2. Is there a seasonality in prices of properties.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Is there a seasonality in the prices of properties llisted in AirBnb-Berlin?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Special highlight\n",
    "#pip install holidays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import initial libraries\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import file\n",
    "dataFrameCalendar=pd.read_csv(\"D:/Raghvendra/Masters/Irish Applications/DCU/DCU subjects study/Sem2_CA683 data analytics and data mining\\Main Assignment/berlin airbnb 2020/calendar_summary.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>adjusted_price</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>maximum_nights</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>355227</td>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>f</td>\n",
       "      <td>$60.00</td>\n",
       "      <td>$60.00</td>\n",
       "      <td>45.0</td>\n",
       "      <td>400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>267951</td>\n",
       "      <td>2020-02-18</td>\n",
       "      <td>f</td>\n",
       "      <td>$60.00</td>\n",
       "      <td>$60.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>267951</td>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>f</td>\n",
       "      <td>$60.00</td>\n",
       "      <td>$60.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>267951</td>\n",
       "      <td>2020-02-20</td>\n",
       "      <td>f</td>\n",
       "      <td>$65.00</td>\n",
       "      <td>$65.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>267951</td>\n",
       "      <td>2020-02-21</td>\n",
       "      <td>f</td>\n",
       "      <td>$65.00</td>\n",
       "      <td>$65.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>300.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   listing_id        date available   price adjusted_price  minimum_nights  \\\n",
       "0      355227  2020-02-19         f  $60.00         $60.00            45.0   \n",
       "1      267951  2020-02-18         f  $60.00         $60.00             2.0   \n",
       "2      267951  2020-02-19         f  $60.00         $60.00             2.0   \n",
       "3      267951  2020-02-20         f  $65.00         $65.00             2.0   \n",
       "4      267951  2020-02-21         f  $65.00         $65.00             2.0   \n",
       "\n",
       "   maximum_nights  \n",
       "0           400.0  \n",
       "1           300.0  \n",
       "2           300.0  \n",
       "3           300.0  \n",
       "4           300.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFrameCalendar.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>adjusted_price</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>maximum_nights</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>9197063</td>\n",
       "      <td>42361801</td>\n",
       "      <td>2021-02-12</td>\n",
       "      <td>t</td>\n",
       "      <td>$46.00</td>\n",
       "      <td>$46.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9197064</td>\n",
       "      <td>42361801</td>\n",
       "      <td>2021-02-13</td>\n",
       "      <td>t</td>\n",
       "      <td>$46.00</td>\n",
       "      <td>$46.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9197065</td>\n",
       "      <td>42361801</td>\n",
       "      <td>2021-02-14</td>\n",
       "      <td>t</td>\n",
       "      <td>$46.00</td>\n",
       "      <td>$46.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9197066</td>\n",
       "      <td>42361801</td>\n",
       "      <td>2021-02-15</td>\n",
       "      <td>t</td>\n",
       "      <td>$46.00</td>\n",
       "      <td>$46.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9197067</td>\n",
       "      <td>42361801</td>\n",
       "      <td>2021-02-16</td>\n",
       "      <td>t</td>\n",
       "      <td>$46.00</td>\n",
       "      <td>$46.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1125.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         listing_id        date available   price adjusted_price  \\\n",
       "9197063    42361801  2021-02-12         t  $46.00         $46.00   \n",
       "9197064    42361801  2021-02-13         t  $46.00         $46.00   \n",
       "9197065    42361801  2021-02-14         t  $46.00         $46.00   \n",
       "9197066    42361801  2021-02-15         t  $46.00         $46.00   \n",
       "9197067    42361801  2021-02-16         t  $46.00         $46.00   \n",
       "\n",
       "         minimum_nights  maximum_nights  \n",
       "9197063             3.0          1125.0  \n",
       "9197064             3.0          1125.0  \n",
       "9197065             3.0          1125.0  \n",
       "9197066             3.0          1125.0  \n",
