Reviews Time Series.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>listing_id</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2515</td>\n",
       "      <td>2008-10-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2515</td>\n",
       "      <td>2009-03-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2515</td>\n",
       "      <td>2009-03-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2515</td>\n",
       "      <td>2009-03-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2515</td>\n",
       "      <td>2009-05-09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   listing_id        date\n",
       "0        2515  2008-10-13\n",
       "1        2515  2009-03-08\n",
       "2        2515  2009-03-25\n",
       "3        2515  2009-03-27\n",
       "4        2515  2009-05-09"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score, mean_squared_error\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "df_samp = pd.read_csv('./data/reviews/reviews_01_2019.csv')\n",
    "df_samp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>listing_id</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008</td>\n",
       "      <td>10</td>\n",
       "      <td>2515</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009</td>\n",
       "      <td>3</td>\n",
       "      <td>2515</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009</td>\n",
       "      <td>3</td>\n",
       "      <td>5441</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009</td>\n",
       "      <td>4</td>\n",
       "      <td>5099</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009</td>\n",
       "      <td>4</td>\n",
       "      <td>5216</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  month  listing_id  count\n",
       "0  2008     10        2515      1\n",
       "1  2009      3        2515      3\n",
       "2  2009      3        5441      1\n",
       "3  2009      4        5099      1\n",
       "4  2009      4        5216      1"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_samp['date'] = pd.to_datetime(df_samp['date'], yearfirst=True)\n",
    "df_samp['year'] = df_samp['date'].dt.year\n",
    "df_samp['month'] = df_samp['date'].dt.month\n",
    "\n",
    "df_samp2 = df_samp.groupby(['year','month','listing_id']).size().reset_index()\n",
    "df_samp2.rename({0: 'count'}, axis=1, inplace=True)\n",
    "\n",
    "df_samp2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  3,  2,  4,  7,  9,  5,  8,  6, 10, 11, 14, 13, 12, 15, 17, 19,\n",
       "       16, 18, 24, 20, 21, 22, 23, 25], dtype=int64)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_samp2['count'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([3.17146e+05, 7.36970e+04, 2.93710e+04, 3.97900e+03, 1.40600e+03,\n",
       "        6.57000e+02, 1.75000e+02, 1.08000e+02, 3.90000e+01, 1.30000e+01]),\n",
       " array([ 1. ,  3.4,  5.8,  8.2, 10.6, 13. , 15.4, 17.8, 20.2, 22.6, 25. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df_samp2['count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        month  listing_id    year  count\n",
      "1           1        2595  2018.5    1.0\n",
      "32635       4        2595  2018.0    1.0\n",
      "44686       5        2595  2018.0    2.0\n",
      "58548       6        2595  2018.0    2.0\n",
      "101308      9        2595  2018.0    1.0\n",
      "118343     10        2595  2018.0    3.0\n",
      "135707     11        2595  2018.0    2.0\n",
      "152175     12        2595  2018.0    2.0\n"
     ]
    }
   ],
   "source": [
    "df_samp3 = df_samp2[(df_samp2.year == 2018) | (df_samp2.year == 2019)]\n",
    "\n",
    "# df_samp3['monthly_availability'] = df_samp3['count']/30\n",
    "# df_samp3 = df_samp3.groupby(['month','listing_id']).size().reset_index()\n",
    "\n",
    "# df_samp3 = df_samp3.groupby(['month', 'listing_id'], as_index=False).mean().groupby('month')['monthly_availability'].mean().reset_index()\n",
    "df_samp3 = df_samp3.groupby(['month', 'listing_id'], as_index=False).mean()\n",
    "print(df_samp3[df_samp3['listing_id'] == 2595])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "skipped\n",
      "skipped\n",
      "skipped\n",
      "skipped\n",
      "              year  month     count\n",
      "date                               \n",
      "2015-01-01  2015.0    1.0  277659.0\n",
      "2015-03-01  2015.0    3.0  280557.0\n",
      "2015-05-01  2015.0    5.0  298181.0\n",
      "2015-06-01  2015.0    6.0  316825.0\n",
      "2015-08-01  2015.0    8.0  352328.0\n",
      "(50, 3)\n"
     ]
    }
   ],
   "source": [
    "skipList = {\n",
    "    2015: {\n",
    "        2: True,\n",
    "        4: True,\n",
    "        7: True\n",
    "    },\n",
    "    2016: {\n",
    "        3: True\n",
    "    },\n",
    "    2019: {\n",
    "        7: True,\n",
    "        8: True,\n",
    "        9: True,\n",
    "        10: True,\n",
    "        11: True,\n",
    "        12: True\n",
    "    },\n",
    "}\n",
    "\n",
    "reviews_df = pd.DataFrame({\n",
    "    \"date\": [],\n",
    "    \"year\": [],\n",
    "    \"month\": [],\n",
    "    \"count\": []\n",
    "})\n",
    "\n",
    "for yr in range(2015, 2020):\n",
    "    for mo in range(1,13):\n",
    "        if yr == 2019 and mo > 6:\n",
    "            break\n",
    "        elif yr in skipList and mo in skipList[yr]:\n",
    "            # do nothing\n",
    "            print('skipped')\n",
    "        else:\n",
    "            zeroStr = '0' if mo < 10 else ''\n",
    "            df_rev = pd.read_csv('./data/reviews/reviews' + '_' + zeroStr + str(mo) + '_' + str(yr) + '.csv')\n",
    "            \n",
    "            dateObj = pd.to_datetime('1/'+str(mo)+'/'+str(yr), dayfirst=True)\n",
    "    \n",
    "            df_temp = pd.DataFrame({\n",
    "                \"date\": [dateObj],\n",
    "                \"year\": [dateObj.year],\n",
    "                \"month\": [dateObj.month],\n",
    "                \"count\": df_rev.shape[0]\n",
    "            })\n",
    "            \n",
    "            reviews_df = reviews_df.append(df_temp)\n",
    "\n",
    "reviews_df = reviews_df.set_index(\"date\")\n",
    "\n",
    "print(reviews_df.head())\n",
    "print(reviews_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(rc={'figure.figsize': (11,4)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reviews_df['count'].plot(linewidth=2);\n",
    "plt.title(\"Total Reviews, NYC\")\n",
    "plt.savefig(\"total_reviews_time.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}