Part 1. Histograms for Price, Latitude and Longitude.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.read_json(\"./dataset/train.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "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>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>building_id</th>\n",
       "      <th>created</th>\n",
       "      <th>description</th>\n",
       "      <th>display_address</th>\n",
       "      <th>features</th>\n",
       "      <th>latitude</th>\n",
       "      <th>listing_id</th>\n",
       "      <th>longitude</th>\n",
       "      <th>manager_id</th>\n",
       "      <th>photos</th>\n",
       "      <th>price</th>\n",
       "      <th>street_address</th>\n",
       "      <th>interest_level</th>\n",
       "      <th>_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>8579a0b0d54db803821a35a4a615e97a</td>\n",
       "      <td>2016-06-16 05:55:27</td>\n",
       "      <td>Spacious 1 Bedroom 1 Bathroom in Williamsburg!...</td>\n",
       "      <td>145 Borinquen Place</td>\n",
       "      <td>[Dining Room, Pre-War, Laundry in Building, Di...</td>\n",
       "      <td>40.7108</td>\n",
       "      <td>7170325</td>\n",
       "      <td>-73.9539</td>\n",
       "      <td>a10db4590843d78c784171a107bdacb4</td>\n",
       "      <td>[https://photos.renthop.com/2/7170325_3bb5ac84...</td>\n",
       "      <td>2400</td>\n",
       "      <td>145 Borinquen Place</td>\n",
       "      <td>medium</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>b8e75fc949a6cd8225b455648a951712</td>\n",
       "      <td>2016-06-01 05:44:33</td>\n",
       "      <td>BRAND NEW GUT RENOVATED TRUE 2 BEDROOMFind you...</td>\n",
       "      <td>East 44th</td>\n",
       "      <td>[Doorman, Elevator, Laundry in Building, Dishw...</td>\n",
       "      <td>40.7513</td>\n",
       "      <td>7092344</td>\n",
       "      <td>-73.9722</td>\n",
       "      <td>955db33477af4f40004820b4aed804a0</td>\n",
       "      <td>[https://photos.renthop.com/2/7092344_7663c19a...</td>\n",
       "      <td>3800</td>\n",
       "      <td>230 East 44th</td>\n",
       "      <td>low</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bathrooms  bedrooms                       building_id              created  \\\n",
       "4        1.0         1  8579a0b0d54db803821a35a4a615e97a  2016-06-16 05:55:27   \n",
       "6        1.0         2  b8e75fc949a6cd8225b455648a951712  2016-06-01 05:44:33   \n",
       "\n",
       "                                         description      display_address  \\\n",
       "4  Spacious 1 Bedroom 1 Bathroom in Williamsburg!...  145 Borinquen Place   \n",
       "6  BRAND NEW GUT RENOVATED TRUE 2 BEDROOMFind you...            East 44th   \n",
       "\n",
       "                                            features  latitude  listing_id  \\\n",
       "4  [Dining Room, Pre-War, Laundry in Building, Di...   40.7108     7170325   \n",
       "6  [Doorman, Elevator, Laundry in Building, Dishw...   40.7513     7092344   \n",
       "\n",
       "   longitude                        manager_id  \\\n",
       "4   -73.9539  a10db4590843d78c784171a107bdacb4   \n",
       "6   -73.9722  955db33477af4f40004820b4aed804a0   \n",
       "\n",
       "                                              photos  price  \\\n",
       "4  [https://photos.renthop.com/2/7170325_3bb5ac84...   2400   \n",
       "6  [https://photos.renthop.com/2/7092344_7663c19a...   3800   \n",
       "\n",
       "        street_address interest_level  _id  \n",
       "4  145 Borinquen Place         medium    4  \n",
       "6        230 East 44th            low    6  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['_id'] = df2.index\n",