       "9197067             3.0          1125.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFrameCalendar.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9197068, 7)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFrameCalendar.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "listing_id        0\n",
       "date              0\n",
       "available         0\n",
       "price             0\n",
       "adjusted_price    0\n",
       "minimum_nights    3\n",
       "maximum_nights    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFrameCalendar.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "listing_id        9197068\n",
       "date              9197068\n",
       "available         9197068\n",
       "price             9197068\n",
       "adjusted_price    9197068\n",
       "minimum_nights    9197065\n",
       "maximum_nights    9197065\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFrameCalendar.notnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dataFrameCalendar['price'].mean()\n",
    "\n",
    "#######calculated as $79.79"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dataFrameCalendar['price'].median()\n",
    "\n",
    "### calculated as $50.00"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#replacing NaN values with 0/mean/mode - check with all three\n",
    "dataFrameCalendar.fillna('$80.00', inplace=True)\n",
    "dataFrameCalendar = dataFrameCalendar[dataFrameCalendar.price != 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Extracting prices from the table\n",
    "price = dataFrameCalendar['price']\n",
    "prices=[]\n",
    "\n",
    "for p in price:\n",
    "    p = re.sub('[^0-9.]+','', p)\n",
    "    prices.append(float(p))\n",
    "    \n",
    "#replace the price column with the new column\n",
    "dataFrameCalendar['price']=prices\n",
    "\n",
    "dataFrameCalendar = dataFrameCalendar[dataFrameCalendar.price >= 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>adjusted_price</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>maximum_nights</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>355227</td>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>f</td>\n",
       "      <td>60.0</td>\n",
       "      <td>$60.00</td>\n",
       "      <td>45</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>267951</td>\n",
       "      <td>2020-02-18</td>\n",
       "      <td>f</td>\n",
       "      <td>60.0</td>\n",
       "      <td>$60.00</td>\n",
       "      <td>2</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>267951</td>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>f</td>\n",
       "      <td>60.0</td>\n",
       "      <td>$60.00</td>\n",
       "      <td>2</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>267951</td>\n",
       "      <td>2020-02-20</td>\n",
       "      <td>f</td>\n",
       "      <td>65.0</td>\n",
       "      <td>$65.00</td>\n",
       "      <td>2</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>267951</td>\n",
       "      <td>2020-02-21</td>\n",
       "      <td>f</td>\n",
       "      <td>65.0</td>\n",
       "      <td>$65.00</td>\n",
       "      <td>2</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   listing_id        date available  price adjusted_price minimum_nights  \\\n",
       "0      355227  2020-02-19         f   60.0         $60.00             45   \n",
       "1      267951  2020-02-18         f   60.0         $60.00              2   \n",
       "2      267951  2020-02-19         f   60.0         $60.00              2   \n",
       "3      267951  2020-02-20         f   65.0         $65.00              2   \n",
       "4      267951  2020-02-21         f   65.0         $65.00              2   \n",
       "\n",
       "  maximum_nights  \n",
       "0            400  \n",
       "1            300  \n",
       "2            300  \n",
       "3            300  \n",
       "4            300  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFrameCalendar.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>adjusted_price</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>maximum_nights</th>\n",
       "      <th>Year</th>\n",
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       "      <td>0</td>\n",
       "      <td>355227</td>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>f</td>\n",
       "      <td>60.0</td>\n",
       "      <td>$60.00</td>\n",
       "      <td>45</td>\n",
       "      <td>400</td>\n",
       "      <td>2020</td>\n",
       "      <td>02</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>267951</td>\n",
       "      <td>2020-02-18</td>\n",
       "      <td>f</td>\n",
       "      <td>60.0</td>\n",
       "      <td>$60.00</td>\n",
       "      <td>2</td>\n",
       "      <td>300</td>\n",
       "      <td>2020</td>\n",
       "      <td>02</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>267951</td>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>f</td>\n",
