Student_Python_for_Data_Science-Tahseen.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Python for Data Science\n",
    "### Learning Center\n",
    "### Sanjay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n30 mins but explains a lot of \\nhttps://www.youtube.com/watch?v=IpGxLWOIZy4\\n--\\n5 mins \\nhttps://www.youtube.com/watch?v=3bJ7RChxMWQ\\n--\\n2 mins \\nhttps://www.youtube.com/watch?v=VUrqddjkxok\\n--\\n2 mins\\nhttps://www.youtube.com/watch?v=ydvDwmE4x-Y\\n--\\n2 mins\\nhttps://www.youtube.com/watch?v=ty-kTUzMnjk\\n'"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "30 mins but explains a lot of \n",
    "https://www.youtube.com/watch?v=IpGxLWOIZy4\n",
    "--\n",
    "5 mins \n",
    "https://www.youtube.com/watch?v=3bJ7RChxMWQ\n",
    "--\n",
    "2 mins \n",
    "https://www.youtube.com/watch?v=VUrqddjkxok\n",
    "--\n",
    "2 mins\n",
    "https://www.youtube.com/watch?v=ydvDwmE4x-Y\n",
    "--\n",
    "2 mins\n",
    "https://www.youtube.com/watch?v=ty-kTUzMnjk\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello Sunday\n"
     ]
    }
   ],
   "source": [
    "print(\"Hello Sunday\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello, Python!\n"
     ]
    }
   ],
   "source": [
    "print(\"Hello, Python!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### --- Exercise 1\n",
    "### Print the following string \"Learning Python for Data Science\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning Python for Data Science\n"
     ]
    }
   ],
   "source": [
    "# Exercise 1 - write code in this cell and click on\n",
    "# \"->|\" below \"Cell\" to run your code in this cell.\n",
    "print(\"Learning Python for Data Science\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n",
      "1000.0\n",
      "John\n",
      "1\n",
      "1\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "counter = 100          # An integer assignment\n",
    "miles   = 1000.0       # A floating point\n",
    "name    = \"John\"       # A string\n",
    "\n",
    "print(counter)\n",
    "print(miles)\n",
    "print(name)\n",
    "\n",
    "a = b = c = 1\n",
    "print(a)\n",
    "print(b)\n",
    "print(c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### --- Exrecise 2\n",
    "### Create variables with your first_name and last_name\n",
    "### print first_name and last_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tahseen Baig\n"
     ]
    }
   ],
   "source": [
    "# Exercise 2 - Python code\n",
    "first_name = \"Tahseen\"\n",
    "last_name = \"Baig\"\n",
    "print(first_name + ' ' +last_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ML class is on seven Saturdays  at Lynbrook\n"
     ]
    }
   ],
   "source": [
    "# Exercise 2b -- write code in this cell and click on \"->|\" below \"Cell\" \n",
    "# to run your code in this cell.\n",
    "# Initialise following variables:\n",
    "class_name = \"ML\"\n",
    "which_day = \"Saturdays \"\n",
    "Num_days = \"seven\"\n",
    "Class_timings = \"8:30 am\"\n",
    "location=\"Lynbrook\"\n",
    "# You want to print the following string:\n",
    "# \"ML class is on seven Saturdays at Lynbrook\"\n",
    "str1 = class_name +\" class is on \" +Num_days+' '+which_day+' at '+location\n",
    "print(str1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello World!\n",
      "H\n",
      "llo W\n",
      "llo World!\n",
      "Hello World!Hello World!\n",
      "Hello World! TEST\n",
      "!\n",
      "ld!\n"
     ]
    }
   ],
   "source": [
    "str = 'Hello World!'\n",
    "\n",
    "a = str\n",
    "b = str[0]\n",
    "c = str[2:7]\n",
    "d = str[2:]\n",
    "e = str * 2\n",
    "f = str + \" \" + \"TEST\"\n",
    "g = str[-1]\n",
    "h = str[-3:]\n",
    "print(a)          # Prints complete string\n",
    "print(b)       # Prints first character of the string\n",
    "print(c)     # Prints characters starting from 3rd to 5th\n",
    "print(d)      # Prints string starting from 3rd character\n",
    "print(e)      # Prints string two times\n",
    "print(f) # Prints concatenated string\n",
    "print(g)\n",
    "print(h)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### --- Exercise 3 \n",
    "### print first two characters of first_name and last two characters of last_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T\n",
      "F\n",
      "B\n"
     ]
    }
   ],
   "source": [
    "# Exercise 3 - write code in this cell \n",
    "# You want to extract first letter from first_name, middle_name, and last_name\n",
    "fn=\"Tahseen\"\n",
    "mn=\"Fatema\"\n",
    "ln=\"Baig\"\n",
    "print(fn[0])\n",
    "print(mn[0])\n",
    "print(ln[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['abcd', 786, 2.23, 'john', 70.2]\n",
      "abcd\n",
      "[786, 2.23]\n",
      "[2.23, 'john', 70.2]\n"
     ]
    }
   ],
   "source": [
    "###########################################################\n",
    "## Python Lists\n",
    "###########################################################\n",
    "list = [ 'abcd', 786 , 2.23, 'john', 70.2 ]\n",
    "\n",
    "print(list)          # Prints complete list\n",
    "print(list[0])       # Prints first element of the list\n",
    "print(list[1:3])     # Prints elements starting from 2nd till 3rd \n",
    "print(list[-3:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 4 -- Lists\n",
    "## Define a new Python list \"new_list\" and initialize it with \"1\",\"A\",\"2\",\"B\",\"3\",\"C\"\n",
    "## Print values with alternate indices 0,2,4 and 1,3,5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['1', '2', '3']\n",
      "['A', 'B', 'C']\n"
     ]
    }
   ],
   "source": [
    "## Write code for Exercise 4 in this cell\n",
    "new_list = [\"1\",\"A\",\"2\",\"B\",\"3\",\"C\"]\n",
    "L1=new_list[0::2]\n",
    "print(L1)\n",
    "\n",
    "L2=new_list[1::2]\n",
    "print(L2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('abcde', 786, 2.23, 'john', 70.2)\n",
      "abcde\n",
      "(786, 2.23)\n",
      "aaaaa\n"
     ]
    }
   ],
   "source": [
    "###########################################################\n",
    "## Python Tuples\n",
    "###########################################################\n",
    "tuple = ('abcd', 786 , 2.23, 'john', 70.2 )\n",
    "tuple = ('abcde', 786 , 2.23, 'john', 70.2 )\n",
