PandasEDA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d7f5046c-4b7f-455d-983b-45aa6de3b511",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4db6e942-00e3-4f9a-80d1-40deb2c8f9d2",
   "metadata": {},
   "source": [
    "#### Conduct exploratory data analysis in order to find out\n",
    "#### where there are outliers or missing values, decide how you will treat them, make sure the dates are formatted correctly,\n",
    "#### make sure values you think are numerical are being treated as such, etc.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1648257d-0cb8-4ab8-bd1d-4db827262d8f",
   "metadata": {},
   "source": [
    "## 1. Reading the Data (4pts)\n",
    "## (a) Load the dataset using pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4af2ff43-90bd-4cfd-8d4b-ef88ff343df3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"rollingsales_brooklyn.csv\",  skiprows = 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cedf195-2a30-4abe-bbe6-ddfd904c2fdb",
   "metadata": {},
   "source": [
    "## (b) Display the shape of the data to make sure it was read\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e01d7588-c187-4cf2-a1b9-f3b82d0f2d20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23373, 21)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f3bc92b-42d1-4788-b40e-08dafe6a84a9",
   "metadata": {
    "tags": []
   },
   "source": [
    "## (c) Display the first 2 rows\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bbdef6b7-71af-4ab6-b3ed-2b6ec96ea9a2",
   "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>BOROUGH</th>\n",
       "      <th>NEIGHBORHOOD</th>\n",
       "      <th>BUILDING CLASS CATEGORY</th>\n",
       "      <th>TAX CLASS AT PRESENT</th>\n",
       "      <th>BLOCK</th>\n",
       "      <th>LOT</th>\n",
       "      <th>EASE-MENT</th>\n",
       "      <th>BUILDING CLASS AT PRESENT</th>\n",
       "      <th>ADDRESS</th>\n",
       "      <th>APART\\nMENT\\nNUMBER</th>\n",
       "      <th>...</th>\n",
       "      <th>RESIDENTIAL UNITS</th>\n",
       "      <th>COMMERCIAL UNITS</th>\n",
       "      <th>TOTAL UNITS</th>\n",
       "      <th>LAND SQUARE FEET</th>\n",
       "      <th>GROSS SQUARE FEET</th>\n",
       "      <th>YEAR BUILT</th>\n",
       "      <th>TAX CLASS AT TIME OF SALE</th>\n",
       "      <th>BUILDING CLASS AT TIME OF SALE</th>\n",
       "      <th>SALE\\nPRICE</th>\n",
       "      <th>SALE DATE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td></td>\n",
       "      <td>15  CONDOS - 2-10 UNIT RESIDENTIAL</td>\n",
       "      <td></td>\n",
       "      <td>814</td>\n",
       "      <td>1103</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>342 53RD    STREET</td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>R1</td>\n",
       "      <td>$403,572</td>\n",
       "      <td>7/9/13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td></td>\n",
       "      <td>15  CONDOS - 2-10 UNIT RESIDENTIAL</td>\n",
       "      <td></td>\n",
       "      <td>814</td>\n",
       "      <td>1105</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>342 53RD    STREET</td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>R1</td>\n",
       "      <td>$218,010</td>\n",
       "      <td>7/12/13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   BOROUGH               NEIGHBORHOOD  \\\n",
       "0        3                              \n",
       "1        3                              \n",
       "\n",
       "                        BUILDING CLASS CATEGORY TAX CLASS AT PRESENT  BLOCK  \\\n",
       "0  15  CONDOS - 2-10 UNIT RESIDENTIAL                                   814   \n",
       "1  15  CONDOS - 2-10 UNIT RESIDENTIAL                                   814   \n",
       "\n",
       "    LOT EASE-MENT BUILDING CLASS AT PRESENT  \\\n",
       "0  1103                                       \n",
       "1  1105                                       \n",
       "\n",
       "                                       ADDRESS APART\\nMENT\\nNUMBER  ...  \\\n",
       "0   342 53RD    STREET                                              ...   \n",
       "1   342 53RD    STREET                                              ...   \n",
       "\n",
       "   RESIDENTIAL UNITS  COMMERCIAL UNITS  TOTAL UNITS  LAND SQUARE FEET  \\\n",
       "0                  0                 0            0                 0   \n",
