Part 1. Hourly Trending and Top Five Busiest Hours of Postings.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "import os\n",
    "from zipfile import ZipFile "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
       "    </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",
       "    </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",
       "    </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  \n",
       "4  145 Borinquen Place         medium  \n",
       "6        230 East 44th            low  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.read_json(\"./train.json\")\n",
    "# df2.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract year, month, day, hour from created column\n",
    "# print(df2['created'].dtype)\n",
    "df2['created'] = pd.to_datetime(df2['created'])\n",
    "# print(df2['created'].dtype)\n",
    "df2['year'] = df2['created'].dt.year\n",
    "df2['month'] = df2['created'].dt.month\n",
    "df2['day'] = df2['created'].dt.day\n",
    "df2['hour'] = df2['created'].dt.hour\n",
    "# df2.head(2)"
   ]
  },
  {
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 5 busiest hours of postings\n"
     ]
    },
    {
     "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>Hour</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>10596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>8318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>7954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>5749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>5021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Hour  Count\n",
       "2     2  10596\n",
       "3     3   8318\n",
       "5     5   7954\n",
       "1     1   5749\n",
       "4     4   5021"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hour_counts = df2['hour'].value_counts().sort_index()\n",
    "hour_counts.plot.bar(x=\"Hour\", y=\"Count\")\n",
    "plt.xlabel('Hours')\n",
    "plt.ylabel('Listing Count')\n",
    "plt.title('Hour-wise Listing Trend by Count')\n",
    "plt.show()\n",
    "\n",
    "formatted_hour_counts = hour_counts.reset_index()\n",
    "formatted_hour_counts.columns = ['Hour', 'Count']\n",
    "\n",
    "print(\"Top 5 busiest hours of postings\")\n",
    "formatted_hour_counts.sort_values(by=['Count'], ascending=False).head(5)"
   ]
  }
 ],
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    "version": 3
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   "file_extension": ".py",
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