Q1- The busiest times of the year and the highest prices.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What are the busiest times of the year to visit Seattle? By how much do prices spike?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Gather"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary libraries\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Magic word for producing visualizations in notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load in calendar data.\n",
    "calendar_df = pd.read_csv('calendar.csv',sep=',')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Assess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1393570, 4)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The number of rows and columns of calendar data\n",
    "calendar_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1393570 entries, 0 to 1393569\n",
      "Data columns (total 4 columns):\n",
      "listing_id    1393570 non-null int64\n",
      "date          1393570 non-null object\n",
      "available     1393570 non-null object\n",
      "price         934542 non-null object\n",
      "dtypes: int64(1), object(3)\n",
      "memory usage: 42.5+ MB\n"
     ]
    }
   ],
   "source": [
    "# The columns information of calendar data\n",
    "calendar_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if there's any duplicate records\n",
    "calendar_df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Clean and Analyze"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1. Handle Inconsistent Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    85.0\n",
       "1    85.0\n",
       "2     NaN\n",
       "3     NaN\n",
       "4     NaN\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Change the type of price feature to float\n",
    "calendar_df['price']= pd.to_numeric(calendar_df['price'].str.translate({ord(i): None for i in '$,'}))\n",
    "calendar_df['price'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2016-01-04\n",
       "1   2016-01-05\n",
       "2   2016-01-06\n",
       "3   2016-01-07\n",
       "4   2016-01-08\n",
       "Name: date, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Change the type of date feature to date\n",
    "calendar_df['date']= pd.to_datetime(calendar_df['date'],errors = 'ignore', format = '%Y-%m-%d')\n",
    "calendar_df['date'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2. Handle Missing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price         0.32939\n",
       "available     0.00000\n",
       "date          0.00000\n",
       "listing_id    0.00000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The average of missing data in each column\n",
    "calendar_df.isnull().mean().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>listing_id</th>\n",
       "      <th>date</th>\n",
       "      <th>available</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>241032</td>\n",
       "      <td>2016-01-06</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>241032</td>\n",
       "      <td>2016-01-07</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>241032</td>\n",
       "      <td>2016-01-08</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>241032</td>\n",
       "      <td>2016-01-09</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>241032</td>\n",
       "      <td>2016-01-10</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   listing_id       date available  price\n",
       "2      241032 2016-01-06         f    NaN\n",
       "3      241032 2016-01-07         f    NaN\n",
       "4      241032 2016-01-08         f    NaN\n",
       "5      241032 2016-01-09         f    NaN\n",
       "6      241032 2016-01-10         f    NaN"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Records with missing price data\n",
    "calendar_df[calendar_df['price'].isnull()].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Only price feature has null values, because of the absence of availability feature.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(934542, 4)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop rows wirh null values\n",
    "calendar_df = calendar_df.dropna(subset=['price'], axis=0) \n",
    "calendar_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Only data with availability feature is important for the price analysis, so rows with missing data are removed**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Model and Visualize"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1 Process Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    934542\n",
       "Name: available, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert 't' to 1 \n",
    "calendar_df.loc[calendar_df['available']=='t','available']='1'\n",
    "calendar_df['available'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create new columns to split date to year, month, and day\n",
    "calendar_df['year'] = calendar_df['date'].dt.year\n",
    "calendar_df['month'] = calendar_df['date'].dt.month\n",
    "calendar_df['day'] = calendar_df['date'].dt.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>listing_id</th>\n",
       "      <th>date</th>\n",
       "      <th>available</th>\n",
       "      <th>price</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>241032</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>85.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>241032</td>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>85.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>241032</td>\n",
       "      <td>2016-01-13</td>\n",
       "      <td>1</td>\n",
       "      <td>85.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>241032</td>\n",
       "      <td>2016-01-14</td>\n",
       "      <td>1</td>\n",
       "      <td>85.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>241032</td>\n",
