{
 "cells": [
  {
   "source": [
    "![NYC Skyline](nyc.jpg)\n",
    "\n",
    "Welcome to New York City, one of the most-visited cities in the world. There are many Airbnb listings in New York City to meet the high demand for temporary lodging for travelers, which can be anywhere between a few nights to many months. In this project, we will take a closer look at the New York Airbnb market by combining data from multiple file types like `.csv`, `.tsv`, and `.xlsx`.\n",
    "\n",
    "Recall that **CSV**, **TSV**, and **Excel** files are three common formats for storing data. \n",
    "Three files containing data on 2019 Airbnb listings are available to you:\n",
    "\n",
    "**data/airbnb_price.csv**\n",
    "This is a CSV file containing data on Airbnb listing prices and locations.\n",
    "- **`listing_id`**: unique identifier of listing\n",
    "- **`price`**: nightly listing price in USD\n",
    "- **`nbhood_full`**: name of borough and neighborhood where listing is located\n",
    "\n",
    "**data/airbnb_room_type.xlsx**\n",
    "This is an Excel file containing data on Airbnb listing descriptions and room types.\n",
    "- **`listing_id`**: unique identifier of listing\n",
    "- **`description`**: listing description\n",
    "- **`room_type`**: Airbnb has three types of rooms: shared rooms, private rooms, and entire homes/apartments\n",
    "\n",
    "**data/airbnb_last_review.tsv**\n",
    "This is a TSV file containing data on Airbnb host names and review dates.\n",
    "- **`listing_id`**: unique identifier of listing\n",
    "- **`host_name`**: name of listing host\n",
    "- **`last_review`**: date when the listing was last reviewed"
   ],
   "metadata": {
    "id": "bA5ajAmk7XH6"
   },
   "id": "c747d469-ee97-4b95-880f-feefd5456042",
   "cell_type": "markdown"
  },
  {
   "source": [
    "# We've loaded your first package for you! You can add as many cells as you need.\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Begin coding here ...\n",
    "price = pd.read_csv(\"datasets/airbnb_price.csv\")\n",
    "room = pd.read_excel(\"datasets/airbnb_room_type.xlsx\")\n",
    "review = pd.read_csv(\"datasets/airbnb_last_review.tsv\", sep='\\t')"
   ],
   "metadata": {
    "executionTime": 1935,
    "lastSuccessfullyExecutedCode": "# We've loaded your first package for you! You can add as many cells as you need.\nimport numpy as np\nimport pandas as pd\n\n# Begin coding here ...\nprice = pd.read_csv(\"data/airbnb_price.csv\")\nroom = pd.read_excel(\"data/airbnb_room_type.xlsx\")\nreview = pd.read_csv(\"data/airbnb_last_review.tsv\", sep='\\t')",
    "executionCancelledAt": null,
    "lastExecutedAt": 1705538073571,
    "lastScheduledRunId": null,
    "collapsed": false
   },
   "id": "1fdc5784-479e-46cb-b05b-68439dcc94a7",
   "cell_type": "code",
   "execution_count": 23,
   "outputs": []
  },
  {
   "source": [
    "price.head()"
   ],
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 62,
    "lastExecutedAt": 1705538073634,
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "price.head()",
    "outputsMetadata": {
     "0": {
      "height": 198,
      "type": "dataFrame"
     }
    }
   },
   "cell_type": "code",
   "id": "d2174228-0d23-4350-8dd0-071ac512ed04",
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "application/com.datacamp.data-table.v2+json": {
       "table": {
        "schema": {
         "fields": [
          {
           "name": "index",
           "type": "integer"
          },
          {
           "name": "listing_id",
           "type": "integer"
          },
          {
           "name": "price",
           "type": "string"
          },
          {
           "name": "nbhood_full",
           "type": "string"
          }
         ],
         "primaryKey": [
          "index"
         ],
         "pandas_version": "1.4.0"
        },
        "data": {
         "index": [
          0,
          1,
          2,
          3,
          4
         ],
         "listing_id": [
          2595,
          3831,
          5099,
          5178,
          5238
         ],
         "price": [
          "225 dollars",
          "89 dollars",
          "200 dollars",
          "79 dollars",
          "150 dollars"
         ],
         "nbhood_full": [
          "Manhattan, Midtown",
          "Brooklyn, Clinton Hill",
          "Manhattan, Murray Hill",
          "Manhattan, Hell's Kitchen",
          "Manhattan, Chinatown"
         ]
        }
       },
       "total_rows": 5,
       "truncation_type": null
      },
      "text/plain": "   listing_id        price                nbhood_full\n0        2595  225 dollars         Manhattan, Midtown\n1        3831   89 dollars     Brooklyn, Clinton Hill\n2        5099  200 dollars     Manhattan, Murray Hill\n3        5178   79 dollars  Manhattan, Hell's Kitchen\n4        5238  150 dollars       Manhattan, Chinatown",
      "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>price</th>\n      <th>nbhood_full</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2595</td>\n      <td>225 dollars</td>\n      <td>Manhattan, Midtown</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>3831</td>\n      <td>89 dollars</td>\n      <td>Brooklyn, Clinton Hill</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>5099</td>\n      <td>200 dollars</td>\n      <td>Manhattan, Murray Hill</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>5178</td>\n      <td>79 dollars</td>\n      <td>Manhattan, Hell's Kitchen</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5238</td>\n      <td>150 dollars</td>\n      <td>Manhattan, Chinatown</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 24
    }
   ],
   "execution_count": 24
  },
  {
   "source": [
    "room.head()"
   ],
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 60,
