san_francisco_housing.ipynb

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   "source": [
    "# Importing the required libraries and dependencies.\n",
    "import pandas as pd\n",
    "import hvplot.pandas\n",
    "from pathlib import Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>neighborhood</th>\n",
       "      <th>sale_price_sqr_foot</th>\n",
       "      <th>housing_units</th>\n",
       "      <th>gross_rent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010</td>\n",
       "      <td>Alamo Square</td>\n",
       "      <td>291.182945</td>\n",
       "      <td>372560</td>\n",
       "      <td>1239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010</td>\n",
       "      <td>Anza Vista</td>\n",
       "      <td>267.932583</td>\n",
       "      <td>372560</td>\n",
       "      <td>1239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010</td>\n",
       "      <td>Bayview</td>\n",
       "      <td>170.098665</td>\n",
       "      <td>372560</td>\n",
       "      <td>1239</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "   year  neighborhood  sale_price_sqr_foot  housing_units  gross_rent\n",
       "0  2010  Alamo Square           291.182945         372560        1239\n",
       "1  2010    Anza Vista           267.932583         372560        1239\n",
       "2  2010       Bayview           170.098665         372560        1239"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>neighborhood</th>\n",
       "      <th>sale_price_sqr_foot</th>\n",
       "      <th>housing_units</th>\n",
       "      <th>gross_rent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>2016</td>\n",
       "      <td>Van Ness/ Civic Center</td>\n",
       "      <td>552.602567</td>\n",
       "      <td>384242</td>\n",
       "      <td>4390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>2016</td>\n",
       "      <td>Visitacion Valley</td>\n",
       "      <td>328.319007</td>\n",
       "      <td>384242</td>\n",
       "      <td>4390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>2016</td>\n",
       "      <td>Westwood Park</td>\n",
       "      <td>631.195426</td>\n",
       "      <td>384242</td>\n",
       "      <td>4390</td>\n",
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      "text/plain": [
       "     year            neighborhood  sale_price_sqr_foot  housing_units  \\\n",
       "394  2016  Van Ness/ Civic Center           552.602567         384242   \n",
       "395  2016       Visitacion Valley           328.319007         384242   \n",
       "396  2016           Westwood Park           631.195426         384242   \n",
       "\n",
       "     gross_rent  \n",
       "394        4390  \n",
       "395        4390  \n",
       "396        4390  "
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   "source": [
    "# Using the read_csv function and Path module, create a DataFrame.\n",
    "\n",
    "sfo_data_df = pd.read_csv(\n",
    "    Path(\"sfo_neighborhoods_census_data.csv\")\n",
    ")\n",
    "\n",
    "# Review the first and last five rows of the DataFrame.\n",
    "display(sfo_data_df.head(3))\n",
    "display(sfo_data_df.tail(3))"
   ]
  },
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   "cell_type": "code",
   "execution_count": 64,
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      "text/plain": [
       "(397, 5)"
      ]
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   "source": [
    "sfo_data_df.shape"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
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   "execution_count": 65,
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sale_price_sqr_foot</th>\n",
       "      <th>housing_units</th>\n",
       "      <th>gross_rent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>369.344353</td>\n",
       "      <td>372560</td>\n",
       "      <td>1239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>341.903429</td>\n",
       "      <td>374507</td>\n",
       "      <td>1530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>399.389968</td>\n",
       "      <td>376454</td>\n",
       "      <td>2324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>483.600304</td>\n",
       "      <td>378401</td>\n",
       "      <td>2971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>556.277273</td>\n",
       "      <td>380348</td>\n",
       "      <td>3528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>632.540352</td>\n",
       "      <td>382295</td>\n",
       "      <td>3739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>697.643709</td>\n",
       "      <td>384242</td>\n",
       "      <td>4390</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "      sale_price_sqr_foot  housing_units  gross_rent\n",
       "year                                                \n",
       "2010           369.344353         372560        1239\n",
       "2011           341.903429         374507        1530\n",
       "2012           399.389968         376454        2324\n",