       "1                  0                 0            0                 0   \n",
       "\n",
       "  GROSS SQUARE FEET YEAR BUILT  TAX CLASS AT TIME OF SALE  \\\n",
       "0                 0          0                          2   \n",
       "1                 0          0                          2   \n",
       "\n",
       "   BUILDING CLASS AT TIME OF SALE SALE\\nPRICE SALE DATE  \n",
       "0                             R1     $403,572    7/9/13  \n",
       "1                             R1     $218,010   7/12/13  \n",
       "\n",
       "[2 rows x 21 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c77e07c-10c3-43f6-8808-891eb5765825",
   "metadata": {},
   "source": [
    "## (d) Display row 14 to see the field names clearly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ed0d3af2-57fc-4914-afb0-3d33711265d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BOROUGH                                                                      3\n",
       "NEIGHBORHOOD                                         BATH BEACH               \n",
       "BUILDING CLASS CATEGORY                                                       \n",
       "TAX CLASS AT PRESENT                                                         4\n",
       "BLOCK                                                                     6370\n",
       "LOT                                                                       1321\n",
       "EASE-MENT                                                                     \n",
       "BUILDING CLASS AT PRESENT                                                   RP\n",
       "ADDRESS                             98 BAY 20TH   STREET                      \n",
       "APART\\nMENT\\nNUMBER                                               P1          \n",
       "ZIP CODE                                                                 11214\n",
       "RESIDENTIAL UNITS                                                            0\n",
       "COMMERCIAL UNITS                                                             0\n",
       "TOTAL UNITS                                                                  1\n",
       "LAND SQUARE FEET                                                             0\n",
       "GROSS SQUARE FEET                                                            0\n",
       "YEAR BUILT                                                                2011\n",
       "TAX CLASS AT TIME OF SALE                                                    4\n",
       "BUILDING CLASS AT TIME OF SALE                                             RP \n",
       "SALE\\nPRICE                                                                 $0\n",
       "SALE DATE                                                              12/7/12\n",
       "Name: 14, dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[14]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "627df760-7b4b-4a28-86d2-36d4eeb4d3aa",
   "metadata": {},
   "source": [
    "## 2. Reformatting column labels (4pts)\n",
    "## (a) Convert the column names to lower case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7fd9c364-53a9-46b8-8557-f7cc616f8671",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['borough', 'neighborhood', 'building class category',\n",
       "       'tax class at present', 'block', 'lot', 'ease-ment',\n",
       "       'building class at present', 'address', 'apart\\nment\\nnumber',\n",
       "       'zip code', 'residential units', 'commercial units', 'total units',\n",
       "       'land square feet', 'gross square feet', 'year built',\n",
       "       'tax class at time of sale', 'building class at time of sale',\n",
       "       'sale\\nprice', 'sale date'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns = df.columns.str.lower()\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e089982-0f32-465f-ae4d-c63f4eaa1860",
   "metadata": {
    "tags": []
   },
   "source": [
    "## (b) Replace the ‘\\n’ in the column names with space\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9540961b-5f9e-4328-83dd-99591a62ae62",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = df.columns.str.replace('\\n', ' ')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cffca68-363b-43a0-ba90-631bf6b0cc20",
   "metadata": {},
   "source": [