    "print(tuple)          # Prints complete list\n",
    "print(tuple[0])       # Prints first element of the list\n",
    "print(tuple[1:3])     # Prints elements starting from 2nd till 3rd \n",
    "var1 = \"jshjah\"\n",
    "var1 = \"aaaaa\"\n",
    "print(var1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4 - Got a false expression value\n",
      "150000\n"
     ]
    }
   ],
   "source": [
    "## Decisions in Python\n",
    "var = 150000\n",
    "if var == 2000:\n",
    "   print(\"1 - Got a true expression value\")\n",
    "   print(var)\n",
    "elif var == 150:\n",
    "   print(\"2 - Got a true expression value\")\n",
    "   print(var)\n",
    "elif var == 100:\n",
    "   print(\"3 - Got a true expression value\")\n",
    "   print(var)\n",
    "elif var == \"Sunnyvale\":\n",
    "    print(\"var is Sunnyvale\")\n",
    "else:\n",
    "   print(\"4 - Got a false expression value\")\n",
    "   print(var)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 5 - if .. elif .. else\n",
    "### Initialize string state_name to be \"California\" or \"Texas\"\n",
    "### write if ... elif ... else checking state_name \n",
    "### to print if state_name is Golden State or Lone Star state\n",
    "### default else to printing \"Not Golden State or Lone Star state\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Golden State\n"
     ]
    }
   ],
   "source": [
    "# Write code for Exercise 5 in this box\n",
    "State=\"California\"\n",
    "if State==\"California\":\n",
    "    print(\"Golden State\")\n",
    "elif State==\"Texas\":\n",
    "    print(\"Lone Star State\")\n",
    "else:\n",
    "    print(\"Not Golden State or Lone Star state\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The count is: 0\n",
      "The count is: 1\n",
      "The count is: 2\n",
      "The count is: 3\n",
      "The count is: 4\n",
      "The count is: 5\n",
      "The count is: 6\n",
      "The count is: 7\n",
      "The count is: 8\n",
      "Current Letter : P\n",
      "Current Letter : y\n",
      "Current Letter : t\n",
      "Current Letter : h\n",
      "Current Letter : o\n",
      "Current Letter : n\n",
      "Current fruit : banana\n",
      "Current fruit : apple\n",
      "Current fruit : mango\n",
      "['tesla', 'toyota', 'honda']\n"
     ]
    }
   ],
   "source": [
    "## For and while Loops\n",
    "## while loop\n",
    "count = 0\n",
    "while (count < 9):\n",
    "   print('The count is:', count)\n",
    "   count += 1\n",
    "\n",
    "## for loop\n",
    "for letter in 'Python':     # First Example\n",
    "   print('Current Letter :', letter)\n",
    "\n",
    "fruits = ['banana', 'apple',  'mango']\n",
    "for fruit in fruits:        # Second Example\n",
    "   print('Current fruit :', fruit)\n",
    "\n",
    "cars = [\"honda\",\"toyota\",\"tesla\"]\n",
    "cars.reverse()\n",
    "print(cars)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 6 -- print fruits in reverse order\n",
    "### mango\n",
    "### apple\n",
    "### banana"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['mango', 'apple', 'banana']\n"
     ]
    }
   ],
   "source": [
    "# Write code for Exercise 6 in this cell\n",
    "fruits.reverse()\n",
    "print(fruits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cats\n",
      "smarter\n",
      "dogs\n"
     ]
    }
   ],
   "source": [
    "## Regular expressions\n",
    "import re\n",
    "\n",
    "line = \"Cats are smarter than dogs\"\n",
    "\n",
    "## Matching using re\n",
    "matchObj = re.match('(.*) are (.*) than (.*)',line,re.M|re.I)\n",
    "\n",
    "print(matchObj.group(1))\n",
    "print(matchObj.group(2))\n",
    "print(matchObj.group(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Phone Num :  4002-959-559 # This is Phone Number\n"
     ]
    }
   ],
   "source": [
    "## search and replace\n",
    "phone = \"2004-959-559 # This is Phone Number\"\n",
    "\n",
    "# make 2004 to be 4002\n",
    "num = re.sub(\"2004\",\"4002\", phone) # this is comment   \n",
    "print(\"Phone Num : \", num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14\n",
      "15\n",
      "14\n",
      "110\n"
     ]
    }
   ],
   "source": [
    "## Functions in Python\n",
    "def add_numbers(a=5,b=10):\n",
    "    c = a + b\n",
    "    return c\n",
    "\n",
    "result = add_numbers(2,12)\n",
    "print(result)\n",
    "result = add_numbers()\n",
    "print(result)\n",
    "result = add_numbers(2,12)\n",
    "print(result)\n",
    "result = add_numbers(100)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 7 -- functions -- \n",
    "### write function to find a^2 + b^2 given a and b\n",
    "### def square_and_sum(a,b)\n",
    "###\n",
    "### write function to add strings \n",
    "### def add_strings(str1,str2)\n",
    "### when a = \"Hello\" and b = \"World\", it should return \"Hello World!!!\" with space and !!!. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13.0\n",
      "Hello World!!!\n"
     ]
    }
   ],
   "source": [
    "# Write code for Exercise 7 in this cell\n",
    "import math\n",
    "def square_and_sum(a,b):\n",
    "    c=math.pow(a,2) + math.pow(b,2)\n",
    "    return(c)\n",
    "\n",
    "def add_strings(str1,str2):\n",
    "    str3=str1 + \" \"+str2+\"!!!\"\n",
    "    return(str3)\n",
    "result=square_and_sum(2,3)\n",
    "print(result)\n",
    "str_result=add_strings('Hello','World')\n",
    "print(str_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "for x in range(10): \n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "3\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "# range also follows the format range(start_value,stop_value,step_value)\n",
    "for x in range(1,6,2):\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 8 -- using range\n",
    "### print numbers 10, 9, 8, ..., 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "9\n",
      "8\n",
      "7\n",
      "6\n",
      "5\n",
      "4\n",
      "3\n",
      "2\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "# Write code for Exercise 8 in this cell\n",
    "for x in range(10,0,-1):\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 11, 111]\n",
      "list1-a = [1, 11, 111, 1111]\n",
      "list1-b = [1, 11, 111, 1111, 2, 22, 222]\n",