    "lastExecutedAt": 1705538073694,
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "room.head()",
    "outputsMetadata": {
     "0": {
      "height": 198,
      "type": "dataFrame"
     }
    }
   },
   "cell_type": "code",
   "id": "cb7b2cea-c995-4bcc-a61d-86266b3c4f7b",
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "application/com.datacamp.data-table.v2+json": {
       "table": {
        "schema": {
         "fields": [
          {
           "name": "index",
           "type": "integer"
          },
          {
           "name": "listing_id",
           "type": "integer"
          },
          {
           "name": "description",
           "type": "string"
          },
          {
           "name": "room_type",
           "type": "string"
          }
         ],
         "primaryKey": [
          "index"
         ],
         "pandas_version": "1.4.0"
        },
        "data": {
         "index": [
          0,
          1,
          2,
          3,
          4
         ],
         "listing_id": [
          2595,
          3831,
          5099,
          5178,
          5238
         ],
         "description": [
          "Skylit Midtown Castle",
          "Cozy Entire Floor of Brownstone",
          "Large Cozy 1 BR Apartment In Midtown East",
          "Large Furnished Room Near B'way",
          "Cute & Cozy Lower East Side 1 bdrm"
         ],
         "room_type": [
          "Entire home/apt",
          "Entire home/apt",
          "Entire home/apt",
          "private room",
          "Entire home/apt"
         ]
        }
       },
       "total_rows": 5,
       "truncation_type": null
      },
      "text/plain": "   listing_id                                description        room_type\n0        2595                      Skylit Midtown Castle  Entire home/apt\n1        3831            Cozy Entire Floor of Brownstone  Entire home/apt\n2        5099  Large Cozy 1 BR Apartment In Midtown East  Entire home/apt\n3        5178            Large Furnished Room Near B'way     private room\n4        5238         Cute & Cozy Lower East Side 1 bdrm  Entire home/apt",
      "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>description</th>\n      <th>room_type</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2595</td>\n      <td>Skylit Midtown Castle</td>\n      <td>Entire home/apt</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>3831</td>\n      <td>Cozy Entire Floor of Brownstone</td>\n      <td>Entire home/apt</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>5099</td>\n      <td>Large Cozy 1 BR Apartment In Midtown East</td>\n      <td>Entire home/apt</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>5178</td>\n      <td>Large Furnished Room Near B'way</td>\n      <td>private room</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5238</td>\n      <td>Cute &amp; Cozy Lower East Side 1 bdrm</td>\n      <td>Entire home/apt</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 25
    }
   ],
   "execution_count": 25
  },
  {
   "source": [
    "review.head()"
   ],
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 56,
    "lastExecutedAt": 1705538073750,
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "review.head()",
    "outputsMetadata": {
     "0": {
      "height": 198,
      "type": "dataFrame"
     }
    }
   },
   "cell_type": "code",
   "id": "062bc9b7-1e7d-4dcf-b779-dc4359a73f64",
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "application/com.datacamp.data-table.v2+json": {
       "table": {
        "schema": {
         "fields": [
          {
           "name": "index",
           "type": "integer"
          },
          {
           "name": "listing_id",
           "type": "integer"
          },
          {
           "name": "host_name",
           "type": "string"
          },
          {
           "name": "last_review",
           "type": "string"
          }
         ],
         "primaryKey": [
          "index"
         ],
         "pandas_version": "1.4.0"
        },
        "data": {
         "index": [
          0,
          1,
          2,
          3,
          4
         ],
         "listing_id": [
          2595,
          3831,
          5099,
          5178,
          5238
         ],
         "host_name": [
          "Jennifer",
          "LisaRoxanne",
          "Chris",
          "Shunichi",
          "Ben"
         ],
         "last_review": [
          "May 21 2019",
          "July 05 2019",
          "June 22 2019",
          "June 24 2019",
          "June 09 2019"
         ]
        }
       },
       "total_rows": 5,
       "truncation_type": null
      },
      "text/plain": "   listing_id    host_name   last_review\n0        2595     Jennifer   May 21 2019\n1        3831  LisaRoxanne  July 05 2019\n2        5099        Chris  June 22 2019\n3        5178     Shunichi  June 24 2019\n4        5238          Ben  June 09 2019",
      "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>host_name</th>\n      <th>last_review</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2595</td>\n      <td>Jennifer</td>\n      <td>May 21 2019</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>3831</td>\n      <td>LisaRoxanne</td>\n      <td>July 05 2019</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>5099</td>\n      <td>Chris</td>\n      <td>June 22 2019</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>5178</td>\n      <td>Shunichi</td>\n      <td>June 24 2019</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5238</td>\n      <td>Ben</td>\n      <td>June 09 2019</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 26
    }
   ],
   "execution_count": 26
  },
  {
   "source": [
    "1. What are the dates of the earliest and most recent reviews? Store these values as two separate variables with your preferred names."
