Housing_numpy_matplotlib.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Name: Vineet Dcunha\n",
    "#\"I have not given or received any unauthorized assistance on this assignment.\"\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.mlab as mlab\n",
    "import os.path\n",
    "import pandas as pd\n",
    "\n",
    "__file__ = 'problem_2.ipynb'\n",
    "my_path = os.path.abspath(os.path.dirname(__file__))\n",
    "fname = os.path.join(my_path, '../data/housing.csv')\n",
    "#fname ='C:\\\\Users\\\\USER\\\\Desktop\\\\DSC\\\\Python\\\\Assignment 4\\\\housing.csv'\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using Pandas to read the file.\n",
      "\n",
      "Displaying the first three records along with the column header\n"
     ]
    },
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] File b'C:\\\\Users\\\\USER\\\\Desktop\\\\DSC\\\\Python\\\\Assignment 4\\\\../data/housing.csv' does not exist: b'C:\\\\Users\\\\USER\\\\Desktop\\\\DSC\\\\Python\\\\Assignment 4\\\\../data/housing.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m~\\Desktop\\DSC\\Python\\Assignment 4\\problem_2.ipynb\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mprint\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m'Using Pandas to read the file.\\n'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mprint\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m'Displaying the first three records along with the column header'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mreadcsv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# reading the casv file using pandas\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[0mreadcsv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Displaying the first three records along with the column header\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[0;32m    683\u001b[0m         )\n\u001b[0;32m    684\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 685\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    686\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    687\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    455\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    456\u001b[0m     \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 457\u001b[1;33m     \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfp_or_buf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    458\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    459\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m    893\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    894\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 895\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    896\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    897\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m   1133\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"c\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1134\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"c\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1135\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1136\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1137\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"python\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m   1915\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"usecols\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1916\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1917\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1918\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munnamed_cols\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munnamed_cols\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1919\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] File b'C:\\\\Users\\\\USER\\\\Desktop\\\\DSC\\\\Python\\\\Assignment 4\\\\../data/housing.csv' does not exist: b'C:\\\\Users\\\\USER\\\\Desktop\\\\DSC\\\\Python\\\\Assignment 4\\\\../data/housing.csv'"
     ]
    }
   ],
   "source": [
    "print ('Using Pandas to read the file.\\n')\n",
    "print ('Displaying the first three records along with the column header')\n",
    "readcsv = pd.read_csv(fname) # reading the casv file using pandas\n",
    "readcsv.head (n = 10) # Displaying the first three records along with the column header"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Problem 2 Section 5\n",
      "\n",
      "The size of the housing dataset. \n",
      "\n",
      "206400\n"
     ]
    }
   ],
   "source": [
    "print ('Problem 2 Section 5\\n')\n",
    "print ('The size of the housing dataset. \\n')\n",
    "size_housing = readcsv.size # Size of a dataframe is the number of fields in the dataframe i.e number of rows * number of columns\n",
    "print (size_housing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Problem 2 Section 6\n",
      "\n",
      "Using Numpy to read the file.\n",
      "\n",
      "[('longitude', '<f8'), ('latitude', '<f8'), ('housing_median_age', '<f8'), ('total_rooms', '<f8'), ('total_bedrooms', '<f8'), ('population', '<f8'), ('households', '<f8'), ('median_income', '<f8'), ('median_house_value', '<f8'), ('ocean_proximity', '<U10')]\n"
     ]
    }
   ],
   "source": [
    "print ('Problem 2 Section 6\\n')\n",
    "print ('Using Numpy to read the file.\\n')\n",
    "csv = np.genfromtxt(fname,  dtype=None,  names=True, delimiter=\",\", encoding=None) # reading the casv file using NumPy\n",
    "print(csv.dtype) # displaying the data types for the columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Problem 2 Section 6\n",
      "\n",
      "Following are the data typs for the columns. \n",
      "\n",
      "longitude             float64\n",
      "latitude              float64\n",
      "housing_median_age    float64\n",
      "total_rooms           float64\n",
      "total_bedrooms        float64\n",
      "population            float64\n",
      "households            float64\n",
      "median_income         float64\n",
      "median_house_value    float64\n",