       "      <td>60.0</td>\n",
       "      <td>$60.00</td>\n",
       "      <td>2</td>\n",
       "      <td>300</td>\n",
       "      <td>2020</td>\n",
       "      <td>02</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>267951</td>\n",
       "      <td>2020-02-20</td>\n",
       "      <td>f</td>\n",
       "      <td>65.0</td>\n",
       "      <td>$65.00</td>\n",
       "      <td>2</td>\n",
       "      <td>300</td>\n",
       "      <td>2020</td>\n",
       "      <td>02</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>267951</td>\n",
       "      <td>2020-02-21</td>\n",
       "      <td>f</td>\n",
       "      <td>65.0</td>\n",
       "      <td>$65.00</td>\n",
       "      <td>2</td>\n",
       "      <td>300</td>\n",
       "      <td>2020</td>\n",
       "      <td>02</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   listing_id        date available  price adjusted_price minimum_nights  \\\n",
       "0      355227  2020-02-19         f   60.0         $60.00             45   \n",
       "1      267951  2020-02-18         f   60.0         $60.00              2   \n",
       "2      267951  2020-02-19         f   60.0         $60.00              2   \n",
       "3      267951  2020-02-20         f   65.0         $65.00              2   \n",
       "4      267951  2020-02-21         f   65.0         $65.00              2   \n",
       "\n",
       "  maximum_nights  Year Month Day  \n",
       "0            400  2020    02  19  \n",
       "1            300  2020    02  18  \n",
       "2            300  2020    02  19  \n",
       "3            300  2020    02  20  \n",
       "4            300  2020    02  21  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#separating date column into day month and year\n",
    "dataFrameCalendar['Year'],dataFrameCalendar['Month'],dataFrameCalendar['Day']=dataFrameCalendar['date'].str.split('-',2).str\n",
    "dataFrameCalendar.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>average_Price</th>\n",
       "      <th>year-Month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "      <td>02</td>\n",
       "      <td>78.065477</td>\n",
       "      <td>2020-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "      <td>03</td>\n",
       "      <td>75.953871</td>\n",
       "      <td>2020-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year Month  average_Price year-Month\n",
       "0  2020    02      78.065477    2020-02\n",
       "1  2020    03      75.953871    2020-03"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFrameYear=dataFrameCalendar.groupby(['Year','Month']).price.mean()\n",
    "dataFrameYear=dataFrameYear.reset_index()\n",
    "dataFrameYear=dataFrameYear.rename(columns={'price':'average_Price'})\n",
    "dataFrameYear['year-Month']=dataFrameYear['Year'].map(str) + \"-\" + dataFrameYear['Month'].map(str)\n",
    "dataFrameYear.to_csv('year_month_data.csv')\n",
    "dataFrameYear.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Average of Price')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualizing the trend of year/Month and average prices of the listing\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "matplotlib.style.use('ggplot')\n",
    "\n",
    "objects = dataFrameYear['year-Month']\n",
    "y_pos = dataFrameYear['average_Price']\n",
    "\n",
    "#https://matplotlib.org/gallery/color/named_colors.html\n",
    "\n",
    "dataFrameYear.plot(kind='bar', x='year-Month', y='average_Price', color = 'yellowgreen', figsize =(10,7), \n",
    "           title = 'Month wise price trend of AirBnb-Berlin', \n",
    "           legend = False)\n",
    "\n",
    "plt.ylabel('Average of Price')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#https://towardsdatascience.com/5-minute-guide-to-detecting-holidays-in-python-c270f8479387\n",
    "\n",
    "https://pypi.org/project/holidays/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Raghvendra\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:19: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n",
      "C:\\Users\\Raghvendra\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:20: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n",
      "C:\\Users\\Raghvendra\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:21: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\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>listing_id</th>\n",
       "      <th>date</th>\n",
       "      <th>available</th>\n",
       "      <th>price</th>\n",
       "      <th>adjusted_price</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>maximum_nights</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>day_Name</th>\n",
       "      <th>holiday</th>\n",
       "      <th>german_holiday_names</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>355227</td>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>f</td>\n",