    "## (c) Replace the spaces in the column names with underscores\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6972caa4-3e9c-46da-909b-451c62ba93a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = df.columns.str.replace(' ', '_')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bfdb3e0-f24c-4f31-a416-8164a0ea6fef",
   "metadata": {},
   "source": [
    "## (d) Display row 14 again to see the change in the field names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c519c677-4f94-47e8-8353-84176264ccae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "borough                                                                      3\n",
       "neighborhood                                         BATH BEACH               \n",
       "building_class_category                                                       \n",
       "tax_class_at_present                                                         4\n",
       "block                                                                     6370\n",
       "lot                                                                       1321\n",
       "ease-ment                                                                     \n",
       "building_class_at_present                                                   RP\n",
       "address                             98 BAY 20TH   STREET                      \n",
       "apart_ment_number                                                 P1          \n",
       "zip_code                                                                 11214\n",
       "residential_units                                                            0\n",
       "commercial_units                                                             0\n",
       "total_units                                                                  1\n",
       "land_square_feet                                                             0\n",
       "gross_square_feet                                                            0\n",
       "year_built                                                                2011\n",
       "tax_class_at_time_of_sale                                                    4\n",
       "building_class_at_time_of_sale                                             RP \n",
       "sale_price                                                                  $0\n",
       "sale_date                                                              12/7/12\n",
       "Name: 14, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[14]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9110be18-3920-40c3-bfd4-f5af89c10afb",
   "metadata": {},
   "source": [
    "## 3. Convert the field “sale_price” to numeric (it is currently formatted as currency which cannot be used in calculations.)\n",
    "(Hints: There is a to_numeric function. You will first need to remove the commas and dollar signs. See slide 33 or\n",
    "35 of the Pandas preprocessing tutorial.) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "81fd557f-ac32-4dcd-8be9-a7976ac2e035",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['sale_price'] = pd.to_numeric(df['sale_price'].replace({'\\$': '', ',': ''}, regex=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d87ec1d-1d3a-4741-86c0-78e1381f7272",
   "metadata": {},
   "source": [
    "## 4. Display a count of missing sale prices (those set to ‘0s’) (1pt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "70336e6c-0214-4d22-90bd-206af3b3e869",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         8791\n",
       "10         241\n",
       "700000     138\n",
       "650000     129\n",
       "300000     120\n",
       "          ... \n",
       "737000       1\n",
       "751000       1\n",
       "498942       1\n",
       "599500       1\n",
       "354969       1\n",
       "Name: sale_price, Length: 3318, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['sale_price'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "314d490b-d371-4e36-8641-5aef21fb031a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8791"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df['sale_price'] == 0).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c152ce87-ed68-4543-af4d-09a2ea799b8b",
   "metadata": {},
   "source": [
    "## 5. Convert “land_square_feet” to numeric (it’s currently a formatted string which cannot be used in calculations) (1pt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "78fce16f-adb4-40a8-a956-d192ad94f89d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['land_square_feet'] = pd.to_numeric(df['land_square_feet'].replace({',': ''}, regex=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06614386-ed9a-4719-8da5-b8de75fd033c",