       "2013           483.600304         378401        2971\n",
       "2014           556.277273         380348        3528\n",
       "2015           632.540352         382295        3739\n",
       "2016           697.643709         384242        4390"
      ]
     },
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   "source": [
    "# Creating a numerical aggregation that groups the data by the year and then averages the results.\n",
    "housing_units_by_year = sfo_data_df.groupby('year').mean()\n",
    "\n",
    "# Review the DataFrame\n",
    "housing_units_by_year"
   ]
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      "text/plain": [
       ":Bars   [year]   (gross_rent)"
      ]
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   "source": [
    "# Create a visual aggregation explore the average gross rent by year using HvPlot.\n",
    "housing_units_by_year.hvplot.bar(\n",
    "    x=\"year\",\n",
    "    y=\"gross_rent\",\n",
    "    title=\"Average Gross Rent in San Francisco from 2010 to 2016\",\n",
    "    color=\"red\",\n",
    "    xlabel=\"Year\",\n",
    "    ylabel=\"Gross Rent\"\n",
    ").opts(yformatter='%.0f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        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></th>\n",
       "      <th>sale_price_sqr_foot</th>\n",
       "      <th>housing_units</th>\n",
       "      <th>gross_rent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>neighborhood</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2010</th>\n",
       "      <th>Alamo Square</th>\n",
       "      <td>291.182945</td>\n",
       "      <td>372560</td>\n",
       "      <td>1239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anza Vista</th>\n",
       "      <td>267.932583</td>\n",
       "      <td>372560</td>\n",
       "      <td>1239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bayview</th>\n",
       "      <td>170.098665</td>\n",
       "      <td>372560</td>\n",
       "      <td>1239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Buena Vista Park</th>\n",
       "      <td>347.394919</td>\n",
       "      <td>372560</td>\n",
       "      <td>1239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Central Richmond</th>\n",
       "      <td>319.027623</td>\n",
       "      <td>372560</td>\n",
       "      <td>1239</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       sale_price_sqr_foot  housing_units  gross_rent\n",
       "year neighborhood                                                    \n",
       "2010 Alamo Square               291.182945         372560        1239\n",
       "     Anza Vista                 267.932583         372560        1239\n",
       "     Bayview                    170.098665         372560        1239\n",
       "     Buena Vista Park           347.394919         372560        1239\n",
       "     Central Richmond           319.027623         372560        1239"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group by year and neighborhood and then create a new dataframe of the mean values\n",
    "\n",
    "#prices_by_year_by_neighborhood = sfo_data_df.groupby(by=['year','neighborhood']).mean()\n",
    "prices_by_year_by_neighborhood = sfo_data_df.groupby(['year', 'neighborhood']).mean()\n",
    "\n",
    "\n",
    "# Review the DataFrame\n",
    "prices_by_year_by_neighborhood.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>sale_price_sqr_foot</th>\n",
       "      <th>gross_rent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>neighborhood</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2010</th>\n",
       "      <th>Alamo Square</th>\n",
       "      <td>291.182945</td>\n",
       "      <td>1239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anza Vista</th>\n",
       "      <td>267.932583</td>\n",
       "      <td>1239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bayview</th>\n",
       "      <td>170.098665</td>\n",
       "      <td>1239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Buena Vista Park</th>\n",
       "      <td>347.394919</td>\n",
       "      <td>1239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Central Richmond</th>\n",
       "      <td>319.027623</td>\n",
       "      <td>1239</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       sale_price_sqr_foot  gross_rent\n",
       "year neighborhood                                     \n",
       "2010 Alamo Square               291.182945        1239\n",
       "     Anza Vista                 267.932583        1239\n",
       "     Bayview                    170.098665        1239\n",
       "     Buena Vista Park           347.394919        1239\n",
       "     Central Richmond           319.027623        1239"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>sale_price_sqr_foot</th>\n",
       "      <th>gross_rent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>neighborhood</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2016</th>\n",
