1.Python_Basics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "# Python Basics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello, Python!\n",
      "hi\n"
     ]
    }
   ],
   "source": [
    "# Printing a string\n",
    "print(\"Hello, Python!\")\n",
    "print('hi')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10   3.146   Hello\n"
     ]
    }
   ],
   "source": [
    "# defining a variable : In Python there is no need to mention the data type\n",
    "\n",
    "var1 = 10      # An integer assignment\n",
    "var2 = 3.146   # A floating point\n",
    "var3 = \"Hello\" # A string\n",
    "\n",
    "print(var1,' ',var2,' ',var3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assignment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1   1   1\n",
      "1   2.5   john\n"
     ]
    }
   ],
   "source": [
    "# Assigning same value to multiple variables\n",
    "\n",
    "var1 = var2 = var3 = 1\n",
    "print(var1,' ',var2,' ',var3)\n",
    "\n",
    "# Assigning Different values to variable in a single expression\n",
    "\n",
    "var1, var2, var3 = 1, 2.5, \"john\"\n",
    "print(var1,' ',var2,' ',var3)\n",
    "\n",
    "# Note: commas can be used for multi-assignments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Slicing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello World!\n",
      "H\n",
      "llo\n",
      "llo World!\n",
      "He\n",
      "Hello World!Hello World!\n",
      "Hello World!TEST\n"
     ]
    }
   ],
   "source": [
    "# String operations\n",
    "\n",
    "str = 'Hello World!'  # A string\n",
    "\n",
    "print(str)          # Prints complete string\n",
    "print(str[0])       # Prints first character of the string\n",
    "print(str[2:5])     # Prints characters starting from 3rd to 5th\n",
    "print(str[2:])      # Prints string starting from 3rd character\n",
    "print(str[:2])\n",
    "print(str * 2)      # Prints string two times\n",
    "print(str + \"TEST\") # Prints concatenated string"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['abcd', 786, 2.23, 'john', 70.2]\n",
      "abcd\n",
      "(786, 2.23)\n"
     ]
    }
   ],
   "source": [
    "# Python Lists\n",
    "list = [ 'abcd', 786 , 2.23, 'john', 70.2 ]  # A list\n",
    "tuple = ( 'abcd', 786 , 2.23, 'john', 70.2  ) # A tuple. Tuples are immutable, i.e. cannot be edit later\n",
    "\n",
    "print(list)            # Prints complete list\n",
    "print(list[0])         # Prints first element of the list\n",
    "print(tuple[1:3])        # Prints elements starting from 2nd till 3rd "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'jack': 4098, 'sape': 4139, 'guido': 4127}\n",
      "4098\n",
      "{'jack': 4098, 'guido': 4127, 'irv': 4127}\n",
      "dict_keys(['jack', 'guido', 'irv'])\n",
      "['guido', 'irv', 'jack']\n",
      "[4098, 4127, 4127]\n",
      "True\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "# Lists are ordered sets of objects, whereas dictionaries are unordered sets. But the main difference is that items in dictionaries are accessed via keys and not via their position.\n",
    "tel = {'jack': 4098, 'sape': 4139}\n",
    "tel['guido'] = 4127\n",
    "print(tel)\n",
    "print(tel['jack'])\n",
    "del tel['sape']\n",
    "tel['irv'] = 4127\n",
    "print(tel)\n",
    "print(tel.keys())\n",
    "print(sorted(tel.keys()))\n",
    "print(sorted(tel.values()))\n",
    "print('guido' in tel)\n",
    "print('jack' not in tel)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Conditioning and looping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Square of  0  is : 0\n",
      "1 is an odd number\n",
      "Square of  2  is : 2\n",
      "3 is an odd number\n",
      "Square of  4  is : 4\n",
      "5 is an odd number\n",
      "Square of  6  is : 6\n",
      "7 is an odd number\n",
      "Square of  8  is : 8\n",
      "9 is an odd number\n"
     ]
    }
   ],
   "source": [
    "# Square of odd numbers\n",
    "\n",
    "for i in range(0,10):\n",
    "    if i%2 == 0:\n",
    "        print(\"Square of \",i,\" is :\",i)\n",
    "        \n",
    "    else:\n",
    "        print(i,\"is an odd number\")\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Built-in Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum of array:  10\n",
      "Length of array:  4\n",
      "Absolute value:  1234\n",
      "Round value:  1\n",
      "Log value:  2.302585092994046\n"
     ]
    }
   ],
   "source": [
    "print(\"Sum of array: \",sum([1,2,3,4]))\n",
    "print(\"Length of array: \",len([1,2,3,4]))\n",
    "print(\"Absolute value: \",abs(-1234))\n",
    "print(\"Round value: \",round(1.2234))\n",
    "\n",
    "import math as mt      # importing a package\n",
    "print(\"Log value: \",mt.log(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Area of rectangle: 200\n"
     ]
    }
   ],
   "source": [
    "def area(length,width):\n",
    "    return length*width\n",
    "are = area(10,20)\n",
    "print(\"Area of rectangle:\",are)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Broadcasting\n",
    "* Subject to certain constraints, the smaller array is “broadcast” across the larger array so that they have compatible shapes\n",
    "\n",
    "### NumPy \n",
    "* Numpy is the fundamental package for scientific computing with Python. It contains among other things:\n",
    "* a powerful N-dimensional array object\n",
    "* sophisticated (broadcasting) functions\n",
    "* tools for integrating C/C++ and Fortran code\n",
    "* useful linear algebra, Fourier transform, and random number capabilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matrix A\n",
      " [0 1 2]\n",
      "Matrix B\n",
      " [5 5 5]\n",
      "Regular matrix addition A+B\n",
      " [5 6 7]\n",
      "Addition using Broadcasting A+5\n",
      " [5 6 7]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np   # Importing libraries\n",
    "\n",
    "a = np.array([0, 1, 2])\n",
    "b = np.array([5, 5, 5])\n",
    "\n",
    "print(\"Matrix A\\n\", a)\n",
    "print(\"Matrix B\\n\", b)\n",
    "\n",
    "print(\"Regular matrix addition A+B\\n\", a + b)\n",
    "\n",
    "print(\"Addition using Broadcasting A+5\\n\", a + 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Broadcasting Rules\n",
    "When operating on two arrays, NumPy compares their shapes element-wise. It starts with the trailing dimensions, and works its way forward. Two dimensions are compatible when\n",
    "\n",
    "1. they are equal, or\n",
    "2.  one of them is 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matrix C\n",
      " [[0 1 2]\n",
      " [3 4 5]\n",
      " [6 7 8]]\n",
      "Matrix D\n",
      " [[1 2 3]\n",
      " [1 2 3]\n",
      " [1 2 3]]\n",
      "Matrix E\n",
      " [[1 2 3]\n",
      " [1 2 3]\n",
      " [1 2 3]]\n",
      "Regular matrix addition C+D\n",
      " [[ 1  3  5]\n",
      " [ 4  6  8]\n",
      " [ 7  9 11]]\n",
      "Addition using Broadcasting C+E\n",
      " [[ 1  3  5]\n",
      " [ 4  6  8]\n",
      " [ 7  9 11]]\n"
     ]
    }
   ],
   "source": [
    "# Lets go for a 2D matrix\n",
    "c = np.array([[0, 1, 2],[3, 4, 5],[6, 7, 8]])\n",
    "d = np.array([[1, 2, 3],[1, 2, 3],[1, 2, 3]])\n",
    "\n",
    "e = np.array([1, 2, 3])\n",
    "\n",
    "print(\"Matrix C\\n\", c)\n",
    "print(\"Matrix D\\n\", d)\n",
    "print(\"Matrix E\\n\", d)\n",
    "\n",
    "print(\"Regular matrix addition C+D\\n\", c + d)\n",
    "\n",
    "print(\"Addition using Broadcasting C+E\\n\", c + e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matrix M:\n",
      " [[1. 1. 1.]\n",
      " [1. 1. 1.]\n",
      " [1. 1. 1.]]\n"
     ]
    }
   ],
   "source": [
    "M = np.ones((3, 3))\n",
    "print(\"Matrix M:\\n\",M)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dimension of M:  (3, 3)\n",
      "Dimension of a:  (3,)\n",
      "Addition using Broadcasting\n",
      "[[1. 2. 3.]\n",
      " [1. 2. 3.]\n",
      " [1. 2. 3.]]\n"
     ]
    }
   ],
   "source": [
    "print(\"Dimension of M: \",M.shape)\n",