   "metadata": {},
   "source": [
    "## 6. Convert “gross_square_feet” to numeric, “sale_date” to datetime, “year_built” to numeric (int32), and “zip_code” to string (4pts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "311d18c4-249f-4362-b357-cb83ca83aaf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['gross_square_feet'] = pd.to_numeric(df['gross_square_feet'].replace({',': ''}, regex=True))\n",
    "df['year_built'] = df['year_built'].astype(np.int32)\n",
    "df['sale_date'] = pd.to_datetime(df['sale_date'])\n",
    "df['zip_code'] = df['zip_code'].astype(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e83d40ed-2187-4acc-9584-5e29bcbdb3e9",
   "metadata": {},
   "source": [
    "## 7. Change EAST/WEST to E/W for each address (2pts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "598d2e16-ef96-4401-ae14-18c9ee649b42",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['address'] = df['address'].replace({'EAST': 'E', 'WEST': 'W'}, regex=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79a774d2-c3f7-4b0a-b989-1b076ce9c6b8",
   "metadata": {},
   "source": [
    "## 8. Change STREET/AVENUE to ST/AVE for each address (2pts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "52fe7c8b-a35e-4866-be44-2cca00979b7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['address'] = df['address'].replace({'STREET': 'ST', 'AVENUE': 'AVE'}, regex=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b474645-5ddd-4610-99dc-5a97284493c4",
   "metadata": {},
   "source": [
    "## 9. Plot the following histograms: (4pts)\n",
    "• sale price by counts\n",
    "\n",
    "• sale price>0\n",
    "\n",
    "• gross sqft where sale price == 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c4c85750-e6f2-4f65-b753-e76446d204f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1440 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "\n",
    "price = df[df['sale_price']>0]\n",
    "zero_price= df[df['sale_price']==0]\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize = (10,20))\n",
    "\n",
    "ax1 = fig.add_subplot(311)\n",
    "ax1.set_title('sale_price by counts', fontsize=20)\n",
    "df['sale_price'].hist(ax=ax1)\n",
    "\n",
    "ax2 = fig.add_subplot(312)\n",
    "ax2.set_title('sale_price > 0', fontsize=20)\n",
    "price['sale_price'].hist(ax=ax2)\n",
    "\n",
    "ax3 = fig.add_subplot(313)\n",
    "ax3.set_title('gross sqft where sale price == 0', fontsize=20)\n",
    "zero_price['gross_square_feet'].hist(ax=ax3);\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5e33b0d-36e2-4117-b96e-d239ccc8b029",
   "metadata": {},
   "source": [
    "## 10. Filter the data by family homes (i.e., return a subset of rows in a temp variable where the \"building_class_category\" contains \"FAMILY\" ) (2pts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a2852848-abe4-4801-ab5e-31149dfcd7bf",
   "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>borough</th>\n",
       "      <th>neighborhood</th>\n",
       "      <th>building_class_category</th>\n",
       "      <th>tax_class_at_present</th>\n",
       "      <th>block</th>\n",
       "      <th>lot</th>\n",
       "      <th>ease-ment</th>\n",
       "      <th>building_class_at_present</th>\n",
       "      <th>address</th>\n",
       "      <th>apart_ment_number</th>\n",
       "      <th>...</th>\n",
       "      <th>residential_units</th>\n",
       "      <th>commercial_units</th>\n",
       "      <th>total_units</th>\n",
       "      <th>land_square_feet</th>\n",
       "      <th>gross_square_feet</th>\n",
       "      <th>year_built</th>\n",
       "      <th>tax_class_at_time_of_sale</th>\n",
       "      <th>building_class_at_time_of_sale</th>\n",
       "      <th>sale_price</th>\n",
       "      <th>sale_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3</td>\n",
       "      <td>BATH BEACH</td>\n",
       "      <td>01  ONE FAMILY HOMES</td>\n",
       "      <td>1</td>\n",
       "      <td>6363</td>\n",
       "      <td>23</td>\n",
       "      <td></td>\n",
       "      <td>A9</td>\n",
       "      <td>8641 16TH AVE</td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2058</td>\n",
       "      <td>1492</td>\n",
       "      <td>1930</td>\n",
       "      <td>1</td>\n",
