1_Getting_Started_with_Python_(Basics).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yEPIrKRwwfgE"
   },
   "source": [
    "# Getting Started with Python\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tB5Z_ffFym-G"
   },
   "source": [
    "Welcome to Python Basics. \n",
    "\n",
    "\n",
    "Instruction: Click on Run (Play icon) or press [Shift key] + [Enter key] together to execute the command."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "yG7gUje3xcGp",
    "outputId": "e49f85f8-4463-4dd1-8564-88c78fb01e6c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello, Business Analytics with Python!\n"
     ]
    }
   ],
   "source": [
    "print(\"Hello, Business Analytics with Python!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6Loed1mfy0DI"
   },
   "outputs": [],
   "source": [
    "# This is a comment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "C8cPLUeSwo7z"
   },
   "source": [
    "## Variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PDHUCGe1zUDN"
   },
   "source": [
    "A variable can have a short name (like x and y) or a more descriptive name (age, carname, total_volume).   \n",
    "Rules for Python variables:  \n",
    "1. A variable name must start with a letter or the underscore character  \n",
    "2. A variable name cannot start with a number  \n",
    "3. A variable name can only contain alpha-numeric characters and underscores (A-z, 0-9, and _ )  \n",
    "4. Variable names are case-sensitive (age, Age and AGE are three different variables)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "OaBMpjN-y_M8",
    "outputId": "4aff3939-e2a4-4735-ac0e-094481159643"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 John and Jane Doe 9.792\n",
      "<class 'int'> <class 'str'> <class 'float'>\n"
     ]
    }
   ],
   "source": [
    "# Creating Variables\n",
    "x = 5\n",
    "y = 'John and Jane Doe'\n",
    "z = 9.792\n",
    "print(x, y, z)\n",
    "print(type(x), type(y), type(z))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "pkfYTCfWzSev",
    "outputId": "69c0bda2-c8a9-4c0b-c747-67be94071c72"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.0 45 9 5\n",
      "<class 'float'> <class 'int'> <class 'int'> <class 'str'>\n"
     ]
    }
   ],
   "source": [
    "# Casting / Changing variable type\n",
    "x = float(5)\n",
    "y = int(\"45\")\n",
    "z = int(9.785)\n",
    "a = str(5)\n",
    "print(x, y, z, a)\n",
    "print(type(x), type(y), type(z), type(a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 153
    },
    "colab_type": "code",
    "id": "f26GuyS9z8SK",
    "outputId": "609f9e0d-0232-4c31-fd4c-21512dba40e4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "e\n",
      "llo\n",
      "Hello, World!\n",
      "Length: 13\n",
      "hello, world!\n",
      "HELLO, WORLD!\n",
      "Jello, World!\n",
      "['Hello', ' World!']\n"
     ]
    }
   ],
   "source": [
    "# String operations\n",
    "\n",
    "a = \"Hello, World!\"\n",
    "print(a[1])\n",
    "\n",
    "b = \"Hello, World!\"\n",
    "print(b[2:5])  # indexing starts with 0\n",
    "\n",
    "c = \"   Hello, World!   \" # string with head and tail whitespaces\n",
    "print(c.strip())\n",
    "\n",
    "a = \"Hello, World!\"\n",
    "print('Length:', len(a))\n",
    "\n",
    "a = \"Hello, World!\"\n",
    "print(a.lower())\n",
    "\n",
    "a = \"Hello, World!\"\n",
    "print(a.upper())\n",
    "\n",
    "a = \"Hello, World!\"\n",
    "print(a.replace(\"H\", \"J\"))\n",
    "\n",
    "a = \"Hello, World!\"\n",
    "print(a.split(\",\")) # returns ['Hello', ' World!']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CEjQBR4PwuT1"
   },
   "source": [
    "## Python Operators"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4qsx2B0t1Gw-"
   },
   "source": [
    "1. Arithmetic operators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lcdprWP80Z27"
   },
   "outputs": [],
   "source": [
    "x = 29\n",
    "y = 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "xLyQoGJp1VDF",
    "outputId": "b4e0cac7-522b-4135-a31e-be316af81ee9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35\n"
     ]
    }
   ],
   "source": [
    "# Addition\n",
    "z = x + y\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "ILRg9ONF1cKM",
    "outputId": "8933e0d7-f391-494d-c51e-2d01c9fa67e9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23\n"
     ]
    }
   ],
   "source": [
    "# Substraction\n",
    "z = x - y\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "kSMsYpsx1fWy",
    "outputId": "e3df6ed2-649a-4b53-a726-b950f89e156c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "174\n"
     ]
    }
   ],
   "source": [
    "# Multiplication\n",
    "z = x * y\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "pkP_ReZu1txW",
    "outputId": "7fbea821-7e0c-41dc-edd4-c6f799741bdb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.833333333333333\n"
     ]
    }
   ],
   "source": [
    "# Division\n",
    "z = x / y\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "DBVjGy0-1x53",
    "outputId": "ee873a67-012a-41a9-edc2-22a2278e95fb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "# Modules\n",
    "z = 10 % 3\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "p63EQhzU150f",
    "outputId": "f978517c-1250-4ea5-8464-11cc69938cd1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16\n"
     ]
    }
   ],
   "source": [
    "# Exponentiation\n",
    "z = 4 ** 2\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "82fccz1f2Gjj"
   },
   "source": [
    "2. Comparison Operators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "m6tJmid62BCK",
    "outputId": "89143e51-e366-46f5-b1fc-d2799aa34293"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "4 == 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "hjaP34tg2OON",
    "outputId": "97463140-c45f-4f86-99bd-d20d57a1e51a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "8 != 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "2SXyaQWN2Qys",
    "outputId": "3758461e-be36-4ef6-cc41-63a28c98f6de"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "4 > 3 and 7 < 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "HFxMIGl-2aVn",
    "outputId": "bc9b2b8c-150e-4ab3-d465-ed393b424d83"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3 > 4 and 7 < 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "Xdt1iBOh2d56",
    "outputId": "6a48916f-67ec-4252-9b48-063fd4774b65"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3 > 4 or 7 < 9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fyNvKs_kxTiY"
   },
   "source": [
    "## Python Conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "QqTxaAKV4Gq4",
    "outputId": "bbb2f040-49fe-4f63-9f3b-60ef7fbb5e11"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a is greater than b\n"
     ]
    }
   ],
   "source": [
    "a = 200\n",
    "b = 33\n",
    "if b > a:\n",
    "  print(\"b is greater than a\")\n",
    "elif a == b:\n",
    "  print(\"a and b are equal\")\n",
    "else:\n",
    "  print(\"a is greater than b\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PEJ0UELpw1Zu"
   },
   "source": [
    "## Lists and Dictionaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "dWYILIUA2zMI",
    "outputId": "aa1f5433-c47c-45ff-b20e-ada5161bdc9d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "thislist = [\"apple\", \"banana\", \"cherry\"]  # This is a list\n",
    "print(len(thislist))  # Length of the list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "5exyO6Ws20sE",
    "outputId": "be999030-1c52-42c1-deb2-a4b93af858ca"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['apple', 'banana', 'cherry', 'orange']\n"
     ]
    }
   ],
   "source": [
    "thislist = [\"apple\", \"banana\", \"cherry\"]\n",
    "thislist.append(\"orange\")   # Add an item to the end of the list\n",
    "print(thislist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "WurJEe4N22me",
    "outputId": "5656078a-5830-4a6f-f627-3c94a5b74db8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['apple', 'orange', 'banana', 'cherry']\n"
     ]
    }
   ],
   "source": [
    "thislist = [\"apple\", \"banana\", \"cherry\"]\n",
    "thislist.insert(1, \"orange\")   # Add an item to a specific location in the list\n",
    "print(thislist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "BIUcHWMH25T-",
    "outputId": "7ca566ae-081e-4947-8aa0-93db53dd4b82"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['banana', 'cherry']\n"
     ]
    }
   ],
   "source": [
    "thislist = [\"apple\", \"banana\", \"cherry\"]\n",
    "del thislist[0]   # Delete a selected item from the list\n",
    "print(thislist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "Ehg_n-nO27Za",
    "outputId": "5c0424c5-7073-47b5-85a4-6976cf3ecddf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['BMW', 'Ford', 'Volvo']\n"
     ]
    }
   ],
   "source": [
    "# Sort a list\n",
    "cars = ['Ford', 'BMW', 'Volvo']\n",
    "cars = sorted(cars, reverse=False)\n",
    "print(cars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "H7Qp5wnP3Eiy",
    "outputId": "cf70eeac-02ff-427e-9606-b45752df01b6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "apple\n",
      "banana\n",
      "cherry\n"
     ]
    }
   ],
   "source": [
    "# Loop through a list\n",
    "fruit = [\"apple\", \"banana\", \"cherry\"]\n",
    "for x in fruit:\n",
    "  print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "6c8Q6sFa3eri",
    "outputId": "9f96be5d-73c3-448d-fac6-a4d884634152"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Yes, 'apple' is in the fruit list\n"
     ]
    }
   ],
   "source": [
    "fruit = [\"apple\", \"banana\", \"cherry\"]\n",
    "\n",
    "# Check if an element is in the list\n",
    "if \"apple\" in fruit:\n",
    "  print(\"Yes, 'apple' is in the fruit list\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "uNRGl83S3je5",
    "outputId": "ccea78c2-fc4b-4eee-b5dd-7f1927bcdca1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'brand': 'Ford', 'model': 'Mustang', 'year': 1964}\n"
     ]
    }
   ],
   "source": [
    "thisdict =\t{\n",
    "  \"brand\": \"Ford\",\n",
    "  \"model\": \"Mustang\",\n",
    "  \"year\": 1964\n",
    "}\n",
    "print(thisdict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "50_GLFKt3yUx",
    "outputId": "71766bdd-40ab-433c-fab6-15fad73a711e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Mustang'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = thisdict[\"model\"]\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "i5Jx1_rd30bK",
    "outputId": "7c7f1def-43ec-45ae-9cef-2ed0422f07fa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'brand': 'Ford', 'model': 'Mustang', 'year': 2018}\n"
