.800125  excellent                 0   \n",
       "\n",
       "  price_doc  \n",
       "0   5850000  \n",
       "1   6000000  \n",
       "2   5700000  \n",
       "3  13100000  \n",
       "4  16331452  \n",
       "\n",
       "[5 rows x 61 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sber_data = pd.read_csv('data/sber_data.csv')\n",
    "sber_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure(figsize=(15, 7))\n",
    "boxplot = sns.scatterplot(sber_data, x='kremlin_km', y='price_doc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "92"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def outliers_iqr_mod(data, feature, left=1.5, right=1.5, log_scale=False):\n",
    "    if log_scale:\n",
    "        x = np.log(data[feature])\n",
    "    else:\n",
    "        x = data[feature]\n",
    "    quartile_1, quartile_3 = x.quantile(0.25), x.quantile(0.75),\n",
    "    iqr = quartile_3 - quartile_1\n",
    "    lower_bound = quartile_1 - (iqr * left)\n",
    "    upper_bound = quartile_3 + (iqr * right)\n",
    "    outliers = data[(x < lower_bound) | (x > upper_bound)]\n",
    "    cleaned = data[(x >= lower_bound) & (x <= upper_bound)]\n",
    "    return outliers, cleaned\n",
    "\n",
    "def outliers_z_score_mod(data, feature, log_scale=False, left=3, right=3):\n",
    "    if log_scale:\n",
    "        x = np.log(data[feature]+1)\n",
    "    else:\n",
    "        x = data[feature]\n",
    "    mu = x.mean()\n",
    "    sigma = x.std()\n",
    "    lower_bound = mu - left * sigma\n",
    "    upper_bound = mu + right * sigma\n",
    "    outliers = data[(x < lower_bound) | (x > upper_bound)]\n",
    "    cleaned = data[(x >= lower_bound) & (x <= upper_bound)]\n",
    "    return outliers, cleaned\n",
    "\n",
    "outliers, cleaned = outliers_iqr_mod(sber_data, 'price_doc', log_scale=True, left=3, right=3)\n",
    "outliers.shape[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x28bce7abfa0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsAAAAFzCAYAAAAwmb+pAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/SrBM8AAAACXBIWXMAAA9hAAAPYQGoP6dpAAA4q0lEQVR4nO3deXhU9d338U9CVpYJhpBNkpCKkoSygzBVETAlYKgLuVupbMp2w52gkPsGmhaQpYqiEBACVGTzLtTq86hFoEAIm0hYDFDZ3dCgZDFiMoAhCWSeP3plHkb2ZMiZcN6v6zpXmPP7zm++5xjJh5Mzv/Gw2+12AQAAACbhaXQDAAAAQG0iAAMAAMBUCMAAAAAwFQIwAAAATIUADAAAAFMhAAMAAMBUCMAAAAAwFQIwAAAATMXL6AbqgsrKSp0+fVqNGjWSh4eH0e0AAADgZ+x2u86ePavw8HB5el7/Gi8B+CacPn1aERERRrcBAACAGzh16pSaNWt23RoC8E1o1KiRpH+fUIvFYnA3QN0WExOjvLw8hYWF6fjx40a3gzqK7yO4At9HdxabzaaIiAhHbrseAvBNqLrtwWKxEICBGqr6tZSnpyf/P6Ha+D6CK/B9dGe6mdtVeRMcAAAATIUADAAAAFMhAAMAAMBUCMAAAAAwFQIwAAAATIUADAAAAFMhAAMAAMBUCMAAAAAwFQIwAAAATIUADAAAAFPho5ABAG4lNzdXRUVF162pqKhwfN2/f/8164KCghQZGenS/gDUfQRgAIDbyM3NVUxMrEpLf7qp+sLCQnXs2PGa4/7+