      "ocean_proximity        object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print ('Problem 2 Section 6\\n')\n",
    "print ('Following are the data typs for the columns. \\n')\n",
    "print (readcsv.dtypes)# displaying the data types for the columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Problem 2 Section 7\n",
      "\n",
      "The dimensions of the housing dataset. \n",
      "\n",
      "Dataframe shape : (20640, 10)\n",
      "\n",
      "Number of rows : 20640\n",
      "\n",
      "Number of columns : 10\n"
     ]
    }
   ],
   "source": [
    "print ('Problem 2 Section 7\\n')\n",
    "print ('The dimensions of the housing dataset. \\n')\n",
    "dim_housing = readcsv.shape # Returns number of rows and the number of columns in the DataFrame\n",
    "print ('Dataframe shape :', dim_housing)\n",
    "print ('\\nNumber of rows :', dim_housing[0]) # Returns number of rows in the DataFrame\n",
    "print ('\\nNumber of columns :', dim_housing[1]) # Returns number of columns in the DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Problem 2 Section 8\n",
      "\n",
      "Histogram of population \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print ('Problem 2 Section 8\\n')\n",
    "print ('Histogram of population \\n')\n",
    "plt.hist(readcsv['population'], bins='auto', Color = 'blue') # Reading the population column from csv file. Setting the number of bins to Auto\n",
    "plt.ylabel('Frequency') # Naming Y axis\n",
    "plt.xlabel('Bins') # Naming X axis\n",
    "plt.title('Population') # Title\n",
    "plt.show() # Displaying plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Problem 2 Section 9\n",
      "\n",
      "Bar Chart for Ocean Proximity \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print ('Problem 2 Section 9\\n')\n",
    "print ('Bar Chart for Ocean Proximity \\n')\n",
    "ax = readcsv.groupby('ocean_proximity')['ocean_proximity'].count().plot(kind = 'bar', legend=True, Color = 'red') # Grouping by based on ocean_proximity and taking count of records\n",
    "plt.xlabel(\"Different Ocean Proximity\") # Naming X axis\n",
    "plt.ylabel(\"Frequency\") # Naming Y axis\n",
    "plt.title('Ocean Proximity') # Title\n",
    "for p in ax.patches: # Looping thru the records to get the values of bar\n",
    "    ax.annotate(np.round(p.get_height(),decimals=0), (p.get_x()+p.get_width()/2.,   p.get_height()), ha='center', va='center', xytext=(2, 10), textcoords='offset points')\n",
    "plt.tight_layout()\n",
    "plt.show() # Displaying bar graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Problem 2 Section 10\n",
      "\n",
      "Scatter plot of age (x-axis) and population (y-axis)\n",
      "Color based on price \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print ('Problem 2 Section 10\\n')\n",
    "print ('Scatter plot of age (x-axis) and population (y-axis)')\n",
    "print ('Color based on price \\n')\n",
    "plt.scatter(readcsv['housing_median_age'], readcsv['population'], c=readcsv['median_house_value']) # Scatter with inputs as folows x value, y value and color based on column.\n",
    "plt.xlabel(\"Age\") # Naming X axis\n",
    "plt.ylabel(\"Population\")# Naming Y axis\n",
    "plt.title('Age Vs Population') # Title\n",
    "cbar= plt.colorbar() # Creating a color bar as per value\n",
    "cbar.set_label(\"House Value ($)\", labelpad=+1) # Naming the color bar\n",
    "plt.show()# Displaying Scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The above scatter plot is reading the columns from housing.csv file.\n",
      "It is plotting the housing_median_age column on the x axis and population column on the y axis.\n",
      "The x axis (age) is equally divided into intervals of 10 starting from 0 to 50 +.\n",
      "The y axis (age) is equally divided into intervals of 5000 starting from 0 to 35000 +.\n",
      "The color of the plots is based on median_housing_value column. The color shade becomes lighter for every interval $100000.\n",
      "The color band starts from $0 upto $500000. As the prices go higher, the color is lighter.\n",
      "The population for majority of the data is between 0 and 10000. There are couple of outliers at 30000 and 35000.\n",
      "The age of the housing is heavy in the first half. Approximately 70% of the data has its age between 0 to 30.\n",
      "Large number of houses priced between $0 to $150000 are aged above 20 years.\n",
      "Majority of the new houses are priced above $200000. Around 25% - 35% of the houses are priced above $300000.\n",
      "We have few houses which are priced around $500000 and are more than 45 years old.\n"
     ]
    }
   ],
   "source": [
    "# Explanation for Scatter plot.\n",
    "\n",
    "print ('The above scatter plot is reading the columns from housing.csv file.')\n",
    "print ('It is plotting the housing_median_age column on the x axis and population column on the y axis.')\n",
    "print ('The x axis (age) is equally divided into intervals of 10 starting from 0 to 50 +.')\n",
    "print ('The y axis (age) is equally divided into intervals of 5000 starting from 0 to 35000 +.')\n",
    "print ('The color of the plots is based on median_housing_value column. The color shade becomes lighter for every interval $100000.')\n",
    "print ('The color band starts from $0 upto $500000. As the prices go higher, the color is lighter.')\n",
    "print ('The population for majority of the data is between 0 and 10000. There are couple of outliers at 30000 and 35000.')\n",
    "print ('The age of the housing is heavy in the first half. Approximately 70% of the data has its age between 0 to 30.')\n",
    "print ('Large number of houses priced between $0 to $150000 are aged above 20 years.')\n",
    "print ('Majority of the new houses are priced above $200000. Around 25% - 35% of the houses are priced above $300000.')\n",
    "print ('We have few houses which are priced around $500000 and are more than 45 years old.')"
   ]
  }
 ],
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