       "      <td>A9</td>\n",
       "      <td>560000</td>\n",
       "      <td>2013-06-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>3</td>\n",
       "      <td>BATH BEACH</td>\n",
       "      <td>01  ONE FAMILY HOMES</td>\n",
       "      <td>1</td>\n",
       "      <td>6366</td>\n",
       "      <td>69</td>\n",
       "      <td></td>\n",
       "      <td>A1</td>\n",
       "      <td>8658 BAY 16TH   ST</td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4833</td>\n",
       "      <td>1724</td>\n",
       "      <td>1930</td>\n",
       "      <td>1</td>\n",
       "      <td>A1</td>\n",
       "      <td>860000</td>\n",
       "      <td>2012-12-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3</td>\n",
       "      <td>BATH BEACH</td>\n",
       "      <td>01  ONE FAMILY HOMES</td>\n",
       "      <td>1</td>\n",
       "      <td>6371</td>\n",
       "      <td>21</td>\n",
       "      <td></td>\n",
       "      <td>A9</td>\n",
       "      <td>73 BAY 20TH   ST</td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2417</td>\n",
       "      <td>2132</td>\n",
       "      <td>1930</td>\n",
       "      <td>1</td>\n",
       "      <td>A9</td>\n",
       "      <td>508000</td>\n",
       "      <td>2012-08-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>3</td>\n",
       "      <td>BATH BEACH</td>\n",
       "      <td>01  ONE FAMILY HOMES</td>\n",
       "      <td>1</td>\n",
       "      <td>6378</td>\n",
       "      <td>64</td>\n",
       "      <td></td>\n",
       "      <td>A1</td>\n",
       "      <td>56 BAY 28TH   ST</td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3867</td>\n",
       "      <td>1704</td>\n",
       "      <td>1899</td>\n",
       "      <td>1</td>\n",
       "      <td>A1</td>\n",
       "      <td>600000</td>\n",
       "      <td>2013-06-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3</td>\n",
       "      <td>BATH BEACH</td>\n",
       "      <td>01  ONE FAMILY HOMES</td>\n",
       "      <td>1</td>\n",
       "      <td>6379</td>\n",
       "      <td>42</td>\n",
       "      <td></td>\n",
       "      <td>S1</td>\n",
       "      <td>2142 86TH   ST</td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1600</td>\n",
       "      <td>2640</td>\n",
       "      <td>1925</td>\n",
       "      <td>1</td>\n",
       "      <td>S1</td>\n",
       "      <td>0</td>\n",
       "      <td>2012-12-14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    borough               neighborhood  \\\n",
       "25        3  BATH BEACH                  \n",
       "26        3  BATH BEACH                  \n",
       "27        3  BATH BEACH                  \n",
       "28        3  BATH BEACH                  \n",
       "29        3  BATH BEACH                  \n",
       "\n",
       "                         building_class_category tax_class_at_present  block  \\\n",
       "25  01  ONE FAMILY HOMES                                            1   6363   \n",
       "26  01  ONE FAMILY HOMES                                            1   6366   \n",
       "27  01  ONE FAMILY HOMES                                            1   6371   \n",
       "28  01  ONE FAMILY HOMES                                            1   6378   \n",
       "29  01  ONE FAMILY HOMES                                            1   6379   \n",
       "\n",
       "    lot ease-ment building_class_at_present  \\\n",
       "25   23                                  A9   \n",
       "26   69                                  A1   \n",
       "27   21                                  A9   \n",
       "28   64                                  A1   \n",
       "29   42                                  S1   \n",
       "\n",
       "                                     address apart_ment_number  ...  \\\n",
       "25   8641 16TH AVE                                              ...   \n",
       "26    8658 BAY 16TH   ST                                        ...   \n",
       "27    73 BAY 20TH   ST                                          ...   \n",
       "28    56 BAY 28TH   ST                                          ...   \n",
       "29    2142 86TH   ST                                            ...   \n",
       "\n",
       "   residential_units  commercial_units  total_units  land_square_feet  \\\n",
       "25                 1                 0            1              2058   \n",