9XX8+DFCMAAnBGAAgNsoKipSaelP6jL0BVnCml+zbsfrqSo7+6N8G92lbs/NuWqNLe9r7Vk2TUVFRQRgAE4IwAAAt2MJa67AyJbXHPes5+X4er06ALga3gQHAAAAUyEAAwAAwFQIwAAAADAVAjAAAABMhQAMAAAAUyEAAwAAwFQIwAAAADAVAjAAAABMhQAMAAAAUyEAAwAAwFQIwAAAADAVQwNw8+bN5eHhccWWnJwsSbpw4YKSk5PVpEkTNWzYUElJSSooKHCaIzc3V4mJiapfv76Cg4M1fvx4Xbx40alm27Zt6tChg3x9fdWiRQutWLGitg4RAAAAbsbQALxv3z7l5eU5tszMTEnSb3/7W0nSuHHj9OGHH+rdd9/V9u3bdfr0afXr18/x/EuXLikxMVHl5eXatWuXVq5cqRUrVmjKlCmOmpMnTyoxMVE9evTQwYMHNXbsWA0fPlwbN26s3YMFAACAW/Ay8sWbNm3q9Pjll1/WPffco4cfflglJSVaunSpVq9erZ49e0qSli9frtjYWO3evVtdu3bVpk2bdPToUW3evFkhISFq166dZsyYoYkTJ2rq1Kny8fHR4sWLFR0drdmzZ0uSYmNjtXPnTqWnpyshIaHWjxkAAADGcpt7gMvLy/XXv/5VQ4cOlYeHh3JyclRRUaH4+HhHTUxMjCIjI5WdnS1Jys7OVuvWrRUSEuKoSUhIkM1m05EjRxw1l89RVVM1x9WUlZXJZrM5bQAAALgzuE0A/uCDD1RcXKxnnnlGkpSfny8fHx81btzYqS4kJET5+fmOmsvDb9V41dj1amw2m0pLS6/ay8yZMxUQEODYIiIianp4AAAAcBNuE4CXLl2qPn36KDw83OhWlJaWppKSEsd26tQpo1sCAACAixh6D3CVb775Rps3b9Z7773n2BcaGqry8nIVFxc7XQUuKChQaGioo2bv3r1Oc1WtEnF5zc9XjigoKJDFYpG/v/9V+/H19ZWvr2+NjwsAAADuxy2uAC9fvlzBwcFKTEx07OvYsaO8vb2VlZXl2HfixAnl5ubKarVKkqxWqw4dOqTCwkJHTWZmpiwWi+Li4hw1l89RVVM1BwAAAMzF8ABcWVmp5cuXa8iQIfLy+v8XpAMCAjRs2DClpqZq69atysnJ0bPPPiur1aquXbtKknr16qW4uDgNGjRI//rXv7Rx40ZNmjRJycnJjiu4o0aN0ldffaUJEybo+PHjWrhwod555x2NGzfOkOMFAACAsQy/BWLz5s3Kzc3V0KFDrxhLT0+Xp6enkpKSVFZWpoSEBC1cuNAxXq9ePa1du1ajR4+W1WpVgwYNNGTIEE2fPt1REx0drXXr1mncuHGaN2+emjVrpjfffJMl0AAAAEzK8ADcq1cv2e32q475+fkpIyNDGRkZ13x+VFSU1q9ff93X6N69uw4cOFCjPgEAAHBnMPwWCAAAAKA2EYABAABgKgRgAAAAmAoBGAAAAKZCAAYAAICpEIABAABgKgRgAAAAmAoBGAAAAKZCAAYAAICpEIABAABgKgRgAAAAmAoBGAAAAKZCAAYAAICpEIABAABgKgRgAAAAmAoBGAAAAKZCAAYAAICpEIABAABgKgRgAAAAmAoBGAAAAKZCAAYAAICpEIABAABgKgRgAAAAmAoBGAAAAKZCAAYAAICpEIABAABgKgRgAAAAmAoBGAAAAKZCAAYAAICpGB6Av/vuOw0cOFBNmjSRv7+/WrdurU8++cQxbrfbNWXKFIWFhcnf31/x8fH6/PPPneY4c+aMBgwYIIvFosaNG2vYsGE6d+6cU82nn36qhx56SH5+foqIiNCsWbNq5fgAAADgXgwNwD/++KMeeOABeXt765///KeOHj2q2bNn66677nLUzJo1S6+//roWL16sPXv2qEGDBkpISNCFCxccNQMGDNCRI0eUmZmptWvXaseOHRo5cqRj3GazqVevXoqKilJOTo5effVVTZ06VW+88UatHi8AAACM52Xki7/yyiuKiIjQ8uXLHfuio6Mdf7bb7Zo7d64mTZqkxx9/XJL01ltvKSQkRB988IH69++vY8eOacOGDdq3b586deokSZo/f74effRRvfbaawoPD9eqVatUXl6uZcuWycfHR61atdLBgwc1Z84cp6AMAACAO5+hV4DXrFmjTp066be//a2Cg4PVvn17LVmyxDF+8uRJ5efnKz4+3rEvICBAXbp0UXZ2tiQpOztbjRs3doRfSYqPj5enp6f27NnjqOnWrZt8fHwcNQkJCTpx4oR+/PHHK/oqKyuTzWZz2gAAAHBnMDQAf/XVV1q0aJHuvfdebdy4UaNHj9Zzzz2nlStXSpLy8/MlSSEhIU7PCwkJcYzl5+crODjYadzLy0uBgYFONVeb4/LXuNzMmTMVEBDg2CIiIlxwtAAAAHAHhgbgyspKdejQQS+99JLat2+vkSNHasSIEVq8eLGRbSktLU0lJSWO7dSpU4b2AwAAANcxNACHhYUpLi7OaV9sbKxyc3MlSaGhoZKkgoICp5qCggLHWGhoqAoLC53GL168qDNnzjjVXG2Oy1/jcr6+vrJYLE4bAAAA7gyGBuAHHnhAJ06ccNr32WefKSoqStK/3xAXGhqqrKwsx7jNZtOePXtktVolSVarVcXFxcrJyXHUbNmyRZWVlerSpYujZseOHaqoqHDUZGZmqmXLlk4rTgAAAODOZ2gAHjdunHbv3q2XXnpJX3zxhVavXq033nhDycnJkiQPDw+NHTtWf/7zn7VmzRodOnRIgwcPVnh4uJ544glJ/75i3Lt3b40YMUJ79+7Vxx9/rJSUFPXv31/h4eGSpKefflo+Pj4aNmyYjhw5or///e+aN2+eUlNTjTp0AAAAGMTQZdA6d+6s999/X2lpaZo+fbqio6M1d+5cDRgwwFEzYcIEnT9/XiNHjlRxcbEefPBBbdiwQX5+fo6aVatWKSUlRY888og8PT2VlJSk119/3TEeEBCgTZs2KTk5WR07dlRQUJCmTJnCEmgAAAAmZGgAlqS+ffuqb9++1xz38PDQ9OnTNX369GvWBAYGavXq1dd9nTZt2uijjz6qdp8AAAC4Mxj+UcgAAABAbSIAAwAAwFQIwAAAADAVAjAAAABMhQAMAAAAUyEAAwAAwFQIwAAAADAVw9cBBgDgdjp27JhL5gkKClJkZKRL5gJgLAIwAOCOVFrygyQPDRw40CXz+fvX1/HjxwjBwB2AAAwAuCNV/HRWkl3tnp6optExNZrLlve19iybpqKiIgIwcAcgAAMA7mgNgyMVGNnS6DYAuBHeBAcAAABTIQADAADAVAjAAAAAMBUCMAAAAEyFAAwAAABTIQADAADAVAjAAAAAMBUCMAAAAEyFAAwAAABTIQADAADAVAjAAAAAMBUCMAAAAEyFAAwAAABTIQADAADAVAjAAAAAMBUCMAAAAEyFAAwAAABTMTQAT506VR4eHk5bTEyMY/zChQtKTk5WkyZN1LBhQyUlJamgoMBpjtzcXCUmJqp+/foKDg7W+PHjdfHiRaeabdu2qUOHDvL19VWLFi20YsWK2jg8AAAAuCHDrwC3atVKeXl5jm3nzp2OsXHjxunDDz/Uu+++q+3bt+v06dPq16+fY/zSpUtKTExUeXm5du3apZUrV2rFihWaMmWKo+bkyZNKTExUjx49dPDgQY0dO1bDhw/Xxo0ba/U4AQAA4B68DG/Ay0uhoaFX7C8pKdHSpUu1evVq9ezZU5K0fPlyxcbGavfu3eratas2bdqko0ePavPmzQoJCVG7du00Y8YMTZw4UVOnTpWPj48WL16s6OhozZ49W5IUGxurnTt3Kj09XQkJCbV6rAAAADCe4VeAP//8c4WHh+sXv/iFBgwYoNzcXElSTk6OKioqFB8f76iNiYlRZGSksrOzJUnZ2dlq3bq1QkJCHDUJCQmy2Ww6cuSIo+byOapqqua4mrKyMtlsNqcNAAAAdwZDA3CXLl20YsUKbdiwQYsWLdLJkyf10EMP6ezZs8rPz5ePj48aN27s9JyQkBDl5+dLkvLz853Cb9V41dj1amw2m0pLS6/a18yZMxUQEODYIiIiXHG4AAAAcAOG3gLRp08fx5/btGmjLl26KCoqSu+88478/f0N6ystLU2pqamOxzabjRAMAABwhzD8FojLNW7cWPfdd5+++OILhYaGqry8XMXFxU41BQUFjnuGQ0NDr1gVourxjWosFss1Q7avr68sFovTBgAAgDuDWwXgc+fO6csvv1RYWJg6duwob29vZWVlOcZPnDih3NxcWa1WSZLVatWhQ4dUWFjoqMnMzJTFYlFcXJyj5vI5qmqq5gAAAIC5GBqA/+d//kfbt2/X119/rV27dunJJ59UvXr19Pvf/14BAQEaNmyYUlNTtXXrVuXk5OjZZ5+V1WpV165dJUm9evVSXFycBg0apH/961/auHGjJk2apOTkZPn6+kqSRo0apa+++koTJkzQ8ePHtXDhQr3zzjsaN26ckYcOAAAAgxh6D/C3336r3//+9/rhhx/UtGlTPfjgg9q9e7eaNm0qSUpPT5enp6eSkpJUVlamhIQELVy40PH8evXqae3atRo9erSsVqsaNGigIUOGaPr06Y6a6OhorVu3TuPGjdO8efPUrFkzvfnmmyyBBgAAYFKGBuC33377uuN+fn7KyMhQRkbGNWuioqK0fv36687TvXt3HThwoFo9AgAA4M7iVvcAAwAAALcbARgAAACmQgAGAACAqRCAAQAAYCoEYAAAAJgKARgAAACmQgAGAACAqRCAAQAAYCoEYAAAAJgKARgAAACmQgAGAACAqRCAAQAAYCoEYAAAAJgKARgAAACmQgAGAACAqRCAAQAAYCpeRjcAAKj7cnNzVVRUVON5jh075oJuAOD6CMAAgBrJzc1VTEysSkt/ctmcFWXlLpsLAH6uWgH4F7/4hfbt26cmTZo47S8uLlaHDh301VdfuaQ5AID7KyoqUmnpT+oy9AVZwprXaK68Q9k6vOYNXbx40TXNAcBVVCsAf/3117p06dIV+8vKyvTdd9/VuCkAQN1jCWuuwMiWNZrDlve1a5oBgOu4pQC8Zs0ax583btyogIAAx+NLly4pKytLzZs3d1lzAAAAgKvdUgB+4oknJEkeHh4aMmSI05i3t7eaN2+u2bNnu6w5AAAAwNVuKQBXVlZKkqKjo7Vv3z4FBQXdlqYAAACA26Va9wCfPHnS1X0AAAAAtaLay6BlZWUpKytLhYWFjivDVZYtW1bjxgAAAIDboVoBeNq0aZo+fbo6deqksLAweXh4uLovAAAA4LaoVgBevHixVqxYoUGDBrm6HwAAAOC28qzOk8rLy/WrX/3K1b0AAAAAt121AvDw4cO1evVqlzby8ssvy8PDQ2PHjnXsu3DhgpKTk9WkSRM1bNhQSUlJKigocHpebm6uEhMTVb9+fQUHB2v8+PFXfILQtm3b1KFDB/n6+qpFixZasWKFS3sHAABA3VGtWyAuXLigN954Q5s3b1abNm3k7e3tND