     ]
    }
   ],
   "source": [
    "# Change the value of a dictionary\n",
    "thisdict =\t{\n",
    "  \"brand\": \"Ford\",\n",
    "  \"model\": \"Mustang\",\n",
    "  \"year\": 1964\n",
    "}\n",
    "thisdict[\"year\"] = 2018\n",
    "print(thisdict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "wRMu05Av34Pl",
    "outputId": "fd641750-60f3-4459-e63a-6f919ba1b6d9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "brand Ford\n",
      "model Mustang\n",
      "year 2018\n"
     ]
    }
   ],
   "source": [
    "for key, value in thisdict.items():\n",
    "  print(key, value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "VX7NRx4X3-2R",
    "outputId": "47d77450-cd17-4bd6-e478-f1aa01d3f9fe"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'brand': 'Ford', 'model': 'Mustang', 'year': 1964, 'color': 'red'}\n"
     ]
    }
   ],
   "source": [
    "# Adding an item to a dictionary\n",
    "thisdict =\t{\n",
    "  \"brand\": \"Ford\",\n",
    "  \"model\": \"Mustang\",\n",
    "  \"year\": 1964\n",
    "}\n",
    "thisdict[\"color\"] = \"red\"\n",
    "print(thisdict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1i_akla9xXhO"
   },
   "source": [
    "## Python Loops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "colab_type": "code",
    "id": "7nev7na74gGH",
    "outputId": "903d7827-33e1-49bf-8088-56de5e147d53"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "for x in range(6):  # Range(): range(start, stop, step)\n",
    "  print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 187
    },
    "colab_type": "code",
    "id": "ZTEqvLzO4wTD",
    "outputId": "7aa489cb-86e7-43da-aed7-c85019154c34"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "5\n",
      "8\n",
      "11\n",
      "14\n",
      "17\n",
      "20\n",
      "23\n",
      "26\n",
      "29\n"
     ]
    }
   ],
   "source": [
    "for x in range(2, 30, 3):\n",
    "  print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Rztfy9W3xdGG"
   },
   "source": [
    "## Python Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_z_7mFuG5Gx4"
   },
   "source": [
    "For a detailed list of python builtin functions: [Visit this link](https://www.w3schools.com/python/python_ref_functions.asp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "eKkEvGy_4_cO",
    "outputId": "90832b02-e97a-4370-9ad9-5f65c0b38cab"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello from a function\n"
     ]
    }
   ],
   "source": [
    "def my_function():\n",
    "  print(\"Hello from a function\")\n",
    "\n",
    "my_function()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "8D3ehynd5JZz",
    "outputId": "6425e695-c952-47bf-f7b1-09889a7c3a84"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WelcomeEmil\n"
     ]
    }
   ],
   "source": [
    "def my_function(fname):\n",
    "  print('Welcome' + fname)\n",
    "\n",
    "my_function(\"Emil\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "StEYxMSw5NPL",
    "outputId": "4f3e39d0-65f6-4f37-c065-918cca96b498"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am from Australia\n",
      "I am from Sweden\n"
     ]
    }
   ],
   "source": [
    "def my_function(country = \"Australia\"):\n",
    "  print(\"I am from \" + country)\n",
    "\n",
    "my_function()\n",
    "my_function(\"Sweden\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "F_eD_Fp85SYX",
    "outputId": "48eedeba-7beb-4cce-bb25-89012ae9ff9a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def my_function(x):\n",
    "  return 5 * x\n",
    "\n",
    "my_function(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Bn82lFRt4uZa"
   },
   "source": [
    "### Lambda functions\n",
    "\n",
    "A lambda function is a straightforward anonymous function. A lambda function can take any number of arguments, but can only have one expression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "rBEZxhebA_mA",
    "outputId": "2185ff76-bfb0-408a-bfa3-447d545c2a60"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29\n"
     ]
    }
   ],
   "source": [
    "# Take this function\n",
    "def f(a):\n",
    "  return a + 10\n",
    "print(f(19))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "GM-de3nb4qUQ",
    "outputId": "9ce99f7f-4e3f-4e53-b70d-06ce46b6aa54"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29\n"
     ]
    }
   ],
   "source": [
    "# Above function is similar to following;\n",
    "f = lambda a : a + 10\n",
    "print(f(19))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "4tr1C9QU4-IZ",
    "outputId": "628a12e3-da98-4cd1-befe-17acf9b5f032"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11\n"
     ]
    }
   ],
   "source": [
    "# Following lambda function is similar to;\n",
    "# def z(a, b):\n",
    "#   return a+b\n",
    "\n",
    "z = lambda a, b : a + b\n",
    "print(z(3, 8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gPROVgSYxhWJ"
   },
   "source": [
    "## Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ClNGU_gR5xTJ"
   },
   "outputs": [],
   "source": [
    "import numpy as np  # Import numpy library (as a given short name) / Here we import the entire library (called alias)\n",
    "import pandas as pd  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GlIrXaA6xqt5"
   },
   "source": [
    "## Wrangling with Datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "so75UC82NaIe"
   },
   "source": [
    "**Loading a dataset from a csv file**\n",
    "\n",
    "Dataset:\n",
    "\n",
    "1. You need to upload the boston dataset to Google Colab.\n",
    "2. You should click on the '>' icon to the left (below the title bar), expand the left menu.\n",
    "3. Select Files tab from the laft menu.\n",
    "4. Click upload to upload the dataset - boston.csv.   \n",
    "\n",
    "Note: You need to upload these datasets everytime you reconnect to the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fAsN-brH5Ydm"
   },
   "outputs": [],
   "source": [
    "boston_dataset = pd.read_csv('tp3_boston_data.csv')  # Load the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "zp6tTcdeM0pX",
    "outputId": "125498b1-c8bf-481c-b007-c469b2209ff7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['crim', 'zn', 'indus', 'chas', 'nox', 'rm', 'age', 'dis', 'rad', 'tax',\n",
      "       'ptratio', 'lstat', 'medv'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(boston_dataset.keys())  # We print the value of the boston_dataset to understand what it contains. print(boston_dataset.keys()) gives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bwZmrMvZNOcJ"
   },
   "outputs": [],
   "source": [
    "df_boston = boston_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 255
    },
    "colab_type": "code",
    "id": "wL2pxjEm9d6X",
    "outputId": "79ada0ba-62ec-4096-fcda-7b7cfea987b1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "crim       0\n",
       "zn         0\n",
       "indus      0\n",
       "chas       0\n",
       "nox        0\n",
       "rm         0\n",
       "age        0\n",
       "dis        0\n",
       "rad        0\n",
       "tax        0\n",
       "ptratio    0\n",
       "lstat      0\n",
       "medv       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for missing values\n",
    "df_boston.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3mTc8OeJ9rKh"
   },
   "source": [
    "**Exercise**\n",
    "1. Read the first 10, 20, 50 records\n",
    "2. View the last 5 records (Hint: Google search for \"pandas read last n rows\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 255
    },
    "colab_type": "code",
    "id": "U3Q9IMHM9lnX",
    "outputId": "bd523cff-76cd-4c29-f525-727aefadd708"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "crim       float64\n",
       "zn         float64\n",
       "indus      float64\n",
       "chas         int64\n",
       "nox        float64\n",
       "rm         float64\n",
       "age        float64\n",
       "dis        float64\n",
       "rad          int64\n",
       "tax          int64\n",
       "ptratio    float64\n",
       "lstat      float64\n",
       "medv       float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for data types\n",
    "df_boston.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jRCtRGs2-bQh"
   },
   "source": [
    "General data types in Pandas  \n",
    "\n",
    "\n",
    "*   object (string)\n",
    "*   int64 (int)\n",
    "*   float64 (float)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nXaKg3mW-vLr"
   },
   "source": [
    "**Pandas dataframe attibutes (df.attribute)  **  \n",
    "\n",
    "*   dtypes\t- list the types of the columns\n",
    "*   columns\t\t- list the column names\n",
    "*   axes\t- \tlist the row labels and column names\n",
    "*   ndim\t- \tnumber of dimensions\n",
    "*   size\t\t- number of elements \n",
    "*   shape\t\t- return a tuple representing the dimensionality \n",
    "*   values\t- \tnumpy representation of the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9G2Le5wl_SB1"
   },
   "source": [
    "**Exercise**\n",
    "\n",
    "1. Find how many records are in this data frame?\n",
    "2. How many columns?     \n",
    "3. What are the column names?\n",
    "4. What types of columns are in this data frame?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZzVexlAi_34C"
   },
   "source": [
    "**Dataframe methods (df.method())**\n",
    "\n",
    "*   head( [n] ), tail( [n] ) - first/last n rows\n",
    "*   describe() - \tgenerate descriptive statistics (for numeric columns only)\n",
    "*   max(), min()\t - return max/min values for all numeric columns\n",
    "*   mean(), median() - \treturn mean/median values for all numeric columns\n",
    "*   std() - \tstandard deviation\n",
    "*   sample([n])\t - returns a random sample of the data frame\n",
    "*   dropna() - \tdrop all the records with missing values\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3F2mne7fATHw"
   },
   "source": [
    "**Exercise**\n",
    "\n",
    "1. Provide a  summary of the numeric columns in the dataset\n",
    "2. Calculate standard deviation for all numeric columns;\n",
    "3. What are the mean values of the first 50 records in the dataset?   (Hint: use head() method to subset the first 50 records and then calculate the mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 221
    },
    "colab_type": "code",
    "id": "JybPD5YI-VD2",
    "outputId": "66bcb177-134e-4d3e-cf0f-ca34c9230eb7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0.00632\n",
       "1      0.02731\n",
       "2      0.02729\n",
       "3      0.03237\n",
       "4      0.06905\n",
       "        ...   \n",
       "501    0.06263\n",