      "list1-c = [1, 11, 111, 1111, 2, 22]\n",
      "list1-d = [1, 2, 11, 22, 111, 1111]\n",
      "list1-e = [1111, 111, 22, 11, 2, 1]\n"
     ]
    }
   ],
   "source": [
    "## Appending and extending Python lists\n",
    "list1 = [1,11,111]\n",
    "list2 = [2,22,222]\n",
    "\n",
    "print(list1)\n",
    "\n",
    "list1.append(1111)\n",
    "print(\"list1-a = %s\" % list1)\n",
    "\n",
    "list1.extend(list2)\n",
    "print(\"list1-b = %s\" % list1)\n",
    "\n",
    "list1.remove(222)\n",
    "print(\"list1-c = %s\" % list1)\n",
    "\n",
    "list1.sort()\n",
    "print(\"list1-d = %s\" % list1)\n",
    "\n",
    "list1.sort(reverse=True)\n",
    "print(\"list1-e = %s\" % list1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 9 -- lists with strings\n",
    "### Initialize states_list with California, Texas, Arizona\n",
    "### step 1: Add Florida\n",
    "### step 2: Remove Texas\n",
    "### step 3: Sort ascending order\n",
    "### step 4: Sort descending order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['California', 'Texas', 'Arizona', 'Florida']\n",
      "['California', 'Arizona', 'Florida']\n",
      "['Arizona', 'California', 'Florida']\n",
      "['Florida', 'California', 'Arizona']\n"
     ]
    }
   ],
   "source": [
    "# Write code for Exercise 9 in this cell\n",
    "states_list=['California','Texas','Arizona']\n",
    "states_list.append('Florida')\n",
    "print(states_list)\n",
    "states_list.remove('Texas')\n",
    "print(states_list)\n",
    "states_list.sort()\n",
    "print(states_list)\n",
    "states_list.sort(reverse=True)\n",
    "print(states_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.5\n",
      "2.5\n",
      "2\n",
      "2.0\n"
     ]
    }
   ],
   "source": [
    "## Integer and floating division\n",
    "def div1(x,y):\n",
    "    print(x/y)\n",
    "    \n",
    "def div2(x,y):\n",
    "    print(x//y) # integer division\n",
    "\n",
    "div1(5,2)\n",
    "div1(5.,2)\n",
    "div2(5,2)\n",
    "div2(5.,2.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    }
   ],
   "source": [
    "rem = 10 % 4\n",
    "print(rem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "2\n",
      "4\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "# Printing list length\n",
    "list = ['a', 'b', 'c', 'd', 'e']\n",
    "print(len(list))\n",
    "lista = [[1,2,3,4],[3,4]]\n",
    "print(lista[0][1])\n",
    "print(len(lista[0]))\n",
    "sizea = np.size(lista)\n",
    "print(sizea)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1-- 1\n",
      "2-- -1\n",
      "3-- 1\n",
      "4-- 7\n",
      "zz liforni\n"
     ]
    }
   ],
   "source": [
    "state_name = \"California\"\n",
    "find_str = state_name.find(\"a\")\n",
    "print(\"1--\",find_str)\n",
    "\n",
    "find_str = state_name.find(\"z\")\n",
    "print(\"2--\",find_str)\n",
    "\n",
    "count_str = state_name.count(\"a\",0,4)\n",
    "print(\"3--\",count_str)\n",
    "\n",
    "find_str = state_name.find(\"nia\")\n",
    "print(\"4--\",find_str)\n",
    "\n",
    "print(\"zz\",state_name[2:state_name.find(\"z\")])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 10 -- find, count --- define the string Albert_Einstein\n",
    "### Albert_Einstein = \"Life is like riding a bicycle. To keep your balance you must keep moving.\"\n",
    "### Find number of occurences for: you, ing, life (all case sensitive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "2\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "# Write code for Wxercise 10 in this cell\n",
    "Albert_Einstein = \"Life is like riding a bicycle. To keep your balance you must keep moving.\"\n",
    "print(Albert_Einstein.count('you'))\n",
    "print(Albert_Einstein.count('ing'))\n",
    "print(Albert_Einstein.count('life'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['This', 'is', 'Learning', 'Center', 'in', 'in', 'Sunnyvale']\n",
      "This is Learning Center in in Sunnyvale\n",
      "{'This': 0, 'is': 0, 'Learning': 0, 'Center': 0, 'in': 0, 'Sunnyvale': 0}\n",
      "{'This': 1, 'is': 1, 'Learning': 1, 'Center': 1, 'in': 2, 'Sunnyvale': 1}\n"
     ]
    }
   ],
   "source": [
    "# Splitting strings\n",
    "ss = \"This is Learning Center in in Sunnyvale\"\n",
    "\n",
    "words = ss.split()\n",
    "print(words)\n",
    "\n",
    "ss1 = \" \".join(words)\n",
    "print(ss1)\n",
    "\n",
    "d = {}\n",
    "# Dictionaries\n",
    "d = {}.fromkeys(words,0)\n",
    "print(d)\n",
    "for w in words:\n",
    "    d[w] += 1\n",
    "print(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Reading csv files\n",
    "def read_file(filename):\n",
    "    file_open = open(filename,\"r\")\n",
    "    data_array = []\n",
    "    for line in iter(file_open):\n",
    "        line_no_newline = line.rstrip()\n",
    "        line_split = line_no_newline.split(\",\")\n",
    "        fn = line_split[0]\n",
    "        work = line_split[2]\n",
    "        concat = [fn,work]\n",
    "        data_array.append(concat)\n",
    "    return data_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['jack', 'engineer'], ['emily', 'lawyer'], ['zen', 'school_a'], ['liz', 'school_b']]\n"
     ]
    }
   ],
   "source": [
    "array1 = read_file(\"family.csv.xls\")\n",
    "print(array1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    name      work\n",
      "0   jack  engineer\n",
      "1  emily    lawyer\n",
      "2    zen  school_a\n",
      "3    liz  school_b\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "family_header=[\"name\",\"work\"]\n",
    "df = pd.DataFrame(array1,columns=family_header)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Regression example in Machine Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"SoSJ_housing_data.csv.xls\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>HomeAge</th>\n",
       "      <th>HomeSqft</th>\n",
       "      <th>LotSize</th>\n",
       "      <th>BedRooms</th>\n",
       "      <th>HighSchoolAPI</th>\n",
       "      <th>ProxFwy</th>\n",
       "      <th>CarGarage</th>\n",
       "      <th>HomePriceK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14</td>\n",
       "      <td>1617</td>\n",
       "      <td>8394</td>\n",
       "      <td>2</td>\n",