       "      <td>2016-01-18</td>\n",
       "      <td>1</td>\n",
       "      <td>85.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    listing_id       date available  price  year  month  day\n",
       "0       241032 2016-01-04         1   85.0  2016      1    4\n",
       "1       241032 2016-01-05         1   85.0  2016      1    5\n",
       "9       241032 2016-01-13         1   85.0  2016      1   13\n",
       "10      241032 2016-01-14         1   85.0  2016      1   14\n",
       "14      241032 2016-01-18         1   85.0  2016      1   18"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calendar_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "2016    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]\n",
       "2017                                        [1]\n",
       "Name: month, dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get months of each year\n",
    "calendar_df.groupby(['year'])['month'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Only year 2016 data is considered, because it includes all months**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>listing_id</th>\n",
       "      <th>date</th>\n",
       "      <th>available</th>\n",
       "      <th>price</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>241032</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>85.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>241032</td>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>85.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>241032</td>\n",
       "      <td>2016-01-13</td>\n",
       "      <td>1</td>\n",
       "      <td>85.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>241032</td>\n",
       "      <td>2016-01-14</td>\n",
       "      <td>1</td>\n",
       "      <td>85.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>241032</td>\n",
       "      <td>2016-01-18</td>\n",
       "      <td>1</td>\n",
       "      <td>85.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    listing_id       date available  price  year  month  day\n",
       "0       241032 2016-01-04         1   85.0  2016      1    4\n",
       "1       241032 2016-01-05         1   85.0  2016      1    5\n",
       "9       241032 2016-01-13         1   85.0  2016      1   13\n",
       "10      241032 2016-01-14         1   85.0  2016      1   14\n",
       "14      241032 2016-01-18         1   85.0  2016      1   18"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calendar_2016_df=calendar_df[calendar_df['year']==2016]\n",
    "calendar_2016_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2 Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this function is for plot graphs\n",
    "def plot_graph(v,title):\n",
    "    plt.figure(figsize=(8,3))\n",
    "    plt.plot(v, 'o-')\n",
    "    plt.title(title)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2.1  Busiest months to visit Seattle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "month\n",
       "12    0.093730\n",
       "3     0.090368\n",
       "10    0.088753\n",
       "11    0.088045\n",
       "5     0.086097\n",
       "9     0.083164\n",
       "6     0.083161\n",
       "8     0.082196\n",
       "4     0.081862\n",
       "7     0.079908\n",
       "2     0.078938\n",
       "1     0.063777\n",
       "Name: available, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count of availability for each month\n",
    "month_avb=calendar_2016_df.groupby(['month'])['available'].count()/calendar_2016_df.shape[0]\n",
    "\n",
    "# Sort values in descending order\n",
    "month_avb.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    12.000000\n",
       "mean      0.083333\n",
       "std       0.007591\n",
       "min       0.063777\n",
       "25%       0.081373\n",
       "50%       0.083163\n",
       "75%       0.088222\n",
       "max       0.093730\n",
       "Name: available, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get statistical information\n",
    "month_avb.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3, 10, 12]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Consider months with the average amount of available present over 0.0883 to be active (above 75% of data)\n",
    "active_months_val=month_avb[month_avb>0.0883]\n",
    "\n",
    "# Get names of active months\n",
    "active_months=active_months_val.index.tolist()\n",
    "active_months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 7]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Consider months with the average amount of available present under 0.081 to be inactive (below 25% of data)\n",
    "inactive_months_val=month_avb[month_avb<0.081]\n",
    "\n",
    "# Get names of active months\n",
    "inactive_months=inactive_months_val.index.tolist()\n",
    "inactive_months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the available_persent by month\n",
    "plot_graph(month_avb,'Available_persent by month')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Based on the above graph,**\n",
    "- December, March, and October are the busiest months of the year.\n",
    "- January, February and July are the least."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2.2  Busiest Days to visit Seattle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.2.2.1  Active months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "day\n",
       "31    0.032738\n",
       "30    0.032631\n",
       "29    0.032620\n",
       "28    0.032608\n",
       "27    0.032604\n",
       "26    0.032466\n",
       "18    0.032414\n",
       "20    0.032367\n",
       "19    0.032359\n",
       "17    0.032347\n",
       "25    0.032343\n",
       "23    0.032335\n",
       "21    0.032332\n",
       "16    0.032320\n",
       "22    0.032316\n",