   ],
   "metadata": {},
   "cell_type": "markdown",
   "id": "436ba6fb-a61c-42ad-a3fb-8006f82a9dd7"
  },
  {
   "source": [
    "review[\"last_review\"] = pd.to_datetime(review[\"last_review\"], infer_datetime_format=True, errors='coerce')\n",
    "review[\"last_review\"].head()"
   ],
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 52,
    "lastExecutedAt": 1705538073802,
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "review[\"last_review\"] = pd.to_datetime(review[\"last_review\"], infer_datetime_format=True, errors='coerce')\nreview[\"last_review\"].head()"
   },
   "cell_type": "code",
   "id": "956ee5da-b06a-4a0b-8628-5edf88ceafad",
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0   2019-05-21\n1   2019-07-05\n2   2019-06-22\n3   2019-06-24\n4   2019-06-09\nName: last_review, dtype: datetime64[ns]"
     },
     "metadata": {},
     "execution_count": 27
    }
   ],
   "execution_count": 27
  },
  {
   "source": [
    "first_review_date = review[\"last_review\"].min()\n",
    "last_review_date = review[\"last_review\"].max()\n",
    "display(first_review_date, last_review_date)"
   ],
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 52,
    "lastExecutedAt": 1705538073854,
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "first_review_date = review[\"last_review\"].min()\nlast_review_date = review[\"last_review\"].max()\ndisplay(first_review_date, last_review_date)",
    "outputsMetadata": {
     "0": {
      "height": 257,
      "type": "stream"
     }
    }
   },
   "cell_type": "code",
   "id": "625df053-4311-4504-b1b0-03a3b7573ba9",
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "Timestamp('2019-01-01 00:00:00')"
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "Timestamp('2019-07-09 00:00:00')"
     },
     "metadata": {}
    }
   ],
   "execution_count": 28
  },
  {
   "source": [
    "2. How many of the listings are private rooms? Save this into any variable."
   ],
   "metadata": {},
   "cell_type": "markdown",
   "id": "3903e04d-a808-40ad-b5b2-abae0fee3be0"
  },
  {
   "source": [
    "room[\"room_type\"] = room[\"room_type\"].str.lower()\n",
    "room[\"room_type\"].value_counts()"
   ],
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 64,
    "lastExecutedAt": 1705538073918,
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "room[\"room_type\"] = room[\"room_type\"].str.lower()\nroom[\"room_type\"].value_counts()"
   },
   "cell_type": "code",
   "id": "8c025a04-57e6-489d-8b95-d8c663adfc17",
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "entire home/apt    13266\nprivate room       11356\nshared room          587\nName: room_type, dtype: int64"
     },
     "metadata": {},
     "execution_count": 29
    }
   ],
   "execution_count": 29
  },
  {
   "source": [
    "private_rooms = room[\"room_type\"].value_counts()[\"private room\"]\n",
    "display(private_rooms)"
   ],
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 51,
    "lastExecutedAt": 1705538073969,
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "private_rooms = room[\"room_type\"].value_counts()[\"private room\"]\ndisplay(private_rooms)"
   },
   "cell_type": "code",
   "id": "e116a011-4ee2-4bee-b7f8-fb6fe6e5c1c7",
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "11356"
     },
     "metadata": {}
    }
   ],
   "execution_count": 30
  },
  {
   "source": [
    "3. What is the average listing price? Round to the nearest penny and save into a variable."