       "      <td>60.0</td>\n",
       "      <td>$60.00</td>\n",
       "      <td>45</td>\n",
       "      <td>400</td>\n",
       "      <td>2020</td>\n",
       "      <td>02</td>\n",
       "      <td>19</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>False</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>267951</td>\n",
       "      <td>2020-02-18</td>\n",
       "      <td>f</td>\n",
       "      <td>60.0</td>\n",
       "      <td>$60.00</td>\n",
       "      <td>2</td>\n",
       "      <td>300</td>\n",
       "      <td>2020</td>\n",
       "      <td>02</td>\n",
       "      <td>18</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>False</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>267951</td>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>f</td>\n",
       "      <td>60.0</td>\n",
       "      <td>$60.00</td>\n",
       "      <td>2</td>\n",
       "      <td>300</td>\n",
       "      <td>2020</td>\n",
       "      <td>02</td>\n",
       "      <td>19</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>False</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>267951</td>\n",
       "      <td>2020-02-20</td>\n",
       "      <td>f</td>\n",
       "      <td>65.0</td>\n",
       "      <td>$65.00</td>\n",
       "      <td>2</td>\n",
       "      <td>300</td>\n",
       "      <td>2020</td>\n",
       "      <td>02</td>\n",
       "      <td>20</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>False</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>267951</td>\n",
       "      <td>2020-02-21</td>\n",
       "      <td>f</td>\n",
       "      <td>65.0</td>\n",
       "      <td>$65.00</td>\n",
       "      <td>2</td>\n",
       "      <td>300</td>\n",
       "      <td>2020</td>\n",
       "      <td>02</td>\n",
       "      <td>21</td>\n",
       "      <td>Friday</td>\n",
       "      <td>False</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   listing_id        date available  price adjusted_price minimum_nights  \\\n",
       "0      355227  2020-02-19         f   60.0         $60.00             45   \n",
       "1      267951  2020-02-18         f   60.0         $60.00              2   \n",
       "2      267951  2020-02-19         f   60.0         $60.00              2   \n",
       "3      267951  2020-02-20         f   65.0         $65.00              2   \n",
       "4      267951  2020-02-21         f   65.0         $65.00              2   \n",
       "\n",
       "  maximum_nights  Year Month Day   day_Name holiday german_holiday_names  \n",
       "0            400  2020    02  19  Wednesday   False                 None  \n",
       "1            300  2020    02  18    Tuesday   False                 None  \n",
       "2            300  2020    02  19  Wednesday   False                 None  \n",
       "3            300  2020    02  20   Thursday   False                 None  \n",
       "4            300  2020    02  21     Friday   False                 None  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#getting day name from date data and holidays\n",
    "\n",
    "from datetime import date\n",
    "import datetime\n",
    "import calendar\n",
    "import holidays\n",
    "\n",
    "dataFrameCalendar.fillna(0, inplace=True)\n",
    "\n",
    "#https://pypi.org/project/holidays/\n",
    "german_holidays = holidays.Germany()\n",
    "\n",
    "dataFrameCalendar['day_Name']='default'\n",
    "dataFrameCalendar['holiday']='False'\n",
    "dataFrameCalendar['german_holiday_names']='working'\n",
    "for index,row in dataFrameCalendar.iterrows():\n",
    "    sdate = datetime.date(int(row['Year']),int(row['Month']),int(row['Day']))\n",
    "    vall=date(int(row['Year']),int(row['Month']),int(row['Day'])) in german_holidays\n",
    "    dataFrameCalendar.set_value(index,'day_Name',calendar.day_name[sdate.weekday()])\n",
    "    dataFrameCalendar.set_value(index,'holiday',vall)\n",
    "    dataFrameCalendar.set_value(index,'german_holiday_names',german_holidays.get(sdate))\n",
    "#calendarDF.to_csv('holidays_data.csv')\n",
    "dataFrameCalendar.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Raghvendra\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:17: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n",
      "C:\\Users\\Raghvendra\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:9: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n",
      "  if __name__ == '__main__':\n",
      "C:\\Users\\Raghvendra\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:19: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n",
      "C:\\Users\\Raghvendra\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:21: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n",
      "C:\\Users\\Raghvendra\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:15: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n",