       "26                 1                 0            1              4833   \n",
       "27                 1                 0            1              2417   \n",
       "28                 1                 0            1              3867   \n",
       "29                 1                 1            2              1600   \n",
       "\n",
       "    gross_square_feet  year_built  tax_class_at_time_of_sale  \\\n",
       "25               1492        1930                          1   \n",
       "26               1724        1930                          1   \n",
       "27               2132        1930                          1   \n",
       "28               1704        1899                          1   \n",
       "29               2640        1925                          1   \n",
       "\n",
       "    building_class_at_time_of_sale sale_price  sale_date  \n",
       "25                             A9      560000 2013-06-06  \n",
       "26                             A1      860000 2012-12-18  \n",
       "27                             A9      508000 2012-08-24  \n",
       "28                             A1      600000 2013-06-18  \n",
       "29                             S1           0 2012-12-14  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "family_homes = df[df['building_class_category'].str.contains('FAMILY')]\n",
    "family_homes.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64e7f8e6-3a2d-4447-b97f-12e7eab793e5",
   "metadata": {},
   "source": [
    "## 11. Create a log scatter plot of “gross sqft” vs “sale price” for family homes (2pts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "297eb72f-f022-4b23-9ab9-ab30dde8b1df",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Mjhon/miniforge3/lib/python3.9/site-packages/pandas/core/arraylike.py:364: RuntimeWarning: divide by zero encountered in log\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,10));\n",
    "x = np.log(family_homes['gross_square_feet']);\n",
    "y = np.log(family_homes['sale_price']);\n",
    "plt.xlabel('gross_square_feet', fontsize=20);\n",
    "plt.ylabel('sale_price', fontsize=20);\n",
    "plt.scatter(x,y);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54614587-f62c-41c9-9d5a-da24245ab70b",
   "metadata": {},
   "source": [
    "## 12. Return a subset of family homes where sale price<100000. How many homes fall in this category? (2pts)\n",
    "\n",
    "4745"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "01d26337-4a16-49d9-94ae-e2e0365d9306",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4745"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# price_condition = family_homes['sale_price'] < 100000\n",
    "subset = family_homes[family_homes['sale_price'] < 100000]\n",
    "subset['sale_price'].value_counts().values.sum()\n",
    "# subset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0eb6a751-0020-445a-800e-b9ed4ae5c6d0",
   "metadata": {},
   "source": [
    "## 13. Return a count of the sale prices (Notice that some homes were sold for a $1, etc. These seem to be outliers) (1pt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d2b0c574-f336-4f60-95ff-85868453774b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       4306\n",
       "1         27\n",
       "10       174\n",
       "100       10\n",
       "500        6\n",
       "1000      22\n",
       "1500       1\n",
       "2000       1\n",
       "2094       1\n",
       "2164       1\n",
       "Name: sale_price, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset['sale_price'].value_counts().sort_index()[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be41a246-87ae-4bb6-8959-862be332d406",
   "metadata": {},
   "source": [
    "## 14. Remove outliers of sales with sale price <= 5 (2pts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7f9e26ec-30cd-45e2-a5ee-15af09975a55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "412"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outliers_removed = subset[subset['sale_price'] > 5]\n",
    "outliers_removed['sale_price'].value_counts().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc0be7d5-6b59-4924-a85f-a3ff73ce5824",
   "metadata": {},
   "source": [
    "## 15. With the outliers removed, create a plot of “gross sqft” vs “sale price” (2pts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7708a1f2-0e1a-4168-bc1f-ddbe1bfc0d54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,10));\n",