5nzpxbmm/fvn36y1/+ojZt2jjtHzdunNatW6d3331XAQEBSklJUb9+/fTxxx9L+veHbyQmJio0NFS7du1SXl6eBg8eLG9vb7300kuS/r1iRWJiokaNGqVVq1YpKytLw4cPV1hYmBISEqpz+AAAAKjDqhWAP/30U7Vr106SdPjwYaexW31D3Llz5zRgwAAtWbJEf/7znx37S0pKtHTpUq1evVo9e/aUJC1fvlyxsbHavXu3unbtqk2bNuno0aPavHmzQkJC1K5dO82YMUMTJ07U1KlT5ePjo8WLFys6OtrxAR2xsbHauXOn0tPTCcAAAAAmVK0AvHXrVpc1kJycrMTERMXHxzsF4JycHFVUVCg+Pt6xLyYmRpGRkcrOzlbXrl2VnZ2t1q1bKyQkxFGTkJCg0aNH68iRI2rfvr2ys7Od5qiqufxWi58rKytTWVmZ47HNZnPBkQIAAMAdVHsdYFd4++23tX//fu3bt++Ksfz8fPn4+Khx48ZO+0NCQpSfn++ouTz8Vo1XjV2vxmazqbS0VP7+/le89syZMzVt2rRqHxcAAADcV7UCcI8ePa57q8OWLVtuOMepU6f0/PPPKzMzU35+ftVp47ZJS0tTamqq47HNZlNERISBHQEAAMBVqhWAq+7/rVJRUaGDBw/q8OHDGjJkyE3NkZOTo8LCQnXo0MGx79KlS9qxY4cWLFigjRs3qry8XMXFxU5XgQsKChQaGipJCg0N1d69e53mrVol4vKan68cUVBQIIvFctWrv5Lk6+srX1/fmzoOAAAA1C3VCsDp6elX3T916lSdO3fupuZ45JFHdOjQIad9zz77rGJiYjRx4kRFRETI29tbWVlZSkpKkiSdOHFCubm5slqtkiSr1aoXX3xRhYWFCg4OliRlZmbKYrEoLi7OUbN+/Xqn18nMzHTMAQAAAHNx6T3AAwcO1P3336/XXnvthrWNGjXSL3/5S6d9DRo0UJMmTRz7hw0bptTUVAUGBspisWjMmDGyWq3q2rWrJKlXr16Ki4vToEGDNGvWLOXn52vSpElKTk52XMEdNWqUFixYoAkTJmjo0KHasmWL3nnnHa1bt86Vhw4AAIA6wqUBODs726X386anp8vT01NJSUkqKytTQkKCFi5c6BivV6+e1q5dq9GjR8tqtapBgwYaMmSIpk+f7qiJjo7WunXrNG7cOM2bN0/NmjXTm2++yRJoAAAAJlWtANyvXz+nx3a7XXl5efrkk080efLkajezbds2p8d+fn7KyMhQRkbGNZ8TFRV1xS0OP9e9e3cdOHCg2n0BAADgzlGtABwQEOD02NPTUy1bttT06dPVq1cvlzQGAAAA3A7VCsDLly93dR8AAABArajRPcA5OTk6duyYJKlVq1Zq3769S5oCAAAAbpdqBeDCwkL1799f27Ztc6zRW1xcrB49eujtt99W06ZNXdkjAAAA4DKe1XnSmDFjdPbsWR05ckRnzpzRmTNndPjwYdlsNj333HOu7hEAAABwmWpdAd6wYYM2b96s2NhYx764uDhlZGTwJjgAAAC4tWpdAa6srJS3t/cV+729vVVZWVnjpgAAAIDbpVoBuGfPnnr++ed1+vRpx77vvvtO48aN0yOPPOKy5gAAAABXq1YAXrBggWw2m5o3b6577rlH99xzj6Kjo2Wz2TR//nxX9wgAAAC4TLXuAY6IiND+/fu1efNmHT9+XJIUGxur+Ph4lzYHAAAAuNotXQHesmWL4uLiZLPZ5OHhoV//+tcaM2aMxowZo86dO6tVq1b66KOPblevAAAAQI3dUgCeO3euRowYIYvFcsVYQECA/vM//1Nz5sxxWXMAAACAq91SAP7Xv/6l3r17X3O8V69eysnJqXFTAAAAwO1ySwG4oKDgqsufVfHy8tL3339f46YAAACA2+WWAvDdd9+tw4cPX3P8008/VVhYWI2bAgAAAG6XWwrAjz76qCZPnqwLFy5cMVZaWqoXXnhBffv2dVlzAAAAgKvd0jJokyZN0nvvvaf77rtPKSkpatmypSTp+PHjysjI0KVLl/SnP/3ptjQKAAAAuMItBeCQkBDt2rVLo0ePVlpamux2uyTJw8NDCQkJysjIUEhIyG1pFAAAAHCFW/4gjKioKK1fv14//vijvvjiC9ntdt1777266667bkd/AAAAgEtV65PgJOmuu+5S586dXdkLAAAAcNvd0pvgAAAAgLqOAAwAAABTqfYtEAAAmM2xY8dqPEdQUJAiIyNd0A2A6iIAAwBwA6UlP0jy0MCBA2s8l79/fR0/fowQDBiIAAwAwA1U/HRWkl3tnp6optEx1Z7Hlve19iybpqKiIgIwYCACMAAAN6lhcKQCI1sa3QaAGuJNcAAAADAVAjAAAABMhQAMAAAAUzE0AC9atEht2rSRxWKRxWKR1WrVP//5T8f4hQsXlJycrCZNmqhhw4ZKSkpSQUGB0xy5ublKTExU/fr1FRwcrPHjx+vixYtONdu2bVOHDh3k6+urFi1aaMWKFbVxeAAAAHBDhgbgZs2a6eWXX1ZOTo4++eQT9ezZU48//riOHDkiSRo3bpw+/PBDvfvuu9q+fbtOnz6tfv36OZ5/6dIlJSYmqry8XLt27dLKlSu1YsUKTZkyxVFz8uRJJSYmqkePHjp48KDGjh2r4cOHa+PGjbV+vAAAADCeoatA/OY3v3F6/OKLL2rRokXavXu3mjVrpqVLl2r16tXq2bOnJGn58uWKjY3V7t271bVrV23atElHjx7V5s2bFRISonbt2mnGjBmaOHGipk6dKh8fHy1evFjR0dGaPXu2JCk2NlY7d+5Uenq6EhISav2YAQAAYCy3uQf40qVLevvtt3X+/HlZrVbl5OSooqJC8fHxjpqYmBhFRkYqOztbkpSdna3WrVsrJCTEUZOQkCCbzea4ipydne00R1VN1RxXU1ZWJpvN5rQBAADgzmB4AD506JAaNmwoX19fjRo1Su+//77i4uKUn58vHx8fNW7c2Kk+JCRE+fn5kqT8/Hyn8Fs1XjV2vRqbzabS0tKr9jRz5kwFBAQ4toiICFccKgAAANyA4QG4ZcuWOnjwoPbs2aPRo0dryJAhOnr0qKE9paWlqaSkxLGdOnXK0H4AAADgOoZ/EpyPj49atGghSerYsaP27dunefPm6amnnlJ5ebmKi4udrgIXFBQoNDRUkhQaGqq9e/c6zVe1SsTlNT9fOaKgoEAWi0X+/v5X7cnX11e+vr4uOT4AAAC4F8OvAP9cZWWlysrK1LFjR3l7eysrK8sxduLECeXm5spqtUqSrFarDh06pMLCQkdNZmamLBaL4uLiHDWXz1FVUzUHAAAAzMXQK8BpaWnq06ePIiMjdfbsWa1evVrbtm3Txo0bFRAQoGHDhik1NVWBgYGyWCwaM2aMrFarunbtKknq1auX4uLiNGjQIM2aNUv5+fmaNGmSkpOTHVdwR40apQULFmjChAkaOnSotmzZonfeeUfr1q0z8tABAABgEEMDcGFhoQYPHqy8vDwFBASoTZs22rhxo379619LktLT0+Xp6amkpCSVlZUpISFBCxcudDy/Xr16Wrt2rUaPHi2r1aoGDRpoyJAhmj59uqMmOjpa69at07hx4zRv3jw1a9ZMb775JkugAQAAmJShAXjp0qXXHffz81NGRoYyMjKuWRMVFaX169dfd57u3bvrwIED1eoRAAAAdxa3uwcYAAAAuJ0IwAAAADAVAjAAAABMhQAMAAAAUyEAAwAAwFQIwAAAADAVAjAAAABMhQAMAAAAUyEAAwAAwFQIwAAAADAVAjAAAABMhQAMAAAAUyEAAwAAwFQIwAAAADAVAjAAAABMhQAMAAAAUyEAAwAAwFQIwAAAADAVAjAAAABMhQAMAAAAUyEAAwAAwFS8jG4AAGCM3NxcFRUV1XieY8eOuaAbAKg9BGAAMKHc3FzFxMSqtPQnl81ZUVbusrkA4HYiAAOACRUVFam09Cd1GfqCLGHNazRX3qFsHV7zhi5evOia5gDgNiMAA4CJWcKaKzCyZY3msOV97ZpmAKCW8CY4AAAAmAoBGAAAAKZCAAYAAICpGBqAZ86cqc6dO6tRo0YKDg7WE088oRMnTjjVXLhwQcnJyWrSpIkaNmyopKQkFRQUONXk5uYqMTFR9evXV3BwsMaPH3/FmzG2bdumDh06yNfXVy1atNCKFStu9+EBAADADRn6Jrjt27crOTlZnTt31sWLF/XHP/5RvXr10tGjR9WgQQNJ0rhx47Ru3Tq9++67CggIUEpKivr166ePP/5YknTp0iUlJiYqNDRUu3btUl5engYPHixvb2+99NJLkqSTJ08qMTFRo0aN0qpVq5SVlaXhw4crLCxMCQkJhh0/AMCcXLV2clBQkCIjI10yF2AmhgbgDRs2OD1esWKFgoODlZOTo27duqmkpERLly7V6tWr1bNnT0nS8uXLFRsbq927d6tr167atGmTjh49qs2bNyskJETt2rXTjBkzNHHiRE2dOlU+Pj5avHixoqOjNXv2bElSbGysdu7cqfT0dAIwAKDWlJb8IMlDAwcOdMl8/v71dfz4MUIwcIvcahm0kpISSVJgYKAkKScnRxUVFYqPj3fUxMTEKDIyUtnZ2eratauys7PVunVrhYSEOGoSEhI0evRoHTlyRO3bt1d2drbTHFU1Y8eOvWofZWVlKisrczy22WyuOkQAgIlV/HRWkl3tnp6optExNZrLlve19iybpqKiIgIwcIvcJgBXVlZq7NixeuCBB/TLX/5SkpSfny8fHx81btzYqTYkJET5+fmOmsvDb9V41dj1amw2m0pLS+Xv7+80NnPmTE2bNs1lxwYAwOUaBkfWeP1lANXnNqtAJCcn6/Dhw3r77beNbkVpaWkqKSlxbKdOnTK6JQAAALiIW1wBTklJ0dq1a7Vjxw41a9bMsT80NFTl5eUqLi52ugpcUFCg0NBQR83evXud5qtaJeLymp+vHFFQUCCLxXLF1V9J8vX1la+vr0uODQAAAO7F0CvAdrtdKSkpev/997VlyxZFR0c7jXfs2FHe3t7Kyspy7Dtx4oRyc3NltVolSVarVYcOHVJhYaGjJjMzUxaLRXFxcY6ay+eoqqmaAwAAAOZh6BXg5ORkrV69Wv/4xz/UqFEjxz27AQEB8vf3V0BAgIYNG6bU1FQFBgbKYrFozJgxslqt6tq1qySpV69eiouL06BBgzRr1izl5+dr0qRJSk5OdlzFHTVqlBYsWKAJEyZo6NCh2rJli9555x2tW7fOsGMHAACAMQy9Arxo0SKVlJSoe/fuCgsLc2x///vfHTXp6enq27evkpKS1K1bN4WGhuq9995zjNerV09r165VvXr1ZLVaNXDgQA0ePFjTp0931ERHR2vdunXKzMxU27ZtNXv2bL355pssgQYAAGBChl4BttvtN6zx8/NTRkaGMjIyrlkTFRWl9evXX3ee7t2768CBA7fcIwAAAO4sbrMKBAAAAFAbCMAAAAAwFQIwAAAATIUADAAAAFMhAAMAAMBUCMAAAAAwFQIwAAAATIUADAAAAFMhAAMAAMBUCMAAAAAwFQIwAAAATIUADAAAAFMhAAMAAMBUCMAAAAAwFQIwAAAATIUADAAAAFMhAAMAAMBUCMAAAAAwFQIwAAAATIUADAAAAFMhAAMAAMBUCMAAAAAwFQIwAAAATIUADAAAAFMhAAMAAMBUCMAAAAAwFQIwAAAATIUADAAAAFPxMvLFd+zYoVdffVU5OTnKy8vT+++/ryeeeMIxbrfb9cILL2jJkiUqLi7WAw88oEWLFunee+911Jw5c0ZjxozRhx9+KE9PTyUlJWnevHlq2LCho+bTTz9VcnKy9u3bp6ZNm2rMmDGaMGFCbR4qALhMbm6uioqKajTHsWPHXNQNANQ9hgbg8+fPq23btho6dKj69et3xfisWbP0+uuva+XKlYqOjtbkyZOVkJCgo0ePys/PT5I0YMAA5eXlKTMzUxUVFXr22Wc1cuRIrV69WpJks9nUq1cvxcfHa/HixTp06JCGDh2qxo0ba+TIkbV6vABQU7m5uYqJiVVp6U8uma+irNwl8wBAXWJoAO7Tp4/69Olz1TG73a65c+dq0qRJevzxxyVJb731lkJCQvTBBx+of//+OnbsmDZs2KB9+/apU6dOkqT58+fr0Ucf1Wuvvabw8HCtWrVK5eXlWrZsmXx8fNSqVSsdPHhQc+bMIQADqHOKiopUWvqTugx9QZaw5tWeJ+9Qtg6veUMXL150XXMwhKuu5gcFBSkyMtIlcwHuztAAfD0nT55Ufn6+4uPjHfsCAgLUpUsXZWdnq3///srOzlbjxo0d4VeS4uPj5enpqT179ujJJ59Udna2unXrJh8fH0dNQkKCXnnlFf3444+66667rnjtsrIylZWVOR7bbLbbdJQAUD2WsOYKjGxZ7efb8r52XTMwRGnJD5I8NHDgQJfM5+9fX8ePHyMEwxTcNgDn5+dLkkJCQpz2h4SEOMby8/MVHBzsNO7l5aXAwECnmujo6CvmqBq7WgCeOXOmpk2b5poDAQDgNqj46awku9o9PVFNo2NqNJct72vtWTZNRUVFBGCYgtsGYCOlpaUpNTXV8dhmsykiIsLAjgD3VJ03Y1VUVDi+7t+/XxK/egVqomFwZI1+GwCYkdsG4NDQUElSQUGBwsLCHPsLCgrUrl07R01hYaHT8y5evKgzZ844nh8aGqqCggKnmqrHVTU/5+vrK19fX5ccB3CnqumbsQoLC9WxY0dJ/OoVAFC73DYAR0dHKzQ0VFlZWY7Aa7PZtGfPHo0ePVqSZLVaVVxcrJycHMcP0i1btqiyslJdunRx1PzpT39SRUWFvL29JUmZmZlq2bLlVW9/AHBzqvtmrB2vp6rs7I/ybXSXuj03h1+9AgBqnaEB+Ny5c/riiy8cj0+ePKmDBw8qMDBQkZGRGjt2rP785z/r3nvvdSyDFh4e7lgrODY2Vr1799aIESO0ePFiVVRUKCUlRf3791d4eLgk6emnn9a0adM0bNgwTZw4UYcPH9a8efOUnp5uxCEDd5xbfTOWZz0vx1d3/7WtK9bbrcJtHgDgPgwNwJ988ol69OjheFx13+2QIUO0YsUKTZgwQefPn9fIkSNVXFysBx98UBs2bHCsASxJq1atUkpKih555BHHB2G8/vrrjvGAgABt2rRJycnJ6tixo4KCgjRlyhSWQANwXa5eb5fbPADAfRgagLt37y673X7NcQ8PD02fPl3Tp0+/Zk1gYKDjQy+upU2bNvroo4+q3ScA83HVersS77AHAHfjtvcAA4A7qOl6uwAA9+NpdAMAAABAbSIAAwAAwFQIwAAAADAVAjAAAABMhQAMAAAAUyEAAwAAwFQIwAAAADAVAjAAAABMhQAMAAAAUyEAAwAAwFQIwAAAADAVAjAAAABMhQAMAAAAUyEAAwAAwFS8jG4AAAC4h2PHjrlknqCgIEVGRrpkLuB2IAADQC1xRbhwVUABLlda8oMkDw0cONAl8/n719fx48cIwXBbBGAAuM1cHS4kqaKs3GVzARU/nZVkV7unJ6ppdEyN5rLlfa09y6apqKiIAAy3RQAGgNvMleEi71C2Dq95QxcvXnRNc8BlGgZHKjCypdFtALcdARgAaokrwoUt72vXNAMAJsYqEAAAADAVAjAAAABMhQAMAAAAU+EeYAAA4HKuWLKP9YRxuxCAAQCAy7hy2T/WE8btQgAG4Bb4BCrgzuCqZf9YTxi3EwEYgKH4BCrgzsSawnBnBGAAhuITqAAAtY0ADMAtcLUIwNVwexRuB1MF4IyMDL366qvKz89X27ZtNX/+fN1///1GtwXAxVzxA9NVP3QBVI+rb4/y9fXT//2//0dhYWGOfRUVFY6v+/fvv+m5CNN1n2kC8N///nelpqZq8eLF6tKli+bOnauEhASdOHFCwcHBRrcHwAVc/QNTkirKyl02F4Cb58rbo77//F86+M489e3b96rjhYWF6tix403Pd7UwXV2EaWOYJgDPmTNHI0aM0LPPPitJWrx4sdatW6dly5bpD3/4g8HdoaZyc3NVVFTkkrnKysrk6+vrNvO461zueIXUlT8w8w5l6/CaN3Tx4kXXNAegWlxxe5Qt72td7e+GHa+nquzsj/JtdJe6PTfnpua6UZi+Va4M0+7488tdA74pAnB5eblycnKUlpbm2Ofp6an4+HhlZ2dfUV9WVqaysjLH45KSEkmSzWa7/c1eJj8/X/n5+TWex9PTU5WVlS7oyD3nKigo0KBBg1VWdsEFXeFWff/FYV0sK