       "      <td>851</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13</td>\n",
       "      <td>1358</td>\n",
       "      <td>6819</td>\n",
       "      <td>2</td>\n",
       "      <td>851</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22</td>\n",
       "      <td>1724</td>\n",
       "      <td>7339</td>\n",
       "      <td>3</td>\n",
       "      <td>975</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12</td>\n",
       "      <td>1943</td>\n",
       "      <td>7249</td>\n",
       "      <td>2</td>\n",
       "      <td>974</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>1836</td>\n",
       "      <td>7027</td>\n",
       "      <td>2</td>\n",
       "      <td>966</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>914</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   HomeAge  HomeSqft  LotSize  BedRooms  HighSchoolAPI  ProxFwy  CarGarage  \\\n",
       "0       14      1617     8394         2            851        2          0   \n",
       "1       13      1358     6819         2            851        3          2   \n",
       "2       22      1724     7339         3            975        3          3   \n",
       "3       12      1943     7249         2            974        2          0   \n",
       "4       13      1836     7027         2            966        3          3   \n",
       "\n",
       "   HomePriceK  \n",
       "0        1138  \n",
       "1         859  \n",
       "2        1038  \n",
       "3        1030  \n",
       "4         914  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>HomeAge</th>\n",
       "      <th>HomeSqft</th>\n",
       "      <th>LotSize</th>\n",
       "      <th>BedRooms</th>\n",
       "      <th>HighSchoolAPI</th>\n",
       "      <th>ProxFwy</th>\n",
       "      <th>CarGarage</th>\n",
       "      <th>HomePriceK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>18</td>\n",
       "      <td>1866</td>\n",
       "      <td>7181</td>\n",
       "      <td>2</td>\n",
       "      <td>854</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>10</td>\n",
       "      <td>1246</td>\n",
       "      <td>6810</td>\n",
       "      <td>2</td>\n",
       "      <td>853</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>17</td>\n",
       "      <td>1881</td>\n",
       "      <td>8921</td>\n",
       "      <td>3</td>\n",
       "      <td>852</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>17</td>\n",
       "      <td>1373</td>\n",
       "      <td>8953</td>\n",
       "      <td>2</td>\n",
       "      <td>851</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>15</td>\n",
       "      <td>1828</td>\n",
       "      <td>6956</td>\n",
       "      <td>3</td>\n",
       "      <td>851</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>916</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    HomeAge  HomeSqft  LotSize  BedRooms  HighSchoolAPI  ProxFwy  CarGarage  \\\n",
       "95       18      1866     7181         2            854        2          3   \n",
       "96       10      1246     6810         2            853        4          3   \n",
       "97       17      1881     8921         3            852        2          0   \n",
       "98       17      1373     8953         2            851        2          0   \n",
       "99       15      1828     6956         3            851        4          3   \n",
       "\n",
       "    HomePriceK  \n",
       "95        1049  \n",
       "96         942  \n",
       "97        1194  \n",
       "98        1190  \n",
       "99         916  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nDataset description\\n1) HomeAge = Age of home in years\\n2) HomeSqft = Square footage of home\\n3) LotSize = LotSize\\n4) BedRooms = Num bedrooms as per county data\\n5) HighSchoolAPI = API for nearest high school\\n6) ProxFwy = Distance in miles to Freeway\\n7) CarGarage = Number of cars in garage; 0 = no garage\\n\\nY) HomePriceK = Home price in $K (Target)\\n\\n7 X Variables; 1 Y variable (Target)\\n\\nData Points = 100\\n'"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Dataset description\n",
    "1) HomeAge = Age of home in years\n",
    "2) HomeSqft = Square footage of home\n",
    "3) LotSize = LotSize\n",
    "4) BedRooms = Num bedrooms as per county data\n",
    "5) HighSchoolAPI = API for nearest high school\n",
    "6) ProxFwy = Distance in miles to Freeway\n",
    "7) CarGarage = Number of cars in garage; 0 = no garage\n",
    "\n",
    "Y) HomePriceK = Home price in $K (Target)\n",
    "\n",
    "7 X Variables; 1 Y variable (Target)\n",
    "\n",
    "Data Points = 100\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "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>HomeAge</th>\n",
       "      <th>HomeSqft</th>\n",
       "      <th>LotSize</th>\n",
       "      <th>BedRooms</th>\n",
       "      <th>HighSchoolAPI</th>\n",
       "      <th>ProxFwy</th>\n",
       "      <th>CarGarage</th>\n",
       "      <th>HomePriceK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.00000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>17.200000</td>\n",
       "      <td>1615.280000</td>\n",
       "      <td>7840.500000</td>\n",
       "      <td>2.71000</td>\n",
       "      <td>904.610000</td>\n",
       "      <td>3.100000</td>\n",
       "      <td>1.520000</td>\n",
       "      <td>1080.990000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.925711</td>\n",
       "      <td>231.759719</td>\n",
       "      <td>1046.107306</td>\n",
       "      <td>0.71485</td>\n",
       "      <td>36.166253</td>\n",
       "      <td>0.758787</td>\n",
       "      <td>1.234848</td>\n",
       "      <td>146.533583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>1215.000000</td>\n",
       "      <td>6056.000000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>851.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>809.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>12.750000</td>\n",
       "      <td>1411.500000</td>\n",
       "      <td>7024.250000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>875.750000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>940.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>1606.500000</td>\n",