       "502    0.04527\n",
       "503    0.06076\n",
       "504    0.10959\n",
       "505    0.04741\n",
       "Name: crim, Length: 506, dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecting a column in a dataframe\n",
    "df_boston['crim']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "W_twLiwOAv0a"
   },
   "source": [
    "**Exercise**\n",
    "\n",
    "1. Calculate the basic statistics for the age column (Hint: .describe())\n",
    "2. Find how many values in the age column (Hint: use count method)\n",
    "3. Calculate the average age\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2Li0lbvGBycX"
   },
   "source": [
    "**Wrangling with a dataset**\n",
    "\n",
    "Dataset:\n",
    "\n",
    "1. You need to upload the titanic train and test datasets to Google Colab.\n",
    "2. You should click on the '>' icon to the left (below the title bar), expand the left menu.\n",
    "3. Select Files tab from the laft menu.\n",
    "4. Click upload to upload the tp3_titanic_data.csv.   \n",
    "\n",
    "Note: You need to upload these datasets everytime you reconnect to the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "P7soWdrvAaNZ"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('tp3_titanic_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "TJqxd5jDBai2",
    "outputId": "996ba645-5041-4c67-b6a6-d2f18ede6599"
   },
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nRZ-63MxyE37"
   },
   "source": [
    "## Advance exploration with Pandas Dataframes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3E41WccmCS3T"
   },
   "source": [
    "**groupby method**\n",
    "\n",
    " Using \"group by\" method we can:\n",
    "*  Split the data into groups based on some criteria\n",
    "*  Calculate statistics (or apply a function) to each group\n",
    "*  Similar to dplyr() function in R\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 142
    },
    "colab_type": "code",
    "id": "iNuYNE6ICvt1",
    "outputId": "b3b94864-adf3-4019-ddfc-0a5d2ce0a478"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>27.915709</td>\n",
       "      <td>44.479818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>30.726645</td>\n",
       "      <td>25.523893</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age       Fare\n",
       "Sex                         \n",
       "female  27.915709  44.479818\n",
       "male    30.726645  25.523893"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculate mean age and mean fare for each passenger based on sex:\n",
    "df.groupby(['Sex'])[['Age', 'Fare']].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UWpUrCssDjVX"
   },
   "source": [
    "**Filter dataframe**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "6iLR9tWEDGWM",
    "outputId": "7a00cdf3-54c1-486e-cd09-654905639bbe"
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
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       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
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       "      <td>Heikkinen, Miss. Laina</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
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       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter only the passengers above 20 years old.\n",
    "df_sub = df[ df['Age'] > 20 ]\n",
    "df_sub.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "7ESEZopjDtH3",
    "outputId": "b1653d73-4b0a-4da7-9234-d5fee51ec4c2"
   },
   "outputs": [
    {
     "data": {
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       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
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       "      <td>Heikkinen, Miss. Laina</td>\n",
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       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
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       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "8            9         1       3   \n",
       "9           10         1       2   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "9                Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "8      2            347742  11.1333   NaN        S  \n",
       "9      0            237736  30.0708   NaN        C  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter only the female passengers.\n",
    "df_sub = df[ df['Sex'] == 'female' ]\n",
    "df_sub.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "_WRkbL2XD4fe",
    "outputId": "701640b5-d307-4209-c4e4-8ab647ee289c"
   },
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113783</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>C103</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Hewlett, Mrs. (Mary D Kingcome)</td>\n",
       "      <td>female</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248706</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vander Planke, Mrs. Julius (Emelia Maria Vande...</td>\n",
       "      <td>female</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>345763</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PassengerId  Survived  Pclass  \\\n",
       "1             2         1       1   \n",
       "3             4         1       1   \n",
       "11           12         1       1   \n",
       "15           16         1       2   \n",
       "18           19         0       3   \n",
       "\n",
       "                                                 Name     Sex   Age  SibSp  \\\n",
       "1   Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "3        Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "11                           Bonnell, Miss. Elizabeth  female  58.0      0   \n",
       "15                   Hewlett, Mrs. (Mary D Kingcome)   female  55.0      0   \n",
       "18  Vander Planke, Mrs. Julius (Emelia Maria Vande...  female  31.0      1   \n",
       "\n",
       "    Parch    Ticket     Fare Cabin Embarked  \n",
       "1       0  PC 17599  71.2833   C85        C  \n",
       "3       0    113803  53.1000  C123        S  \n",
       "11      0    113783  26.5500  C103        S  \n",
       "15      0    248706  16.0000   NaN        S  \n",
       "18      0    345763  18.0000   NaN        S  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter only the female passengers who are above 30 yrs.\n",
    "df_sub = df[(df['Sex'] == 'female') & (df['Age'] > 30)]  # Boolean operators '&' for AND, '|' for OR\n",
    "df_sub.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0Xj6RK4fEVM0"
   },
   "source": [
    "**Dataframe Slicing and Indexing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 221
    },
    "colab_type": "code",
    "id": "-AlTFcBYEBpj",
    "outputId": "820218e1-b4c4-4688-8513-0cfa150ca671"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      22.0\n",
       "1      38.0\n",
       "2      26.0\n",
       "3      35.0\n",
       "4      35.0\n",
       "       ... \n",
       "886    27.0\n",
       "887    19.0\n",
       "888     NaN\n",
       "889    26.0\n",
       "890    32.0\n",
       "Name: Age, Length: 891, dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Select column salary:\n",
    "df['Age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "colab_type": "code",
    "id": "5vrcW_vpEfCh",
    "outputId": "d6e6ea3b-20f6-44c7-bf6a-4587f593cc24"
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Age</th>\n",
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       "  <tbody>\n",
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       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
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       "      <td>53.1000</td>\n",
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       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>886</th>\n",
       "      <td>27.0</td>\n",
       "      <td>13.0000</td>\n",
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       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>19.0</td>\n",
       "      <td>30.0000</td>\n",
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       "    <tr>\n",
       "      <th>888</th>\n",
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       "      <th>889</th>\n",
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       "    <tr>\n",
       "      <th>890</th>\n",
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       "      <td>7.7500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age     Fare\n",
       "0    22.0   7.2500\n",
       "1    38.0  71.2833\n",
       "2    26.0   7.9250\n",
       "3    35.0  53.1000\n",
       "4    35.0   8.0500\n",
       "..    ...      ...\n",
       "886  27.0  13.0000\n",
       "887  19.0  30.0000\n",
       "888   NaN  23.4500\n",
       "889  26.0  30.0000\n",
       "890  32.0   7.7500\n",
       "\n",
       "[891 rows x 2 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new dataframe with only 2 columns\n",
    "df[['Age','Fare']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 359
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    "colab_type": "code",
    "id": "Z_j9gB_fEka1",
    "outputId": "4b349be7-be23-418c-9fb2-c17821db318a"
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   "outputs": [
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       "    PassengerId  Survived  Pclass  \\\n",
       "10           11         1       3   \n",
       "11           12         1       1   \n",
       "12           13         0       3   \n",
       "13           14         0       3   \n",
       "14           15         0       3   \n",
       "15           16         1       2   \n",
       "16           17         0       3   \n",
       "17           18         1       2   \n",
       "18           19         0       3   \n",
       "19           20         1       3   \n",
       "\n",
       "                                                 Name     Sex   Age  SibSp  \\\n",
       "10                    Sandstrom, Miss. Marguerite Rut  female   4.0      1   \n",
       "11                           Bonnell, Miss. Elizabeth  female  58.0      0   \n",
       "12                     Saundercock, Mr. William Henry    male  20.0      0   \n",
       "13                        Andersson, Mr. Anders Johan    male  39.0      1   \n",
       "14               Vestrom, Miss. Hulda Amanda Adolfina  female  14.0      0   \n",
       "15                   Hewlett, Mrs. (Mary D Kingcome)   female  55.0      0   \n",
       "16                               Rice, Master. Eugene    male   2.0      4   \n",
       "17                       Williams, Mr. Charles Eugene    male   NaN      0   \n",