      "  from ipykernel import kernelapp as app\n",
      "C:\\Users\\Raghvendra\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:11: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n",
      "  # This is added back by InteractiveShellApp.init_path()\n",
      "C:\\Users\\Raghvendra\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:13: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n",
      "  del sys.path[0]\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>day_Name</th>\n",
       "      <th>Average_Price</th>\n",
       "      <th>day_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Monday</td>\n",
       "      <td>69.291058</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>69.331959</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>69.268131</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>69.559435</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Friday</td>\n",
       "      <td>106.562470</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>106.583259</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>69.265595</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    day_Name  Average_Price  day_num\n",
       "1     Monday      69.291058        1\n",
       "5    Tuesday      69.331959        2\n",
       "6  Wednesday      69.268131        3\n",
       "4   Thursday      69.559435        4\n",
       "0     Friday     106.562470        5\n",
       "2   Saturday     106.583259        6\n",
       "3     Sunday      69.265595        7"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculating Average price for each day\n",
    "\n",
    "dataFrameDay=dataFrameCalendar.groupby('day_Name').price.mean()\n",
    "dataFrameDay=dataFrameDay.reset_index()\n",
    "dataFrameDay['day_num']=0\n",
    "\n",
    "for index,row in dataFrameDay.iterrows():\n",
    "    if row['day_Name']=='Monday':\n",
    "        dataFrameDay.set_value(index,'day_num',1)\n",
    "    if row['day_Name']=='Tuesday':\n",
    "        dataFrameDay.set_value(index,'day_num',2)\n",
    "    if row['day_Name']=='Wednesday':\n",
    "        dataFrameDay.set_value(index,'day_num',3)\n",
    "    if row['day_Name']=='Thursday':\n",
    "        dataFrameDay.set_value(index,'day_num',4)\n",
    "    if row['day_Name']=='Friday':\n",
    "        dataFrameDay.set_value(index,'day_num',5)\n",
    "    if row['day_Name']=='Saturday':\n",
    "        dataFrameDay.set_value(index,'day_num',6)\n",
    "    if row['day_Name']=='Sunday':\n",
    "        dataFrameDay.set_value(index,'day_num',7)\n",
    "dataFrameDay=dataFrameDay.sort_values('day_num',ascending=[1])\n",
    "dataFrameDay=dataFrameDay.rename(columns={'price':'Average_Price'})\n",
    "dataFrameDay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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"
    }
   ],
   "source": [
    "#https://www.tutorialspoint.com/matplotlib/matplotlib_scatter_plot.htm\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "fig=plt.figure()\n",
    "ax=fig.add_axes([0,0,1,1])\n",
    "ax.scatter(dataFrameDay[\"day_Name\"], dataFrameDay[\"Average_Price\"], color='green')\n",
    "ax.set_xlabel('Name of Day')\n",
    "ax.set_ylabel('Average price of property')\n",
    "ax.set_title('Scatter plot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>german_holiday_names</th>\n",
       "      <th>listing_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Christi Himmelfahrt</td>\n",
       "      <td>25197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Erster Mai</td>\n",
       "      <td>25197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Erster Weihnachtstag</td>\n",
       "      <td>25197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Karfreitag</td>\n",
       "      <td>25197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Neujahr</td>\n",
       "      <td>25197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Ostermontag</td>\n",
       "      <td>25197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Pfingstmontag</td>\n",
       "      <td>25197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Tag der Deutschen Einheit</td>\n",
       "      <td>25197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Zweiter Weihnachtstag</td>\n",
       "      <td>25197</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        german_holiday_names  listing_id\n",
       "0        Christi Himmelfahrt       25197\n",
       "1                 Erster Mai       25197\n",
       "2       Erster Weihnachtstag       25197\n",
       "3                 Karfreitag       25197\n",
       "4                    Neujahr       25197\n",
       "5                Ostermontag       25197\n",
       "6              Pfingstmontag       25197\n",
       "7  Tag der Deutschen Einheit       25197\n",