       "      <td>7822.000000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>901.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>21.250000</td>\n",
       "      <td>1836.000000</td>\n",
       "      <td>8839.250000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>936.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1191.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>9476.000000</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>975.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1336.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          HomeAge     HomeSqft      LotSize   BedRooms  HighSchoolAPI  \\\n",
       "count  100.000000   100.000000   100.000000  100.00000     100.000000   \n",
       "mean    17.200000  1615.280000  7840.500000    2.71000     904.610000   \n",
       "std      4.925711   231.759719  1046.107306    0.71485      36.166253   \n",
       "min     10.000000  1215.000000  6056.000000    2.00000     851.000000   \n",
       "25%     12.750000  1411.500000  7024.250000    2.00000     875.750000   \n",
       "50%     17.000000  1606.500000  7822.000000    3.00000     901.500000   \n",
       "75%     21.250000  1836.000000  8839.250000    3.00000     936.000000   \n",
       "max     25.000000  1994.000000  9476.000000    4.00000     975.000000   \n",
       "\n",
       "          ProxFwy   CarGarage   HomePriceK  \n",
       "count  100.000000  100.000000   100.000000  \n",
       "mean     3.100000    1.520000  1080.990000  \n",
       "std      0.758787    1.234848   146.533583  \n",
       "min      2.000000    0.000000   809.000000  \n",
       "25%      3.000000    0.000000   940.000000  \n",
       "50%      3.000000    2.000000  1100.000000  \n",
       "75%      4.000000    3.000000  1191.000000  \n",
       "max      4.000000    3.000000  1336.000000  "
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "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>HomeAge</th>\n",
       "      <th>HomeSqft</th>\n",
       "      <th>LotSize</th>\n",
       "      <th>BedRooms</th>\n",
       "      <th>HighSchoolAPI</th>\n",
       "      <th>ProxFwy</th>\n",
       "      <th>CarGarage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14</td>\n",
       "      <td>1617</td>\n",
       "      <td>8394</td>\n",
       "      <td>2</td>\n",
       "      <td>851</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13</td>\n",
       "      <td>1358</td>\n",
       "      <td>6819</td>\n",
       "      <td>2</td>\n",
       "      <td>851</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   HomeAge  HomeSqft  LotSize  BedRooms  HighSchoolAPI  ProxFwy  CarGarage\n",
       "0       14      1617     8394         2            851        2          0\n",
       "1       13      1358     6819         2            851        3          2"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.drop('HomePriceK',axis=1)\n",
    "X.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60.645262694757776\n",
      "[ 0.70906208  0.01544843  0.13235772  2.79002749 -0.03065946 -9.62684217\n",
      " -2.98573246]\n",
      "Number of coefficients is  7\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lm = LinearRegression()\n",
    "lm.fit(X,df.HomePriceK)\n",
    "print(lm.intercept_)\n",
    "print(lm.coef_)\n",
    "print (\"Number of coefficients is \",len(lm.coef_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1166.79814669,  938.02621945, 1014.89044119, 1015.09550212,\n",
       "        966.42940391])"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.predict(X)[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Predicted Prices')"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df.HomePriceK,lm.predict(X))\n",
    "plt.xlabel(\"Actual Prices\")\n",
    "plt.ylabel(\"Predicted Prices\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "38.43327976784298\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "mseFull = math.sqrt(np.mean((df.HomePriceK - lm.predict(X)) ** 2))\n",
    "print(mseFull)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "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>HomeAge</th>\n",
       "      <th>HomeSqft</th>\n",
       "      <th>LotSize</th>\n",
       "      <th>BedRooms</th>\n",
       "      <th>HighSchoolAPI</th>\n",
       "      <th>ProxFwy</th>\n",
       "      <th>CarGarage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14</td>\n",
       "      <td>1617</td>\n",
       "      <td>8394</td>\n",
       "      <td>2</td>\n",
       "      <td>851</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13</td>\n",
       "      <td>1358</td>\n",
       "      <td>6819</td>\n",
       "      <td>2</td>\n",
       "      <td>851</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   HomeAge  HomeSqft  LotSize  BedRooms  HighSchoolAPI  ProxFwy  CarGarage\n",
       "0       14      1617     8394         2            851        2          0\n",
       "1       13      1358     6819         2            851        3          2"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[0:2] ## first two data rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    1138\n",
      "1     859\n",
      "Name: HomePriceK, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df.HomePriceK[0:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1166.79814669,  938.02621945])"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row_1 = [14,1617,8394,2,851,2,0]\n",
    "row_2 = [13,1358,6819,2,851,3,2]\n",
    "tests = [row_1,row_2]\n",