    "df2.set_index('_id')\n",
    "df2.sort_index()\n",
    "df2.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1) Histogram of \"price\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "count    4.935200e+04\n",
       "mean     3.830174e+03\n",
       "std      2.206687e+04\n",
       "min      4.300000e+01\n",
       "25%      2.500000e+03\n",
       "50%      3.150000e+03\n",
       "75%      4.100000e+03\n",
       "max      4.490000e+06\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = np.linspace(df2['price'].min(), df2['price'].max(), 50)\n",
    "plt.hist(df2['price'], bins )\n",
    "plt.xlabel('price')\n",
    "plt.ylabel('frequency/count')\n",
    "plt.title(\"price distribution\")\n",
    "plt.show()\n",
    "df2['price'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZ4AAAEWCAYAAABWn/G6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAboklEQVR4nO3de5RdZZnn8e9jJUDEkXDJILlAgtBxwmATqQZs7F5Id5OAaNK0o9C2oKLoiI46s2gTsRdeGzTdgzDSIiMoInIZpZFG7QyC2HY7AhWhDQEjJReT4hYJAZWIITzzx3krnJRVlaqiznvq8v2sdVbt/e537/fde9c5v9qX2icyE0mSanlBuzsgSZpcDB5JUlUGjySpKoNHklSVwSNJqsrgkSRVZfBIOxARfxQRayu0c39E/GkZ/lBEfGEUl/2riNi/DH8pIj4xisu+MCL+ZrSWp4lvSrs7II11mfl9YH7lNv92KPUi4mbgK5k5aEhl5otGo18R8Rbg7Zn5qqZlv2s0lq3JwyMeaRARMa7/OBvv/dfEZPBo0imntJZHxF0R8XhEfDEidinTjoqI9RHxwYh4GPhib1nT/HMi4pqI2BARj0XEZ5umvS0i7i7LXRkR+w3SjzdHxANlGWf2mfaRiPhKGd4lIr5S6m2KiNsiYu+I+CTwR8Bny6m0z5b6GRGnR8Q9wD1NZQc0NbFXRNwQEb+MiO/19jMi5pa6U5r6cnNEvD0i/hNwIfDK0t6mMn27U3cR8Y6I6I6IjRFxXUTMbJqWEfGuiLinrMsFERFD3nmaEAweTVZvAhYBLwV+D/hw07SXAHsA+wGnNc8UER3A9cADwFxgFnBlmbYE+BBwAjAD+D5wRX+NR8QC4HPAm4GZwJ7A7AH6egqwGzCn1HsXsDkzzyxtvCczX5SZ72maZylwOLBgkPX/OLAXcAdw+QD1tsnMu0vb/6+0N72f9ToaOBt4A7APje10ZZ9qxwN/ALy81Fu0o7Y1sRg8mqw+m5nrMnMj8EngpKZpzwJnZebTmbm5z3yH0QiKMzLz15n5m8z81zLtXcDZmXl3Zj4D/C1wyABHPa8Hrs/Mf8nMp4G/Ke32ZwuNwDkgM7dm5qrMfHIH63d2Zm7sp/+9vtnU9pk0jmLm7GCZQ/Em4JLM/FFZ9vKy7LlNdc7JzE2Z+XPgu8Aho9CuxhGDR5PVuqbhB2iESa8NmfmbAeabAzxQgqWv/YDzyimkTcBGIGgcFfU1s7kPmflr4LEB2rwMWAlcGREPRsSnI2LqAHV7rRvq9Mz8VenrzIGrD9lMGtuzedmPsf02eLhp+ClgVG580Phh8Giyav7rfl/gwabxwR7Zvg7Yd4CL9uuAd2bm9KbXtMz8QT91H2ruQ0S8kMZRze/IzC2Z+dHMXAD8IY1TVSfvoK87eux8c9svonFq8UHg16X4hU11XzKM5T5II4B7l70rjfXq2cF8mkQMHk1Wp0fE7IjYg8appquGON+tNELjnIjYtVz4P7JMuxBYHhEHAUTEbhHxXwZYzteA4yPiVRGxE/AxBng/RsSrI+Lgcn3pSRqn3npPyz0C7D/Evjc7rqntjwM/LKceN9AIib+KiI6IeBuN62C9HgFml/n6cwXw1og4JCJ2pnG68ZbMvH8EfdQEZfBosvoq8H+Be4GfAUP6h8rM3Aq8FjgA+DmwHnhjmfaPwKdonBJ7ErgTOHaA5awBTi/9eAh4vCyrPy+hEVRPAncD36Nx+g3gPOD15S6684eyDsVXgbNonGI7FPirpmnvAM6gcYrsIKD5iO0mYA3wcET8op/1+g6N61VfL+v1UuDEYfRLk0D4RXCabCLifhr/BPmddvdFmow84pEkVWXwSJKq8lSbJKkqj3gkSVX5AMEd2GuvvXLu3Lnt7oYkjSurVq36RWbO6G+awbMDc+fOpaurq93dkKRxJSIeGGiap9okSVUZPJKkqgweSVJVBo8kqSqDR5JUlXe1acy59vYeVqxcy4ObNjNz+jTOWDSfpQv7+0obSeORwaMx5drbe1h+zWo2b9kKQM+mzSy/ZjWA4SNNEJ5q05iyYuXabaHTa/OWraxYubZNPZI02gwejSkPbto8rHJJ44/BozFl5vRpwyqXNP4YPBpTzlg0n2lTO7Yrmza1gzMWzW9TjySNNm8u0JjSewOBd7VJE5fBozFn6cJZBo00gXmqTZJUlcEjSarK4JEkVWXwSJKqMngkSVUZPJKkqgweSVJVBo8kqSqDR5JUlcEjSarK4JEkVWXwSJKqMngkSVUZPJKkqloePBHRERG3R8T1ZXxeRNwSEd0RcVVE7FTKdy7j3WX63KZlLC/layNiUVP54lLWHRHLmsqH3YYkqY4aRzzvA+5uGv8UcG5mHgA8Dpxayk8FHi/l55Z6RMQC4ETgIGAx8A8lzDqAC4BjgQXASaXusNuQJNXT0uCJiNnAa4AvlPEAjga+VqpcCiwtw0vKOGX6n5T6S4ArM/PpzLwP6AYOK6/uzLw3M38LXAksGWEbkqRKWn3E8xngr4Fny/iewKbMfKaMrwd6v2pyFrAOoEx/otTfVt5nnoHKR9LGdiLitIjoioiuDRs2DH+tJUkDalnwRMTxwKOZuapVbbRKZl6UmZ2Z2Tljxox2d0eSJpQpLVz2kcDrIuI4YBfgxcB5wPSImFKOOGYDPaV+DzAHWB8RU4DdgMeayns1z9Nf+WMjaEOSVEnLjngyc3lmzs7MuTRuDrgpM98EfBd4fal2CvCNMnxdGadMvykzs5SfWO5ImwccCNwK3AYcWO5g26m0cV2ZZ7htSJIqaeURz0A+CFwZEZ8AbgcuLuUXA5dFRDewkUaQkJlrIuJq4C7gGeD0zNwKEBHvAVYCHcAlmblmJG1IkuoJ/+AfXGdnZ3Z1dbW7G5I0rkTEqszs7G+aTy6QJFVl8EiSqjJ4JElVGTySpKoMHklSVQaPJKkqg0eSVJXBI0mqyuCRJFVl8EiSqjJ4JElVGTySpKoMHklSVQaPJKkqg0eSVJXBI0mqyuCRJFVl8EiSqjJ4JElVGTySpKoMHklSVQaPJKkqg0eSVJXBI0mqyuCRJFVl8EiSqjJ4JElVGTySpKoMHklSVQaPJKkqg0eSVJXBI0mqyuCRJFVl8EiSqjJ4JElVtSx4ImKXiLg1Iv49ItZExEdL+byIuCUiuiPiqojYqZTvXMa7y/S5TctaXsrXRsSipvLFpaw7IpY1lQ+7DUlSHa084nkaODozfx84BFgcEUcAnwLOzcwDgMeBU0v9U4HHS/m5pR4RsQA4ETgIWAz8Q0R0REQHcAFwLLAAOKnUZbhtSJLqaVnwZMOvyujU8krgaOBrpfxSYGkZXlLGKdP/JCKilF+ZmU9n5n1AN3BYeXVn5r2Z+VvgSmBJmWe4bUiSKmnpNZ5yZHIH8ChwA/AzYFNmPlOqrAdmleFZwDqAMv0JYM/m8j7zDFS+5wja6Nvv0yKiKyK6NmzYMLKVlyT1q6XBk5lbM/MQYDaNI5SXtbK90ZKZF2VmZ2Z2zpgxo93dkaQJpcpdbZm5Cfgu8EpgekRMKZNmAz1luAeYA1Cm7wY81lzeZ56Byh8bQRuSpEpaeVfbjIiYXoanAX8G3E0jgF5fqp0CfKMMX1fGKdNvysws5SeWO9LmAQcCtwK3AQeWO9h2onEDwnVlnuG2IUmqZMqOq4zYPsCl5e6zFwBXZ+b1EXEXcGVEfAK4Hbi41L8YuCwiuoGNNIKEzFwTEVcDdwHPAKdn5laAiHgPsBLoAC7JzDVlWR8cThuSpHrCP/gH19nZmV1dXe3uhiSNKxGxKjM7+5vmkwskSVUZPJKkqgweSVJVBo8kqSqDR5JUlcEjSarK4JEkVWXwSJKqMngkSVUZPJKkqgweSVJVBo8kqSqDR5JUlcEjSarK4JEkVWXwSJKqGlLwRMQu/ZTtNfrdkSRNdEM94rktIo7oHYmIvwB+0JouSZImsilDrPeXwCURcTMwE9gTOLpVnZIkTVxDCp7MXB0RnwQuA34J/HFmrm9pzyRJE9KQgiciLgZeCrwc+D3g+oj4X5l5QSs7J0maeIZ6jWc18OrMvC8zVwKHA69oXbckSRPVkIInMz8D7BIR88v4E5l5akt7JkmakIZ6O/VrgTuAfy7jh0TEda3smCRpYhrqqbaPAIcBmwAy8w5g/xb1SZI0gQ01eLZk5hN9yp4d7c5Ikia+of4fz5qI+EugIyIOBP4b/gOpJGkEhnrE817gIOBp4ArgSeD9reqUJGniGuo/kD4FnFlekiSN2KDBExH/BORA0zPzdaPeI0nShLajI56/Kz9PAF4CfKWMnwQ80qpOSZImrkGDJzO/BxARf5+ZnU2T/ikiulraM0nShDTUmwt2jYht/7cTEfOAXVvTJUnSRDbU26k/ANwcEfcCAewHvLNlvZIkTVhDvavtn8v/77ysFP0kM59uXbckSRPVUE+1ARxK4395fh94Y0ScPFjliJgTEd+NiLsiYk1EvK+U7xERN0TEPeXn7qU8IuL8iOiOiB9HxCualnVKqX9PRJzSVH5oRKwu85wfETHSNiRJdQz1IaGX0bjD7VXAH5RX56AzwTPA/8jMBcARwOkRsQBYBtyYmQcCN5ZxgGOBA8vrNOBzpe09gLNofBXDYcBZvUFS6ryjab7FpXxYbUiS6hnqNZ5OYEFmDvg/PX1l5kPAQ2X4lxFxNzALWAIcVapdCtwMfLCUf7m08cOImB4R+5S6N2TmRoCIuAFYXL6G+8WZ+cNS/mVgKfDt4bZR+ipJqmCop9rupPF/PCMSEXOBhcAtwN5NH/QPA3uX4VnAuqbZ1peywcrX91POCNro29/TIqIrIro2bNgwtJWUJA3JUI949gLuiohbaTyvDRjakwsi4kXA14H3Z+aT5TJM7/wZEUM+ihqJkbSRmRcBFwF0dna2tH+SNNkMNXg+MpKFR8RUGqFzeWZeU4of6T29VU6lPVrKe4A5TbPPLmU9PHfarLf85lI+u5/6I2lDklTJUL/6+nv9vQabp9xhdjFwd2b+z6ZJ1wG9d6adAnyjqfzkcufZEcAT5XTZSuCYiNi93FRwDLCyTHsyIo4obZ3cZ1nDaUOSVMmOHhL6r5n5qoj4Jds/LDRonMV68SCzHwm8GVgdEXeUsg8B5wBXR8SpwAPAG8q0bwHHAd3AU8BbaTSyMSI+DtxW6n2s90YD4N3Al4BpNG4q+HYpH1YbkqR6Yhg3qk1KnZ2d2dXlY+kkaTgiYlWfZ3xuM5x/IJUk6XkzeCRJVRk8kqSqDB5JUlUGjySpKoNHklSVwSNJqsrgkSRVZfBIkqoyeCRJVRk8kqSqDB5JUlUGjySpKoNHklSVwSNJqsrgkSRVZfBIkqoyeCRJVRk8kqSqDB5JUlUGjySpKoNHklSVwSNJqsrgkSRVZfBIkqoyeCRJVRk8kqSqDB5JUlVT2t0BjZ5rb+9hxcq1PLhpMzOnT+OMRfNZunBWu7slSdsxeCaIa2/vYfk1q9m8ZSsAPZs2s/ya1QCGj6QxxVNtE8SKlWu3hU6vzVu2smLl2jb1SJL6Z/BMEA9u2jyscklqF0+1TRAzp0+jp5+QmTl9Wht6o8EMdC3Oa3SaLAyeCeKMRfO3u8YDMG1qB2csmt/GXqmvga7FdT2wka+v6vEanSYFT7VNEEsXzuLsEw5m1vRpBDBr+jTOPuFgP7TGmIGuxV1xyzqv0WnSaNkRT0RcAhwPPJqZ/7mU7QFcBcwF7gfekJmPR0QA5wHHAU8Bb8nMH5V5TgE+XBb7icy8tJQfCnwJmAZ8C3hfZuZI2pgoli6cZdCMcQNdc9uaOaz60njWyiOeLwGL+5QtA27MzAOBG8s4wLHAgeV1GvA52BZUZwGHA4cBZ0XE7mWezwHvaJpv8UjakGoa6JpbR8Sw6kvjWcuCJzP/BdjYp3gJcGkZvhRY2lT+5Wz4ITA9IvYBFgE3ZObGzHwcuAFYXKa9ODN/mJkJfLnPsobThlTNGYvmM21qx3Zl06Z2cNLhc/ot9xqdJqLaNxfsnZkPleGHgb3L8CxgXVO99aVssPL1/ZSPpI2H6CMiTqNxVMS+++47xFWTdqz3VGh/d6917reHd7VpUmjbXW3lekz/J7bb3EZmXgRcBNDZ2dnSPmryGehanNfoNFnUvqvtkd7TW+Xno6W8B5jTVG92KRusfHY/5SNpQ5JUUe3guQ44pQyfAnyjqfzkaDgCeKKcLlsJHBMRu5ebCo4BVpZpT0bEEeVutZP7LGs4bUiSKmrl7dRXAEcBe0XEehp3p50DXB0RpwIPAG8o1b9F4zbnbhq3Or8VIDM3RsTHgdtKvY9lZu8NC+/mudupv11eDLcNSVJdkQP8/4AaOjs7s6urq93dkKRxJSJWZWZnf9N8coEkqSqDR5JUlcEjSarK4JEkVWXwSJKqMngkSVUZPJKkqgweSVJVBo8kqSqDR5JUlcEjSarK4JEkVWXwSJKqMngkSVUZPJKkqgweSVJVBo8kqSqDR5JUlcEjSarK4JEkVWXwSJKqMngkSVUZPJKkqgweSVJVBo8kqSqDR5JUlcEjSarK4JEkVWXwSJKqmtLuDkiSxo5rb+9hxcq1PLhpMzOnT+OMRfNZunDWqLZh8IwBNXa0JO3Itbf3sPya1WzeshWAnk2bWX7NaoBR/UwyeNqs1o5We/nHRV0j2d7jfR+NRv9XrFy77bOo1+YtW1mxcq3BM14M5Reh1o5W+zzfPy7G+wficD3f9R3J9h7vfwCOVv8f3LR5WOUjFZk5qgucaDo7O7Orq2vY8/X9RQCYNrWDs084eLtfhHnLvslAe+Azbzzkef3St/IDazSW3buMnk2b6YhgayazWvjBOtI+P991PfKcm+jp543bu669y95t2lR++8xWntryLAC7v3Aqr3n5Pnx9Vc8Of48miqG+b/qbr3c7vqD8LvXVEcGzmf3uw8H20b8tO/p5rtXIf4c+fO1qrrhlHVsz6YjgpMPn8ImlB/9OvYH6P9g699e//pYBI9sOEbEqMzv7nWbwDG6kwTP/w9/m6WeebUGPJKmeAM4dwR/BgwXPpLudOiIWR8TaiOiOiGWtaGPusm8aOpImhATef9Udo7rMSRU8EdEBXAAcCywAToqIBaPZxtxl3xzNxUnSmDCan22TKniAw4DuzLw3M38LXAksaXOfJGlSmWzBMwtY1zS+vpRtJyJOi4iuiOjasGFDtc5J0mQw2YJnSDLzoszszMzOGTNmtLs7kjShTLbg6QHmNI3PLmWSpEomW/DcBhwYEfMiYifgROC60Wzg/nNeM5qLk6QxYTQ/2ybVkwsy85mIeA+wEugALsnMNaPdjuEjSQObVMEDkJnfAr7V7n5I0mQ12U61SZLazOCRJFVl8EiSqjJ4JElV+XTqHYiIDcADI5x9L+AXo9iddnAdxo6JsB6uw9hQYx32y8x+/wPf4GmhiOga6LHg44XrMHZMhPVwHcaGdq+Dp9okSVUZPJKkqgye1rqo3R0YBa7D2DER1sN1GBvaug5e45EkVeURjySpKoNHklSVwdMiEbE4ItZGRHdELGtzX+ZExHcj4q6IWBMR7yvle0TEDRFxT/m5eymPiDi/9P3HEfGKpmWdUurfExGnNJUfGhGryzznR0S0aF06IuL2iLi+jM+LiFtKu1eVr7sgInYu491l+tymZSwv5WsjYlFTeZV9FhHTI+JrEfGTiLg7Il453vZFRHyg/C7dGRFXRMQuY31fRMQlEfFoRNzZVNby7T5QG6O4DivK79KPI+IfI2J607Rhbd+R7MMRyUxfo/yi8ZULPwP2B3YC/h1Y0Mb+7AO8ogz/B+CnwALg08CyUr4M+FQZPg74NhDAEcAtpXwP4N7yc/cyvHuZdmupG2XeY1u0Lv8d+CpwfRm/GjixDF8I/Ncy/G7gwjJ8InBVGV5Q9sfOwLyynzpq7jPgUuDtZXgnYPp42hc0vi7+PmBa0z54y1jfF8AfA68A7mwqa/l2H6iNUVyHY4ApZfhTTesw7O073H044vVoxRtrsr+AVwIrm8aXA8vb3a+m/nwD+DNgLbBPKdsHWFuGPw+c1FR/bZl+EvD5pvLPl7J9gJ80lW9XbxT7PRu4ETgauL68wX/R9Kbbtt1pfOfSK8vwlFIv+u6L3nq19hmwG40P7ehTPm72BY3gWUfjw3dK2ReLxsO+AOay/Yd2y7f7QG2M1jr0mfbnwOX9bbcdbd+RvJ9Gug6eamuN3jdmr/WlrO3KIfJC4BZg78x8qEx6GNi7DA/U/8HK1/dTPto+A/w18GwZ3xPYlJnP9NPutr6W6U+U+sNdt9E2D9gAfDEapwy/EBG7Mo72RWb2AH8H/Bx4iMa2XcX42xdQZ7sP1EYrvI3G0RYMfx1G8n4aEYNnEomIFwFfB96fmU82T8vGnzJj9t76iDgeeDQzV7W7L8/TFBqnSj6XmQuBX9M4/bLNONgXuwNLaIToTGBXYHFbOzUKamz3VrYREWcCzwCXt2L5o8ngaY0eYE7T+OxS1jYRMZVG6FyemdeU4kciYp8yfR/g0VI+UP8HK5/dT/loOhJ4XUTcD1xJ43TbecD0iOj9Jt3mdrf1tUzfDXhsB+tQY5+tB9Zn5i1l/Gs0gmg87Ys/Be7LzA2ZuQW4hsb+GW/7Aups94HaGDUR8RbgeOBNJdzYQV/7K3+M4e/DkRnNc7++tp0znULjouM8nrt4d1Ab+xPAl4HP9ClfwfYXPT9dhl/D9hdWby3le9C4PrF7ed0H7FGm9b2welwL1+conru54P+w/cXQd5fh09n+YujVZfggtr/gei+Ni63V9hnwfWB+Gf5I2Q/jZl8AhwNrgBeWNi4F3jse9gW/e42n5dt9oDZGcR0WA3cBM/rUG/b2He4+HPE6tOKN5WvbXTE/pXH3yJlt7suraBze/xi4o7yOo3GO9kbgHuA7TW+gAC4ofV8NdDYt621Ad3m9tam8E7izzPNZnseFxyGsz1E8Fzz7lzd8d3nT7FzKdynj3WX6/k3zn1n6uZamO75q7TPgEKCr7I9rywfYuNoXwEeBn5R2LisfbmN6XwBX0LgmtYXGkeepNbb7QG2M4jp007j+0vvevnCk23ck+3AkLx+ZI0mqyms8kqSqDB5JUlUGjySpKoNHklSVwSNJqsrgkcaIiPjBMOsfFeUp3dJ4YvBIY0Rm/mG7+yDVYPBIY0RE/Kr8PCoibo7nvrPn8qbvdllcyn4EnNA0767lu1puLQ8fXVLKPxARl5Thg8v357ywDasnbWPwSGPTQuD9NL5TZX/gyIjYBfjfwGuBQ4GXNNU/E7gpMw8DXg2sKE+9Pg84ICL+HPgi8M7MfKreaki/y+CRxqZbM3N9Zj5L4zEoc4GX0Xg45z3ZeOTIV5rqHwMsi4g7gJtpPOJk3zL/W2g81uZ7mflv9VZB6t+UHVeR1AZPNw1vZcfv1QD+IjPX9jPtQOBXNL7CQGo7j3ik8eMnwNyIeGkZP6lp2krgvU3XghaWn7sB59P4yuQ9I+L1Ffsr9cvgkcaJzPwNcBrwzXJzQfP3unwcmAr8OCLWlHGAc4ELMvOnNJ5kfE5E/MeK3ZZ+h0+nliRV5RGPJKkqg0eSVJXBI0mqyuCRJFVl8EiSqjJ4JElVGTySpKr+PxYnNn2OKFCyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df2['_id'], df2['price'])\n",
    "plt.xlabel('index')\n",
    "plt.ylabel('index')\n",
    "plt.title('price distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "because of ourliers, the historgram is extremely skewed towards those outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1) Histogram of prices within 75% "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "count    37097.000000\n",
       "mean      2808.845891\n",
       "std        664.375558\n",
       "min         43.000000\n",
       "25%       2300.000000\n",
       "50%       2800.000000\n",
       "75%       3325.000000\n",