       "24    0.032288\n",
       "15    0.032249\n",
       "14    0.032209\n",
       "13    0.032166\n",
       "6     0.032158\n",
       "7     0.032111\n",
       "8     0.032103\n",
       "9     0.032095\n",
       "11    0.032095\n",
       "10    0.032087\n",
       "12    0.032055\n",
       "5     0.032012\n",
       "4     0.032000\n",
       "3     0.031894\n",
       "2     0.031890\n",
       "1     0.031791\n",
       "Name: available, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a datframe with only active months\n",
    "active_months_df=calendar_2016_df[calendar_2016_df['month'].isin(active_months)] \n",
    "\n",
    "# Count of availability for each day\n",
    "active_months_avb_day=active_months_df.groupby(['day'])['available'].count()/active_months_df.shape[0]\n",
    "\n",
    "# Sort values in descending order\n",
    "active_months_avb_day.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the available_persent by day\n",
    "plot_graph(active_months_avb_day,'Available_persent by day for active months')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.2.2.3  Inactive months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "day\n",
       "26    0.035646\n",
       "27    0.035535\n",
       "25    0.035511\n",
       "29    0.035429\n",
       "24    0.035380\n",
       "28    0.035351\n",
       "21    0.035255\n",
       "20    0.035230\n",
       "19    0.035119\n",
       "22    0.035119\n",
       "23    0.035114\n",
       "18    0.034810\n",
       "17    0.034036\n",
       "12    0.033717\n",
       "11    0.033683\n",
       "10    0.033576\n",
       "13    0.033499\n",
       "16    0.033427\n",
       "15    0.033412\n",
       "14    0.033388\n",
       "9     0.032774\n",
       "8     0.032319\n",
       "7     0.032164\n",
       "5     0.032106\n",
       "6     0.031898\n",
       "4     0.031415\n",
       "3     0.023227\n",
       "2     0.023198\n",
       "31    0.023097\n",
       "1     0.023034\n",
       "30    0.022531\n",
       "Name: available, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a datframe with only inactive months\n",
    "inactive_months_df=calendar_2016_df[calendar_2016_df['month'].isin(inactive_months)] \n",
    "\n",
    "# Count of availability for each day\n",
    "inactive_months_avb_day=inactive_months_df.groupby(['day'])['available'].count()/inactive_months_df.shape[0]\n",
    "\n",
    "# Sort values in descending order\n",
    "inactive_months_avb_day.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the available_persent by day\n",
    "plot_graph(inactive_months_avb_day,'Available_persent by day for inactive months')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2.3  Price trends of months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "month\n",
       "7     152.094150\n",
       "8     150.656594\n",
       "6     147.473137\n",
       "9     143.255949\n",
       "5     139.538183\n",
       "12    137.251835\n",
       "10    137.031939\n",
       "11    135.688738\n",
       "4     135.097005\n",
       "3     128.644488\n",
       "2     124.293927\n",
       "1     121.568409\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Average price per months\n",
    "month_price=calendar_2016_df.groupby(['month'])['price'].mean()\n",
    "\n",
    "# Sort values in descending order\n",
    "month_price.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     12.000000\n",
       "mean     137.716196\n",
       "std        9.715239\n",
       "min      121.568409\n",
       "25%      133.483876\n",
       "50%      137.141887\n",
       "75%      144.310246\n",
       "max      152.094150\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get statistical information\n",
    "month_price.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[6, 7, 8]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Consider months with avergae price over 147 to be expensive (above 75% of data)\n",
    "high_price_months_val=month_price[month_price>147]\n",
    "\n",
    "# Get names of high price months\n",
    "high_price_months=high_price_months_val.index.tolist()\n",
    "high_price_months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Consider months with avergae price under 133 to be cheap (under 25% of data)\n",
    "low_price_months_val=month_price[month_price<133]\n",
    "\n",
    "# Get names of high price months\n",
    "low_price_months=low_price_months_val.index.tolist()\n",
    "low_price_months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the average price per months\n",
    "plot_graph(month_price,'Average price per months')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Based on the above graph,**\n",
    "- prices increase in summer season, mostly in June, July and August.\n",
    "- They decrease in winter season in January, February, and March."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get monhts that are active and expensive\n",
    "list(set(high_price_months) & set(active_months) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[7]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get monhts that are inactive and expensive\n",
    "list(set(high_price_months) & set(inactive_months) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get monhts that are active and cheap\n",
    "list(set(low_price_months) & set(active_months) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get monhts that are inactive and cheap\n",
    "list(set(low_price_months) & set(inactive_months) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**In Conclusion,** January and February are less crowded and cheaper. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:Anaconda3]",
   "language": "python",
   "name": "conda-env-Anaconda3-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}