       "18  Vander Planke, Mrs. Julius (Emelia Maria Vande...  female  31.0      1   \n",
       "19                            Masselmani, Mrs. Fatima  female   NaN      0   \n",
       "\n",
       "    Parch     Ticket     Fare Cabin Embarked  \n",
       "10      1    PP 9549  16.7000    G6        S  \n",
       "11      0     113783  26.5500  C103        S  \n",
       "12      0  A/5. 2151   8.0500   NaN        S  \n",
       "13      5     347082  31.2750   NaN        S  \n",
       "14      0     350406   7.8542   NaN        S  \n",
       "15      0     248706  16.0000   NaN        S  \n",
       "16      1     382652  29.1250   NaN        Q  \n",
       "17      0     244373  13.0000   NaN        S  \n",
       "18      0     345763  18.0000   NaN        S  \n",
       "19      0       2649   7.2250   NaN        C  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select only 10th to 20th rows\n",
    "df[10:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {
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    "id": "s2Oa8C1sE0qZ",
    "outputId": "85ac53ba-d1d6-4e6a-8bc5-fa81c3698304"
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       "     Age     Fare\n",
       "10   4.0  16.7000\n",
       "11  58.0  26.5500\n",
       "12  20.0   8.0500\n",
       "13  39.0  31.2750\n",
       "14  14.0   7.8542\n",
       "15  55.0  16.0000\n",
       "16   2.0  29.1250\n",
       "17   NaN  13.0000\n",
       "18  31.0  18.0000\n",
       "19   NaN   7.2250\n",
       "20  35.0  26.0000"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select a range of rows, using their labels we can use method loc method\n",
    "df.loc[10:20, ['Age','Fare']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 359
    },
    "colab_type": "code",
    "id": "ge1vRSSbFGE1",
    "outputId": "6f5d7909-d2c6-46d4-8900-f9d8928caacb"
   },
   "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>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PassengerId  Pclass     Sex\n",
       "10           11       3  female\n",
       "11           12       1  female\n",
       "12           13       3    male\n",
       "13           14       3    male\n",
       "14           15       3  female\n",
       "15           16       2  female\n",
       "16           17       3    male\n",
       "17           18       2    male\n",
       "18           19       3  female\n",
       "19           20       3  female"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select a range of rows and/or columns, using their positions we can use method iloc\n",
    "df.iloc[10:20, [0, 2, 4]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rvgpWY27F4A1"
   },
   "source": [
    "**Exercise**\n",
    "\n",
    "*  df.iloc[0]  # First row of a data frame\n",
    "*  df.iloc[-1] # Last row \n",
    "*  df.iloc[:, 0]  # First column\n",
    "*  df.iloc[:, -1] # Last column \n",
    "*  df.iloc[0:7]       #First 7 rows \n",
    "*  df.iloc[:, 0:2]    #First 2 columns\n",
    "*  df.iloc[1:3, 0:2]  #Second through third rows and first 2 columns\n",
    "*  df.iloc[[0,5], [1,3]]  #1st and 6th rows and 2nd and 4th columns\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "If1y-XmYGHhp"
   },
   "source": [
    "**Sorting**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "TjtLFgvfFs5c",
    "outputId": "f55835b2-584e-4252-b6d4-2b012f716821"
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>803</th>\n",
       "      <td>804</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Thomas, Master. Assad Alexander</td>\n",
       "      <td>male</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>8.5167</td>\n",
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       "      <td>Hamalainen, Master. Viljo</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>250649</td>\n",
       "      <td>14.5000</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>644</th>\n",
       "      <td>645</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Baclini, Miss. Eugenie</td>\n",
       "      <td>female</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2666</td>\n",
       "      <td>19.2583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>470</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Baclini, Miss. Helene Barbara</td>\n",
       "      <td>female</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2666</td>\n",
       "      <td>19.2583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Caldwell, Master. Alden Gates</td>\n",
       "      <td>male</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>248738</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                             Name     Sex  \\\n",
       "803          804         1       3  Thomas, Master. Assad Alexander    male   \n",
       "755          756         1       2        Hamalainen, Master. Viljo    male   \n",
       "644          645         1       3           Baclini, Miss. Eugenie  female   \n",
       "469          470         1       3    Baclini, Miss. Helene Barbara  female   \n",
       "78            79         1       2    Caldwell, Master. Alden Gates    male   \n",
       "\n",
       "      Age  SibSp  Parch  Ticket     Fare Cabin Embarked  \n",
       "803  0.42      0      1    2625   8.5167   NaN        C  \n",
       "755  0.67      1      1  250649  14.5000   NaN        S  \n",
       "644  0.75      2      1    2666  19.2583   NaN        C  \n",
       "469  0.75      2      1    2666  19.2583   NaN        C  \n",
       "78   0.83      0      2  248738  29.0000   NaN        S  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new data frame from the original sorted by the column Age\n",
    "df_sorted = df.sort_values( by ='Age')\n",
    "df_sorted.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "cGtBiskIGNDx",
    "outputId": "3e02b253-c338-4ab7-e281-a2b9fe8e1bbc"
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
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       "      <th>Name</th>\n",
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       "      <td>631</td>\n",
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       "      <td>Barkworth, Mr. Algernon Henry Wilson</td>\n",
       "      <td>male</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27042</td>\n",
       "      <td>30.0000</td>\n",
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       "      <td>Svensson, Mr. Johan</td>\n",
       "      <td>male</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>347060</td>\n",
       "      <td>7.7750</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>96</th>\n",
       "      <td>97</td>\n",
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       "      <td>1</td>\n",
       "      <td>Goldschmidt, Mr. George B</td>\n",
       "      <td>male</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17754</td>\n",
       "      <td>34.6542</td>\n",
       "      <td>A5</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>494</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Artagaveytia, Mr. Ramon</td>\n",
       "      <td>male</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17609</td>\n",
       "      <td>49.5042</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>117</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Connors, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>70.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370369</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                  Name  \\\n",
       "630          631         1       1  Barkworth, Mr. Algernon Henry Wilson   \n",
       "851          852         0       3                   Svensson, Mr. Johan   \n",
       "96            97         0       1             Goldschmidt, Mr. George B   \n",
       "493          494         0       1               Artagaveytia, Mr. Ramon   \n",
       "116          117         0       3                  Connors, Mr. Patrick   \n",
       "\n",
       "      Sex   Age  SibSp  Parch    Ticket     Fare Cabin Embarked  \n",
       "630  male  80.0      0      0     27042  30.0000   A23        S  \n",
       "851  male  74.0      0      0    347060   7.7750   NaN        S  \n",
       "96   male  71.0      0      0  PC 17754  34.6542    A5        C  \n",
       "493  male  71.0      0      0  PC 17609  49.5042   NaN        C  \n",
       "116  male  70.5      0      0    370369   7.7500   NaN        Q  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sorted = df.sort_values( by =['Age', 'Fare'], ascending = [False, True])\n",
    "df_sorted.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "dmlgo3FIyNQI"
   },
   "source": [
    "## Handling missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ylgMB1tFGdXF"
   },
   "source": [
    "df.method()\n",
    "\n",
    "*  dropna() - Drop missing observations\n",
    "*  dropna(how='all')- \tDrop observations where all cells is NA\n",
    "*  dropna(axis=1, how='all')- \tDrop column if all the values are missing\n",
    "*  dropna(thresh = 5)- \tDrop rows that contain less than 5 non-missing values\n",
    "*  fillna(0)\t- Replace missing values with zeros\n",
    "*  isnull()\t- returns True if the value is missing\n",
    "*  notnull()\t- Returns True for non-missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "tUHUbt7jGW0y",
    "outputId": "d45eec93-3c8d-4947-841e-701536f32bb5"
   },
   "outputs": [
    {
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       "      <th>0</th>\n",
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       "      <td>Braund, Mr. Owen Harris</td>\n",
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       "      <th>2</th>\n",
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       "      <td>Heikkinen, Miss. Laina</td>\n",
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       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass                            Name     Sex  \\\n",
       "0            1         0       3         Braund, Mr. Owen Harris    male   \n",
       "2            3         1       3          Heikkinen, Miss. Laina  female   \n",
       "4            5         0       3        Allen, Mr. William Henry    male   \n",
       "5            6         0       3                Moran, Mr. James    male   \n",
       "7            8         0       3  Palsson, Master. Gosta Leonard    male   \n",
       "\n",
       "    Age  SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "0  22.0      1      0         A/5 21171   7.2500   NaN        S  \n",
       "2  26.0      0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "4  35.0      0      0            373450   8.0500   NaN        S  \n",