       "max       4100.000000\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf = df2[df2.price <= df2['price'].quantile(0.75)]\n",
    "bins = np.linspace(newdf['price'].min(), newdf['price'].max(), 50)\n",
    "plt.hist(newdf['price'], bins )\n",
    "plt.xlabel('price')\n",
    "plt.ylabel('frequency/count')\n",
    "plt.title(\"price distribution\")\n",
    "plt.show()\n",
    "newdf['price'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When outliers are removed, we can see the distributions of values more clearly"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2) Histogram of \"longitude\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "count    49352.000000\n",
       "mean       -73.955716\n",
       "std          1.177912\n",
       "min       -118.271000\n",
       "25%        -73.991700\n",
       "50%        -73.977900\n",
       "75%        -73.954800\n",
       "max          0.000000\n",
       "Name: longitude, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = np.linspace(df2['longitude'].min(), df2['longitude'].max(), 50)\n",
    "plt.hist(df2['longitude'], bins )\n",
    "plt.xlabel('longitude')\n",
    "plt.ylabel('frequency/count')\n",
    "plt.title(\"longitude distribution\")\n",
    "plt.show()\n",
    "df2['longitude'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, there seems to be outliers that make the graph skewed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1) Histogram of longitude within 75% "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "count    37046.000000\n",
       "mean       -73.987467\n",
       "std          0.269446\n",
       "min       -118.271000\n",
       "25%        -73.996300\n",
       "50%        -73.984200\n",
       "75%        -73.973900\n",
       "max        -73.954800\n",
       "Name: longitude, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf = df2[df2.longitude <= df2['longitude'].quantile(0.75)]\n",
    "bins = np.linspace(newdf['longitude'].min(), newdf['longitude'].max(), 50)\n",
    "plt.hist(newdf['longitude'], bins )\n",
    "plt.xlabel('longitude')\n",
    "plt.ylabel('frequency/count')\n",
    "plt.title(\"longitude distribution\")\n",
    "plt.show()\n",
    "newdf['longitude'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2) Histogram of longitude within 50% "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "count    24777.000000\n",
       "mean       -73.997672\n",
       "std          0.328958\n",
       "min       -118.271000\n",
       "25%        -74.002200\n",
       "50%        -73.991700\n",
       "75%        -73.984000\n",
       "max        -73.977900\n",
       "Name: longitude, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf = df2[df2.longitude <= df2['longitude'].quantile(0.50)]\n",
    "bins = np.linspace(newdf['longitude'].min(), newdf['longitude'].max(), 50)\n",
    "plt.hist(newdf['longitude'], bins )\n",
    "plt.xlabel('longitude')\n",
    "plt.ylabel('frequency/count')\n",
    "plt.title(\"longitude distribution\")\n",
    "plt.show()\n",
    "newdf['longitude'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, there are large datapoints that have relatively close values. The y-axis shows the count of datapoints that have a specific degree of longitude, x, on x-axis. With this in mind, we assume that a significant number of datapoints are neary -73.984200. The graph below demonstrates our educated guess!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "count    24756.000000\n",
       "mean       -73.975782\n",
       "std          0.010537\n",
       "min        -73.991700\n",
       "25%        -73.984300\n",
       "50%        -73.977900\n",
       "75%        -73.967375\n",
       "max        -73.954800\n",