    "print(\"Dimension of a: \",a.shape)\n",
    "print(\"Addition using Broadcasting\")\n",
    "print(M + a)\n",
    "# Broadcasting array with matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## All in one program"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original List [10, 11, 15, 19, 21, 32] \n",
      "\n",
      "10 is not a prime number ( 2 times 5 is 10 )\n",
      "11 is a prime number\n",
      "15 is not a prime number ( 3 times 5 is 15 )\n",
      "19 is a prime number\n",
      "21 is not a prime number ( 3 times 7 is 21 )\n",
      "32 is not a prime number ( 2 times 16 is 32 )\n",
      "\n",
      "Non-prime List [10, 15, 21, 32] \n",
      "\n",
      "Time Taken to run the program: 0.0077969133200327 seconds\n"
     ]
    }
   ],
   "source": [
    "# Importing libraries\n",
    "import timeit\n",
    "\n",
    "# Usage of builtin functions\n",
    "start = timeit.default_timer()   \n",
    "\n",
    "# Defining a list\n",
    "array_list = [10,11,15,19,21,32]      \n",
    "array_np_list = []\n",
    "\n",
    "# Print the list\n",
    "print(\"Original List\",array_list,\"\\n\")   \n",
    "\n",
    "# Defining a function\n",
    "def prime(num):      \n",
    "    if num > 1:     \n",
    "        \n",
    "        # check for factors\n",
    "        # Iterating a range of numbers\n",
    "        for i in range(2,num):    \n",
    "            if (num % i) == 0:\n",
    "                \n",
    "                # Appending data to list\n",
    "                array_np_list.append(num)           \n",
    "                print(num,\"is not a prime number (\",i,\"times\",num//i,\"is\",num,\")\")\n",
    "                \n",
    "                # Terminating a loop run\n",
    "                break         \n",
    "        else:\n",
    "            print(num,\"is a prime number\")\n",
    "            \n",
    "# Iterating a list\n",
    "for item in array_list:\n",
    "    \n",
    "    # Calling a function\n",
    "    prime(item)         \n",
    "\n",
    "print(\"\\nNon-prime List\",array_np_list,\"\\n\")\n",
    "\n",
    "end = timeit.default_timer()\n",
    "\n",
    "# Computing running time\n",
    "print(\"Time Taken to run the program:\",end - start, \"seconds\")       "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Note:\n",
    "* Python is a procedural Language\n",
    "* Two versions of Python 2 vs 3\n",
    "* No braces. i.e. indentation\n",
    "* No need to explicitly mention data type"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unvectorized vs Vectorized Implementations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matrix A:  [[6, 7, 8], [5, 4, 5], [1, 1, 1]] \n",
      "\n",
      "Matrix B:  [[1, 2, 3], [1, 2, 3], [1, 2, 3]] \n",
      "\n",
      "Unvectorized Matrix Multiplication\n",
      " [[21, 42, 63], [14, 28, 42], [3, 6, 9]] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Importing libraries\n",
    "import numpy as np\n",
    "\n",
    "# Defining matrices\n",
    "mat_a = [[6, 7, 8],[5, 4, 5],[1, 1, 1]]\n",
    "mat_b = [[1, 2, 3],[1, 2, 3],[1, 2, 3]]\n",
    "\n",
    "# Getting a row from matrix\n",
    "def get_row(matrix, row):\n",
    "    return matrix[row]\n",
    "\n",
    "# Getting a coloumn from matrix\n",
    "def get_column(matrix, column_number):\n",
    "    column = []\n",
    " \n",
    "    for i in range(len(matrix)):\n",
    "        column.append(matrix[i][column_number])\n",
    " \n",
    "    return column\n",
    "\n",
    "# Multiply a row with coloumn\n",
    "def unv_dot_product(vector_one, vector_two):\n",
    "    total = 0\n",
    " \n",
    "    if len(vector_one) != len(vector_two):\n",
    "        return total\n",
    " \n",
    "    for i in range(len(vector_one)):\n",
    "        product = vector_one[i] * vector_two[i]\n",
    "        total += product\n",
    " \n",
    "    return total\n",
    "\n",
    "# Multiply two matrixes\n",
    "def matrix_multiplication(matrix_one, matrix_two):\n",