       "      <th>Telegraph Hill</th>\n",
       "      <td>903.049771</td>\n",
       "      <td>4390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Twin Peaks</th>\n",
       "      <td>970.085470</td>\n",
       "      <td>4390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Van Ness/ Civic Center</th>\n",
       "      <td>552.602567</td>\n",
       "      <td>4390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Visitacion Valley</th>\n",
       "      <td>328.319007</td>\n",
       "      <td>4390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Westwood Park</th>\n",
       "      <td>631.195426</td>\n",
       "      <td>4390</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             sale_price_sqr_foot  gross_rent\n",
       "year neighborhood                                           \n",
       "2016 Telegraph Hill                   903.049771        4390\n",
       "     Twin Peaks                       970.085470        4390\n",
       "     Van Ness/ Civic Center           552.602567        4390\n",
       "     Visitacion Valley                328.319007        4390\n",
       "     Westwood Park                    631.195426        4390"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Filter out the housing_units\n",
    "prices_by_year_by_neighborhood = prices_by_year_by_neighborhood.drop(columns=['housing_units'])\n",
    "\n",
    "# Review the first and last five rows of the DataFrame\n",
    "display(prices_by_year_by_neighborhood.head())\n",
    "display(prices_by_year_by_neighborhood.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
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      "text/plain": [
       ":DynamicMap   [neighborhood]\n",
       "   :NdOverlay   [Neighborhood]\n",
       "      :Curve   [year]   (value)"
      ]
     },
     "execution_count": 72,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "3492"
      }
     },
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    }
   ],
   "source": [
    "sfo_data_df['neighborhood'] = sfo_data_df['neighborhood'].astype(str)\n",
    "\n",
    "prices_by_year_by_neighborhood = sfo_data_df.groupby(['year', 'neighborhood']).mean()\n",
    "prices_by_year_by_neighborhood.reset_index(inplace=True)\n",
    "\n",
    "prices_by_year_by_neighborhood.hvplot.line(\n",
    "    x='year',\n",
    "    y=['sale_price_sqr_foot', 'gross_rent'],\n",
    "    xlabel='Year',\n",
    "    ylabel='Price per Square Foot',\n",
    "    title='Average Price per Square Foot and Gross Rent in San Francisco by Neighborhood',\n",
    "    groupby=['neighborhood'],\n",
    "    group_label='Neighborhood'\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load neighborhoods coordinates data\n",
    "\n",
    "neighborhood_locations_df = pd.read_csv(\n",
    "    Path(\"neighborhoods_coordinates.csv\"),\n",
    "    index_col=\"Neighborhood\")\n",
    "\n",
    "# Review the DataFrame\n",
    "neighborhood_locations_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the mean values for each neighborhood\n",
    "all_neighborhood_info_df = sfo_data_df.groupby(by=\"neighborhood\").mean()\n",
    "\n",
    "# Review the resulting DataFrame\n",
    "all_neighborhood_info_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using the Pandas `concat` function, join the \n",
    "# neighborhood_locations_df and the all_neighborhood_info_df DataFrame\n",
    "# The axis of the concatenation is \"columns\".\n",
    "# The concat function will automatially combine columns with\n",
    "# identical information, while keeping the additional columns.\n",
    "all_neighborhoods_df = pd.concat(\n",
    "    [neighborhood_locations_df, all_neighborhood_info_df], \n",
    "    axis=\"columns\",\n",
    "    sort=False\n",
    ")\n",
    "\n",
    "# Review the resulting DataFrame\n",
    "#display(all_neighborhoods_df.head())\n",
    "#display(all_neighborhoods_df.tail()\n",
    "all_neighborhoods_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Call the dropna function to remove any neighborhoods that do not have data\n",
    "all_neighborhoods_df = all_neighborhoods_df.reset_index().dropna()\n",
    "\n",
    "# Rename the \"index\" column as \"Neighborhood\" for use in the Visualization\n",
    "all_neighborhoods_df = all_neighborhoods_df.rename(columns={\"index\": \"Neighborhood\"})\n",
    "\n",
    "# Review the resulting DataFrame\n",
    "display(all_neighborhoods_df.head())\n",
    "display(all_neighborhoods_df.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a plot to analyze neighborhood info\n",
    "all_neighborhoods_df.hvplot.points(\n",
    "    'Lon',\n",
    "    'Lat',\n",
    "    geo=True,\n",
    "    color='gross_rent',\n",
    "    size='sale_price_sqr_foot',\n",
    "    tiles='OSM',\n",
    "    frame_width=700,\n",
    "    frame_height=500,\n",
    "    hover_cols=['Neighborhood'],\n",
    "    title='Avg Sale Price per square food and Gross Rent')"
   ]
  }
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