   ],
   "metadata": {},
   "cell_type": "markdown",
   "id": "ee3b9f65-2fac-435d-8cbc-04018fc41938"
  },
  {
   "source": [
    "price[\"price\"].head()"
   ],
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 53,
    "lastExecutedAt": 1705538074022,
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "price[\"price\"].head()"
   },
   "cell_type": "code",
   "id": "68af15ee-6cba-49d0-b2d3-c311b55d00ae",
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0    225 dollars\n1     89 dollars\n2    200 dollars\n3     79 dollars\n4    150 dollars\nName: price, dtype: object"
     },
     "metadata": {},
     "execution_count": 31
    }
   ],
   "execution_count": 31
  },
  {
   "source": [
    "price[\"price\"] = price[\"price\"].str.replace(\" dollars\", \"\")\n",
    "price[\"price\"].head()"
   ],
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 60,
    "lastExecutedAt": 1705538074082,
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "price[\"price\"] = price[\"price\"].str.replace(\" dollars\", \"\")\nprice[\"price\"].head()"
   },
   "cell_type": "code",
   "id": "64f4f301-8bca-47e6-bc07-8f6eb7b17dc9",
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0    225\n1     89\n2    200\n3     79\n4    150\nName: price, dtype: object"
     },
     "metadata": {},
     "execution_count": 32
    }
   ],
   "execution_count": 32
  },
  {
   "source": [
    "price[\"price\"] = price[\"price\"].astype(\"float64\")\n",
    "average_price = round(price[\"price\"].mean(), 2)\n",
    "display(average_price)"
   ],
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 52,
    "lastExecutedAt": 1705538074134,
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "price[\"price\"] = price[\"price\"].astype(\"float64\")\naverage_price = round(price[\"price\"].mean(), 2)\ndisplay(average_price)",
    "outputsMetadata": {
     "0": {
      "height": 177,
      "type": "stream"
     }
    }
   },
   "cell_type": "code",
   "id": "85383eb9-134b-4b23-ba88-31c45393d1f3",
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "141.78"
     },
     "metadata": {}
    }
   ],
   "execution_count": 33
  },
  {
   "source": [
    "4. Combine the new variables into one DataFrame called review_dates with four columns in the following order: first_reviewed, last_reviewed, nb_private_rooms, and avg_price. The DataFrame should only contain one row of values."
   ],
   "metadata": {},
   "cell_type": "markdown",
   "id": "a3a191be-fa5a-43c7-88b8-d01389d22882"
  },
  {
   "source": [
    "results = {\n",
    "    \"first_reviewed\": [first_review_date],\n",
    "    \"last_reviewed\": [last_review_date],\n",
    "    \"nb_private_rooms\": [private_rooms],\n",
    "    \"avg_price\": [average_price]\n",
    "}\n",
    "review_dates = pd.DataFrame(results)\n",
    "display(review_dates)"
   ],
   "metadata": {
    "executionCancelledAt": null,
    "executionTime": 56,
    "lastExecutedAt": 1705538074190,
    "lastScheduledRunId": null,
    "lastSuccessfullyExecutedCode": "results = {\n    \"first_reviewed\": [first_review_date],\n    \"last_reviewed\": [last_review_date],\n    \"nb_private_rooms\": [private_rooms],\n    \"avg_price\": [average_price]\n}\nreview_dates = pd.DataFrame(results)\ndisplay(review_dates)",
    "outputsMetadata": {
     "0": {
      "height": 98,
      "type": "dataFrame"
     }
    }
   },
   "cell_type": "code",
   "id": "c016b4c3-4591-4132-9135-c49e659f7de2",
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "application/com.datacamp.data-table.v2+json": {
       "table": {
        "schema": {
         "fields": [
          {
           "name": "index",
           "type": "integer"
          },
          {
           "name": "first_reviewed",
           "type": "datetime"
          },
          {
           "name": "last_reviewed",
           "type": "datetime"
          },
          {
           "name": "nb_private_rooms",
           "type": "integer"
          },
          {
           "name": "avg_price",
           "type": "number"
          }
         ],
         "primaryKey": [
          "index"
         ],
         "pandas_version": "1.4.0"
        },
        "data": {
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          "2019-01-01T00:00:00.000"
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      },
      "text/plain": "  first_reviewed last_reviewed  nb_private_rooms  avg_price\n0     2019-01-01    2019-07-09             11356     141.78",
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   "execution_count": 34
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