    "    m_rows = len(matrix_one)\n",
    "    p_columns = len(matrix_two[0])\n",
    "    result = []\n",
    "    \n",
    "    for i in range(m_rows):\n",
    "        row_result = []\n",
    " \n",
    "        for j in range(p_columns):\n",
    "            row = get_row(matrix_one, i)\n",
    "            column = get_column(matrix_two, j)\n",
    "            product = unv_dot_product(row, column)\n",
    "            \n",
    "            row_result.append(product) \n",
    "        result.append(row_result)\n",
    "        \n",
    "    return result\n",
    "\n",
    "print(\"Matrix A: \", mat_a,\"\\n\")\n",
    "print(\"Matrix B: \", mat_b,\"\\n\")\n",
    "\n",
    "print(\"Unvectorized Matrix Multiplication\\n\",matrix_multiplication(mat_a,mat_b),\"\\n\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vectorized Matrix Multiplication\n",
      " [[21 42 63]\n",
      " [14 28 42]\n",
      " [ 3  6  9]] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Vectorized Implementation\n",
    "npm_a = np.array(mat_a)\n",
    "npm_b = np.array(mat_b)\n",
    "\n",
    "print(\"Vectorized Matrix Multiplication\\n\",npm_a.dot(npm_b),\"\\n\") \n",
    "# A.dot(B) is a numpy built-in function for dot product"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tip:\n",
    "* Vectorization reduces number of lines of code\n",
    "* Always prefer libraries and avoid coding from scratch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Essential Python Packages: Numpy, Pandas, Matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load library\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vector: [1 2 3 4 5 6]\n",
      "Element 2 in Vector is 2\n"
     ]
    }
   ],
   "source": [
    "# Create row vector\n",
    "vector = np.array([1, 2, 3, 4, 5, 6])\n",
    "print(\"Vector:\",vector)\n",
    "\n",
    "# Select second element\n",
    "print(\"Element 2 in Vector is\",vector[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matrix\n",
      " [[1 2 3]\n",
      " [4 5 6]\n",
      " [7 8 9]]\n",
      "Second row of Matrix\n",
      " [4 5 6]\n",
      "Third coloumn of Matrix\n",
      " [3 6 9]\n"
     ]
    }
   ],
   "source": [
    "# Create matrix\n",
    "matrix = np.array([[1, 2, 3],\n",
    "                   [4, 5, 6],\n",
    "                   [7, 8, 9]])\n",
    "\n",
    "print(\"Matrix\\n\",matrix)\n",
    "\n",
    "# Select second row\n",
    "print(\"Second row of Matrix\\n\",matrix[1,:])\n",
    "print(\"Third coloumn of Matrix\\n\",matrix[:,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor\n",
      " [[[[1 1]\n",
      "   [1 1]]\n",
      "\n",
      "  [[2 2]\n",
      "   [2 2]]]\n",
      "\n",
      "\n",
      " [[[3 3]\n",
      "   [3 3]]\n",
      "\n",
      "  [[4 4]\n",
      "   [4 4]]]]\n"
     ]
    }
   ],
   "source": [
    "# Create Tensor\n",
    "tensor = np.array([ [[[1, 1], [1, 1]], [[2, 2], [2, 2]]],\n",
    "                    [[[3, 3], [3, 3]], [[4, 4], [4, 4]]] ])\n",
    "\n",
    "print(\"Tensor\\n\",tensor)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Matrix properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matrix Shape: (3, 3)\n",
      "Number of elements: 9\n",
      "Number of dimentions: 2\n",
      "Average of matrix: 5.0\n",
      "Maximum number: 9\n",
      "Coloumn with minimum numbers: [1 4 7]\n",
      "Diagnol of matrix: [1 5 9]\n",
      "Determinant of matrix: 0.0\n"
     ]
    }
   ],
   "source": [
    "# Create matrix\n",
    "matrix = np.array([[1, 2, 3],\n",
    "                   [4, 5, 6],\n",
    "                   [7, 8, 9]])\n",
    "\n",
    "print(\"Matrix Shape:\",matrix.shape)\n",
    "print(\"Number of elements:\",matrix.size)\n",
    "print(\"Number of dimentions:\",matrix.ndim)\n",
    "print(\"Average of matrix:\",np.mean(matrix))\n",
    "print(\"Maximum number:\",np.max(matrix))\n",
    "print(\"Coloumn with minimum numbers:\",np.min(matrix, axis=1))\n",
    "print(\"Diagnol of matrix:\",matrix.diagonal())\n",