73p+ksXKxxfCz8/KFveN5Kkku8+l7eXR416qZrrUkXZLfV01T4ryl3elzvN5Y493cpcP/8+cpe+amseM8xVG3832O12x9eb/Tuj7GyxJLt+0f23CghpVqO+Sk5/pa8++ofLwrQ78vPz1yef7FNERMRtf62qnFb13/V6POw3U1XHnT59Wnfffbd27dolq9Xq2D9hwgRt375de/bscaqfOnWqpk2bVtttAgAAoIZOnTqlZs2u/48TU1wBvlVpaWlKTU11PK6srNSZM2fUpEkTeXjU7F+hdwKbzaaIiAidOnVKFovF6HbqHM5fzXD+ao5zWDOcv5rjHNYM5+/q7Ha7zp49q/Dw8BvWmiIABwUFqV69eiooKHDaX1BQoNDQ0CvqfX19r7j3pXHjxrezxTrJYrHwP14NcP5qhvNXc5zDmuH81RznsGY4f1cKCAi4qTrP29yHW/Dx8VHHjh2VlZXl2FdZWamsrCynWyIAAABw5zPFFWBJSk1N1ZAhQ9SpUyfdf//9mjt3rs6fP+9YFQIAAADmYJoA/NRTT+n777/XlClTlJ+fr3bt2mnDhg0KCQkxurU6x9fXVy+88ILLlkgxG85fzXD+ao5zWDOcv5rjHNYM56/mTLEKBAAAAFDFFPcAAwAAAFUIwAAAADAVAjAAAABMhQAMAAAAUyEA46p27Nih3/zmNwoPD5eHh4c++OADx1hFRYUmTpyo1q1bq0GDBgoPD9fgwYN1+vRp4xp2Q9c7h9K/P3I7JiZGDRo00F133aX4+PgrPpbbzG50/i43atQoeXh4aO7cubXWX11wo3P4zDPPyMPDw2nr3bu3Mc26oZv5Hjx27Jgee+wxBQQEqEGDBurcubNyc3Nrv1k3dKPz9/Pvvart1VdfNaZhN3Sjc3ju3DmlpKSoWbNm8vf3V1xcnBYvXmxMs3UMARhXdf78ebVt21YZGRlXjP3000/av3+/Jk+erP379+u9997TiRMn9NhjjxnQqfu63jmUpPvuu08LFizQoUOHtHPnTjVv3ly9evXS999/X8uduqcbnb8q77//vnbv3n1TH31pNjdzDnv37q28vDzH9re//a0WO3RvNzp/X375pR588EHFxMRo27Zt+vTTTzV58mT5+fnVcqfu6Ubn7/Lvu7y8PC1btkweHh5KSkqq5U7d143OYWpqqjZs2KC//vWvOnbsmMaOHauUlBStWbOmljutg+zADUiyv//++9et2bt3r12S/ZtvvqmdpuqYmzmHJSUldkn2zZs3105Tdci1zt+3335rv/vuu+2HDx+2R0VF2dPT02u9t7riaudwyJAh9scff9yQfuqaq52/p556yj5w4EBjGqpjbubvwMcff9zes2fP2mmoDrraOWzVqpV9+vTpTvs6dOhg/9Of/lSLndVNXAGGS5SUlMjDw0ONGzc2upU6qby8XG+88YYCAgLUtm1bo9upEyorKzVo0CCNHz9erVq1MrqdOmvbtm0KDg5Wy5YtNXr0aP3www9Gt1QnVFZWat26dbrvvvuUkJCg4OBgdenS5bq36uDaCgoKtG7dOg0bNszoVuqUX/3qV1qzZo2+++472e12bd26VZ999pl69epldGtujwCMGrtw4YImTpyo3//+97JYLEa3U6esXbtWDRs2lJ+fn9LT05WZmamgoCCj26oTXnnlFXl5eem5554zupU6q3fv3nrrrbeUlZWlV155Rdu3b1efPn106dIlo1tze4WFhTp37pxefvll9e7dW5s2bdKTTz6pfv36afv27Ua3V+esXLlSjRo1Ur9+/YxupU6ZP3++4uLi1KxZM/n4+Kh3797KyMhQt27djG7N7Znmo5Bxe1RUVOh3v/ud7Ha7Fi1aZHQ7dU6PHj108OBBFRUVacmSJfrd736nPXv2KDg42OjW3FpOTo7mzZun/fv3y8PDw+h26qz+/fs7/ty6dWu1adNG99xzj7Zt26ZHHnnEwM7cX2VlpSTp8ccf17hx4yRJ7dq1065du7R48WI9/PDDRrZX5yxbtkwDBgzg/ulbNH/+fO3evVtr1qxRVFSUduzYoeTkZIWHhys+Pt7o9twaV4BRbVXh95tvvlFmZiZXf6uhQYMGatGihbp27aqlS5fKy8tLS5cuNbott/fRRx+psLBQkZGR8vLykpeXl7755hv993//t5o3b250e3XWL37xCwUFBemLL74wuhW3FxQUJC8vL8XFxTntj42NZRWIW/TRRx/pxIkTGj58uNGt1CmlpaX64x//qDlz5ug3v/mN2rRpo5SUFD311FN67bXXjG7P7XEFGNVSFX4///xzbd26VU2aNDG6pTtCZWWlysrKjG7D7Q0aNOiKqxsJCQkaNGiQnn32WYO6qvu+/fZb/fDDDwoLCzO6Fbfn4+Ojzp0768SJE077P/vsM0VFRRnUVd20dOlSdezYkfc/3KKKigpVVFTI09P5Wma9evUcv6HAtRGAcVXnzp1zugp08uRJHTx4UIGBgQoLC9N//Md/aP/+/Vq7dq0uXbqk/Px8SVJgYKB8fHyMatutXO8cNmnSRC+++KIee+wxhYWFqaioSBkZGfruu+/029/+1sCu3cf1zl9kZOQV/+jy9vZWaGioWrZsWdutuq3rncPAwEBNmzZNSUlJCg0N1ZdffqkJEyaoRYsWSkhIMLBr93Gj78Hx48frqaeeUrdu3dSjRw9t2LBBH374obZt22Zc027kRudPkmw2m959913Nnj3bqDbd2o3O4cMPP6zx48fL399fUVFR2r59u9566