       "5   NaN      0      0            330877   8.4583   NaN        Q  \n",
       "7   2.0      3      1            349909  21.0750   NaN        S  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select the rows that have at least one missing value\n",
    "df[df.isnull().any(axis=1)].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "mwhJuvzVHpgz"
   },
   "source": [
    "**Aggregation Functions in Pandas**  \n",
    "agg() method are useful when multiple statistics are computed per column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 142
    },
    "colab_type": "code",
    "id": "U-WgjB9bGim8",
    "outputId": "7e2080dd-4fba-407f-ae98-4a8ec0fdc66d"
   },
   "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>Age</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>29.699118</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Age        Fare\n",
       "min    0.420000    0.000000\n",
       "mean  29.699118   32.204208\n",
       "max   80.000000  512.329200"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Age','Fare']].agg(['min','mean','max'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gtC7qwDhIBLx"
   },
   "source": [
    "**Basic Descriptive Statistics**  \n",
    "\n",
    "*  describe -\tBasic statistics (count, mean, std, min, quantiles, max)\n",
    "*  min, max -\t\tMinimum and maximum values\n",
    "*  mean, median, mode -\t\tArithmetic average, median and mode\n",
    "*  var, std -\t\tVariance and standard deviation\n",
    "*  sem\t -\tStandard error of mean\n",
    "*  skew\t -\tSample skewness\n",
    "*  kurt -\t\tkurtosis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "06UXGQ8syVe2"
   },
   "source": [
    "## Visualization and plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sUsj0E7DIEUD"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\21361535\\AppData\\Local\\anaconda3\\envs\\data_analysis\\lib\\site-packages\\scipy\\__init__.py:155: UserWarning: A NumPy version >=1.18.5 and <1.26.0 is required for this version of SciPy (detected version 1.26.4\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "LICWSqHDIcGi",
    "outputId": "4aacc5c0-321f-4eea-8c44-d3fcab4958dd"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>crim</th>\n",
       "      <th>zn</th>\n",
       "      <th>indus</th>\n",
       "      <th>chas</th>\n",
       "      <th>nox</th>\n",
       "      <th>rm</th>\n",
       "      <th>age</th>\n",
       "      <th>dis</th>\n",
       "      <th>rad</th>\n",
       "      <th>tax</th>\n",
       "      <th>ptratio</th>\n",
       "      <th>lstat</th>\n",
       "      <th>medv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1</td>\n",
       "      <td>296</td>\n",
       "      <td>15.3</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      crim    zn  indus  chas    nox     rm   age     dis  rad  tax  ptratio  \\\n",
       "0  0.00632  18.0   2.31     0  0.538  6.575  65.2  4.0900    1  296     15.3   \n",
       "1  0.02731   0.0   7.07     0  0.469  6.421  78.9  4.9671    2  242     17.8   \n",
       "2  0.02729   0.0   7.07     0  0.469  7.185  61.1  4.9671    2  242     17.8   \n",
       "3  0.03237   0.0   2.18     0  0.458  6.998  45.8  6.0622    3  222     18.7   \n",
       "4  0.06905   0.0   2.18     0  0.458  7.147  54.2  6.0622    3  222     18.7   \n",
       "\n",
       "   lstat  medv  \n",
       "0   4.98  24.0  \n",
       "1   9.14  21.6  \n",
       "2   4.03  34.7  \n",
       "3   2.94  33.4  \n",
       "4   5.33  36.2  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reuse the boston dataset loaded previously\n",
    "df_boston.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "colab_type": "code",
    "id": "y77z74I1Ipe6",
    "outputId": "e265af6e-028c-4671-e691-3d95635bc524"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\21361535\\AppData\\Local\\anaconda3\\envs\\data_analysis\\lib\\site-packages\\seaborn\\axisgrid.py:123: UserWarning: The figure layout has changed to tight\n",
      "  self._figure.tight_layout(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let’s first plot the distribution of the target variable MEDV. We will use the distplot function from the seaborn library.\n",
    "sns.displot(df_boston['medv'], bins=30)  #Histogram of MEDV by binning to 30 bins\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "colab_type": "code",
    "id": "OCv8zesGKLhr",
    "outputId": "62936bcf-4832-4f6e-ff9c-abe35263c331"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create box plots\n",
    "df_boston.boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 292
    },
    "colab_type": "code",
    "id": "wgJyQ3xWIwzo",
    "outputId": "03a7c2eb-d5ca-4276-9642-360d80ba5105"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a correlation matrix that measures the linear relationships between the variables. \n",
    "# The correlation matrix can be formed by using the corr function from the pandas dataframe library. \n",
    "# We will use the heatmap function from the seaborn library to plot the correlation matrix.\n",
    "correlation_matrix = df_boston.corr().round(2)\n",
    "sns.heatmap(data=correlation_matrix, annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WH5qRki9Je16"
   },
   "source": [
    "Observations:\n",
    "*  To fit a linear regression model, we select those features which have a high correlation with our target variable MEDV. By looking at the correlation matrix we can see that RM has a strong positive correlation with MEDV (0.7) where as LSTAT has a high negative correlation with MEDV(-0.74).\n",
    "*  An important point in selecting features for a linear regression model is to check for multi-co-linearity. The features RAD, TAX have a correlation of 0.91. These feature pairs are strongly correlated to each other. We should not select both these features together for training the model. Same goes for the features DIS and AGE which have a correlation of -0.75."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "colab_type": "code",
    "id": "z8GDyzkHJDRp",
    "outputId": "b104d73e-d065-4ac5-ea12-a98fc3e29c6c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7cmRGA+bBEBFRLLPbdY7BjEbMgyEiolhmp+scgxmN9N6NlIiISAtejxjMaKL3bqRERERa8HrUjAnAKum1GykREVEkeD36EYMZFcLtRgo070ba5FW7qwUREZE4Xo/8MZhRQY/dSImIiCLF65E/5swIkJOr3hEcstOytwUREZGoSHfHjjUMZsIIllwVjujeFkRERFqUn6gTely8XI8YzIQgJ1eJzjgq7c1ERESkl40lFXh68xchHxNv1yPmzCgIlVwVjJX3rCAiotggem2SEF/XIwYzCsIlVwWy8p4VREQUG0SvTbOu7QlXajLWFX+D3UcqY35VE6eZFKhJmspKT8bcMfFVoIiIiKJP9Nq0YtdRPP3ely3/HeuF9Dgyo0BN0lRVbSOmr4qvAkVERBR9otemU2fP+f13rBfSYzCjYHB+FnJdKRCZbYzHAkVERBR9aq5NvmL9OsVgRkFiggOP/rwAAIQDmngqUERERNGn9trkK5avUwxmQhhdmIulkwfB7RKfcoqXAkVERGQOpWtTu7QkoeeruU41eSXsPlJp+URiJgCHMbowF6MK3FixswzzNxwK+/jyE7VRaBUREcUz+dq0t+wkjp+uR6eMFHi9Em594cOwzxXNu7HTjtwcmRGQmODA7cPyheYpn9p8OGYTrIiIyDoSExwY2qMDxg24AEN7dMCQHh1CXqccaA5GRArp2W1HbgYzgnznKUNxIHYTrIiIyLpC5dOoKexqxx25GcyoMLowF7NGXhTyMbGcYEVERNamlE+jprCrHXfkZs6MSt2y04Qex0RgIiIyQ7B8msH5WcJbG9hxR24GMwKavFLLQXHidIPQc+Jlp1IiIjKO7/VHTVAi59NoIXr9stJ1jsFMGMGyuR0OQFKYKoy3nUqJiMgYkawm0hoEAT8W5vNU1wfNm7HidY7BTAhyNnfgHzNUIAPE106lRESkP6Xrj7yaKFT+S6RLquVE4ntX7ocD8GuDVa9zTABWILrNui/unE1ERJGKZDWRXkuq9UgkjiaOzCgQ3WZdlpWehG2//ymS2zA+JCIi7dSsJvLNiwkXBMmlQ0YVuIVGVSJNJI4mBjMK1GZpn6w9h4+PVmlOuCIiIgK0rybSGgSFEkkicTQxmFGQne5U/Ryty9QiSdQiIqLYInr9CXycHZdU64XBjBINsYSWZWp22vuCiIiiQPD6s6/8JIb1ym75bzsuqdYLEzwUnDgjVk8GULffhS+77X1BRETGE73+PP2e/16A8pJqPfZmshsGMwrURq5ql6nZce8LIiIynprrj+91Qq+9mWRNXgm7j1RiXfE32H2k0tLXI04zKQhXNEjmAPCbq/NVTwkZkahFRET2J19/RFbUBl4n5CXVgekLbpXpC3ZLgWAwoyBU0aBAz28vw8Au7VX9geM5UYuIiJTJ15+pK/cLPT7wOhHpkupICvaZhdNMIcgRbk6m8pCf1imheE7UIiKi0EYX5uK+kRcJPfbE6YZW1x95SfW4ARdgaI8OqqaW7JgCwWBGQMP58yF/r2U79HhO1CIiovBmjOgJd2b4ZdrzNxzCVYu26LJoRE0KhJUwmAlBHmqrqgsdzMjUTAnpnahFRESxJTHBgaIb+8CB8Ku19VoFa9cUCAYzCrTszaR2Sshue18QEVF0KV0nAuk1BWTXFAgmACtQszdTJNuh22nvCyIiij75OrFiZxnmbzik+Dg9VsGGW8kbyfXOSAxmgmjyStj55feqnhPJlJBd9r4gIiJzJCY4kJ0hts1BJFNA4VbySgAmXN5F8+sbhdNMATaWVOCqRVuwZOsRocd3SE/mlBARERkuWlNA4aa2ntr8hW4Jx3phMONDaXsBJVnpSdg951oGMkREZLhoroIdXZiLHbNH4L6RvYL+3mrb7jCY+YGahF85s3zBL/oiuU2CrUo+ExGRcYy8HpixCnbNvmNBf261mjPMmfmBmoRf37LQdiv5TERExojG9UCv7QpE2GnbHQYzPxBNmJrx0564b9RFSExw2LLkMxER6S+a14NorYK1U80ZTjP9QDRhaljPbCQmOGxb8pmIiPRlxvVA63YFatip5gyDmR+oTayya8lnIiLSV6xeD+y07Q6DmR+oTayy0/AbEREZJ1avB3badofBjA812wvYafiNiIiME8vXA7tsu8ME4ACiiVV2LflMRET6ivXrgR223WEwE4TI9gKhSj5bbfiNiIiMEw/XA6tvu8NppgjYZfiNiIiMxeuBuSwTzCxcuBAOhwOzZs1q+ZkkSSgqKkJeXh5SU1MxfPhwfPbZZ+Y1Mgi55PPqu4fgmQkDsPruIdgxewQPXKIosWvfQbGH1wPzWGKaad++fXj++efRr18/v58vXrwYTz75JFasWIGLLroIjz/+OEaNGoXPP/8cGRkZJrW2NT2G35q8kqXnI4msyO59B8Ues6dj4vVaYnowc+bMGdx6661Yvnw5Hn/88ZafS5KEp59+Gg8//DDGjx8PAHj55ZeRk5ODVatW4Z577jGrybrjlghE6rHvIPIXz9cS06eZpk+fjjFjxmDkyJF+Py8rK4PH48F1113X8jOn04lrrrkGu3btinYzDaO0U7fVdiQlspp47zuIfMX7tcTUkZk1a9Zg//792LdvX6vfeTweAEBOTo7fz3NycnD06FHF12xoaEBDQ0PLf9fU1OjUWv2FK4HtQHMJ7FEF7rgYJiQSFe99B5EvXktMHJk5duwYZs6ciZUrVyIlRbmQkMPh/8VLktTqZ74WLlwIl8vV8q9z5866tVlvsVoCm8hI7DuI/PFaYmIw8/HHH+P48eO49NJL0aZNG7Rp0wbbtm3DH//4R7Rp06blrkq+y5IdP3681R2Xrzlz5qC6urrl37Fjxwz9HJGI1RLYREZi30Hkj9cSE6eZrr32Whw8eNDvZ3fccQcuueQSzJ49G927d4fb7camTZswcOBAAEBjYyO2bduGRYsWKb6u0+mE0+k0tO16ieUS2ERGYd9B5I/XEhODmYyMDBQWFvr9LD09HR06dGj5+axZs7BgwQL06tULvXr1woIFC5CWloZJkyaZ0WTdxXoJbCIjsO8g8sdriQWWZofywAMP4OzZs5g2bRqqqqpwxRVX4N1337VMnYhI1/PHQwlsIjNYve8g0lMk15JYqUvjkCQpWCAXM2pqauByuVBdXY3MzEzdXlfP9fzxXBuAYp9R56DR7Npuil9qryVWv/aoOQcZzGggr+cP/OLkWFbLPhyxEh0TBbJrUGDXdlN8E72WGHEd05uac9DS00xWZNR6frNLYBMRkf2JXEtisS6N6RWA7Ybr+YmIyM5i8TrGYEYlrucnIiI7i8XrGKeZVBJdp3/idAOavJJthuiIiMh4VsiPjMW6NAxmVAq3nl82f8Mh/GVHmWWywomIyFxWWT0Ui3VpOM2kkryeH/gx61tJvOxWSkREoVlpV+tQ1zG71jhjMCOgySth95FKrCv+BruPVGJUgRtLJw+C2xV6CE6OeOe9WYomb0yvgCciIgXhVg8B0b9OjC7MxbOTBqJ9epLfz92uFEssy1aL00xhhBoW3DF7BFbsLMP8DYcUn++bFc6l10RE8UfN6qFoXSc2llRg/oZDOFl7ruVnWenJmDvGnqkRDGZCUCoqVFFdj6kr9+O+kb3QJStN6LXslBVORETaBSb5emqstXpI6dpWVduI6av2Y2kCR2ZiRqhhQdlTmw8jKz1Z6PXslBVORETaBBvNzwqYylESjetELBbMA5gzoyjcsKDsZG2j0OtV1TZE2iQiIrKwjSUVmBokydd3KkdJu7SkqKweisWCeQCDGUV6D/fN33CIScBERDGqySvhwTcOan5+tMZAYrFgHsBgRpHew312jHSJiEjMki1f4lRd+BEYJVV156JyjYjFgnkAgxlFclEhPaNlT/VZHV+NiIisoMkr4aWdZRG/TjRGQ8Jd2xxoXrFrp4J5AIMZRb5FhfQiml8TWNeG01NERNa1t+wkTp3VPioji8ZoSCwWzAO4miksV1pSREOHvrLaOsM+xirlromISIweIyoOR/QWiowuzMXSyYNaXWvcNr7WMJhRoLQOP5ADCPsYmTszdNSt9J5yuWu5KqMVNiojIrIiM/pHPUZUJAmYtuoTLEtwRCWYGF2Yi1EF7pi5ljCYCUKkxozM7UrB3DEFmL+hNORyt4QgUbfvSZfd1omi9Z+FXfvv9UqYv+EQR26IiAKYNbItsgFxu9Q2aGzyoq7RG/K1HlpbgrONTXC7Ug0PLhITHDFTmd4hSVJMJ2TU1NTA5XKhuroamZmZQs/ZfaQSE5fvCfu4uWN64/Zh+UhMcAiN5DiAltGVYCedVvKhbsf9NCj2aTkHrcCu7Y5XSn1wtPpH+f2B1qP1DgBj+7nx5gGPqteM9xtVNecgE4CDEJ3/zM5wtkTNzZt2DUK4IHrem6V4+0Dw3VO14oaWRBTPrLCRo5yHErgBca4rBc9OGoQPDleqfk0zdtS2K04zBaF1HX779GSEOlfkyoqPrCsRzrMRxQ0tiSheWWUjR6U8FK2rney8vUC0MZgJItz8pwPNuTKB6/BFR3REl2hrYbeqjUREkbJSVdtgeSiRvC9vVMVwmikItevw5bowX3hOR6+RCuxWtZGIKFJWq2obWCssOz18WY5weKMaGkdmFIiuw1eTyOsA0D49SWjTsWCy0pNQVXtO1WgREVGs0zqaboRg1wR3ZgrapSWhui54/y0iVCDGch0MZkIKtw5ftBYN8OOIzi8HXYAXdpSHzK0JJteVgrljemP6qk9a1baxc9VGIqJIyaPp967cH7X+MVgAsanUE/Sa8F2N8pLtcMIFYiy02ozBTBhK6/DV1KIBgJxMJ8YNyMPz28tUHdS+J+LowlwsTXDEVNVGIiI9RLOqbfDRFyfqz3tD1gprl5YEZ5sEeGrEKv2GC8REC63GAwYzPtQM1YXLng+0aHw/PLj2YOg6NA7AldIGp86eb/lZ4IkYa1UbiYj0Eo3+UTGACBOgSGjeGfvVX1+B0m+r8Ye3/xX2vbLSk/GHXxQGDUjCLUePt1VQDGZ+oHaozlOjLhnr3lc/Rt250JUfJQl49tZLkeBwhDwRY6lqIxGRnozsH9WOyAfz4o6v8NHRU0KPfWRMb8WRFassR7cKBjNQP1S3saQC89/6TNV7hAtkZCfONGDcgAtUvTYRERlP7Yh8MO/963vhx7pdqYq/s9JydCuI+6XZaitHyoGP1hVJ4XBpNRGRNUUzMGiXlhRy9ZXVlqObLe6DGTVDdXoMMYbSPszBS0RE5olmYHCq7hw2lSrv5SQvR1fKhnGgOVUiXq4pcR/MqBmq02OIMRTuqkREZF3hAgg9yQm8SvtJqS3uGuviPphRM1Rn9BDjqbpz2Ft20tD3ICIibUIFEHrznRVQorS5pduVElfLsgEmAKuqHBmNQCNekrWIiOxIqZ5NrisFZ881RVTlN5hw1wSW62gW98GMmsqR4QIfPYiOFLF8NRGROZQCCLkCsJ5Ergks18FgBoB45chQgY+v9mlJkNA8bdTyWj9Uh1SK2tXsHcLy1URE5goWQChdS2Tt05JQVSe2Epb77anjkCQppvNOa2pq4HK5UF1djczMzJCPFR3tCBZMZKUn4RcDLsDIAnfLwRcqag82AiQyx6lUE0fNaxBFk5pz0Ers2m4yn3wt8dTU4+SZBmSlJ8PtSsXg/Cws3ngIf95eFvL57M+bqTkHGcyEoRTgaJ3mefvAt3hkXYlfnRrRUZUmr4SrFm1RXFElR/I7Zo/glBNZhl2DAru2m6wh2DUCQMg+XObOdOI/xxagfbozrlMJ1JyDnGYKIdx0jpwULC/bDnWwNZ734qE3DuDtEg/qGptafp6Vnoy5Y8Smh1i+mojI+pSuHRMu7yxU3uOWyzrjsbcO+W2b485MQdGNTCVQwmBGQbgtDn5zdT7WFX/rt7lYTkYyJl3RFd2y0/0i6YVvlyrull1V24jpq/ZjaUL44USWryYiso5goy+bSj2YGiQJ2FNdj6c2HxZ63T9u+bL182vqMXXlfiyL86knJQxmghDZ4iDYnOd3pxv9DtZcVwoKL8jEptLjiu8VbHdTpSkslq8mIrKGYKMv7swU1NQHT/DVK59jzhsH42YnbDUYzAShV6VfT3W90Ov4Tg9Vn21UnNoaVeAWrolDRETGUBy5rzF+VLyq7hz2fFWJYT2zDX8vO4n7CsDB6DVNozYS3/zDaqfAAEie2tpU6mH5aiIiExm9R5+I3UcqTXx3a2IwE4RZ0zRri78Ju3v3qAI3y1cTEZnE6D36xMT0ImRNOM0URDQq/QbqkJ6MytpGxd/7TkWxfDURkXFCld6IxlRSOEO7c4opEIOZIEQr/erFAWDcgDy8uLM87GPlKTCWryYi0l+okhwAMP+tz8xqGgCgXVoShrDvb4XTTAqUdiPVW7ozEUsnD8KoArfQ47lSiYjIGHJib7C8xakr92Pqyv1+BU/N8MT4vhyFD4LBTAijC3Ox5T+GIyPFmAGsAZ1deH7yZRj1wxYI7dKSFB/rQPPdAVcqERHpT6QkRzSlJvlfnt2ZTtaYCYHTTCFsLKnAQ2tLcLr+vCGvX3ysGre+8CFyXSm4sX+u38aUgSRwpRIRkVH0SuxNS05EcpuEkP25iL9MuRwJDgfzIgUxmFGgVEfACBXV9WE3HmufliQ8FUVEROroVZLjbGMT6hqbMOvanlix6yhOnVUX1Mj1woZ078DgRQVOMwURaR2BtsmJurYHaC6UtLfspO6vS0RE+uUjylXdX9mjLZAB0JJsvPtIJdYVf4PdRyrR5OVy7FA4MhOEluHGdqmJuLJHNiQ4sOerSsBnM0m9cM8lIiJjyCU59JhqkgBNicJuVwrmjinA557TePD1g37BkO8mx9Qag5kgtAQNp8424e2S7wxozY+4komIyBiJCQ7c2D837JS/Ue4b2Qu9OrXFQ/84GDTfRq4Ez+KowXGaKQirBQ1cyUREZKyNJRV4PkQgM7af+gAiKz251dYzwTgAvLSrHNNXfaKYOOxbCZ5TTq0xmAlCHm40I/WKey4REUVXuDxJB4CPj1bBnekUfs22zjZ4fFyh0GMlAKfqzoXN0/StBE/+GMwEIVcABloHF0a65+p8XfdcavJKTCAjIgojXJ6kHERMHNxF+DXPNJxHQgKwdPIgtEtVriGmBfMnW2POjAK5AnBgWWuj3DeyF2aOvAgPjO6ty55LoUpyc76ViOhHosFBt+x0jO2Xi7cOVAg9ft6bpdgxewQynEm49YUPI2miH6ulQlgBg5kQfDd03PnlCSzZ+qUh75PrSsGMEb0A6LPnklKNnEgTyEJtvkZEZFeiwUHZ92fwUXmV8OtWVNfjqU2fY2iPbLgznfiupiHi2mXMnwyOwUwYcnAxOD8Lr+//P11HaYzIhwlXktuB5ruFUQVuVe/JkR4iilVynqSnuj5ksPH0e+pvaJdsPYIlW4+gXVpSSx8cSUDD/MngmDMjSM6jieQQCpw3jSQfRono3K+aBLJQm6/du3I/NpaIDbkSEVmRb56kUap/WKXkCtiDLytdLJ8m3ZnIvZlC4MiMCnIezUNrS3CytlH185+dNAgJCer22lA7tSM69yv6OKNGeoiIrGR0YS5mjbwIT23+wpDXl3z+x8M3XILsjBS4M1PgqanHfX8rDvv8x2/qy0AmBAYzKo0uzMXZxibc99qnws9p2Wujh7q9NrRM7YjO/Yo+Ts1IT6S5PkREZuqWnWb4e5w6ew5/ePtfLX25O1OsLxZ9XLxiMKOB25Wq+jnB5jl9R12y2zoBCThR24BOGSmoqm3A9FWfqE7iDTf3KwdWoglkeo/0EBFZVTRXCcl9+bOTBuraZ8crU3Nmli5din79+iEzMxOZmZkYOnQo3nnnnZbfS5KEoqIi5OXlITU1FcOHD8dnn31mYoubyQGDqFkjL2oVeGwsqcCwJ7Zg4vI9mLmmGLf+5UPc+sKHmLmmGBOX78GM1a0DGSB8FchQNXK0JBzrPdJDFCm79htkfdEsmCr33vM3HMLcMfr12fHK1GDmwgsvxBNPPIGPPvoIH330EUaMGIFx48a1dDyLFy/Gk08+iSVLlmDfvn1wu90YNWoUTp8+bWazVSeLBQ5dbiypwNSV++GpUR7NCFXfLlwSr5zbE1iAz5WahFkje2FUgVu47eFObm61QNFm136DrC8xwYG5Y3pHvHxalNyXt09PDtpnG7FIJFY5JEmyVFnYrKws/Nd//RfuvPNO5OXlYdasWZg9ezYAoKGhATk5OVi0aBHuueceoderqamBy+VCdXU1MjMzI2qb37RQuhP3rPwIZxrC7469+u4hLfkkTV4Jlz6+SXH/DTWemTAA4wZcELK9S7Z8iZd2loXdfTVUorG8mgnwX1IoBzg82SgUPc9BJXr3G9FqNxlDa02sjSUVKFr/GTw1DVFo5Y/kvpy1vPypOQctkzPT1NSEv//976itrcXQoUNRVlYGj8eD6667ruUxTqcT11xzDXbt2qXYKTU0NKCh4ccDsaamRpf2BUvGFZGVnuQ3arHnq0pdAhkg/NTOplIPnt78Rdi8m3CJxkrVkN2sM0Mm06vfAIzrOyi6tNbEUio2Gg1yX65H0dR4ZXowc/DgQQwdOhT19fVo27Yt1q5di4KCAuzatQsAkJOT4/f4nJwcHD16VPH1Fi5ciHnz5unaxkgO8r55LuwtO9kSYe8+Uhlxe0QSwkSXVHu9klCisW81ZN41kNn07jcAY/oOii6t1c/DbTRpFCb36sf0onkXX3wxiouLsWfPHtx7772YMmUKSktLW37vcPhfLCVJavUzX3PmzEF1dXXLv2PHjkXUvkgP8m2HT2Di8j24atGWH4rLRXa6iCaEiS6pfmRdiXCisXzXMG7ABRiqcpk5kZ707jcA/fsOiq5wN3CA8sKJcP2lEZjcqy9VwczTTz+Nkyf13Xo8OTkZPXv2xGWXXYaFCxeif//+eOaZZ+B2Nyepejwev8cfP3681V2XL6fT2bLKQf4XCb0OcvnOQO1BG/hw0YQw0aXSJ2uVp7y43TzpSc++Q+9+A9C/76DoiqT6uRmlJZjcqy9Vwcy8efOQl5eHW265Be+++y6MyB2WJAkNDQ3Iz8+H2+3Gpk2bWn7X2NiIbdu24corr9T9fZXodZDL39Tf9h1TtR38b0f0wqt3XYFnJgzA6ruHYMfsEUIHv55LpVlDhvRwySWXGNZ3WK3foOiLpCZW2fe1ejdH0Yyf9lDVl5MYVcGMx+PBCy+8gJMnT+L6669H165d8eijj6KsrEzTmz/00EP44IMPUF5ejoMHD+Lhhx/G+++/j1tvvRUOhwOzZs3CggULsHbtWpSUlOD2229HWloaJk2apOn9tNAzKJAAeGoacMewfOHnPP3eYfzufz+Fs02CqqkdkSXVHdKThV6LNWRID0uWLNGl77BDv0HRp7Um1saSCjz93mEjmtSKwwEU5GZymt4AqoIZp9OJW2+9FZs3b8aRI0dwxx134JVXXkGvXr0wcuRIrFmzxm81QDjfffcdbrvtNlx88cW49tpr8eGHH2Ljxo0YNWoUAOCBBx7ArFmzMG3aNFx22WX45ptv8O677yIjI0Pdp4yAEUWUumSlol2a+OhMsA0dm7wSdh+pxLrib7D7SGWreWCR4nnzxxWyhgxFzS233KJL32GHfoOiT0tNrMbzXjy0tiQq7QMASQKmrfoEbx/g5rx606XOzObNm/HSSy/hH//4B1JSUlBZGfmKHb3oUStCLnKnl8Hd2mNveZWq58hZ7ztmj8CmUo/w0sNwyxRZQ4aMFuocjPW+g6JLTX+2saQCD609GDJvUFRmShsM7NwO2w6fEHp8ggNYMnEgbuiXF/F7x7Ko15lJSEiAw+GAJEnwer16vKTltEtL0q0+jNpABvgxeW3Gqv14p8TT6vdKSw/DLalmDRkyUzz0HRQ9ov2Z3jVlaurPtwQyDoRfs+r9YYRmWYKDfaxONI/MHD16FCtWrMCKFStw7NgxXH311bjrrrvwy1/+Eikp1smxiPTuysxCSmr5jt6onY9l5UkySuA5GC99B5knVH/W5JVw1aItUV+KHUyuxv46Xhg2MlNfX4/XX38dL774IrZt24bc3FxMmTIFd955J7p37x5Ro63IrEJKWvkuPVRbRZKVJ8lor732GlavXh0XfQeZK1R/ZkZNGSVa+2tqTVUw43a7UV9fj7Fjx+LNN9/Ez372MyQkmF53zzBWOujV4FJqsqIZM2bETd9B1rWptPU0vZnYX+tDVTDzn//5n/jVr36F7Oxso9pjKXY9yEItUeR0Epnl0KFDyM8XL0tAsS/a/dHGkgq8uLPcsNfXgqUv9KEqmLn//vsBAIcPH8a6detQXl4Oh8OB/Px83HTTTTE3XCx6kA3Nz8KespOWmI4KtZRa6wZsRHro0KFD3PQdFF60+yM5bcAquC+TvlSP8y5cuBC9e/fG7Nmz8frrr+Pvf/87fv/73+OSSy7Bf//3fxvRRtOI1C1ol5ZkmUAGACZc3iXonY2cyBw4bRashg2REf7nf/4nbvoOCs2M/siMtIHf/rRH0J9zXyb9qQpmtm7dikceeQSPPPIITpw4gYqKCng8Hnz//fd48MEH8eCDD2L79u1GtTXqwhWekwMYqwQyQHNBvkCRbMBmpnCFAcleHn/88ZjrO3iMqmdWfySaNnDbkM66vWePnAwsmzwIuS7/UX7uy6Q/VdNMy5Ytw69//WsUFRX5/TwrKwuPPfYYPB4Pli5diquvvlrPNpoqVN2CCZd3xlObo1MGW9T8DYeQmpzod5Ko2YDNKln1nBKLPb/61a9iqu/gMaqNWf2RaNpAtw5tdX3PoT06hKz1RfpQNTKzd+9e3HbbbYq/v+2227Bnz56IG2U1owtzsWP2CKy+e4jfho/dstNVv9bNgy4U3mhSLr/93KSBcGeKnYhVtY2thmkj2YDNDJwSi00TJkxQ/J3d+g4eo9qZ1R+JbneQ1dap23tW1TZv0SEvFR834ALuy2QQVcHMd999h27duin+Pj8/Hx6PtZa96SXYwagmC10+URbd3A97Hx6JLIFNHiU0z6ne0C8P2x/4KdKdiULPAfyHabVuwGYGea8Uu02JUXhdunRR/J2d+g67TttaRTT6o2DTfyL71T368wJ00jGYeewtHgfRorpoXnKy8kU4KSkJjY2NETfKLuRI31NdHzZvRgIwd0xvJCY4sLfsJE7Whv+e7hvZq2W4+uOjVahtaBJqV+Awbbh2WiWrXmSvFCtOiZGYWOk77DhtayVG90fBpv+y0pPwiwEXYGSBG89OGoT5G4JvdwAA//H3TzW9bzCemgYeB1Giem+mv/zlL2jbNvic4unTpyNukJ3Ikf69K/cL7ccxf8MhJCQ40HBebA+amrM/XtS1DLnKzwnVTqtk1avdNsIqU2Ik7uWXX1asUWWnvsNu07ZWY2R/pNSPnKw9hxd2luOFneXIdaVg7pjeaJ/u9Mth2VTqMWTrGh4H0aEqmOnSpQuWL18e9jHxRClBOBh5Pn3WyF5Cr722+Bs8NKZA9ZSWzPc5Vt5QUsu2EVaYEiN1Xn755ZBVf+3Sd9hp2taqjOiPRPsRT3U9pq/6BEsnD8K4AReoeq4W2TpOW5EyVcFMeXm5Qc2wN3ln6j1fVWLXlyewYlc5ahtbTwnJJ8qKnWVIdyaGnTY6WXtOeKrIl9IwbbgdtM2ipv6DVabESL2DBw/GxIaNdpm2tTq9+yPRfkRC899o3pulGFXgbpn6N6oGzX+8VoyiG/twhZvBuDmKTjaVevC7v3+KZ98/EjSQ8VV19rxw/svmUk9Lye8bCt3Cdw5Kw7RqsuqjVUND7TCs2VNiFN9EE0l5jEaXmn7EN69J7XPV8tQ0cIVbFKgambnhhhuwevVquFwuAMAf/vAHTJ8+He3atQMAVFZW4ic/+QlKS61TMjoa1OZ7qPHCznK8/sk3OFWnnBTrK8EBLJkYeTGmaNbQEB2O75CejD/8opB3ODZVXV3dMjJj977DytO2dqF3H6NlWk8OYspP1Kl+rhoS/EeCSH8OSZKEr8GJiYmoqKhAp06dAACZmZkoLi5u2Vflu+++Q15eHpqaxEYdoqGmpgYul8uvI9VTk1fCVYu2WGp37dV3D4koe14pOJNPQb0rV8rfYagptKz0JOyZMxLJbTiYaDfyOfjll1+iR4/m8u6x0ndw41ZtjOhjRPqRQKvvHoLqs42G3YwGez+ubBKn5vqt6soQGPeoiINilhn7fYQTyZCpGTU0wg3bOwAs+EVfBjIxJFb6DhZDU8+oPsa3HwlHrvt1adf2hiX+BsOVTcbh1SFCVjw4I1lFoaaGhp7kYXs39zAhimlG9jFyPxK4F5Iv37ymj49WRfVmlCvcjKMqZ8bhcMDhcLT6WTyz0sGpxyoKM2toWHW1FemDfQcBxvcxvv3IplIP/lH8rV+RUt+8pnXF32h6Dy2y0pO4ws1AqoIZSZJw++23w+lsXjdfX1+PqVOnIj29eY+ihoYG/VtocWqWTBtJr1UUZtfQkIftKfbce++9LX0F+474FY0+Ru5HhvbogIfHFCjeIEXzZvTxcYW8MTOQqmDmV7/6ld/d1OTJk4M+Jp6orQJsFL1WUbCGBhmlY8eOSEpq3mSVfUf8inYfE+oGKVo3o/dcnY8b+uUZ+A6kajWTHRm9mkmmtMxwbL9cvL7/G6G9mAK1S0vCqbpziiW/Z428CN2y03SfjpFXGkDhfZnDQmpE6xzUm13bbQdG9zFqVpkptUWty7q2wzen6v2uAR3SkzF/XCFu6Mf+Ugs156CqYObOO+8M+xiHw4EXXnhB9CUNF80OKfAEqqptbLWhmRL5buS/b+6PE7UNfvuFRKvei69o1pmh2Cafg5MnT24ZmQkmnvuOeGRUHxNuo8lggY1SW+aOKYArNQnTV+3HqbPKtb5yXSnYMXsEADDnT0eGBTMJCQno2rUrBg4cGHJp5dq1a8VbazCzOiQthfSWKdyNBAZJl3Ztj4+PVhl+wrCGBulBPge7dOmCSy+9lH0HtdC7jxHpd5UCpmBtAdCSSPziznLFUXKOVhtDzTmoKmdm6tSpWLNmDb766ivceeedmDx5MrKymDsRqPG8Fw+tPah6yNLrlRRPbnnOd2NJBa5evBWemh/vIFypSbhzWD5mjOipa7DBZFzSU01NDfsO8qNnH9PklVC0/jOhjSbvXbkfz04aCFdaMnYfqQQgYWj3bFyen9Vyo7hky5dYvfdrv77W4QB8Y3FXWhLuuDIfowrcIdvFm0Ljqc6ZaWhowBtvvIEXX3wRu3btwpgxY3DXXXfhuuuus+RSS73vrsIdmBtLKvDQ2hJNOTIOB+BKSfIbzvS9i9hYUoGpP8ztBtMuLQlPjO/LOwSyFPkcPH78ODZv3hy3fQcZ65nNh/HU5i+EHx8YmKiRlpyIOp89+NyZTkwc3AXdstP9rgucro+MYdNMgY4ePYoVK1bglVdewblz51BaWoq2bdtqfTlD6NkhhTswjdijSe7in500CL//30/DbmIJAM9NGsSEM7KMYOdgvPUdpK9g+YnTVinf6EVbrisFN/bPxfPby6K2LUwsMmyaKZBcRE+SJHi93kheyvKUApUfhywHYf4G/ctiy9vVP7KuRCiQAYAZq/djCQZqWgrIIVGKhnjqO0hfbx/4Fo+sK8HJ2h9HsK3WRVVU1+PP28uC/k7u07nxpL5UBzO+00w7duzA2LFjsWTJEowePRoJCbG5O0K4vUQcAOauK0GlhqklERKgatrKKwHTVn2C+46faTXsGUqokSdW5qVINTQ0YPXq1XHVd5C+Fr5dGjRI0HGruKjw3bKBeYn6UBXMTJs2DWvWrEGXLl1wxx13YM2aNejQIfb/ECJ7iRgVyETiqc2HW/63yNJEpZGnqSv3t9S8kXHel9S66KKL0LVr17jqO0g/bx+oUBztsCsr7u1nV6qXZnfp0gUDBw4MmbD3xhtv6NI4Pegx772u+BvMXFOsb8NMFBiINHklXLVoi6oN1zjvS6J8l2YPGjQorvoO0keTV8Llf9isaWGFla2+ewhHZkIwLGcmcDuDeGGlzST1IOf5yIFIuJGnYDjvS2r95Cc/CVk0j0jJ3rKTMRXIcFsY/akKZlasWGFQM6zNKptJ6iUwENE61Ml5X1Ljueee4wgHaRJL0zF6bQpM/ph1J0DeTBL48UCU2fVQlAORPV9VRjzyFEsdTTQ1eSXsPlKJdcXfYPeRSjTZLYuRSIAex3ksjY67XSmcnjdAREuz48nowlwsnTyo1WoftysFD1/fG7/92ye2y6gHgOmv7seCXxRGNPIUSx1NtLCYFsUDvY7zqtoGJDisuWopJSkBKW0SQ+7dJLtv5EW6V2qnZhyZUWF0YS52zB6B1XcPwTMTBmD13UOwY/YIdMhwWvIkE3Hq7DlMX/UJbuzf3LGoOcUcaO6YOO+rjrxyLDBPSc5l2lhSYVLLiPSj13G+saQC01dZ92ax/pwX1WfPYea1vdAuNXRO2IpdsbUay0oYzKgk7yUybsAFGNqjAxITHNhU6jG7WRFb/2kFnp00CDmZ/qMs7dOaT06l6TXO+6oTrmYR0JzLxCknsjO9jvNQr2MlEoC/7ikPOzpTVXcOS7YcDvkY0obBTIQ2llTgxZ3lZjcjInL+zOHjZ4CAbsPZJgF3/6Qb2qf733HoPe8bL/kjIjWL5KRqIrvS6zjXstLSLL4ViUN5aWd5zPZvZmLOTATku4ZYEWyTNk9NA5Z/UO73s7bORPzbZZ1D7hQbjNJWCfGUPyKaLM2k6tgUL9uFiB6/O7880fJdXNq1fcuO1fJ3E4vnwamz57gC1AAMZiJgp7sGPZ1paMLT7x3Git3lwrt0KwUsSpuxBdbCiRWiydJMqo498RS0ix6/S7Z+2fK/AxN8c10p+LfLOuvdNEO1dSbiTEP4PfRiMUgzG6eZIhDvB+SpunOYKpDIFyoR8M9BAhkgdvNH5JpFSvfiTKqOTfGW9B3uOA8m8DT3VNfj6ffskV8in7e/vqq70ON5s6I/BjMR4AHZLFTAIZIIqCQW80dEahYxqTq2xGPSd6jjXJTdvo1Hf16Af7+2F9qlKa9o4s2KcRjMRKAqhsprRyJUwKHHVFysjYDJNYvcLv9gmMW0YlO8Jn0rHeex6DdX52N0YS4SExx4YnzfoI/hzYqxmDOjUZNXwvwNsZP8GylP9VnsPlLZKrFRj0AkFkfARhfmYlSBOy6SQeNdPCd9+x7nnuqzmLuuRCinxG7Wf1qBB0b3RmKCA6MLc7FMocBqLOZHWQWDGY3iNflXyfwNh/w2gpMTGyMJRGJ9Mza5ZhHFtnhP+paP891HKmMykAFa71HHm5Xo4zSTRrF4FxWJwB1t5cTGqtoG1YmAAIdkKXYw6btZrPeZgZ8vWIFVMg6DGY1i9S4qmPTkRNXPkZP35m84hLljClQ/n/kjFCuY9N0s1vvMrNTkuCj8aVWcZtJIvtvSujmjHbRLTcJVvbLxwRffB/19VnpSyKqXcmJj+/RkLJ08CA++flBoM7YZP+2B+0ZdHPOdO8WPUBvVWjWPQu8Cf7HeZ05ZsbdVnRyr/m3NZFThSAYzGsl3W/eu3G92UwxTf+483jqgXP9iXP88vLTraNjX8dTUw52ZgklXdMFz7x8J+/hhPTuqOrjjpaoq2Zud8iiMKPDn22c6EHzpdUpSAurPebU12mTB6uTEYuHPSBhZONIhSVIsBsktampq4HK5UF1djczMTN1ff2NJBYrWl8JTE9vzwYEcANIFq12GG8HxfU23KwU7Zo8Q7uDjqaqqXRl9DhrFru2OlFzgL/DCIJ+RkV6cg52z7dKScKpObG8jO9HSp8UqLceVmnOQOTMRGl2Yi50PjsDMa3ua3ZSokgDhlQmigQygLncg3qqqEhktGgX+RhfmYsfsEVh99xA8M2EAXr3rCqS0UZ+XZwexWkNIrWgcVwxmdJCY4MCQ7tlmN8PWXGlJqu744rGqKpHRolXgz3elT0KCI+ZHtmN9JVc40TiuGMzoJN4P1khVqxxijteqqkRGMqPAXzz0nbG+kiucaBxXDGZ0Eu8Hqx7UjKTEc1VVIqOYUeDPbn1ngkN8v6l4qSEUTjSOKwYzOtGySyz9SO1ISrxXVSUyghkF/uzWd3olsU0w46mGUDjROK4YzOhEpDBW3wviY0WEA4A70wl3pvoOSnQkhVVVifSnZ4G/Jq8kVEQuMcGBuWMKLFd7Jis9WdXjA78SFv78UTQKR7LOjI6UCmO50pJwx5X5mDGiJ/5ZUoFH1pX4rfBpn5aEJq8XNfX237dEPhSLbuwDACFrSgQjOpISqmYF74iItNOjwJ+akgkbSyost2lvrisFW/5jOIYtei/kasys9CTMHdsH7swUXNq1PT4+WmX5GkJmMbpwJOvMGKDJK2HJlsN4aWe5X8Vb+WT2evFDQPPjfkbpzkTUxsAmbIEdVrBOLRit9RhYZ8b67Fqvxa7t1ovWYpRq6okoPdZMDjS30ZWajInL94R9/Oq7h3DDWBXUHFdqzkGOzBhgU6kHT28+3OoE9VTXY6pCxWA7BzI3D7oAw3pmw+1KbXVg+lY93VTqwYs7y4O+hgRtIyl2qqpKZCdadnUPVzLBgeZE/1EFbuCH/22lQMb3Rmhd8TdCz+EiA3W0HFciGMzoTKT+Saz53/3fYMvnx/GLARcAQKtgQj54q882KgYzkTDq5CAidURLJuz5qhIJDkfYEdtocTiAV24fjCt6dMDHR6uwrvgbnDjdIPRcLjKwBgYzOgt3Mseqk7Xn8MLOcrywszzoNI8c5CnxvWPjqAqRPYmOUkx/dT9+OegCg1sjTpKA/cdO4YE3Dvj13wmO1nsuyeSpcS4ysAauZtIZhxyDbydgdpE70ZUVRKSd6CjFqbPNNz9W8tTmL1r1UaECGYCLDKzE1GBm4cKFuPzyy5GRkYFOnTrhpptuwueff+73GEmSUFRUhLy8PKSmpmL48OH47LPPTGpxeBxybA5MJAAPrT2IxvPNO+CaWeRuY0kFrlq0BROX78HMNcWYuHwPrlq0hXs32VQs9huxYnB+FtqlJQk/Xk0BOjNx2bX1mRrMbNu2DdOnT8eePXuwadMmnD9/Htdddx1qa2tbHrN48WI8+eSTWLJkCfbt2we3241Ro0bh9OnTJrZcmd0KQBnpZO05DFn4HjaWVBhS5E5ktIWbUcaeWOw3YsWmUo+q3a9FC9CZzSsBc8f0xjMTBmD13UOwY/YIBjIWY6ml2d9//z06deqEbdu24eqrr4YkScjLy8OsWbMwe/ZsAEBDQwNycnKwaNEi3HPPPWFf04zllfIFFGhd/0QK8r9jnQPAs5MGYf6GUniq64N+brVLs0WWZDd5JVy1aIvi9JbW5eCkjtHnoBH9RjTaHWvCnW9Kri/MwTsl3xnUKv08M2EAxg2wTp5PPFBzDloqZ6a6uhoAkJXVnFBVVlYGj8eD6667ruUxTqcT11xzDXbt2hX0NRoaGlBTU+P3L9rk4kBul/8og9uVgmWTB2FZkN/FuvkbSjF3TG8AwStASgCuL2xeYh0un0V0tMXsPB2KDj36DcAafYedaV38kNIm0YDW6I8pBNZmmdVMkiTh/vvvx1VXXYXCwkIAgMfjAQDk5OT4PTYnJwdHjx4N+joLFy7EvHnzjG2sgHD1T3x/V36iFk9tPmxyi40jBw3t051BK0A6HM2rCV7cWY4XFVZDydTUsRDNv3nnh+CH9WnsR69+A7BO32FXWvLdEhzA2uJvDWiNvrg1ivVZJpiZMWMGDhw4gB07drT6ncPhf4GRJKnVz2Rz5szB/fff3/LfNTU16Ny5s76NFRSq/kng7+oaz+PP28ui1TRTbC71YO7P+7QEcptLPXhhZ3mrFQPyCEuwBDs1oy2id1Kv7D6KV3YfZeVgG9Kr3wCs1XfYkZaRC7ssKuSqJeuzxDTTv//7v2P9+vXYunUrLrzwwpafu93NVSLlOy3Z8ePHW911yZxOJzIzM/3+WV2TV8L6T2M/EfW1j4+h8bwXiQkOXNq1PdYqVNiU+7d5b5a2mnJSsypKbTI2k4LtRc9+A7Bn32El8vkWa+4beRFvcGzA1GBGkiTMmDEDb7zxBrZs2YL8/Hy/3+fn58PtdmPTpk0tP2tsbMS2bdtw5ZVXRru5homXQnun65swZOF7WPh2KYYsDL2Bm1I+i5pVUaF2alV6TyB4EEXWwX7DmnzPt1jhSm2DGSN6mt0MEmBqMDN9+nSsXLkSq1atQkZGBjweDzweD86ePQugeZh41qxZWLBgAdauXYuSkhLcfvvtSEtLw6RJk8xsuq7iqdDeydpG/Hl7md8mm6EEfjeid39Vtc2lyJWSsZUwKdj62G9Y1+jCXPxyYOys+EkIMS1J1mJqzszSpUsBAMOHD/f7+UsvvYTbb78dAPDAAw/g7NmzmDZtGqqqqnDFFVfg3XffRUZGRpRbaxxmySsL/G4SExyYO6YA01YF37BTNn/DIfysMBeJCQ6/ZOx3Sirwym7lJFBZPAWYdsN+w9quuqgjXv9EbJNGq6uqO4e9ZSe595sNmBrMiJS4cTgcKCoqQlFRkfENMok82hBqqqmtsw3ONJyPYqvMFWrfk/bpyWGfL4+uyJ2Qb8K1SDDDANO62G9Ymzszts4d3tjYgyUSgONdYoIDN/YPnWA2tEf8LQtUWkEQydYI4ZKCHeAyTKJIiEwFt1ex5YHZeGNjDwxmLEBkNdN7h45HqTXW8Jur8xVXEESyNUKopGBuHkcUOfkccyD4OeYAcMtlF7Z+ogW1T0vijY1NMJixAJHVTPG2uGb9pxWKK4oiHV0JVaGZm8cRRS7UOfbspIG2KUVRVXcOm0o94R9IprNM0bx4xjnZ1gJzXnzJd373rtzfao8r0dGVcBWaiSgySueYnUpR+FYUZ99gbQxmLIBzssGFCvLkO7/ArRHcKqr4hqrQrJcmr2SJgMkq7SBzGPX3D/e6wc4xO928+ZZqiOaKJp6v6jGYsYDB+VnISk8KWUQuHoUL8qw+uiKys3c8tYPMYdTfX+vr2vHmLZoBGM9XbZgzYwGJCQ78glvL+xFNvJPv/MYNuABDe3SwVCAjsrN3vLSDzGHU3z+S17XjtgfRCsB4vmrHYMYiRha4hR6XlW6fJY2R8ArUErGqcDt7A9HZMsEq7SBzGPX3j/R17bbtQbRKNfB8jQyDGYsQXaGzZ85IrL57CJ6ZMACv/voKuDOdwhsp2kn12fOmbynQ5JWw+0gl1hV/g91HKoU7ETU7exvJKu0gcxj199fjdT/5ukrVe5opWqUaeL5GhjkzFiG6Qie5TYJfIlrRjX0wdWXo0v52JTJPbVSiXKh563B5OpEU9dOTVdpB5jDq7x/p6759oAJ/3l6m6j3Nct/IXhhV4MbuI5WG5+XxfI0MgxkL0bJCZ1SBG+3SknCqLnzysN2SjE+cbsC64m8UOxAjExvvXbm/1XCvp7oeU1fub/V9B75nJEX99GSVdpA5jPr7R/K6TV4Jj6wrUfV+Zsl1paBXpwxctWhLVJJxeb5GhsGMxahdobO37KRQIDN3TG9ktXXivr8V69xiYyQ4mjeLlAV2IKECjntX7tdc/E5k3jrw+w58T3nK0FNdH/R1Qu07pSertIPMYdTfP5LX3Vt2EidrG1W9n1lu7J+L6av072OU8HyNDHNmLEjNCh3RIcfsDGdUNoDrk6fPrsSB6Sm+2fxGJsppKegV+J5W2TLBKu0gcxj194/kde00RfK3j/4vqsm4PF8jw2DG5tQMTV7atb3hycI3DRDfc8WhojG+HcieI5WGJcpp7WwD39MqWyZYpR1kDqP+/lpf1+wpknYqNrgMNeJtVDIuz1ftOM1kceESXNUMTe4tOxn0MXq6xJ2BXFdKyGCjXWoSnr11EC7vloXntn6JZduOoP68N+xryx3I7q9OCLVFS2ASaWfr+56iU4ZGV/u0enFBMpZRf38trzs4PwvuzBR4aswZoXl24iB8dLQKT23+QpfXM2KkieerNgxmLEwkwVXNPkXRGOI9caYBEy7vjKc2H271O7k9T/yyL4b1zMbGkgo8/V7rx4UndlJrCUzCBYdq3zPclgnRqvYZja0byLqM+vurfd3EBAeKbiwwbQXmidoGdMtO0+31jBpp4vmqHqeZLEpNJchwQ5Py0sLD350Wfv/2KoZjfc3fcChoIAMArrQkzBp5EUYVuNHklVC0vlTTe1wR4a7ZoYSatw5Fy3uy2ifZldYaTEBzf/XcpEGm1MfqlJGC7LZOXV4rwQFU2SSZOR5wZMaCwiW4BtvJVWloclOpp9XSwnBmXdsLXbPTNa18CrVS4VTdOTy1+Qus3nsUEwd30TzU/Pv//RTjBuTh+e1lmnfNDkVpiby8JFuP99TyNyaKJqXpTz1GE39W6EZmahtUnz1vVPP9OADkZDrhlSTs+lJsmjocrwRMX7UfSxOYy2IFDGYsSE0lSN+hyMChSaXly6GMKuiEWaMuwu4jlRpaLsZT06A4eiPiu5oGPL+9DL+5Oh/rP63QvGt2KKGCw0h26pZp/RsTRYNSwHJj/1w8v70s4uXKe8tORjWQkQDUn/fi1r98qPvr86bDGhjMWJAelSBD3fmHsrn0ODaWVGBUgdvURL1Q5JGL9Z9WYNvvf4qPj1YZkigXbN5ar+Q8Vvskq1K6Caqorles3Kt2NDGax7XrhxFVkXpcavGmwzqYM2NBelSC1FIvRTbvzeZclnEDrDt0KnciHx+tivqu2cHqAKnNIWC1T7IirTdBgLrlytE4rtOSE/HXOwcjpU2i4e/Fmw7zcWTGgvSoBBlpvZQ9X1Vi/afWT0A1oxMJzCWoqm3A/A2HVOUQsNonWVEkN0EypXPS97zJbuuEy+CcmXuu7o4PyyqjMrrMmw7zMZixIDXLrZVEenLtDlOYzrc9v/5JN7x1wKOpE7y+0I3dX1W2GgJOT05AbWP42jOdMlIMr9PiK1guQTDhcgj0+BsT6U2Pm4NgfY/oeaOH9OQEJLVJjCgvTxRvOqyD00wWFWklyMFhli+HJzbQLAH4ywflKLwgU9O7TB7SFQtuKmy1FDwjJRnt0pLCLr+uqm3AVYu2YOLyPZi5phgTl+/BVYu2GLKsWWkpdTAiJc9Z7ZOsJpKbIKXyBGrOGz3UNnp1zY+RqwZziwFr48iMhUWSbOp756+GfKcxtHs2lmw9Ivy89w4dV/U+sndLPHh5z9FWP/+u5sfpF6WRi+aN4D6JykZwWnIJRJIDWe2TrERr0UilC3skOThW8fiNhThyohYv7SzDqbM/Bkl6rZwkfTCYsTiRSpBK0yxK9VKUyEHDhMs74/iZBmSlJ+Fkbfg7HAmApLG3ChbIyK/pQPNdkbNNAjw1DS2/c6Ul4fah3bBm37Go1WmJJJcg3NA9q32SVWi9CVK6sOuRg2O23/7tE7+Nb9ulJuGOYd0wY0QvXW86ojldHosYzNhcuAJWowtzMeKSHAxZuDlsYOL6YTg1GnPNIiQAVXXn8Oqvr8BH5Sfx0s5ynDrbvMQy3DYIei+ZjCSXgMmBZCejC3Px7KRBmLF6f6vd631lpSdh7tg+cGcqX3g3lXoMbGl0BH4Hp86ea+kj9QpoorWtSSxjzoyNiZbD//holdAIi1G1GCK15dB3eHrzYb8hXlF6rXbSEpBEsq0CkZnapyeHDGQA4GTtObgzUxRLIjR5Jfyj+FuDWmi+pzYfxrAnIs/P47Ym+mAwY1PhyuEDPyafRnpBdwBwmDjaubb4G81z7nqNiqhNqBZJDoxkfxsiozR5JewULPkfqm/ZW3Yy5PYmscBTE1nAoaYfp9A4zWRTasrhR3pBl1r+T+tkXKOlJycIjSoF0nvJZKil1MGESw7ksDJZkdol1KH6lngqJKc1P4/bmuiHwYxNqSmHP7ZfnqYVCoHuGtYNb5f415MRTRLWqvG8+hYbtWRSKaE615WCuWMK0D49WSh5T6lcvO8qLL1XODG5kMJRs5ebyM2CUbliacmJqGtsMuS1tYgk4OC2JvphMGNTasrhqx1VUDKywI2HxhT4XRQrTp3F/X//VOMrhndOw/CqkUsmI11KLbJb9pw3DqJo/Wd+K7giGbXhKBCFo3YJtYTwNwtVtQ1IcLROoPXlcKhfCVnX2ISbB12A/93/jbonqhSu7YG0BBzc1kQ/DGZsSm05fLXLtJVey3cZ8caSCjz+dmlkHyRCDgA5mU78zy0DcOJMQ1RGHSJZSi0yrFwVJAlba+0ckVEgBjSk9xLqjSUVQWtABdJa0mGzxrpWalxf6MaGg+KrsU6cbkCTV1LV93BbE/0wAdim5NEWQLwy5ejCXOyYPQKr7x6CGT/tqer9Al9LvkgaOcUkqujGPhjWMzvsZpPBEm6jnYQbyZ5ZgLpkQCYXkii1x6VcxynYsSMyypPgaK7XopWWlY1qbTjoQbu0JLhSxO755284pLr6uJZ+nILjyIyNKY22hJpmkUcVRDuvdmlJeGJ8X7/XajzvxUNrS0yv6tkuNQlP/LKv0MhCsKkWuUy573J0PaZfQuWnRDJcrHZu3sjkQubgGCva36/a4zLUsSMyyuOVohOQREptqQotI55a+nFqjcGMzWnN4RDtvJ6dOAjDemW3/PfGkgo8tPagJUZknr11EIb1zA77OKWplmAdVaTTL+HyU7SWi/eld9LgplKPqmCGOTjGMuP7lY9LtVNNwY6xeE5W1Vp9nNuaRI7TTDFAHm0JN83iK1zdFLng2xCfi5yVppYyUhLhbZLCTg9pSWwEtE2/iBS/CjWsLErvpMEXd5YLD42zwJexzPp+ExMcuLG/+kAp2DEW78mqvqNWamjpx+lHDGbilNq5WqttGHe6vgm3vbQ37E7ZWhIbRTsj33ybnYdPoGi9WH6K0m7Zua4UoZ3CRZMB5YA1nFD5D76Yg2MsM7/fJq+E9Z+KB0qhjsXB+VlwZzp1bJ09xfMIlRkYzMQxpYuq25XSaprF6hvGVVTXY+rK/Xir2H+5ZiQdSqjnbiypwFWLtmDi8j2YuaYYt77wITw1YvkpgH8y9jMTBmD13UOwY/YIPDG+LwB9kgF9A9ZQRIM3NTk4pJ6Z36+a8zvcsZiY4MDEwV0ibtO1l3QUCsatKt5HqKKNOTNxTnSuNpp3GRnOBJxp8GoaBZqxphifflONh8c0X8Qj6VCUnqumuFgg3+8x2BJvvZMBRxfm4q5h3fDCznJVbdPye7WPI39mfr9qXlPpWPRNWv6X53TEbbqyRzaWTr4MS98/ghd3fIXq+vMRv6Zs2vDu6JWTiZ2Hv8e7pd+hRsfX5nJqczCYIaG6KdG8y7iujxuv79e+Qd3yD8qQ4ADm3FCAAZ3baXqNdmlJQTujSKfbRL5HvZMBRxa4hYKZcG1jgS9jmfn9ir7m3DG9cfuw/FbHotptEETM33AIf9r6pSGb3zrbJGLxxn8ZMtosUlCQ9MdpJhKidqPFSLytolCVkuUflKHxvBerPjyq6flKnzOS6TY1+S56JgOKJnuHa5ter0PBmfn9ir63UiATLGlZD0YEMu1S2+CpzYcNmzZvl5aEUQVuQ16blDGYISF6rMIRdfacN+LX8ErAy7vK8aHG/IKqunNBcxMiGeKfO8acuzW9CnOxwJexzPx+tb631RYGiNCyRYoapxT6DjIWgxkSppQwbFVPbfoC75Z+p/n5wQKXSIb426cna35upNQke0fjdSg4M79fLe9t9YUBwdQ2GL9JJfPGoo85M6RKYD7H4e9OY8nWI2Y3K6i6c5F1WsECl0iK3pndwemVi8MCX8Yy8/tV+95mH9NWxbyx6GMwQ6r5JgzvPlJp2WAmEkq5CZHsQG6FDi6STTKNeB0KzszvV817qz2m5XNmbN8cbDj4na2mp0RwJZN5OM1EERmcn4X2adGLieeO6Y1nJgzA2L7GTmecPdeETaXBE5HVTrcxMZZildqFAa60JLRLS8JbNglkAgek2qUlIT05MehjmTdmLo7MUEQSExwY2r0D3i5Rn5uidmTD4QDapSWjU0YKnpk4EBe0T8HyD8rgm8/ncACSDr1kdd25kHs0BQ7Hl5+oxVObD7f6TOzgKJaFGqmUj/ZZIy9Ct+w0lJ+ow9Obv4hKEONsk4CG85EvJFgycSDapzv9ptwAYMmWw3hpZ7nfZpncGNJcDGYsxo67EXfvmAFALJiRN8wDoLouhSQB//H3T/1e51/zL8Ffd5fj6Mk6dM1Kw0WdMnDbS3tVf4ZW7/XD/y9a/5nihnGBw/EXuzO48y3pwk79gFKhx/bpSXh8XCFu6JeHJq+EqxZtidpozF1XdcNz738V0WvcN7IXbuiXF/R3M0dehBkjetnmbxQPGMxYiF13Ix7aowOWbP0y7OMevqE37rzqxzoVowrc2HOkEtNX7fe7wxHhu7v1XT/p3vLzJq+kafdfxfepacCSLV9i5sheYR8rmjxppwsVRZ/Z/YCW43N0YS68XuCRdSU4WdsIADhZew7zNxxCQoIDrtTkqK16SnAAyYnBp4LU6JadHvL3zBuzFock6TEob101NTVwuVyorq5GZmam2c1RpFQiX+5CrLTsNbCzu7RrewxesDlkgau2zjaYP64P3K7UVp3jxpIKTF25X3U75GS7HbNHtHo9rdsNKFmm0/dv9oXKDHY5BwOZ0W6z+wGtx2e4dt8xrBteFKhCHch36krNtLQDzfk51XXnNPcD9428SOgmhoyj5hxkMGMB8hCs0p2L0kXbDEqd3Y39c/Hn7WVCrxGsc3z7QAVmrN4PLfWsXv31FRjWMztsOyORq8P3b/aFyix2OAeDiXa7ze4HtB6f4doNNN/MnGlQv/+R1mlpB4A0Z2JENWUciN1z0i7UnINczWQBdtmNWKlsuae6Hs9vL8M9V+fDnRl+hY88RbSxpKLlZzf0y8WSiQM1tWv6q/6vBfy4K/XcMb01vWagSL//UJVS5Z/Ne7MUTQZXJyXrMrMfiOT4FCmcpyaQmXltL7+d5EcX5rbaZT7ceS1Bn+J4PCftg8GMBdhhN2KRzm79pxXY/sBPsfruIXjq3wYgKz0p6GspdY439MvDssmDkKuywvCps+daBUdA85x2doZT1WuFEsn3b5eAlcxjZj8QyfGpZ3scAF776BjG9strtSeZ735lep7XSnhO2guDGQuww27Eop3dx0erMLRHB7gzU3CyVjmHRqmj8L0De+qW/shKTxauYRHsLkrP7yyS17JDwErmMrMfiOT41LM9ogFENPtCnpP2wGDGAuywG7Hazi6SzlG+A/vFoAux4BeFQq8jd4Irdpb5BTR67Patx/dvh4CVzGVmPxDJ8anHORYoXP9hxHsq4TlpDwxmLMAOuxFntxUb1pVPfL0u3nINi3apwaesAs3fcAhXLdrSMuXk+91qodf3b4eAlcxlZj8QyfEZqt1ahesX9HhPkcrlHdKTcWnX9hrfgaKJwYxFWHk34o0lFfiP14pDPiaws9Pz4j26MBfPThok3N7ABOPRhbmYNfIi4ef70uv7t0PASuYzqx+I9PhUu8VHKMH6hSavhN1HKrGu+BvsPlKJJq+k+J7t08Lf+OS6UvDhQ6NwX5h+obK2Edf819ZW+XhkPVyabTFWK6gmUrNFaemm/FwgeJlzNZ2zvPxTdLfqwGWs64q/wcw1xWGfd/uVXfGzPrmGff+sM2P9c1BmZrvN6gciPT6bvBL2fFWJ6a+qL4QpC6zpFK5Nvt9Vdlsn/uO1YnhqGkK+x90/ycfDYwoUX99XrJdOsDLWmfFh147UCkTqRwChOzs9L95aiuHNHdMbtw/Lx96yk5i4fE/Yx+tZKEvpgmS1gNVodj0H7dpuJaLHnR7Hp9KNTDiB59/bByowbVXrgppKAcbuI5VC53lWehL2PTyq5XM1nvdiyMLNiosWrFTrK56oOQe5nQEpEqkfAQD/fXN/