    "print(\"Determinant of matrix:\",np.linalg.det(matrix))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Matrix Operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Flattened Matrix\n",
      " [1 2 3 4 5 6 7 8 9]\n",
      "Reshaping Matrix\n",
      " [[1]\n",
      " [2]\n",
      " [3]\n",
      " [4]\n",
      " [5]\n",
      " [6]\n",
      " [7]\n",
      " [8]\n",
      " [9]]\n",
      "Transposed Matrix\n",
      " [[1 4 7]\n",
      " [2 5 8]\n",
      " [3 6 9]]\n"
     ]
    }
   ],
   "source": [
    "print(\"Flattened Matrix\\n\",matrix.flatten())\n",
    "print(\"Reshaping Matrix\\n\",matrix.reshape(9,1))\n",
    "print(\"Transposed Matrix\\n\",matrix.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matrix Addition\n",
      " [[ 2  4  2]\n",
      " [ 2  4  2]\n",
      " [ 2  4 10]]\n",
      "Scalar Multiplication\n",
      " [[ 1  3  1]\n",
      " [ 1  3  1]\n",
      " [ 1  3 16]]\n",
      "Matrix Addition\n",
      " [[ 3  9 10]\n",
      " [ 3  9 10]\n",
      " [ 4 12 18]]\n"
     ]
    }
   ],
   "source": [
    "# Create matrix\n",
    "matrix_a = np.array([[1, 1, 1],\n",
    "                     [1, 1, 1],\n",
    "                     [1, 1, 2]])\n",
    "\n",
    "# Create matrix\n",
    "matrix_b = np.array([[1, 3, 1],\n",
    "                     [1, 3, 1],\n",
    "                     [1, 3, 8]])\n",
    "\n",
    "print(\"Matrix Addition\\n\",np.add(matrix_a, matrix_b))\n",
    "print(\"Scalar Multiplication\\n\",np.multiply(matrix_a, matrix_b))\n",
    "print(\"Matrix Addition\\n\",np.dot(matrix_a, matrix_b))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GEOID</th>\n",
       "      <th>State</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>04000US01</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>37150</td>\n",
       "      <td>37952</td>\n",
       "      <td>42212</td>\n",
       "      <td>44476</td>\n",
       "      <td>39980</td>\n",
       "      <td>40933</td>\n",
       "      <td>42590</td>\n",
       "      <td>43464</td>\n",
       "      <td>41381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>04000US02</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>55891</td>\n",
       "      <td>56418</td>\n",
       "      <td>62993</td>\n",
       "      <td>63989</td>\n",
       "      <td>61604</td>\n",
       "      <td>57848</td>\n",
       "      <td>57431</td>\n",
       "      <td>63648</td>\n",
       "      <td>61137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>04000US04</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>45245</td>\n",
       "      <td>46657</td>\n",
       "      <td>47215</td>\n",
       "      <td>46914</td>\n",
       "      <td>45739</td>\n",
       "      <td>46896</td>\n",
       "      <td>48621</td>\n",
       "      <td>47044</td>\n",
       "      <td>50602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>04000US05</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>36658</td>\n",
       "      <td>37057</td>\n",
       "      <td>40795</td>\n",
       "      <td>39586</td>\n",
       "      <td>36538</td>\n",
       "      <td>38587</td>\n",
       "      <td>41302</td>\n",
       "      <td>39018</td>\n",
       "      <td>39919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>04000US06</td>\n",
       "      <td>California</td>\n",
       "      <td>51755</td>\n",
       "      <td>55319</td>\n",
       "      <td>55734</td>\n",
       "      <td>57014</td>\n",
       "      <td>56134</td>\n",
       "      <td>54283</td>\n",
       "      <td>53367</td>\n",
       "      <td>57020</td>\n",
       "      <td>57528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>04000US07</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       GEOID       State   2005   2006   2007   2008   2009   2010   2011  \\\n",
       "0  04000US01     Alabama  37150  37952  42212  44476  39980  40933  42590   \n",
       "1  04000US02      Alaska  55891  56418  62993  63989  61604  57848  57431   \n",