       "Name: longitude, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf = df2[ (df2.longitude <= df2['longitude'].quantile(0.75)) & (df2.longitude >= df2['longitude'].quantile(0.25))]\n",
    "bins = np.linspace(newdf['longitude'].min(), newdf['longitude'].max(), 50)\n",
    "plt.hist(newdf['longitude'], bins )\n",
    "plt.xlabel('longitude')\n",
    "plt.ylabel('frequency/count')\n",
    "plt.title(\"longitude distribution\")\n",
    "plt.show()\n",
    "newdf['longitude'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3) Histogram of \"latitude\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAEWCAYAAACEz/viAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAZrElEQVR4nO3dfbRddX3n8ffHIGgFBOSWBoIGaLQLWRohoo4Pg1J5iNbg1FEYl0RlRCrM1HFcCtoRaovFpzrDqsVBTYlPIIrU1MFiRJbUGRECRkgQJEAoiYFEo4JPSOQ7f5zfpZvLvTc3996cczHv11pn3b2/++l79kru5+6Hs0+qCknSju0xg25AkjR4hoEkyTCQJBkGkiQMA0kShoEkCcNAfZRkbZI/nuSyL0xyyzT2ckSSddO0rtcn+VZn/OdJDpymdb8rySfa8NwklWSnaVr3k1uvs6ZjfXp0Mww0I7Vfen84PF5V/1JVT+tMn3SwbG9VtWtV3T7ePBMNo6p6X1X95+noa+Q+q6p/bb3+djrWr0c3w0CaoabrCECaCMNAA5Hk8CTfTvLTJBuS/F2Sndu0q9ps32unMV7T/Us6yaeBJwP/1Ka/Y7S/tLt/CSd5fJILkvwkyU3As0fMu2+SS5JsSnJHkv86Tu9PSrIsyb1JrgEOGjH9oaOaJAuT3JTkviTrk7w9yROArwL7tv5/3rZ/VpIvJvlMknuB17faZ0a08MYkP2z77e2d7V6Q5K8741vbZw877dR6WJZkc5I1Sd7UWddZSS5O8qn2XlYnWTDWPtKjj2GgQfkt8N+AvYHnAUcCbwGoqhe1eZ7ZTmN8vrtgVb0O+FfgT9r0D0xge2fS+6V9EHA0sHh4QpLHAP8EfA/Yr/Xy1iRHj7GujwK/BmYDb2yvsXwSeHNV7QYcAnyjqn4BHAv8sPW/a1X9sM2/CPgisAfw2THW+WJgHnAU8M6JnC6b4D67CFgH7Au8Cnhfkpd0pr+izbMHsAz4u61tV48ehoEGoqquq6qrq2pLVa0F/jfw77fjJl8NnF1Vm6vqLuDczrRnA0NV9d6q+k073/9x4PiRK2kXW/8UeE9V/aKqVgFLx9nuA8DBSXavqp9U1fVb6fPbVfWPVfVgVf1qjHn+sm37RuAfgBO2ss6tSrI/8HzgnVX166paCXwCOLEz27eq6rJ2jeHTwDOnul3NHIaBBiLJU5N8Jcnd7ZTI++gdJWwv+wJ3dcbv7Aw/hd4pm58Ov4B3AfuMsp4hYKdx1jXSnwILgTuTfDPJ87bS511bmT5ynjvpvbep2hfYXFX3jVj3fp3xuzvDvwQe53WN3x2GgQblPOBmYF5V7U7vl2+2YfmRj9v9BfB7wyPtL/ihzvQNwP6d8Sd3hu8C7qiqPTqv3apq4Sjb3QRsGWddD2+y6tqqWgT8PvCPwMVj9M9W6l0jtz18iulh+wD4g21Y9w+BvZLsNmLd6yfQj34HGAYalN2Ae4GfJ/kj4M9GTL8HGO9e/ZHTf0DvL9WXJXks8BfALp3pFwNnJNkzyRzgv3SmXQPcl+Sd7ULzrCSHJHnYRWaAdorkS8BZSX4vycF0rj90Jdk5yWuTPLGqHmjv98FO/09K8sRx3uNY/kfb9tOBNwDD11RWAguT7JXkD4C3jlhuzH3aTp39P+BvkjwuyTOAk4CRF6/1O8ow0KC8HfhPwH30zs9/fsT0s4Cl7bTNq0dZ/m+Av2jT315VP6N3AfoT9P6a/QW9i6HD/pLeaY87gK/RO+cNPPQL/uXA/Db9R209Y/2iPg3Yld5pkwvonbcfy+uAte1U2CnAa9s2bwYuBG5v72FbTvV8E1gDXAF8qKq+1uqfpncRfG17jyP36cP22SjrPQGYS+8o4VLgzKr6+jb0pUex+OU2kiSPDCRJhoEkyTCQJGEYSJLofXjmUWnvvfeuuXPnDroNSXpUue66635UVUMj64/aMJg7dy4rVqwYdBuS9KiSZNRPzHuaSJJkGEiSDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJPIo/gSxJv8vmnv5/Rq2vPedl22V7HhlIkgwDSZJhIEnCMJAkYRhIkjAMJEkYBpIkDANJEoaBJAnDQJLEBMIgyZIkG5Os6tQ+n2Rle61NsrLV5yb5VWfaxzrLHJbkxiRrkpybJK2+V5LlSW5tP/fcHm9UkjS2iRwZXAAc0y1U1Wuqan5VzQcuAb7UmXzb8LSqOqVTPw94EzCvvYbXeTpwRVXNA65o45KkPtpqGFTVVcDm0aa1v+5fDVw43jqSzAZ2r6qrq6qATwHHtcmLgKVteGmnLknqk6leM3ghcE9V3dqpHZDku0m+meSFrbYfsK4zz7pWA9inqja04buBfcbaWJKTk6xIsmLTpk1TbF2SNGyqYXACDz8q2AA8uaqeBbwN+FyS3Se6snbUUONMP7+qFlTVgqGhocn2LEkaYdLfZ5BkJ+A/AIcN16rqfuD+NnxdktuApwLrgTmdxee0GsA9SWZX1YZ2OmnjZHuSJE3OVI4M/hi4uaoeOv2TZCjJrDZ8IL0Lxbe300D3Jnluu85wIvDlttgyYHEbXtypS5L6ZCK3ll4IfBt4WpJ1SU5qk47nkReOXwTc0G41/SJwSlUNX3x+C/AJYA1wG/DVVj8HeGmSW+kFzDlTeD+SpEnY6mmiqjphjPrrR6ldQu9W09HmXwEcMkr9x8CRW+tDkrT9+AlkSZJhIEkyDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAkYRhIkjAMJElMIAySLEmyMcmqTu2sJOuTrGyvhZ1pZyRZk+SWJEd36se02pokp3fqByT5Tqt/PsnO0/kGJUlbN5EjgwuAY0apf6Sq5rfXZQBJDgaOB57elvn7JLOSzAI+ChwLHAyc0OYFeH9b1x8CPwFOmsobkiRtu62GQVVdBWye4PoWARdV1f1VdQewBji8vdZU1e1V9RvgImBRkgAvAb7Yll8KHLeN70GSNEVTuWZwWpIb2mmkPVttP+CuzjzrWm2s+pOAn1bVlhH1USU5OcmKJCs2bdo0hdYlSV2TDYPzgIOA+cAG4MPT1tE4qur8qlpQVQuGhob6sUlJ2iHsNJmFquqe4eEkHwe+0kbXA/t3