    "x_removed = outliers_removed['gross_square_feet'];\n",
    "y_removed = outliers_removed['sale_price'];\n",
    "\n",
    "plt.xlabel('gross_square_feet', fontsize=20);\n",
    "plt.ylabel('sale_price', fontsize=20);\n",
    "plt.scatter(x_removed,y_removed);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8828d130-a8ef-4093-a8ee-bf3a0227e9cb",
   "metadata": {},
   "source": [
    "## 16. Create a plot of the top 10 home sales for family homes. (5pts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bbdc34df-fcfc-4703-bb74-6bb251945efd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# topvalues = top['sale_price'].value_counts().nlargest(10).index\n",
    "# topvalues\n",
    "# topten = top[top['sale_price'].isin(topvalues)]\n",
    "# topten"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5b847e5c-3fb6-439c-86b7-674a38c03942",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0         4306\n",
      "10         174\n",
      "600000      82\n",
      "700000      77\n",
      "650000      75\n",
      "300000      71\n",
      "800000      61\n",
      "550000      61\n",
      "450000      59\n",
      "200000      59\n",
      "Name: sale_price, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(family_homes['sale_price'].value_counts().nlargest(10))\n",
    "top = family_homes[family_homes['sale_price'] > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6f63bb50-e317-47a5-b410-ea79fec245ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# outliers_removed \n",
    "\n",
    "fig = plt.figure(figsize = (10,10));\n",
    "\n",
    "color = plt.cm.rainbow(np.linspace(0, 1, 10))\n",
    "\n",
    "plt.xlabel('House Price', fontsize=20);\n",
    "plt.ylabel('House Sale Counts', fontsize=20);\n",
    "\n",
    "\n",
    "index = np.arange(10)  \n",
    "\n",
    "# std = outliers_removed['sale_price'].std()\n",
    "# # mean = topten['sale_price'].mstd)\n",
    "# print(std)\n",
    "\n",
    "y = top['sale_price'].value_counts()[:10].index\n",
    "x = top['sale_price'].value_counts()[:10].values\n",
    "\n",
    "# plt.bar(index, y, yerr=std, width=0.90, color=color)\n",
    "plt.bar(index, y, width=0.90, color=color)\n",
    "plt.xticks(index, x);\n",
    "plt.show()\n",
    "# outliers_removed['sale_price'].value_counts()[:10].plot(kind='bar', rot=0);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "966e8ffa-d155-4c82-94f3-a9f76b8c7061",
   "metadata": {},
   "source": [
    "## MEME TIME / SPAGHETTI CODE \n",
    "NO LOOPS JUST A LOT OF BROADCASTING PAIN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7bfce6e5-2a84-4d97-97b7-16a39991a12b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_border(img, color):\n",
    "    h, w, c = img.shape\n",
    "    border = 10\n",
    "    new_img_array = np.full((h+2*border, w+2*border, c), color) \n",
    "    new_img_array[border:-border, border:-border] = img\n",
    "    return new_img_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9552f2ca-f2d8-4867-91df-ac40c184d698",
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image, ImageDraw, ImageFont\n",
    " \n",
    "file = 'spaghetticode.png'\n",
    "img = Image.open(file)\n",
    "img = np.array(img)\n",
    "\n",
    "# print(img.shape)\n",
    "magic = img[50:335,45:335]\n",
    "# print(magic.shape)\n",
    "spaghetti = img[50:335, 370:660]\n",
    "# img.save('pil_red.png')\n",
    "plt.figure(figsize = (10,10))\n",
    "\n",
    "#Ignore this...wanted to add rainbow to each tiles but can't use loops :(\n",
    "border_color = (plt.cm.rainbow(np.linspace(0, 1, 9))[:,:3] * 255).astype(np.uint8)\n",
    "\n",
    "border = add_border(magic, border_color[0])\n",
    "border2 = add_border(spaghetti, border_color[3])\n",
    "\n",
    "new = np.tile(border, (3,3,1))\n",
    "h,w = border2.shape[:2]\n",
    "new[h:-h, w:-w] = border2\n",
    "\n",
    "\n",
    "meme = np.full((100+new.shape[0], new.shape[1], new.shape[2]), (255,255,255), dtype = new.dtype) \n",
    "meme[100:, :] = new\n",
    "\n",
    "draw = Image.fromarray(meme)\n",
    "fnt = ImageFont.truetype('/Library/Fonts/Arial.ttf', 40)\n",
    "d = ImageDraw.Draw(draw)\n",
    "d.text((150,25), 'The truth inside software application', font=fnt, fill=(0, 0, 0))\n",
    "d.text((340,525), 'spaghetti code',font=fnt, fill=(255, 255, 255))\n",
    "\n",
    "plt.imshow(draw);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}