DOuVHfR3owtzMeKSHL/9k24b2g3JbdTPcMrTRU9t/kL4OfM3HMJfdpRh7pgCuDOdYe/Y1uz7GjNG9AzbYYXr8MMFcSyDTtGk5qYikjL98nnRcN6LWSN7YfXer8OeczJ3phMzRvRs+e+3D3yLGas/CfpYCc0Bxrw3S/32ThNdeHCy9hz2lp1s+ZwfH60SXn0p8t3E2w2LFTCYIUWiHcPurypxorZB+KL+lx1lmqdVumWnqX6Op7oe01ftx9h+brx5wBPysSIdVrgLg9IIku9+UqE+OztC0pOa4zGSYy/YeeHOTMF9Lbtmh95ZvujGPi3vtbGkAtNWBQ9kZMECDDUrj3z7Nz1LJ8TjVLIVMJghRaIdg+8mk3pe1CNpky/5Lu6Dw5VCjw/VYYX7TM9OGoT5G5SLCwa7mwx8fXaEpJdwxS59j8dNpR7Nx57SefFdTT2e3vwFlk4ehJkjLxLaWV5usyjf83Vwfhay0pNbNrsMxbcv0Wv1pRF9HokxdTXT9u3b8fOf/xx5eXlwOBz4xz/+4fd7SZJQVFSEvLw8pKamYvjw4fjss8/MaWwc0lLLQT5p3z5QYUj5fq31JSRAOCFRqcMSqYI8d12J5kqqobaLCFbhOJ6x7xAjWuxyyZYvNR97arZCCNyWwHfLAtE2B/I9XxMTHHh8XPjaVO3TkvxWTOmx+pJblpjL1GCmtrYW/fv3x5IlS4L+fvHixXjyySexZMkS7Nu3D263G6NGjcLp06ej3NL4pKWWgx4Xdb3b5KtdapLmDkvkwlApcEcItB79YUeoDvsOMaLTJy/tLNN87KndCsF3W4LALQvUtBkIfr7+rNCNtOTEkM8L/CR6lE7gliXmMjWYuf766/H4449j/PjxrX4nSRKefvppPPzwwxg/fjwKCwvx8ssvo66uDqtWrTKhtfFJS/2ISC7qRrVJdsewfADqOiy5xsU7Oo6MBI7+sCNUh32HGNHpk1Cjlr7VtX3rvMj03qpDzVTyhMu7tDpf95adRF1j6E0mT9WdC7qVSiQ1frhlibksmzNTVlYGj8eD6667ruVnTqcT11xzDXbt2oV77rnHxNbFl9GFuRhV4G5JDDz83Wks2XpEl9c+/N0Z7D5SqTrJVW7TU5s+F25LrisFM0b0xMXutq3m7V1pSbjjynyMKnD7PSdcDYpgstKTUFV7LuidrrzEM/Bukh2hfth3/EiePlGqz+QA4EpNEpqCnb/hUMv/9s2l0XurjsH5WWgn2KZgCwJEzxFP9VnsPlLZXJ8m3Qk4gIbzXvz3/+sPSFBc1KCEW5aYy7IVgD2e5lUnOTk5fj/Pyclp+V0wDQ0NqKmp8ftHkfMdGh7Ws6PQc7LSlad0ZEu2fomJy/f4bUGgpk2ibQF+HHWR5+3vG9mrZZuEU3Xn8NTmL/zaoZTDokSeppLn7NWM/rAj1A/7jh+JTJ/cMayb6tf1zaXRe6uOxARHywhqOMHOB9FzZP6GQ5i4fA9mrinGrS98iFv/8mHz//7Lh/jd/34KZ5uEoNNgSrhlibksG8zIHA7/Q0OSpFY/87Vw4UK4XK6Wf507dza6iXFH9KRVuqgHozXJVSQhOMEBPDfJf5h4U6kHT28+3OruTySBORjfQOWGfnmqh6vZEeqPfUezcNMnM0b0Up1U75tLA0D3rTpmjOiJdiG2JQh1PoguEgi14klLf8QtS8xl2WDG7W4e7g+8kzp+/HirOy5fc+bMQXV1dcu/Y8eOGdrOeCR60ipd1IPRmuQqkhC8ZOJA3NDvxwBCj1VJgQIDFZFVG6Kfgx2hOuw7Wgt1PGpNqvfN49J7T6nEBAeeGN836O/CnQ96bEKptT+y8h57sc6yOTP5+flwu93YtGkTBg4cCABobGzEtm3bsGjRIsXnOZ1OOJ1iOzyTdvJJG65mhG++zc4vvw+Z36K2yma4tijVyNBzVdKvhnbF9YW5QefV1VZSFf1OKTT2HcGFOh6Vjj0Rco5KYG5dpAUfRxfmYpnG80Hp82SlJ4Ws9Osrkv5Iz++BxJgazJw5cwZffvljwbWysjIUFxcjKysLXbp0waxZs7BgwQL06tULvXr1woIFC5CWloZJkyaZ2GqSiZ60cidqZJKrmg5EzyTa63XemoAdoRj2HfoLPPZOnG7wS/pVEljnxSrnQ7Dnemrqcd/filW1QUt/off3QOGZGsx89NFH+OlPf9ry3/fffz8AYMqUKVixYgUeeOABnD17FtOmTUNVVRWuuOIKvPvuu8jIyDCryRRAzUmrR5JrqHLrom0RbUeoVUlAc80aryShySsx2Igy9h3N9N76wvccavJK+MuOspAroYKtzNNbsPM63OcO/P3YfnlITHBg9xGxKuC+mHRvD9w1m6JG3oU7XOeotDOtXqX+Rdsxd0xvTP9hf5hQJ4me2w3E4nYGdj0Hrd7uaBwrSrtfK+1aHQ0ie6Mp/X5UgTvkue+LO2WbT805yGCGokpt5yjfYW0q9eDFneWtXk9rpyraDpE6M/JzZv2woZ7WO2SlfV3MvHDowa7noJXbHc1jRe+gSWQ0Sekx4T73b67Ox/PbW1cz9v1eAAQ995Ueb8dzLlYwmPFh5Q4pXol2jm8f+BaPrCsJm7AX6g4qVMcZrB0d0pMxf1xhq9VPe45UYvqq/cL7O6nt7OXRIqWgSetdohV24LbrOWjVdht1rIR7TzXHUahgJNy5r/SYuWN647G3SuGpaVB83wQHoLT4yPd7Cbappi/R89cK51csU3MOWnY1E8UukaS+hW+X4s/by4ReT2nVQbiOc3RhLrxeyS9gqqxtxPwNpUhIQEtHlpjgQEKCQziQAdTvkqtmOwPRHKVYnLIiY46VcNTkxikddzf2zw06auJ7rgBQ3HV62g9TvqGEWkUduJTctw+SKwCfOCNe9Zfnl7UwmCFThOoc3z5QIRzI+PJddaA0HB3YcU5f9UnIx8idktoVDRKa7wTnvVmKUQXuoB2j713d4e/ENkAUbYfI52eHa09mbn0RbiRC6birqK5XPKd9zxVJkkLWf9KD/L1EsuKI55f1MJghS2n6YaREC3nVQbiieA4ARes/A+AI+RjfQETLioZQd8ha9nwCxFZWiHz+UEEWWZtZW1+EG4kIddyFI58r0RDp98Lzy5osWwGY4tPespMhy4wHE1jaXGQY3lPTAE+N+C7VoiXSgwm8Q1a75xOgbjsD7sAd28zY+kLpmPUt+x/uuIsGB5Sr/ur1vfD8siYGM2QpaofGg5U213N43XdIWmuJdN87QS13r2q3M+AO3LEt2ltfiGz/Me/NUniqz+ryfpEYcUnzxrNGfi88v6yJwQxZitoh4GB7nug5vN4pIwVNXgm7j1Si4bwXs0ZehJxMsdcPdieo5e5V7b4u3IE79kVzDyDRkQi1I6q+5HPFnenUvJ8SAPz6Jz0M/154flkTc2bIUuQh9HAX/Duu7Irr+gTfE0l+jVBF8XIynQAc+K4mdOG8qtqGVstg3ZlO3DeyF7plp6P8RC2e2nwYDgSvVxN4Jyh6tzbjpz3RK6dtqyRLkaWgIp8/GpVbyVjR2PqiySth55cnhB6b1dYZ8riThTpXgObVTIGPESHfOCQmOAz9XqpqG4SWgPP8ii4GM2Qp8hB6sJUCsrH9cvHI2D6KnZPvayh1nEU39gEQvOOUH3Nj/9ygq52+q2nA05sPY+nkQZg58iJc7M4Q3gwvu63YRoZDe3RAgsOB46eb594H52cFrY0RbCmoyOfnDtyxwcg9gNQmqXfKcGLC5V3w1OYvWv3Ot6jd+k8rQp4rwTaIbJeWhFN1oUsj+B7Tob6XwBuCS7u2x8dHq4QCn40lFUH7hFBtoehg0TyypHAdqUg9h8gKdBVg/gbl9w8sTCYyYrKxpAJF6z8LWfTLgeaO29kmwe9xSp15qEqlVqmDYddz0K7t1oPS0uNglI5ZX4GrnrRUAN5U6sGDbxxsdR60T0vCwvF9hY7pYOdE4CiL0jkSrlih/FpLJg7yK7pJ2rECsI947pDsrskrYcmWw3hq8+FWvxMtNx6q45R/56mpx8kzDchKT4bblYrB+VnYW3YSE5fvCdvG1XcPEbozFrk4aBlal5+npQKyqEhfw67noF3bHenfS+SiLRM5Zmde2xOD8zuoKkgXqm17jlRi91cnADSPvgzp3kHo9UQDNKW+ZfeRSl37BAqPFYApZqzZdyzoz0XrOSgNN4catUhMcOi6YkF0BZPblYKz55rCDqcHClXPJtJpCKuM7pAYPf5eapLURY7ZP235El7pS83t8ZWY4MCwXtkY1itb1fPUrCJU6lu4isnauJqJLMuoeg4iNTP0XLEgenG4c1g31YGML707UZHviaxDr7+XeJJ6D/z3zf3DHrOBibJmHD9qVxEG61u4isnaGMyQZRlxJyRaM+PSru11K0wm2r5jVZHV6dCzExX9nppCbYZDUaPn30v0OBrWsyNO1Crnfykx4/jRGuj7Ps+MYoUkjsEMWZYRd0Kioz0fH63SrTCZaPu6ZqUJPS6QEZ0oq5zai55/LzUXba0BdLSPH63t9H1etIsVkjoMZsiyjLgTUjPao1dhMpHP0SE9Ge3SkpGVniT0mr7PBfTvRJkfYC96/r3UXLQj2eZDtD0AWgpXriv+BruPVKoe0VHbTqW+JZrFCkkdJgCTZRlRL0XtaI8ehclCfQ788N+VtY34j79/qvga8vMCl2gr1bOJFPMD7EXvv5d80Q5XPyncsa1He/RIalbTznB9SzSKFZJ6XJpNlqfnihp52Wm46rjBljlHSutO2cCPnzdanaie35Ndz0E7tduo41p0mbdI/RYt7VFaTi1amkFLO7lazzpYZ8aHnTokUqZHvRSZ3EECwUd7jBwu9q1tM/+tz3CyNvhKEAeArPRkPDKmd0vtm2jf+en1Pdn1HLRbu808roHW52hVbSOmr9LennD1bvQK0NRUAKboYp0Zijl6lm0XHUI3gvw5dh+pVAxkgB+nntyuVNMKcJn5PZF6Zv+9gp2jSxO0t0dNUrOacyRYO1nkzv4YzFBc0mPeO5LRIrsk2DI/wF6s9veKpD2ix/47P9Sr4XEZ3xjMUNyKZLQn0jweOyXYGrmZIenPan8vre0RPfZf2X0Ur+w+ylyXOMel2UQq6VFplQW4iEJTu5yalanjG4MZIhX0qrTKAlxEoYU6R4JhZer4xmCGSAU9K62yABdRaErniBJWpo5fzJmhmKfnsm69E3cDEySz2zoBCThR24DdRyqZ1EgxQeQcVHqM7znyTkkFXtl9NOz7mZ04T9HHYIZimp4F9wBjEnflBMmNJRX43d8/1a2tRFYgcg6Ge4xvErFIMGOFxHmKLk4zUczSI1E3kFGJu0a0lchsIse1mmOfifOkhMEMxSS9EnUDGZG4a1RbicwkclwXrf8MRevFj30mzpMSBjMUk/RM1A2kd+KukW0lMovIce2paYCnRt2xz8R5CoY5MxSTjK6wq2elVbtUAyZSQ8/jNfC1rFbpmMzHYIZiUjQq7OpVadVO1YCJROl5vAZ7LatVOiZzcZqJYpKdEgXt1FYiUSLHtTvTCXcmj32KHIMZikl2ShS0U1uJRIkc10U39kHRjTz2KXIMZihm2SlR0E5tJRIlclzz2Cc9OCRJiun1njU1NXC5XKiurkZmZqbZzSET6FkB2Gh2aqsou56Ddm23FUVSAZjil5pzkAnAFPPslChop7YSiRI5rnnsUyQ4zURERES2xmCGiIiIbI3BDBEREdkagxkiIiKyNQYzREREZGsMZoiIiMjWGMwQERGRrTGYISIiIltjMENERES2FvMVgOXdGmpqakxuCVF8ks89u+2cwr6DyFxq+o6YD2YqKysBAJ07dza5JUTx7fTp03C5XGY3Q9jp06cBsO8gMptI3xHzG02eOnUK7du3x9dff22rjlSNmpoadO7cGceOHYvZDfFi/TPG8ueTJAmnT59GXl4eEhLsM7Pt9Xrx7bffIiMjAw6HsRsexvLf32j87rSxw/empu+I+ZEZ+QtwuVyW/YPpJTMzk5/R5mL189nxRiIhIQEXXnhhVN8zVv/+0cDvThurf2+ifYd9bpOIiIiIgmAwQ0RERLYW88GM0+nEo48+CqfTaXZTDMPPaH+x/vkoNP79teN3p02sfW8xnwBMREREsS3mR2aIiIgotjGYISIiIltjMENERES2xmCGiIiIbC3mg5nnnnsO+fn5SElJwaWXXooPPvjA7CZptn37dvz85z9HXl4eHA4H/vGPf/j9XpIkFBUVIS8vD6mpqRg+fDg+++wzcxqrwcKFC3H55ZcjIyMDnTp1wk033YTPP//c7zF2/oxLly5Fv379WopUDR06FO+8807L7+382ShyCxcuhMPhwKxZs8xuii188803mDx5Mjp06IC0tDQMGDAAH3/8sdnNsrzz58/jkUceQX5+PlJTU9G9e3c89thj8Hq9ZjctIjEdzPztb3/DrFmz8PDDD+OTTz7BT37yE1x//fX4+uuvzW6aJrW1tejfvz+WLFkS9PeLFy/Gk08+iSVLlmDfvn1wu90YNWpUyx4zVrdt2zZMnz4de/bswaZNm3D+/Hlcd911qK2tbXmMnT/jhRdeiCeeeAIfffQRPvroI4wYMQLjxo1rCVjs/NkoMvv27cPzzz+Pfv36md0UW6iqqsKwYcOQlJSEd955B6Wlpfif//kftGvXzuymWd6iRYuwbNkyLFmyBIcOHcLixYvxX//1X/jTn/5kdtMiI8WwwYMHS1OnTvX72SWXXCI9+OCDJrVIPwCktWvXtvy31+uV3G639MQTT7T8rL6+XnK5XNKyZctMaGHkjh8/LgGQtm3bJklSbH7G9u3bS3/5y19i8rORmNOnT0u9evWSNm3aJF1zzTXSzJkzzW6S5c2ePVu66qqrzG6GLY0ZM0a68847/X42fvx4afLkySa1SB8xOzLT2NiIjz/+GNddd53fz6+77jrs2rXLpFYZp6ysDB6Px+/zOp1OXHPNNbb9vNXV1QCArKwsALH1GZuamrBmzRrU1tZi6NChMfXZSJ3p06djzJgxGDlypNlNsY3169fjsssuw//7f/8PnTp1wsCBA7F8+XKzm2ULV111Fd577z188cUXAIBPP/0UO3bswA033GByyyITsxtNnjhxAk1NTcjJyfH7eU5ODjwej0mtMo78mYJ93qNHj5rRpIhIkoT7778fV111FQoLCwHExmc8ePAghg4divr6erRt2xZr165FQUFBS8Bi589G6q1Zswb79+/Hvn37zG6KrXz11VdYunQp7r//fjz00EPYu3cvfvvb38LpdOJXv/qV2c2ztNmzZ6O6uhqXXHIJEhMT0dTUhD/84Q+YOHGi2U2LSMwGMzKHw+H335IktfpZLImVzztjxgwcOHAAO3bsaPU7O3/Giy++GMXFxTh16hRef/11TJkyBdu2bWv5vZ0/G6lz7NgxzJw5E++++y5SUlLMbo6teL1eXHbZZViwYAEAYODAgfjss8+wdOlSBjNh/O1vf8PKlSuxatUq9OnTB8XFxZg1axby8vIwZcoUs5unWcxOM2VnZyMxMbHVKMzx48db3f3GArfbDQAx8Xn//d//HevXr8fWrVtx4YUXtvw8Fj5jcnIyevbsicsuuwwLFy5E//798cwzz8TEZyN1Pv74Yxw/fhyXXnop2rRpgzZt2mDbtm344x//iDZt2qCpqcnsJlpWbm4uCgoK/H7Wu3dv2y7uiKbf//73ePDBBzFhwgT07dsXt912G+677z4sXLjQ7KZFJGaDmeTkZFx66aXYtGmT3883bdqEK6+80qRWGSc/Px9ut9vv8zY2NmLbtm22+bySJGHGjBl44403sGXLFuTn5/v9PhY+YyBJktDQ0BCTn41Cu/baa3Hw4EEUFxe3/Lvssstw6623ori4GImJiWY30bKGDRvWqmzDF198ga5du5rUIvuoq6tDQoL/pT8xMdH2S7NjejXTmjVrpKSkJOmFF16QSktLpVmzZknp6elSeXm52U3T5PTp09Inn3wiffLJJxIA6cknn5Q++eQT6ejRo5IkSdITTzwhuVwu6Y033pAOHjwoTZw4UcrNzZVqampMbrmYe++9V3K5XNL7778vVVRUtPyrq6treYydP+OcOXOk7du3S2VlZdKBAwekhx56SEpISJDeffddSZLs/dlIH1zNJGbv3r1SmzZtpD/84Q/S4cOHpVdffVVKS0uTVq5caXbTLG/KlCnSBRdcIL311ltSWVmZ9MYbb0jZ2dnSAw88YHbTIhLTwYwkSdKzzz4rde3aVUpOTpYGDRrUsszXjrZu3SoBaPVvypQpkiQ1L11+9NFHJbfbLTmdTunqq6+WDh48aG6jVQj22QBIL730Ustj7PwZ77zzzpZjsWPHjtK1117bEshIkr0/G+mDwYy4N998UyosLJScTqd0ySWXSM8//7zZTbKFmpoaaebMmVKXLl2klJQUqXv37tLDDz8sNTQ0mN20iDgkSZLMGRMiIiIiilzM5swQERFRfGAwQ0RERLbGYIaIiIhsjcEMERER2RqDGSIiIrI1BjNERERkawxmiIiIyNYYzFBUDR8+HLNmzTK7GUREFEMYzJAlaQ16br/9dtx00026t4eIiKyLwQwREcWExsZGs5tAJmEwQ6Z57rnn0KtXL6SkpCAnJwc333wzgObRlW3btuGZZ56Bw+GAw+FAeXk5mpqacNdddyE/Px+pqam4+OKL8cwzz7S8XlFREV5++WWsW7eu5Xnvv/++SZ+OiIw2fPhwzJgxA/fffz+ys7MxatQoOBwO/POf/8TAgQORmpqKESNG4Pjx43jnnXfQu3dvZGZmYuLEiairqzO7+aSjNmY3gOLTRx99hN/+9rf461//iiuvvBInT57EBx98AAB45pln8MUXX6CwsBCPPfYYAKBjx47wer248MIL8dprryE7Oxu7du3Cb37zG+Tm5uKWW27B7373Oxw6dAg1NTV46aWXAABZWVmmfUYiMt7LL7+Me++9Fzt37sTWrVuxfft2FBUVYcmSJUhLS8Mtt9yCW265BU6nE6tWrcKZM2fwi1/8An/6058we/Zss5tPOmEwQ6b4+uuvkZ6ejrFjxyIjIwNdu3bFwIEDAQAulwvJyclIS0uD2+1ueU5iYiLmzZvX8t/5+fnYtWsXXnvtNdxyyy1o27YtUlNT0dDQ4Pc8IopdPXv2xOLFiwEAFRUVAIDHH38cw4YNAwDcddddmDNnDo4cOYLu3bsDAG6++WZs3bqVwUwM4TQTmWLUqFHo2rUrunfvjttuuw2vvvqq0LDvsmXLcNlll6Fjx45o27Ytli9fjq+//joKLSYiK7rsssta/axfv34t/zsnJwdpaWktgYz8s+PHj0elfRQdDGbIFBkZGdi/fz9Wr16N3Nxc/Od//if69++PU6dOKT7ntddew3333Yc777wT7777LoqLi3HHHXcw6Y8ojqWnp7f6WVJSUsv/djgcfv8t/8zr9RreNooeBjNkmjZt2mDkyJFYvHgxDhw4gPLycmzZsgUAkJycjKamJr/Hf/DBB7jyyisxbdo0DBw4ED179sSRI0f8HhPseUREFNuYM0OmeOutt/DVV1/h6quvRvv27fH222/D6/Xi4osvBgB069YNH374IcrLy9G2bVtkZWWhZ8+eeOWVV/DPf/4T+fn5+Otf/4p9+/YhPz+/5XW7deuGf/7zn/j888/RoUMHuFyuVndlREQUWzgyQ6Zo164d3njjDYwYMQK9e/fGsmXLsHr1avTp0wcA8Lvf/Q6JiYkoKChAx44d8fXXX2Pq1KkYP348/u3f/g1XXHEFKisrMW3aNL/Xvfvuu3HxxRe35NXs3LnTjI9HRERR5JAkSTK7EURERERacWSGiIiIbI3BDBEREdkagxkiIiKyNQYzREREZGsMZoiIiMjWGMwQERGRrTGYISIiIltjMENERES2xmCGiIiIbI3BDBEREdkagxkiIiKyNQYzREREZGv/H4ytZeSICghDAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Based on the above observations we will RM and LSTAT as our features. Using a scatter plot let’s see how these features vary with MEDV.\n",
    "features = ['lstat', 'rm']\n",
    "target = df_boston['medv']\n",
    "\n",
    "for i, col in enumerate(features):\n",
    "    plt.subplot(1, len(features) , i+1)\n",
    "    x = df_boston[col]\n",
    "    y = target\n",
    "    plt.scatter(x, y, marker='o')\n",
    "    plt.title(col)\n",
    "    plt.xlabel(col)\n",
    "    plt.ylabel('MEDV')\n",
    "\n",
    "plt.show()"
   ]
  },
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   "source": []
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