       "8      Zweiter Weihnachtstag       25197"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking which holiday has maximum listings\n",
    "\n",
    "dataFrameHoliday=dataFrameCalendar.groupby('german_holiday_names').listing_id.count()\n",
    "dataFrameHoliday=dataFrameHoliday.reset_index()\n",
    "dataFrameHoliday=dataFrameHoliday.sort_values('listing_id',ascending=[0])\n",
    "dataFrameHoliday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'Overall listings'), Text(0.5, 0, 'German holiday names')]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1368x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualizing\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(19,8))\n",
    "\n",
    "#https://seaborn.pydata.org/tutorial/color_palettes.html\n",
    "\n",
    "ax = sns.barplot(\"german_holiday_names\", y=\"listing_id\", data=dataFrameHoliday,palette=\"BuGn_r\")\n",
    "ax.set(xlabel='German holiday names', ylabel='Overall listings')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>german_holiday_names</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Neujahr</td>\n",
       "      <td>111.014446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Tag der Deutschen Einheit</td>\n",
       "      <td>106.432194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Erster Mai</td>\n",
       "      <td>106.284875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        german_holiday_names       price\n",
       "4                    Neujahr  111.014446\n",
       "7  Tag der Deutschen Einheit  106.432194\n",
       "1                 Erster Mai  106.284875"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DataFrameHolidayPrice=dataFrameCalendar.groupby('german_holiday_names').price.mean()\n",
    "DataFrameHolidayPrice=DataFrameHolidayPrice.reset_index()\n",
    "DataFrameHolidayPrice=DataFrameHolidayPrice.sort_values('price',ascending=[0])\n",
    "\n",
    "DataFrameHolidayPrice.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'Average Price'), Text(0.5, 0, 'German holiday names')]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1368x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualizing\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(19,8))\n",
    "\n",
    "#https://seaborn.pydata.org/tutorial/color_palettes.html\n",
    "\n",
    "ax = sns.barplot(\"german_holiday_names\", y=\"price\", data=DataFrameHolidayPrice,palette=\"GnBu_d\")\n",
    "ax.set(xlabel='German holiday names', ylabel='Average Price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>german_holiday_names</th>\n",
       "      <th>number_Of_Listings</th>\n",
       "      <th>average_Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Christi Himmelfahrt</td>\n",
       "      <td>25197</td>\n",
       "      <td>70.379251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Erster Mai</td>\n",
       "      <td>25197</td>\n",
       "      <td>106.284875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Erster Weihnachtstag</td>\n",
       "      <td>25197</td>\n",
       "      <td>106.152161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Karfreitag</td>\n",
       "      <td>25197</td>\n",
       "      <td>106.189784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Neujahr</td>\n",
       "      <td>25197</td>\n",
       "      <td>111.014446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Ostermontag</td>\n",
       "      <td>25197</td>\n",
       "      <td>68.108307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Pfingstmontag</td>\n",
       "      <td>25197</td>\n",
       "      <td>69.642656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Tag der Deutschen Einheit</td>\n",
       "      <td>25197</td>\n",
       "      <td>106.432194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Zweiter Weihnachtstag</td>\n",
       "      <td>25197</td>\n",
       "      <td>106.181331</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        german_holiday_names  number_Of_Listings  average_Price\n",
       "0        Christi Himmelfahrt               25197      70.379251\n",
       "1                 Erster Mai               25197     106.284875\n",
       "2       Erster Weihnachtstag               25197     106.152161\n",
       "3                 Karfreitag               25197     106.189784\n",
       "4                    Neujahr               25197     111.014446\n",
       "5                Ostermontag               25197      68.108307\n",
       "6              Pfingstmontag               25197      69.642656\n",
       "7  Tag der Deutschen Einheit               25197     106.432194\n",
       "8      Zweiter Weihnachtstag               25197     106.181331"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DataFrameMerged=pd.merge(dataFrameHoliday,DataFrameHolidayPrice,on='german_holiday_names')\n",
    "DataFrameMerged=DataFrameMerged.rename(columns={'listing_id':'number_Of_Listings'})\n",