       "2  04000US04     Arizona  45245  46657  47215  46914  45739  46896  48621   \n",
       "3  04000US05    Arkansas  36658  37057  40795  39586  36538  38587  41302   \n",
       "4  04000US06  California  51755  55319  55734  57014  56134  54283  53367   \n",
       "5  04000US07     Chicago   -999   -999   -999   -999   -999   -999   -999   \n",
       "\n",
       "    2012   2013  \n",
       "0  43464  41381  \n",
       "1  63648  61137  \n",
       "2  47044  50602  \n",
       "3  39018  39919  \n",
       "4  57020  57528  \n",
       "5   -999   -999  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"Income.csv\")\n",
    "print(\"Data\\n\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top Elements\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GEOID</th>\n",
       "      <th>State</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>04000US01</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>37150</td>\n",
       "      <td>37952</td>\n",
       "      <td>42212</td>\n",
       "      <td>44476</td>\n",
       "      <td>39980</td>\n",
       "      <td>40933</td>\n",
       "      <td>42590</td>\n",
       "      <td>43464</td>\n",
       "      <td>41381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>04000US02</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>55891</td>\n",
       "      <td>56418</td>\n",
       "      <td>62993</td>\n",
       "      <td>63989</td>\n",
       "      <td>61604</td>\n",
       "      <td>57848</td>\n",
       "      <td>57431</td>\n",
       "      <td>63648</td>\n",
       "      <td>61137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>04000US04</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>45245</td>\n",
       "      <td>46657</td>\n",
       "      <td>47215</td>\n",
       "      <td>46914</td>\n",
       "      <td>45739</td>\n",
       "      <td>46896</td>\n",
       "      <td>48621</td>\n",
       "      <td>47044</td>\n",
       "      <td>50602</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       GEOID    State   2005   2006   2007   2008   2009   2010   2011   2012  \\\n",
       "0  04000US01  Alabama  37150  37952  42212  44476  39980  40933  42590  43464   \n",
       "1  04000US02   Alaska  55891  56418  62993  63989  61604  57848  57431  63648   \n",
       "2  04000US04  Arizona  45245  46657  47215  46914  45739  46896  48621  47044   \n",
       "\n",
       "    2013  \n",
       "0  41381  \n",
       "1  61137  \n",
       "2  50602  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Top Elements\\n\")\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bottom Elements\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GEOID</th>\n",
       "      <th>State</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>04000US05</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>36658</td>\n",
       "      <td>37057</td>\n",
       "      <td>40795</td>\n",
       "      <td>39586</td>\n",
       "      <td>36538</td>\n",
       "      <td>38587</td>\n",
       "      <td>41302</td>\n",
       "      <td>39018</td>\n",
       "      <td>39919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>04000US06</td>\n",
       "      <td>California</td>\n",
       "      <td>51755</td>\n",
       "      <td>55319</td>\n",
       "      <td>55734</td>\n",
       "      <td>57014</td>\n",
       "      <td>56134</td>\n",
       "      <td>54283</td>\n",
       "      <td>53367</td>\n",
       "      <td>57020</td>\n",
       "      <td>57528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>04000US07</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       GEOID       State   2005   2006   2007   2008   2009   2010   2011  \\\n",
       "3  04000US05    Arkansas  36658  37057  40795  39586  36538  38587  41302   \n",
       "4  04000US06  California  51755  55319  55734  57014  56134  54283  53367   \n",
       "5  04000US07     Chicago   -999   -999   -999   -999   -999   -999   -999   \n",
       "\n",
       "    2012   2013  \n",