    "lm.predict(tests)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x24ea19eed68>]"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df.HomePriceK)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x24ea1a4a710>"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY4AAAEWCAYAAABxMXBSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzt3XucXHV9//HXm80Cm2jZcLE/WAiJNkXxUqKrYmkVoRJQgYit4BXwQluxFfsrP0O1P8BLjdIW29pi4QcqilxabQxFjQheKop1A5GLEo1yS0CJkmAhETbJ5/fHOZNMZs+Zy+45M2d238/HYx87c+bM7Gcmk/nM9/v5XhQRmJmZtWu3XgdgZmb9xYnDzMw64sRhZmYdceIwM7OOOHGYmVlHnDjMzKwjThxmdSR9SdKpvY4jj6TzJH2moMeaJ+lRSQNFPJ7NHE4c1hck3SPpDyZxv5D0Ww3H/krS3emH5jpJV9dui4jjIuJTRcTcbZKOlLQ9fV7/I2mNpNPzzo+I+yLiSRGxrZtxWv9z4rAZJW1NvBH4g4h4EjAK3NDbqAr1QPq8fgN4N3CJpEMbT5I0q+uR2bThxGF9T9LbJK2V9LCkFZIOSI9/Mz3l++m38JOB5wMrI+InABHxs4i4uO6xvi7prenl2v1qPyHpyPS2wyV9W9Km9Lwjm8S3VNJP0lbADyS9qu620yR9S9LfStqYtoSOq7t9gaRvpPe9Hti3ndckEsuBjcChkuan8b9F0n3AjXXHZqV/a29Jn5D0QBrL8ro4Xilpdfp8vy3pOe3EYdOTE4f1NUlHAR8CXgPsD9wLXAUQES9OT/udtEvmauBm4E2SzpY02qx/PyJq93sS8BfAGuAWSSPAdcAHgL2BvwQ+J2m/nIf6CfD7wF7A+cBnJO1fd/sL08feF/gIcKkkpbd9FliV3vZ+oK36i6Td0gQ1DNxed9NLgGcAizPu9mlgNvBM4CnAheljPRe4DPhjYB/gX4EVkvZoJxabhiLCP/6p/A9wD0n3UuPxS4GP1F1/EjAOzE+vB/BbDfd5PfBV4DHgl8DSutu+Dry14fzfAx4Cfju9/m7g0w3nrARObfO5rAZOTC+fBqytu212GvP/AuYBW4E5dbd/FvhMzuMeCWwHNgEPp3/nlPS2+enjPrXu/NqxWSRJdzswN+NxLwLe33BsDfCSXr8v/NObH/dzWr87ALildiUiHpX0S2CEJNlMEBFXAFdIGgSWpJdvjYiVjedKOgi4hiQp/Cg9fDDwR5KOrzt1EPha1t+T9CaSFsv89NCT2LXL6Wd1sW1OGxu1czZGxGN1594LHJT1d1IPRMSBTW6/P+f4QcDDEbEx47aDgVMl/Vndsd1JXnubgdxVZf3uAZIPNgAkzSHpTlnf6o4RMR4R/wbcBjyr8XZJQ8By4KMR8aW6m+4naXEM1/3MiYhlGY9xMHAJ8A5gn4gYBu4A1HhuhgeBuelzqpnXxv2ayVsO+35gb0nDObd9sOH5zo6IK6cYi/UpJw7rJ4OS9qz7mUXSdXO6pMPSPve/Ab4bEfek9/k58NTaA6TF6FdIenJaBziOpE//uxl/7zLgroj4SMPxzwDHS1osaSCN5UhJWd/055B8WG9I//7pZCSpLBFxLzAGnC9pd0m/Bxzf4m6TEhEPAl8C/kXSXEmDkmo1okuAP5H0QiXm1F7DMmKx6nPisH7yRWBL3c95EXED8NfA50i+oT8NOKXuPucBn0pHA70G+BXwV8B9JLWAjwB/GhHfyvh7pwCvahhZ9fsRcT9wYvo4G0i+kZ9Nxv+niPgB8HfAd0iS2LOBmzp4zq8jKZ4/DJwLXN7BfTv1RpL60F0kNZ2zACJiDHgb8DGSUVprSWozNkMpwhs5mZlZ+9ziMDOzjjhxmJlZR5w4zMysI04cZmbWkWk5AXDfffeN+fPn9zoMM7O+smrVql9ERN7SOTtMy8Qxf/58xsbGeh2GmVlfkXRvO+e5q8rMzDrixGFmZh1x4jAzs444cZiZWUecOMzMrCPTclSVmdl0sPzW9Vywcg0PbNrCAcNDnL34EJYsGul1WE4cZmZV9N7lt3PFzfft2EBl/aYtnPP5ZBfgXicPd1WZmVXM8lvX75I0araMb+OClWt6ElM9Jw4zs4q5YOWa3K0aH9i0pauxZHHiMDOrmGbJ4YDhoS5Gks2Jw8ysYvKSg4CzFx/S3WAyOHGYmVXM2YsPYWhwYJdjAl5/+LyeF8bBo6rMzCqnlhyqOBQXnDjMzCppyaKRyiSKRu6qMjOzjjhxmJlZR9xVZWZWUV5yxMzM2rb81vWc8/nb2TK+DfCSI2Zm1sIFK9fsSBo1037JEUmXSXpI0h11x94v6TZJqyV9RdIB6XFJ+kdJa9Pbn1t3n1Ml/Tj9ObWseM3Mpmr5res5YtmNLFh6HUcsu5Hlt66f9GPlzR6f7kuOfBI4tuHYBRHxnIg4DPhP4P+mx48DFqY/ZwAXAUjaGzgXeCHwAuBcSXNLjNnMbFJqXUvrN20h2Nm1NNnkkTd7vApLjpRW44iIb0qa33DsV3VX58COdbxOBC6PiABuljQsaX/gSOD6iHgYQNL1JMnoyrLiNjObjLyupfNW3NlWgbuxEP7Sp+/H51at3+UxhwYHKrHkSNeL45I+CLwJeAR4aXp4BLi/7rR16bG841mPewZJa4V58+YVG7SZWQt5XUibtoyzacs4kF/gziqEf27Vel79vBG+dtcGj6qKiPcA75F0DvAOkq4oZZ3a5HjW414MXAwwOjqatyKxmVmhai2Fdj90agXu+gSQ11r52l0buGnpUQVGW4xejqr6LPDq9PI64KC62w4EHmhy3Mys5+rrGp1obJ1UuRCepauJQ9LCuqsnAHell1cAb0pHVx0OPBIRDwIrgWMkzU2L4sekx8zMei6rpVAzMjzE3NmDmbc1FrinUggvciRXu8ocjnsl8B3gEEnrJL0FWCbpDkm3kSSBd6anfxH4KbAWuAR4O0BaFH8/8L305321QrmZWa/ltQgE3LT0KM49/pkTlkcHeOzxrbt8wGcto95OIbzokVztKnNU1WszDl+ac24AZ+bcdhlwWYGhmZkV4oDhocxuqlpLoVbHOP/aO9m4eXzH7Zu2jO9SJJ/sMurNJgmWWUT3kiNmZpN09uJDdhkNBUlr46VP32/H9SWLRrhg5ZpdEgdM/ICfzDLqvaqNOHGYmU3SkkUjjN37MFfcfN+OUVUBfG5V0lVUG0qbN+Jqqh/wrVo8ZfFaVWZmU/C1uzZMSAxbxrdxxc337ag95Gn8gO+00J23xWx9i6cMThxmZlOQ12poNa+jsfidVeh+19Wrmd8kiSxZNMKrnzeyy4S3WounzAK5E4eZ2RR02i0kkqG6Hzrp2S0nAdaST7PRUnktnjJX0XXiMDObgrzuoiwjw0PcvewV3LT0qAmF8Fb1jrxk0IsCuROHmdkULFk0wodOejYjw0M7WhOvP3xex/My2mm5ZCWDXqyi61FVZmZTlDWUdvTgvSfMywA4YtmNmXM1sob2NspKBln3K3sVXScOM7MSNCaTVlvB1k8CXL9pC2LXAnteMpjs5MGpcOIwM+uCVlvB1n/wf/TkwyYca5YMJjN5cCqUrPYxvYyOjsbY2FivwzAz22HB0utyh+gODQ5M6GpqHHXVSuNGUJNpdUhaFRGjrc5zcdzMrAvyitUDUtOWSDu6vdihE4eZWRfkrYC7LafXp5PhtK26wYrmGoeZ9YX6rpjh2YNEwCNbxiu1pWozeUXsWjG8USfDabs9l8OJw8wqr3FEUv1Ks3n7eFdRXhF7qsNpu73YobuqzKzymu20B+UvsVGmrAmEnRbGJ7sR1GS5xWFmlVTfNdXO2M+q7s/djqkOp+32XA4nDjOrnMauqXaUvQdF1XVzLoe7qsysclp1TTUqe4kN25VbHGZWOc26nQR9OapqOnHiMLPKyRslNDI8xE1Lj+pBRNmKmK3dj9xVZWaV0+1RQpPRyWztTreErTonDjOrnCKGqJat3dna3V4OpBvcVWVmldTtFV871e5s7WYJpsrPrxm3OMzMJqHdnfd6sbVr2Zw4zMwmod06TC+2di2bE4eZ2SS0W4fJSjCDu4nNT2zt22K5axxmZpPUTh2mcTmQvYYGeeyJrTsWauynRRprSmtxSLpM0kOS7qg7doGkuyTdJuk/JA3X3XaOpLWS1khaXHf82PTYWklLy4rXzKwsSxaNcNPSo7h72SuYs8csxrftuvpWvy3SWGZX1SeBYxuOXQ88KyKeA/wIOAdA0qHAKcAz0/v8i6QBSQPAPwPHAYcCr03PNbOKm25zF4oyHYrlpSWOiPgm8HDDsa9ExNb06s3AgenlE4GrIuLxiLgbWAu8IP1ZGxE/jYgngKvSc82swqbj3IWiTIdieS+L428GvpReHgHur7ttXXos7/gEks6QNCZpbMOGDSWEa2bt6vZWplPVzdZRP8yKb6UniUPSe4CtwBW1QxmnRZPjEw9GXBwRoxExut9++xUTqJlNSj91x2S1js66ejWL3veVUhJIP8yKb6Xro6oknQq8Ejg6Yscu7euAg+pOOxB4IL2cd9zMKqrbW5lORd4S7hs3j5c22qnqs+Jb6WqLQ9KxwLuBEyJic91NK4BTJO0haQGwEPhv4HvAQkkLJO1OUkBf0c2YzVpxEXiiKnXHtPr3adYKqnL3Wi+VORz3SuA7wCGS1kl6C/Ax4MnA9ZJWS/o4QETcCVwD/AD4MnBmRGxLC+nvAFYCPwSuSc81qwQXgbNVpTumnX+fVq2g9Zu2zPh/z0ba2Vs0fYyOjsbY2Fivw7AZ4IhlN/bFvhFV0cn+FUXsddHOv08729QODQ70XR1iMiStiojRVud55rjZFPRLEbgKGw41fkA3mzHdybnNtPPvU3u8d129OnvkDf2/mm3RvFaV2RT0w5j8qnSn5Q3RPf/aib3PRQ3nzft3GJ49uEvdox1V+zLQS04cZlNQpSJwnqrMqcj74N24ebztgnWnH96ZCwwOiEd/vXVCIh2ePdj0sar0ZaDXnDjMpqAqReBmqtKd1uyDtzGJFdWSy/r3mbP7LMa3T1wrKoIJSaamal8Ges01DrMpqvqY/KrMqTh78SGcdfXqzNtqSaxWi1m/aQti19m+k/3wbvz3WbD0uszzHtkyzoUnH7bj7w9IbItgpEc1oSpz4jCb5s5efMiEUUNFfIPutOC+ZNEI5624k01bxifcdsDw0ISCeG3piIBCP7ybJdKqfwmoCndVmU1zZXSnTbbgft4Jz8ytCWXVYmpJ46alRxX2gd4Pdamqc4vDbAYo+pt0s4J7q1ZH7f6NLZV3tejGKkqzGKw9Thxm1rGpFNzzklg3azHukpoad1WZWcfKmL/iLqT+4RaH2TRW9IzxMkY91bgLqX84cZhNU0Ut25H3eGWMenIXUn9wV5XZNFX0jPFujXqy6nPiMJumip4xXpUZ6NZ7TROHpL2a3Pb84sMxs6IUXcDuhwUdrTtatThukDS38aCkY4DPlxOS2fTWrR0Dix6l1OrxvBPizNGqOP6vwNckvSwiNgBIeh3wQeAVZQdnNt0UXbBupuhRSs0er5vPy3qv5Q6Akt4I/B/gGOBk4E+AYyPintKjmyTvAGhVNR13DFx+63r+9zXfZ1vGZ0k/P6+ZqLAdACPi05J+DdwK3AccERG/LCBGs57qxa54Uy0wV2Env8Z4zvn87ZlJAzornFftuVm+polD0u3sHK49G9iHpOtKQETEc8oP0ax4vepamcqyGlXsDsoaoluv3cJ5FZ+b5WtVHH8lcHz6+9nAUXXXjy83NLPy9GpXvKkUrKuyk1+9Zi2KTgrxVXxulq9piyMi7q1dlnQwsDAivippqNV9zaqsV3MSplKwruI8irwW1IDU0dLtVXxulq+tD39JbwPOAPYGngYcCHwcOLq80MzK08td8Sa7rEZVdvKrl7dJVKf7fVTxuVm+dmeOnwkcAfwKICJ+DDylrKDMyrT81vU89vjWCcervhJrVjcXwGOPb+3ZnImiNonyyrj9pd3upscj4omkJg6SZrHrwphmfaGxCFszd/Yg5x7/zMILsVMZKZR13w+d9GzOv/ZONm7euf3qpi3jPS0kF7EwoVfG7S/tJo5vSPorYEjSy4C3A9eWF5ZZOfJGAc3efVYpSWOyI4Xy7vuhk57N7N1n7ZI4oL3d96rOK+P2j3a7qpYCG4DbgT8Gvgi8t6ygzMrSzSLsVEYKNbuvC8nWa+22OIaAyyLiEgBJA+mxzWUFZlaGbhZhp/IB3+y+LiRbr7Xb4riBJFHUDAFfLT4cs3J1swg7ldVkm9232XPwQoPWDe0mjj0j4tHalfTy7GZ3kHSZpIck3VF37I8k3Slpu6TRhvPPkbRW0hpJi+uOH5seWytpaZvxmmUqahRQO6aSpJrdN+85AJzz+dtZv2kLwc66iJOHFa3lIocAkm4C/iwibkmvPw/4WES8qMl9Xgw8ClweEc9Kjz0D2E6y6u5fRsRYevxQ4ErgBcABJK2Z304f6kfAy4B1wPeA10bED5rF60UOrSraHVWVdR50NspoOi6gaN1V2CKHqbOAf5P0QHp9f5KVcnNFxDclzW849sM0uMbTTwSuiojHgbslrSVJIgBrI+Kn6f2uSs9tmjjMqqKdkULNRlB18oHvorl1S1tdVRHxPeDpwJ+SDMV9RkSsKjCOEeD+uuvr0mN5xyeQdIakMUljGzZsKDA0s3IVtU6Td+izbmm1dexR6e+TSBY1/G1gIXB8eqwoE5og7FyVN+v4xIMRF0fEaESM7rfffgWGZlauoloKnn1t3dKqq+olwI1kr4QbFLd97DrgoLrrBwK1brG842bTQlHDaz372rql1eq450raDfhSRFxTYhwrgM9K+nuS4vhC4L9JWhwLJS0A1gOnAK8rMQ6zrstbKHAyLQXPvrZuaGcHwO2S3gF0lDgkXQkcCewraR1wLvAw8E/AfsB1klZHxOKIuFPSNSRF763AmRGxLX2cdwArgQGSSYh3dhKHWdWV2VLwrnpWhnaH4/41sAW4GnisdjwiHi4vtMnzcFyz7AUdJ7Pkuc0cRQ/HfXP6+8y6YwE8tdPAzKw9U20tNBut5cRhU9FW4oiIBWUHYmY7k8X6TVsQO4cQTmYPbs/rsLK0Go67UNIXJN0h6UpJ/ppi01av13mqdS3VRlg1diJ3OrfD8zqsLK1aHJcBlwPfBE4gKWwXOX/DrBKmsnfGZP9efTfUS5++H1d+9362tag5dtJaKHK0llm9VjPHnxwRl0TEmoi4AJjfhZjMuq6o2dvtqG9Z1BYj/MzN97VMGtBZa6GbCzrazNKqxbGnpEXsnME9VH+9tuihWb/r9QZP7ZhMa8HzOqwMrRLHg8Df113/Wd31ALzkpk0LebO3d5NYsPS6QudAdJKMagXyEc/BsAppNXP8pd0KxKyXsuoBwI7uoyJrHnlJqtGAxN+95necLKxy2lodV9JsSe+VdHF6faGkV5Ybmln3NNYDBiYu/V9YzSNrMcJGQ4MDThpWWe3uAPgJ4Angd9Pr64APlBKRWY8sWTTCTUuP4u5lr2B7TqG6iJpHVtH6DYfPcxHb+ka7M8efFhEnS3otQERsUcZuTGZF6uU6S0WtWJvHRWvrZ+22OJ6QNEQ6J0nS04DHS4vKZrysIavd3D/be1uY5Wu3xXEu8GXgIElXAEcAp5UVlNlk11kqqpXivS3M8rW7VtX1km4BDicZIfjOiPhFqZHZjJZXS2g2Gqno2d/uTjLL1m5XFSR7fQ8AuwMvLnjrWLNd5NUSBLndVXmtlPOv9RYuZkVqdzjuZSTrVr2aZBvZ4wEPx7XSnL34kNwN5/OGxOa1UjZuHu/6goVm01m7LY7DI2I0Ik6NiNPTnze3vpvZ5CxZNDJhddiavATRbMRTGWtOmc1U7SaO70g6tNRIzBqM5CSC4dmDmcufNxvx5D0ozIrTbuL4FEnyWCPpNkm3S7qtzMDMsobEDg6IR3+9NXOY7pJFIwwPDWY+lvegMCtOu4njMuCNwLHsrG8cX1ZQZpA9w3rO7rMY375rJ1b9UiDnnfDMiclmN7H5ia0926DJbLppdx7HfRGxotRIzDI0DoldsPS6zPNqXVGN8y/2GhrksSe2snHzOFD+Bk1mM0G7ieMuSZ8FrqVuxnhEfL6UqMxy5C0FstdQUvdonKx3xLIb2bRlfJdz25lIaGb52k0cQyQJ45i6YwE4cVhXZS1/PribeOyJrTsSRH2ropsbNJnNFO3OHD+97EDM2pG1FMjmuq6omlqrouzFChv1cmFGs25pK3FIOhD4J5I1qgL4FsmyI+tKjM0sUyd1jwtPPmxCC6WsxQqLXvLErKo62Y9jBXAAydIj16bHzHour/VwwPBQ5sissva6aLYwo9l00m6NY7+IqE8Un5R0VhkBmXUqq+5R36ro1mKFrqfYTNFui+MXkt4gaSD9eQPwyzIDM2tXN1sVzTRr+ZhNJ+22ON4MfAy4kKTG8e30WK50YcRXAg9FxLPSY3sDVwPzgXuA10TExnQ3wX8AXg5sBk6LiFvS+5wKvDd92A9ExKfafXI2c3RzCfS8Anirlo/ZdNHuqKr7gBM6fOxPkiSby+uOLQVuiIhlkpam198NHAcsTH9eCFwEvDBNNOcCoyQJa5WkFRGxscNYzArRTgHco6psumuaOCT9E+QuUkpE/HmT274paX7D4ROBI9PLnwK+TpI4TgQuj4gAbpY0LGn/9NzrI+LhNJ7rSZY9ubJZ3GZlabUzoTd/spmgVYtjrO7y+STf/qfiNyPiQYCIeFDSU9LjI8D9deetS4/lHZ9A0hnAGQDz5s2bYphWRVWYI+ECuFmLxFFfT5B0Von1hbw9e/KOTzwYcTFwMcDo6GhuK8mqq1liqMociW5PKDSrok62ji3iw/jnaRcU6e+H0uPrgIPqzjsQeKDJcZtmaokha7l0qM4ciayl3l0At5mmk8RRhBXAqenlU4Ev1B1/kxKHA4+kXVorgWMkzZU0l2StrJVdjtm6oFViqEoXUVWG/pr1Uqvi+P+ws6UxW9KvajcBERG/0eS+V5IUt/eVtI6kPrIMuEbSW4D7gD9KT/8iyVDctSTDcU8n+QMPS3o/8L30vPfVCuU2vbRKDFXqInIB3Ga6VjWOJ0/2gSPitTk3HZ1xbgBn5jzOZSQbSdk01ioxeI6EWXV0u6vKLFOr2sFUu4iW37o+c59yM+tcuzPHzUrVzuS5yXYRVWVEltl04cRhpepk7kVZtYNWk/bMrDNOHFaaqnzTr8qILLPpwjUOK01V5l541VqzYjlxWGmq8k3fk/bMiuXEYaWpyjd9T9ozK5ZrHFaaKs298KQ9s+I4cVhpvD+F2fTkxGGlauebfhWWSzez9jlxzBBV+nCuj2V49iCP/nor49uTJdE8Oc+s+lwcnwFaLVney1g2bh7fkTRqejFk18za58QxA1RlPkVeLFk8Oc+sutxVNQNMZj5FWV1b7SYET84zqy63OGaATudTlNm11U5C8OQ8s2pz4pgBOp05XWbXVlYsgwNieGjQk/PM+oS7qvrIZLuPOp1PUfRSIY1xv/p5I3ztrg2VGOFlZp1z4ugTU11ptpOZ00Vu05oV9+dWrXerwqyPuauqT3RzZFSRiwJWaUSXmRXDLY4+0c2VZotcKqQqK+SaWXGcOPpEkd1H7ShqUcBux21m5XNXVZ/o1z0l+jVuM8vnFkef6LT7qCprUzWLuyoxmllnFBGtz+ozo6OjMTY21usweqZxJBMk3/LLHsnUSSLoVYxmlk/SqogYbXWeWxwVU8S38GYjmYr4UM6KEcgdLlyLqf78smM0s/I4cVTIVOdq1JQ5kikvxj0Hd8tMBOdfeye/Ht8+4fy8hQ492sqs+lwcr5Ci5jyUudd3XowbN49nnr9x83jm+QNSaTGaWbmcOCqkqJZCWSOZlt+6PnNo7WRsi/BoK7M+5cTRZctvXc8Ry25kwdLrOGLZjbusOFtUS2HJohE+dNKzGRkeKmzhwFoXVZ7hocHMRJDdroABqfAYzaw7elLjkPRO4G2AgEsi4qOS9gauBuYD9wCviYiNkgT8A/ByYDNwWkTc0ou4p6pVDePsxYdkjjSazLfwoibw1TTbgGlocIDzTnjmjvPqi+BnXb068z7bIgqP0cy6o+uJQ9KzSJLGC4AngC9Lui49dkNELJO0FFgKvBs4DliY/rwQuCj93XdajSQqcqmPqWocOdWsi6q+pdAY6wUr12Ted8S1DLO+1YsWxzOAmyNiM4CkbwCvAk4EjkzP+RTwdZLEcSJweSQTTm6WNCxp/4h4sOjAyp6Q1k4NowrfwrNaRgKyZvyMDA81jbfIVpSZVUMvahx3AC+WtI+k2SRdUAcBv1lLBunvp6TnjwD3191/XXpsF5LOkDQmaWzDhg0dB1Xmrnc1ZY52KlJWyyhgQr2inQRQRr3FzHqr6y2OiPihpA8D1wOPAt8Htja5S1Z9dcKX34i4GLgYkpnjncbVqhupiNZIv3z7zmsZBckH/2Q2knKiMJs+elIcj4hLgUsBJP0NSSvi57UuKEn7Aw+lp68jaZHUHAg8UHRMzbqRJjMxr1miqUINo5m8msbI8BA3LT2qBxGZWZX0alTVUyLiIUnzgJOAFwELgFOBZenvL6SnrwDeIekqkqL4I2XUN5ot/93p8hitEk3VEkWjfmkZmVlv9Goex+ck/QC4FjgzIjaSJIyXSfox8LL0OsAXgZ8Ca4FLgLeXEVCzSXOdTszrx13v6ueXXLByDa9+3ojrEmaWqVddVb+fceyXwNEZxwM4s+yYmnUj5Q0pzStq99uud94X3Mw64UUO6+R1I3XaddNvu955pVoz64QTRxvaKWrXF8OHZw8yuJsY375zcFeVawT91kIys95y4mhTs6J2Y1fPxs3jDA6I4aFBHtkyXtnRUzX91kIys95y4uhA3hDbrK6e8W3BnD1msfrcY3oUbfs8isrMOuHE0aZmQ2x70dVT5PIo/TK/xMyqwYkjQ9aHcrMCcre7eoraKbBeP8wvMbNq8H4cDfLWrMpbHfaBTVtK2zgpT7fmiTTbO8TMZi63OBrkfSgPSGyLiUtgHVC3Omy3unq60TVWRqvGzKYHJ44GeR8TzAwyAAAJwklEQVS+ta1O8wrI3ezq6UbXmOd2mFked1U1yPvwrS27UYVlOLrRNea5HWaWxy2OBs2GplalgNyNrjHP7TCzPE4cDfplaGrZScxzO8wsjxNHhnY+lMveZrbX+iWBmln3OXFMwkwZcVSVrjkzqxYXxyehH/fbMDMrihPHJHjEkZnNZE4ck5A3ssgjjsxsJnDiyNFsuY1uLzFiZlYlLo5naFX89ogjM5vJnDgytLPchkccmdlM5a6qDC5+m5nlc4sjQzvLbUz3CYBmZnnc4sjQqvidt2eH96sws5nAiSPDkkUjTVfC9QRAM5vJ3FWVo1nx2zUQM5vJnDja0FjPGJ49yMbN4xPO8wRAM5sJnDhayJrTMbibGBwQ49t2biXrCYBmNlO4xtFCVj1jfHswZ/dZldgN0Mys23rS4pD0LuCtQAC3A6cD+wNXAXsDtwBvjIgnJO0BXA48D/glcHJE3NOtWPPqFo9sGWf1ucd0Kwwzs8roeotD0gjw58BoRDwLGABOAT4MXBgRC4GNwFvSu7wF2BgRvwVcmJ7XNV7Q0MxsV73qqpoFDEmaBcwGHgSOAv49vf1TwJL08onpddLbj5akbgXqBQ3NzHbV9cQREeuBvwXuI0kYjwCrgE0RsTU9bR1QKxiMAPen992anr9P4+NKOkPSmKSxDRs2FBZvqzkdZmYzTddrHJLmkrQiFgCbgH8Djss4tTZkKat1ERMORFwMXAwwOjo64fap8IKGZmY79aKr6g+AuyNiQ0SMA58HfhcYTruuAA4EHkgvrwMOAkhv3wt4uLshm5lZTS8Sx33A4ZJmp7WKo4EfAF8D/jA951TgC+nlFel10ttvjIhCWxRmZta+XtQ4vktS5L6FZCjubiRdTO8G/kLSWpIaxqXpXS4F9kmP/wWwtNsxm5nZTpqOX95HR0djbGys12GYmfUVSasiYrTVeZ45bmZmHZmWLQ5JG4B72zx9X+AXJYZTtH6LF/ov5n6LF/ov5n6LF/ov5snEe3BE7NfqpGmZODohaaydpllV9Fu80H8x91u80H8x91u80H8xlxmvu6rMzKwjThxmZtYRJ450tnkf6bd4of9i7rd4of9i7rd4of9iLi3eGV/jMDOzzrjFYWZmHXHiMDOzjkzLxCFpWNK/S7pL0g8lvUjS3pKul/Tj9Pfc9FxJ+kdJayXdJum5dY9zanr+jyWdmv8XS4n3PEnrJa1Of15ed/45abxrJC2uO35semytpNKWZpF0SF1cqyX9StJZVX2Nm8Rb2dc4/VvvknSnpDskXSlpT0kLJH03fb2ulrR7eu4e6fW16e3zWz2XLsX7SUl3173Gh6Xn9vz/Xfq33pnGe6eks9JjlXwfN4m3++/jiJh2PyQbP701vbw7MAx8BFiaHlsKfDi9/HLgSyTLtx8OfDc9vjfw0/T33PTy3C7Gex7wlxnnHgp8H9iDZGn6n5DsojiQXn5q+hjfBw7twms9APwMOLjKr3FOvJV9jUn2obkbGEqvXwOclv4+JT32ceBP08tvBz6eXj4FuLrZc+livJ8E/jDj/J6/J4BnAXeQbCY3C/gqsLCq7+Mm8Xb9fTztWhySfgN4MekiiRHxRERsYtedBBt3GLw8EjeTLO++P7AYuD4iHo6IjcD1wLFdjDfPicBVEfF4RNwNrAVekP6sjYifRsQTJPu3n1h0vBmOBn4SEfdS0de4Sbx5qvIaF7FTZt5z6Ua8DzQ5twrviWcAN0fE5kg2ifsG8Cqq+z7OizdPae/jaZc4SLLoBuATkm6V9P8kzQF+MyIeBEh/PyU9f8cOg6na7oN5x7sVL8A70ibxZbXmcgXibXQKcGV6uaqvcb36eKGir3EUt1NmV2LOijcivpLe/MH0Nb5Q0h6N8TbE1c33xB3AiyXtI2k2SYviIKr7Ps6LF7r8Pp6OiWMW8FzgoohYBDxG86XY83YYbGvnwQLkxXsR8DTgMJL/iH+Xnt/reHdI+9dPINnFsempGce6HnNGvJV9jbXrTpkHAHOY3E6ZXYk5K15JbwDOAZ4OPJ+kK+fdVYgXICJ+CHyYpIXwZZIum61N7tLTmJvE2/X38XRMHOuAdZHs+wFJs/25wM/TZiXp74fqzj+o7v613Qfzjncl3oj4eURsi4jtwCXs7F7odbz1jgNuiYifp9er+hpnxlvx17ionTK7FXNmvBHxYNq18zjwCar1GhMRl0bEcyPixSSv14+p8Ps4K95evI+nXeKIiJ8B90s6JD1U22GwfifBxh0G35SOmDicpIn9ILASOEbS3PTb1DHpsa7EW3vjpl5F0kytxXtKOopmAUlx7L+B7wELlYy62Z2kS2ZF0fE2eC27dvtU8jXOi7fir3FRO2XmPZduxPvDug9gkdQK6l/jnr8nJD0l/T0POInk/VHZ93FWvD15H3dSSe+XH5Im2xhwG7CcZKTDPsANJN8obgD2Ts8V8M8kowxuB0brHufNJAWltcDpXY7302k8t6X/qPvXnf+eNN41wHF1x18O/Ci97T0lv8azgV8Ce9Udq/JrnBVv1V/j84G7SD4IPk0yOuapJP/515J0ue2Rnrtnen1tevtTWz2XLsV7Y/oa3wF8BnhSVd4T6d/6L5KE/H3g6D54H2fF2/X3sZccMTOzjky7riozMyuXE4eZmXXEicPMzDrixGFmZh1x4jAzs444cZh1QNKjHZy7RNKhddcPV7Jy7WolqyCflx4/YVIrlJr1iIfjmnVA0qMR8aQ2z/0k8J8R8e/p9TXAayLi+5IGgEMi4gflRWtWDrc4zKZI0sGSbkgXmbtB0jxJv0uyLtYFaQvjaSSL5dUWz9tWSxqSTpP0sfRy/b4hWyS9RNKcdPG676ULYXZj1WOzXE4cZlP3MZLltp8DXAH8Y0R8m2QW79kRcVhE/AS4EFgj6T8k/bGkPRsfKD33MOCvSVYT+DbJ7N8bI+L5wEtJktGcxvuadYsTh9nUvQj4bHr508DvZZ0UEe8DRoGvAK8jWeF0AkkLgQuAkyNZMPAYYKmk1cDXSZYXmVdg/GYdmdX6FDPrUG7hMG15XCTpEmCDpH3qb09bEtcAb4uI2oqlAl4dEWvKCtisE25xmE3dt0lWGAV4PfCt9PL/AE+unSTpFekqsZCsVLoNaNzt8RPAJyLiv+qOrQT+rHZfSYuKDd+sMx5VZdYBSdvZde+CvyfZe+IyYF+S3RxPj4j7JB1Bsj/C4yRLnX+QZG+YzSQb8LwnIlZKOo2kC+sCkn27b6t7/LcCdwIfJdmPQ8A9EfHKsp6jWStOHGZm1hF3VZmZWUecOMzMrCNOHGZm1hEnDjMz64gTh5mZdcSJw8zMOuLEYWZmHfn/S9daomIfj4gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"LotSize and Price\") \n",
    "plt.xlabel(\"LotSize\") \n",
    "plt.ylabel(\"HomePriceK\") \n",
    "plt.scatter(df['LotSize'],df['HomePriceK'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x24ea1aab208>"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"BedRooms and Price\") \n",
    "plt.xlabel(\"BedRooms\") \n",
    "plt.ylabel(\"HomePriceK\") \n",
    "plt.scatter(df['BedRooms'],df['HomePriceK'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "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>HomeAge</th>\n",
       "      <th>HomeSqft</th>\n",
       "      <th>LotSize</th>\n",
       "      <th>BedRooms</th>\n",
       "      <th>HighSchoolAPI</th>\n",
       "      <th>ProxFwy</th>\n",
       "      <th>CarGarage</th>\n",
       "      <th>HomePriceK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HomeAge</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.005498</td>\n",
       "      <td>0.133927</td>\n",
       "      <td>0.005164</td>\n",
       "      <td>-0.004151</td>\n",
       "      <td>-0.035133</td>\n",
       "      <td>-0.100304</td>\n",
       "      <td>0.154894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HomeSqft</th>\n",
       "      <td>0.005498</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.084197</td>\n",
       "      <td>0.032016</td>\n",
       "      <td>0.046302</td>\n",
       "      <td>-0.035486</td>\n",
       "      <td>-0.046856</td>\n",
       "      <td>-0.051960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotSize</th>\n",
       "      <td>0.133927</td>\n",
       "      <td>-0.084197</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.529351</td>\n",
       "      <td>-0.141361</td>\n",
       "      <td>-0.146570</td>\n",
       "      <td>-0.023795</td>\n",
       "      <td>0.962220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BedRooms</th>\n",
       "      <td>0.005164</td>\n",
       "      <td>0.032016</td>\n",
       "      <td>0.529351</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.098336</td>\n",
       "      <td>0.054004</td>\n",
       "      <td>0.012358</td>\n",
       "      <td>0.510955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HighSchoolAPI</th>\n",
       "      <td>-0.004151</td>\n",
       "      <td>0.046302</td>\n",
       "      <td>-0.141361</td>\n",
       "      <td>0.098336</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.087640</td>\n",
       "      <td>0.104783</td>\n",
       "      <td>-0.137037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ProxFwy</th>\n",
       "      <td>-0.035133</td>\n",
       "      <td>-0.035486</td>\n",
       "      <td>-0.146570</td>\n",
       "      <td>0.054004</td>\n",
       "      <td>-0.087640</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.073306</td>\n",
       "      <td>-0.190496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CarGarage</th>\n",
       "      <td>-0.100304</td>\n",
       "      <td>-0.046856</td>\n",
       "      <td>-0.023795</td>\n",
       "      <td>0.012358</td>\n",
       "      <td>0.104783</td>\n",
       "      <td>0.073306</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.055459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HomePriceK</th>\n",
       "      <td>0.154894</td>\n",
       "      <td>-0.051960</td>\n",
       "      <td>0.962220</td>\n",
       "      <td>0.510955</td>\n",
       "      <td>-0.137037</td>\n",
       "      <td>-0.190496</td>\n",
       "      <td>-0.055459</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                HomeAge  HomeSqft   LotSize  BedRooms  HighSchoolAPI  \\\n",
       "HomeAge        1.000000  0.005498  0.133927  0.005164      -0.004151   \n",
       "HomeSqft       0.005498  1.000000 -0.084197  0.032016       0.046302   \n",
       "LotSize        0.133927 -0.084197  1.000000  0.529351      -0.141361   \n",
       "BedRooms       0.005164  0.032016  0.529351  1.000000       0.098336   \n",
       "HighSchoolAPI -0.004151  0.046302 -0.141361  0.098336       1.000000   \n",
       "ProxFwy       -0.035133 -0.035486 -0.146570  0.054004      -0.087640   \n",
       "CarGarage     -0.100304 -0.046856 -0.023795  0.012358       0.104783   \n",
       "HomePriceK     0.154894 -0.051960  0.962220  0.510955      -0.137037   \n",
       "\n",
       "                ProxFwy  CarGarage  HomePriceK  \n",
       "HomeAge       -0.035133  -0.100304    0.154894  \n",
       "HomeSqft      -0.035486  -0.046856   -0.051960  \n",
       "LotSize       -0.146570  -0.023795    0.962220  \n",
       "BedRooms       0.054004   0.012358    0.510955  \n",
       "HighSchoolAPI -0.087640   0.104783   -0.137037  \n",
       "ProxFwy        1.000000   0.073306   -0.190496  \n",
       "CarGarage      0.073306   1.000000   -0.055459  \n",
       "HomePriceK    -0.190496  -0.055459    1.000000  "
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}