    "DataFrameMerged=DataFrameMerged.rename(columns={'price':'average_Price'})\n",
    "DataFrameMerged"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the prices in January and February 2021 are higher. Let us check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>holiday</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2021</td>\n",
       "      <td>01</td>\n",
       "      <td>False</td>\n",
       "      <td>83.281311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>01</td>\n",
       "      <td>True</td>\n",
       "      <td>111.014446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2021</td>\n",
       "      <td>02</td>\n",
       "      <td>False</td>\n",
       "      <td>82.988975</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year Month  holiday       price\n",
       "0  2021    01    False   83.281311\n",
       "1  2021    01     True  111.014446\n",
       "2  2021    02    False   82.988975"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#analyzing data of January and February\n",
    "\n",
    "DataFrameCheckHolidays=dataFrameCalendar[(dataFrameCalendar['Year'] == '2021') & ((dataFrameCalendar['Month'] == '01') |(dataFrameCalendar['Month'] == '02'))]\n",
    "DataFrameCheckHolidays=DataFrameCheckHolidays.groupby(['Year','Month','holiday']).price.mean()\n",
    "DataFrameCheckHolidays=DataFrameCheckHolidays.reset_index()\n",
    "DataFrameCheckHolidays.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month\n",
       "01    97.027252\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#analyzing longweekednd holiday days\n",
    "\n",
    "columbusDF=dataFrameCalendar[(dataFrameCalendar['Year'] == '2021') & (dataFrameCalendar['Month'] == '01' ) & ((dataFrameCalendar['Day'] == '01') | (dataFrameCalendar['Day'] == '02') | (dataFrameCalendar['Day'] == '03'))]\n",
    "columbusDF.groupby('Month').price.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month\n",
       "01    84.175929\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#analyzing rest of the days\n",
    "\n",
    "NocolumbusDF=dataFrameCalendar[(dataFrameCalendar['Year'] == '2021') & (dataFrameCalendar['Month'] == '01' ) & ((dataFrameCalendar['Day'] != '01') | (dataFrameCalendar['Day'] != '02') | (dataFrameCalendar['Day'] != '03'))]\n",
    "NocolumbusDF.groupby('Month').price.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>Day</th>\n",
       "      <th>Average_Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>01</td>\n",
       "      <td>111.014446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>02</td>\n",
       "      <td>108.735008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>03</td>\n",
       "      <td>71.332301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>04</td>\n",
       "      <td>72.398460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>05</td>\n",
       "      <td>72.665000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>06</td>\n",
       "      <td>72.642180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>07</td>\n",
       "      <td>71.390364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>08</td>\n",
       "      <td>109.489423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>09</td>\n",
       "      <td>109.492162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>71.139064</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Day  Average_Price\n",
       "0  01     111.014446\n",
       "1  02     108.735008\n",
       "2  03      71.332301\n",
       "3  04      72.398460\n",
       "4  05      72.665000\n",
       "5  06      72.642180\n",
       "6  07      71.390364\n",
       "7  08     109.489423\n",
       "8  09     109.492162\n",
       "9  10      71.139064"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# analyzing data from date 1st of January to date 10th of January which includes both long weekend and normal workdays\n",
    "\n",
    "janDF=dataFrameCalendar[(dataFrameCalendar['Year'] == '2021') & (dataFrameCalendar['Month'] == '01' )& ((dataFrameCalendar['Day'] == '01' )| (dataFrameCalendar['Day'] == '02' )| (dataFrameCalendar['Day'] == '03' )| (dataFrameCalendar['Day'] == '04' )| (dataFrameCalendar['Day'] == '05' )| (dataFrameCalendar['Day'] == '06' )| (dataFrameCalendar['Day'] == '07' )| (dataFrameCalendar['Day'] == '08' )| (dataFrameCalendar['Day'] == '09' )| (dataFrameCalendar['Day'] == '10' ))]\n",
    "janDF=janDF.groupby('Day').price.mean()\n",
    "janDF=janDF.reset_index()\n",
    "janDF=janDF.sort_values('Day',ascending=[1])\n",
    "janDF=janDF.rename(columns={'price':'Average_Price'})\n",
    "janDF.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x=janDF['Day'].tolist()\n",
    "y=janDF['Average_Price'].tolist()\n",
    "\n",
    "plt.plot(x,y, 'go-')\n",
    "plt.ylabel('Average Price')\n",
    "plt.xlabel('Days')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}