Q2- The more crowded and expensive neighborhood.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Which neighborhoods are more crowded and expensive?"
   ]
  },
  {
   "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 listings data.\n",
    "listings_df = pd.read_csv('listings.csv',sep=',')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Assess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>id</th>\n",
       "      <th>neighbourhood_group_cleansed</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>241032</td>\n",
       "      <td>Queen Anne</td>\n",
       "      <td>$85.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>953595</td>\n",
       "      <td>Queen Anne</td>\n",
       "      <td>$150.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3308979</td>\n",
       "      <td>Queen Anne</td>\n",
       "      <td>$975.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7421966</td>\n",
       "      <td>Queen Anne</td>\n",
       "      <td>$100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>278830</td>\n",
       "      <td>Queen Anne</td>\n",
       "      <td>$450.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id neighbourhood_group_cleansed    price\n",
       "0   241032                   Queen Anne   $85.00\n",
       "1   953595                   Queen Anne  $150.00\n",
       "2  3308979                   Queen Anne  $975.00\n",
       "3  7421966                   Queen Anne  $100.00\n",
       "4   278830                   Queen Anne  $450.00"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new dataframe with only needed columns\n",
    "needed_columns=['id','neighbourhood_group_cleansed','price']\n",
    "listings_df_clean= pd.DataFrame(data=listings_df, index=listings_df.index, columns=needed_columns) \n",
    "listings_df_clean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3818, 3)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The number of rows and columns of listings data\n",
    "listings_df_clean.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3818 entries, 0 to 3817\n",
      "Data columns (total 3 columns):\n",
      "id                              3818 non-null int64\n",
      "neighbourhood_group_cleansed    3818 non-null object\n",
      "price                           3818 non-null object\n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 89.6+ KB\n"
     ]
    }
   ],
   "source": [
    "# The columns information of listings data\n",
    "listings_df_clean.info()"
   ]
  },
  {
   "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    150.0\n",
       "2    975.0\n",
       "3    100.0\n",
       "4    450.0\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Change the type of price feature to float\n",
    "listings_df_clean['price']= pd.to_numeric(listings_df_clean['price'].str.translate({ord(i): None for i in '$,'}))\n",
    "listings_df_clean['price'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2. Handle Missing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price                           0.0\n",
       "neighbourhood_group_cleansed    0.0\n",
       "id                              0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Missing data in each column\n",
    "listings_df_clean.isnull().mean().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**No Missing Data is found**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Model and Visualize"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1. Price Variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "neighbourhood_group_cleansed\n",
       "Magnolia               177.672131\n",
       "Queen Anne             157.223729\n",
       "Downtown               154.405660\n",
       "West Seattle           141.359606\n",
       "Cascade                140.101124\n",
       "Capitol Hill           130.592593\n",
       "Central Area           128.338753\n",
       "Ballard                124.682609\n",
       "Other neighborhoods    119.968514\n",
       "Interbay               109.727273\n",
       "Seward Park            108.477273\n",
       "Beacon Hill             94.669492\n",
       "University District     93.639344\n",
       "Lake City               92.671642\n",
       "Rainier Valley          91.899371\n",
       "Northgate               88.550000\n",
       "Delridge                83.278481\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Average price for each neighbourhood group\n",
    "neighbourhood_groups_price=listings_df_clean.groupby(['neighbourhood_group_cleansed'])['price'].mean()\n",
    "\n",
    "# Sort values in descending order\n",
    "neighbourhood_groups_price=neighbourhood_groups_price.sort_values(ascending=False)\n",
    "neighbourhood_groups_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     17.000000\n",
       "mean     119.838682\n",
       "std       27.847598\n",
       "min       83.278481\n",
       "25%       93.639344\n",
       "50%      119.968514\n",
       "75%      140.101124\n",
       "max      177.672131\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get statistical information\n",
    "neighbourhood_groups_price.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Magnolia', 'Queen Anne', 'Downtown', 'West Seattle', 'Cascade']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Consider neighbourhood with avergae price over 140 to be expensive (above 75% of data)\n",
    "high_price_neighbourhood_val=neighbourhood_groups_price[neighbourhood_groups_price>140]\n",
    "\n",
    "# Get names of high price months\n",
    "high_price_neighbourhood=high_price_neighbourhood_val.index.tolist()\n",