y3NmTPHwK7rCKOXoYB72rp1q13SFduQIUPsJ0+evOqYJPvWrVuNbt1tXO8clpaW2p988kl7eHi43cfHxx4WFmZ/7LHH7Hv37jW6bbdxvfN3NSyDdqXrncOffvrJ3qtXL3vTpk3t3t7e9qioKPuIESPs+fn5RrftNm7me3Dp0qX2Fi1a2P38/Oxt27a1f/DBB8Y17GZu5vz95S9/sfv7+9uLi4uNa9SN3egc5uXl2Z955hl7eHi43c/Pz96yZUv77Nmz7ZWVlcY2Xgd42O12+21L1wAAAICb4U1wAAAAMBUCMAAAAEyFAAwAAABTIQADAADAVAjAAAAAMBUCMAAAAEyFAAwAAABTIQADQB3y9ddfy8PDQwcPHjTl6wOAK/BRyABQh0RERCgvL09BQUFGtwIAdRYBGADqiPLycvn4+Cg0NNToVgCgTuMWCAAwSPfu3ZWSkqKUlBQFBAQoKChIkydPVtUn1Ddv3lwzZszQ4MGDZbFYNHLkyKvegnDkyBH17dtXFotFjRo10kMPPaQvv/zSMf7mm28qNjZWfn5+iomJ0cKFC2+6x71796p9+/by8/NTp06ddODAgStqtm/frvvvv1++vr4KCwvTH/7wB128eNExXllZqVmzZqlFixby9fVVZGSkXnzxxWqcMQBwDa4AA4CBVq5cqWHDhmnv3r365JNPNHLkSEVGRmrEiBGSpNdee01TpkzRCy+8cNXnf/fdd+rWrZu6d++uLVu2yGKx6OOPP3YE0FWrVmnKlClasGCB2rdvrwMHDmjEiBFq0KCBhgwZct3ezp07p759++rXv/61/vrXv+rkyZN6/vnnr3j9Rx99VM8884zeeustHT9+XCNGjJCfn5+mTp0qSUpLS9OSJUuUnp6uBx98UHl5eTp+/HgNzxwA1IAdAGCIhx9+2B4bG2uvrKx07Js4caI9NjbWbrfb7VFRUfYnnnjC6TknT560S7IfOHDAbrfb7Wlpafbo6Gh7eXn5VV/jnnvusa9evdpp34wZM+xWq/WG/f3lL3+xN2nSxF5aWurYt2jRIqfX/+Mf/2hv2bKl0zFkZGTYGzZsaL906ZLdZrPZfX197UuWLLnh6wFAbeEKMAAYqGvXrvLw8HA8tlqtmj17ti5duiRJ6tSp03Wff/DgQT300EPy9va+Yuz8+fP68ssvNWzYMMcVZUm6ePGiAgICbtjbsWPH1KZNG/n5+Tn19/Maq9XqdAwPPPCAzp07p2+//Vb5+fkqKyvTI488csPXA4DaQgAGADfWoEGD6477+/tfc+zcuXOSpCVLlqhLly5OY/Xq1at5czfhev0BgFF4ExwAGGjPnj1Oj3fv3q177733pgNqmzZt9NFHH6miouKKsZCQEIWHh+urr75SixYtnLbo6Ogbzh0bG6tPP/1UFy5ccOrv5zXZ2dmON+5J0scff6xGjRqpWbNmuvfee+Xv76+srKybOh4AqA0EYAAwUG5urlJTU3XixAn97W9/0/z58694o9n1pKSkyGazqX///vrkk0/0+eef63//93914sQJSdK0adM0c+ZMvf766/rss8906NAhLV++XHPmzLnh3E8//bQ8PDw0YsQIHT16VOvXr9drr73mVPNf//VfOnXqlMaMGaPjx4/rH//4h1544QWlpqbK09NTfn5+mjhxoiZMmKC33npLX375pXbv3q2lS5fe2okCABfiFggAMNDgwYNVWlqq+++/X/Xq1dPzzz+vkSNH3vTzmzRpoi1btmj8+PF6+OGHVa9ePbVr104PPPCAJGn48OGqX7++Xn31VY0fP14NGjRQ69atNXbs2BvO3bBhQ3344YcaNWqU2rdvr7i4OL3yyitKSkpy1Nx9991av369xo8fr7Zt2yowMFDDhg3TpEmTHDWTJ0+Wl5eXpkyZotOnTyssLEyjRo26+ZMEAC7mYb/891YAgFrTvXt3tWvXTnPnzjW6FQAwFW6BAAAAgKkQgAHApF566SU1bNjwqlufPn2Mbg8AbhtugQAAkzpz5ozOnDlz1TF/f3/dfffdtdwRANQOAjAAAABMhVsgAAAAYCoEYAAAAJgKARgAAACmQgAGAACAqRCAAQAAYCoEYAAAAJgKARgAAACmQgAGAACAqfw/hnVKaZ0KmBcAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1, figsize=(8,4))\n",
    "log_price = np.log(sber_data['price_doc']+1)\n",
    "hplot = sns.histplot(log_price, bins=30, ax=ax)\n",
    "hplot.axvline(log_price.mean(), color='k', lw=2)\n",
    "hplot.axvline(log_price.mean() + log_price.std() * 3, color='k', lw=2)\n",
    "hplot.axvline(log_price.mean() - log_price.std() * 3, color='k', lw=2)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.7 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "e77e67092b78210dc1d194fa44dc28add182a422c35c9d6db1b008835d4b98bf"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}