Zp3TaoxR/zGwR5Kd2tFBd35JUp9M6sggyezO6CuB4TuNlgHHJ9klyQHAPOAa4FpgXrtzaGd6F5mXVVUBVwKvassvBr48mZ4kSZO31SODJBcCRwB7J1kHnAkckWQ+UMBa4M0AVbU6ycXATcAW4NSq+m1bz2nA5cAsYElVrW6beCdwUZK/Br4LfHLa3p0kaUK2GgZVdcIo5TF/YVfV2cDZo9QvAy4bpX47vbuNJEkD4ieQJUmGgSTJMJAkYRhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgSWICYZBkSZKNSVZ1ah9McnOSG5JcmmSPVp+b5FdJVrbXxzrLHJbkxiRrkpybJK2+V5LlSW5tP/fcHm9UkjS2iRwZXAAcM6K2HDikqp4B/AA4ozPttqqa316ndOrnAW8C5rXX8DpPB66oqnnAFW1cktRHWw2DqroK2Dyi9rWq2tJGrwbmjLeOJLOB3avq6qoq4FPAcW3yImBpG17aqUuS+mQ6rhm8EfhqZ/yAJN9N8s0kL2y1/YB1nXnWtRrAPlW1oQ3fDewz1oaSnJxkRZIVmzZtmobWJUkwxTBI8m5gC/DZVtoAPLmqngW8Dfhckt0nur521FDjTD+/qhZU1YKhoaEpdC5J6tppsgsmeT3wcuDI9kucqrofuL8NX5fkNuCpwHoefippTqsB3JNkdlVtaKeTNk62J0nS5EzqyCDJMcA7gFdU1S879aEks9rwgfQuFN/eTgPdm+S57S6iE4Evt8WWAYvb8OJOXZLUJ1s9MkhyIXAEsHeSdcCZ9O4e2gVY3u4QvbrdOfQi4L1JHgAeBE6pquGLz2+hd2fS4+ldYxi+znAOcHGSk4A7gVdPyzuTJE3YVsOgqk4YpfzJMea9BLhkjGkrgENGqf8YOHJrfUiSth8/gSxJMgwkSYaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkiQmGQZIlSTYmWdWp7ZVkeZJb2889Wz1Jzk2yJskNSQ7tLLO4zX9rksWd+mFJbmzLnJsk0/kmJUnjm+iRwQXAMSNqpwNXVNU84Io2DnAsMK+9TgbOg154AGcCzwEOB84cDpA2z5s6y43cliRpO5pQGFTVVcDmEeVFwNI2vBQ4rlP/VPVcDeyRZDZwNLC8qjZX1U+A5cAxbdruVXV1VRXwqc66JEl9MJVrBvtU1YY2fDewTxveD7irM9+6Vhuvvm6U+iMkOTnJiiQrNm3aNIXWJUld03IBuf1FX9Oxrq1s5/yqWlBVC4aGhrb35iRphzGVMLinneKh/dzY6uuB/TvzzWm18epzRqlLkvpkKmGwDBi+I2gx8OVO/cR2V9FzgZ+100mXA0cl2bNdOD4KuLxNuzfJc9tdRCd21iVJ6oOdJjJTkguBI4C9k6yjd1fQOcDFSU4C7gRe3Wa/DFgIrAF+CbwBoKo2J/kr4No233uravii9Fvo3bH0eOCr7SVJ6pMJhUFVnTDGpCNHmbeAU8dYzxJgySj1FcAhE+lFkjT9/ASyJMkwkCQZBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSUwhDJI8LcnKzuveJG9NclaS9Z36ws4yZyRZk+SWJEd36se02pokp0/1TUmSts2EvgN5NFV1CzAfIMksYD1wKfAG4CNV9aHu/EkOBo4Hng7sC3w9yVPb5I8CLwXWAdcmWVZVN022N0nStpl0GIxwJHBbVd2ZZKx5FgEXVdX9wB1J1gCHt2lrqup2gCQXtXkNA0nqk+m6ZnA8cGFn/LQkNyRZkmTPVtsPuKszz7pWG6v+CElOTrIiyYpNmzZNU+uSpCmHQZKdgVcAX2il84CD6J1C2gB8eKrbGFZV51fVgqpaMDQ0NF2rlaQd3nScJjoWuL6q7gEY/gmQ5OPAV9roemD/znJzWo1x6pKkPpiO00Qn0DlFlGR2Z9orgVVteBlwfJJdkhwAzAOuAa4F5iU5oB1lHN/mlST1yZSODJI8gd5dQG/ulD+QZD5QwNrhaVW1OsnF9C4MbwFOrarftvWcBlwOzAKWVNXqqfQlSdo2UwqDqvoF8KQRtdeNM//ZwNmj1C8DLptKL5KkyfMTyJIkw0CSZBhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSmIYwSLI2yY1JViZZ0Wp7JVme5Nb2c89WT5Jzk6xJckOSQzvrWdzmvzXJ4qn2JUmauOk6MnhxVc2vqgVt/HTgiqqaB1zRxgGOBea118nAedALD+BM4DnA4cCZwwEiSdr+ttdpokXA0ja8FDiuU/9U9VwN7JFkNnA0sLyqNlfVT4DlwDHbqTdJ0gjTEQYFfC3JdUlObrV9qmpDG74b2KcN7wfc1Vl2XauNVX+YJCcnWZFkxaZNm6ahdUkSwE7TsI4XVNX6JL8PLE9yc3diVVWSmobtUFXnA+cDLFiwYFrWKUmahiODqlrffm4ELqV3zv+edvqH9nNjm309sH9n8TmtNlZdktQHUwqDJE9IstvwMHAUsApYBgzfEbQY+HIbXgac2O4qei7ws3Y66XLgqCR7tgvHR7WaJKkPpnqaaB/g0iTD6/pcVf1zkmuBi5OcBNwJvLrNfxmwEFgD/BJ4A0BVbU7yV8C1bb73VtXmKfYmSZqgKYVBVd0OPHOU+o+BI0epF3DqGOtaAiyZSj+SpMnxE8iSJMNAkmQYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAkMYUwSLJ/kiuT3JRkdZI/b/WzkqxPsrK9FnaWOSPJmiS3JDm6Uz+m1dYkOX1qb0mStK2m8h3IW4D/XlXXJ9kNuC7J8jbtI1X1oe7MSQ4GjgeeDuwLfD3JU9vkjwIvBdYB1yZZVlU3TaE3SdI2mHQYVNUGYEMbvi/J94H9xllkEXBRVd0P3JFkDXB4m7amqm4HSHJRm9cwkKQ+mZZrBknmAs8CvtNKpyW5IcmSJHu22n7AXZ3F1rXaWPXRtnNykhVJVmzatGk6WpckMQ1hkGRX4BLgrVV1L3AecBAwn96Rw4enuo1hVXV+VS2oqgVDQ0PTtVpJ2uFN5ZoBSR5LLwg+W1VfAqiqezrTPw58pY2uB/bvLD6n1RinLknqg6ncTRTgk8D3q+pvO/XZndleCaxqw8uA45PskuQAYB5wDXAtMC/JAUl2pneRedlk+5IkbbupHBk8H3gdcGOSla32LuCEJPOBAtYCbwaoqtVJLqZ3YXgLcGpV/RYgyWnA5cAsYElVrZ5CX5KkbTSVu4m+BWSUSZeNs8zZwNmj1C8bbzlJ0vblJ5AlSYaBJMkwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAkYRhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJDGDwiDJMUluSbImyemD7keSdiQzIgySzAI+ChwLHAyckOTgwXYlSTuOGREGwOHAmqq6vap+A1wELBpwT5K0w9hp0A00+wF3dcbXAc8ZOVOSk4GT2+jPk9wyye3tDfxokstuLzOxJ5iZfdnTxM3EvmZiTzAz+3pET3n/lNf5lNGKMyUMJqSqzgfOn+p6kqyoqgXT0NK0mYk9wczsy54mbib2NRN7gpnZVz97mimnidYD+3fG57SaJKkPZkoYXAvMS3JAkp2B44FlA+5JknYYM+I0UVVtSXIacDkwC1hSVau34yanfKppO5iJPcHM7MueJm4m9jUTe4KZ2VffekpV9WtbkqQZaqacJpIkDZBhIEna8cJgJj72IsnaJDcmWZlkxQD7WJJkY5JVndpeSZYnubX93HMG9HRWkvVtf61MsrDPPe2f5MokNyVZneTPW31g+2qcnga9rx6X5Jok32t9/WWrH5DkO+3/4efbjSOD7umCJHd09tX8fvXU6W1Wku8m+Uob799+qqod5kXv4vRtwIHAzsD3gINnQF9rgb1nQB8vAg4FVnVqHwBOb8OnA++fAT2dBbx9gPtpNnBoG94N+AG9x6gMbF+N09Og91WAXdvwY4HvAM8FLgaOb/WPAX82A3q6AHjVoPZV6+dtwOeAr7Txvu2nHe3IwMdejKOqrgI2jygvApa24aXAcTOgp4Gqqg1VdX0bvg/4Pr1P0Q9sX43T00BVz8/b6GPbq4CXAF9s9X7vq7F6Gqgkc4CXAZ9o46GP+2lHC4PRHnsx8P8w9P4hfi3Jde2RGzPJPlW1oQ3fDewzyGY6TktyQzuN1NdTV11J5gLPovfX5YzYVyN6ggHvq3bqYyWwEVhO7+j8p1W1pc3S9/+HI3uqquF9dXbbVx9Jsks/ewL+J/AO4ME2/iT6uJ92tDCYqV5QVYfSe2rrqUleNOiGRlO9Y9WB/wUFnAccBMwHNgAfHkQTSXYFLgHeWlX3dqcNal+N0tPA91VV/baq5tN7ssDhwB/1u4eRRvaU5BDgDHq9PRvYC3hnv/pJ8nJgY1Vd169tjrSjhcGMfOxFVa1vPzcCl9L7DzNT3JNkNkD7uXHA/VBV97T/zA8CH2cA+yvJY+n90v1sVX2plQe6r0braSbsq2FV9VPgSuB5wB5Jhj/0OrD/h52ejmmn2qqq7gf+gf7uq+cDr0iylt7p65cA/4s+7qcdLQxm3GMvkjwhyW7Dw8BRwKrxl+qrZcDiNrwY+PIAewEe+kU77JX0eX+1c7mfBL5fVX/bmTSwfTVWTzNgXw0l2aMNPx54Kb3rGVcCr2qz9XtfjdbTzZ0gD71z833bV1V1RlXNqaq59H4vfaOqXks/99Mgr5wP4gUspHenxW3Au2dAPwfSu6vpe8DqQfYEXEjvVMID9M5PnkTvvOUVwK3A14G9ZkBPnwZuBG6g9wt4dp97egG9U0A3ACvba+Eg99U4PQ16Xz0D+G7b/irgPa1+IHANsAb4ArDLDOjpG21frQI+Q7vjqN8v4Aj+7W6ivu0nH0chSdrhThNJkkZhGEiSDANJkmEgScIwkCRhGEgPSfLzrUzfI8lbOuP7JvliG54/mSeCtqeKvn3bu5Wml2EgTdwewENhUFU/rKrhDwTNp3dfv/SoZBhIIyTZNckVSa5P73smhp9sew5wUHvW/QeTzE2yqn2a/b3Aa9q014z8i7/NN7cNvzvJD5J8C3haZ56Dkvxze2DhvyQZ+DN8tOPYaeuzSDucXwOvrKp7k+wNXJ1kGb3vKDikeg84G346KFX1myTvARZU1Wlt2lmjrTjJYfQeNzCf3v+/64Hhh5OdD5xSVbcmeQ7w9/SeUSNtd4aB9EgB3teeHvsgvccGT9fjqF8IXFpVvwRoITP8tNF/B3yh92gcAPr9CGXtwAwD6ZFeCwwBh1XVA+1Jko/bxnVs4eGnYbe2/GPoPbu+71+1KIHXDKTRPJHes+UfSPJi4Cmtfh+9r5Qczchpa+l9XSdJDgUOaPWrgOOSPL49rfZPAKr33QN3JPmPbZkkeeb0vSVpfIaB9EifBRYkuRE4EbgZoKp+DPzfdjH4gyOWuRI4ePgCMr3vFdgryWrgNHpPyqV6X035eXpPqf0qvceqD3stcFKS4SfY+pWs6hufWipJ8shAkmQYSJIwDCRJGAaSJAwDSRKGgSQJw0CSBPx/Dve6UT4VUdwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "count    49352.000000\n",
       "mean        40.741545\n",
       "std          0.638535\n",
       "min          0.000000\n",
       "25%         40.728300\n",
       "50%         40.751800\n",
       "75%         40.774300\n",
       "max         44.883500\n",
       "Name: latitude, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = np.linspace(0, df2.loc[:,\"latitude\"].mean(), 50)\n",
    "plt.hist(df2['latitude'], bins)\n",
    "plt.xlabel('latitude')\n",
    "plt.title(\"latitude distribution\")\n",
    "plt.show()\n",
    "df2['latitude'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By looking at the table of value, it shows the similar result as \"longitude\", where a significant number of values are within a specific range. In this case, they are near 40.7."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "count    24749.000000\n",
       "mean        40.751827\n",
       "std          0.013233\n",
       "min         40.728300\n",
       "25%         40.741100\n",
       "50%         40.751800\n",
       "75%         40.763300\n",
       "max         40.774300\n",
       "Name: latitude, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf = df2[ (df2.latitude <= df2['latitude'].quantile(0.75)) & (df2.latitude >= df2['latitude'].quantile(0.25))]\n",
    "bins = np.linspace(newdf['latitude'].min(), newdf['latitude'].max(), 50)\n",
    "plt.hist(newdf['latitude'], bins )\n",
    "plt.xlabel('latitude')\n",
    "plt.ylabel('frequency/count')\n",
    "plt.title(\"latitude distribution\")\n",
    "plt.show()\n",
    "newdf['latitude'].describe()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}