    "high_price_neighbourhood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Lake City', 'Rainier Valley', 'Northgate', 'Delridge']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Consider neighbourhood with avergae price under 93 to be cheap (under 25% of data)\n",
    "low_price_neighbourhood_val=neighbourhood_groups_price[neighbourhood_groups_price<93]\n",
    "\n",
    "# Get names of high price months\n",
    "low_price_neighbourhood=low_price_neighbourhood_val.index.tolist()\n",
    "low_price_neighbourhood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the average price per neighbourhoods\n",
    "plt.figure(figsize=(14,7))\n",
    "plt.bar(neighbourhood_groups_price.index,neighbourhood_groups_price)\n",
    "plt.xticks(rotation=90)\n",
    "plt.xlabel('Neighborhoods')\n",
    "plt.ylabel('Price')\n",
    "plt.title('Price variance among neighborhoods')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Based on the above graph,**\n",
    "- Magnolia, Queen Anne, Downtown, and Cascade are the most expensive.\n",
    "- Delridge, Northgate, Rainier Valley, and Lake City are the cheapest."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2. Listings Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "neighbourhood_group_cleansed\n",
       "Other neighborhoods    0.207962\n",
       "Capitol Hill           0.148507\n",
       "Downtown               0.138816\n",
       "Central Area           0.096647\n",
       "Queen Anne             0.077266\n",
       "Ballard                0.060241\n",
       "West Seattle           0.053169\n",
       "Rainier Valley         0.041645\n",
       "University District    0.031954\n",
       "Beacon Hill            0.030906\n",
       "Cascade                0.023311\n",
       "Northgate              0.020953\n",
       "Delridge               0.020691\n",
       "Lake City              0.017548\n",
       "Magnolia               0.015977\n",
       "Seward Park            0.011524\n",
       "Interbay               0.002881\n",
       "Name: id, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The average number of listings for each neighbourhood group\n",
    "neighbourhood_group_popular=listings_df_clean.groupby(['neighbourhood_group_cleansed'])['id'].count()/listings_df_clean.shape[0]\n",
    "\n",
    "# Sort values in descending order\n",
    "neighbourhood_group_popular=neighbourhood_group_popular.sort_values(ascending=False)\n",
    "neighbourhood_group_popular"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    17.000000\n",
       "mean      0.058824\n",
       "std       0.057739\n",
       "min       0.002881\n",
       "25%       0.020691\n",
       "50%       0.031954\n",
       "75%       0.077266\n",
       "max       0.207962\n",
       "Name: id, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get statistical information\n",
    "neighbourhood_group_popular.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Other neighborhoods', 'Capitol Hill', 'Downtown', 'Central Area']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Consider neighbourhood with The average number of listings over 0.08 to be popular (above 75% of data)\n",
    "pop_neighbourhood_val=neighbourhood_group_popular[neighbourhood_group_popular>0.08]\n",
    "\n",
    "# Get names of high price months\n",
    "pop_neighbourhood=pop_neighbourhood_val.index.tolist()\n",
    "pop_neighbourhood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Lake City', 'Magnolia', 'Seward Park', 'Interbay']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Consider neighbourhood with The average number of listings under 0.02 to be unpopular (under 25% of data)\n",
    "unpop_neighbourhood_val=neighbourhood_group_popular[neighbourhood_group_popular<0.02]\n",
    "\n",
    "# Get names of high price months\n",
    "unpop_neighbourhood=unpop_neighbourhood_val.index.tolist()\n",
    "unpop_neighbourhood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Average number of listings per neighbourhoods\n",
    "plt.figure(figsize=(14,7))\n",
    "plt.bar(neighbourhood_group_popular.index,neighbourhood_group_popular)\n",
    "plt.xticks(rotation=90)\n",
    "plt.xlabel('Neighborhoods')\n",
    "plt.ylabel('Average number of listings')\n",
    "plt.title('Listings distribution among neighborhoods')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Based on the above graph,**\n",
    "- Capitol Hill, Downtown, and Central Area are the most crowded.\n",
    "- Interbay, Seward Park, Magnolia, and Lake City are the least crowded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Downtown']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get monhts that are expensive and crowded\n",
    "list(set(high_price_neighbourhood) & set(pop_neighbourhood) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Magnolia']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get monhts that are expensive and less crowded\n",
    "list(set(high_price_neighbourhood) & set(unpop_neighbourhood) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get monhts that are cheap and crowded\n",
    "list(set(low_price_neighbourhood) & set(pop_neighbourhood) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Lake City']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get monhts that are cheap and less crowded\n",
    "list(set(low_price_neighbourhood) & set(unpop_neighbourhood) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**In Conclusion,** \n",
    "- Downtown is expensive and more crowded\n",
    "- Magnolia is expensive and less crowded \n",
    "- Lake City is cheap and less crowded"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python [conda env:Anaconda3]",
   "language": "python",
   "name": "conda-env-Anaconda3-py"
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  "language_info": {
   "codemirror_mode": {
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