       "3  39018  39919  \n",
       "4  57020  57528  \n",
       "5   -999   -999  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Bottom Elements\\n\")\n",
    "df.tail(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Specific Coloumn\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0    Alabama\n",
       "1     Alaska\n",
       "2    Arizona\n",
       "Name: State, dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Specific Coloumn\\n\")\n",
    "df['State'].head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Replace negative numbers with NaN\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GEOID</th>\n",
       "      <th>State</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>04000US01</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>37150.0</td>\n",
       "      <td>37952.0</td>\n",
       "      <td>42212.0</td>\n",
       "      <td>44476.0</td>\n",
       "      <td>39980.0</td>\n",
       "      <td>40933.0</td>\n",
       "      <td>42590.0</td>\n",
       "      <td>43464.0</td>\n",
       "      <td>41381.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>04000US02</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>55891.0</td>\n",
       "      <td>56418.0</td>\n",
       "      <td>62993.0</td>\n",
       "      <td>63989.0</td>\n",
       "      <td>61604.0</td>\n",
       "      <td>57848.0</td>\n",
       "      <td>57431.0</td>\n",
       "      <td>63648.0</td>\n",
       "      <td>61137.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>04000US04</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>45245.0</td>\n",
       "      <td>46657.0</td>\n",
       "      <td>47215.0</td>\n",
       "      <td>46914.0</td>\n",
       "      <td>45739.0</td>\n",
       "      <td>46896.0</td>\n",
       "      <td>48621.0</td>\n",
       "      <td>47044.0</td>\n",
       "      <td>50602.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>04000US05</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>36658.0</td>\n",
       "      <td>37057.0</td>\n",
       "      <td>40795.0</td>\n",
       "      <td>39586.0</td>\n",
       "      <td>36538.0</td>\n",
       "      <td>38587.0</td>\n",
       "      <td>41302.0</td>\n",
       "      <td>39018.0</td>\n",
       "      <td>39919.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>04000US06</td>\n",
       "      <td>California</td>\n",
       "      <td>51755.0</td>\n",
       "      <td>55319.0</td>\n",
       "      <td>55734.0</td>\n",
       "      <td>57014.0</td>\n",
       "      <td>56134.0</td>\n",
       "      <td>54283.0</td>\n",
       "      <td>53367.0</td>\n",
       "      <td>57020.0</td>\n",
       "      <td>57528.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>04000US07</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       GEOID       State     2005     2006     2007     2008     2009  \\\n",
       "0  04000US01     Alabama  37150.0  37952.0  42212.0  44476.0  39980.0   \n",
       "1  04000US02      Alaska  55891.0  56418.0  62993.0  63989.0  61604.0   \n",
       "2  04000US04     Arizona  45245.0  46657.0  47215.0  46914.0  45739.0   \n",
       "3  04000US05    Arkansas  36658.0  37057.0  40795.0  39586.0  36538.0   \n",
       "4  04000US06  California  51755.0  55319.0  55734.0  57014.0  56134.0   \n",
       "5  04000US07     Chicago      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "      2010     2011     2012     2013  \n",
       "0  40933.0  42590.0  43464.0  41381.0  \n",
       "1  57848.0  57431.0  63648.0  61137.0  \n",
       "2  46896.0  48621.0  47044.0  50602.0  \n",
       "3  38587.0  41302.0  39018.0  39919.0  \n",
       "4  54283.0  53367.0  57020.0  57528.0  \n",
       "5      NaN      NaN      NaN      NaN  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Replace negative numbers with NaN\\n\")\n",
    "df.replace(-999,np.nan)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nbuser/anaconda3_420/lib/python3.5/site-packages/matplotlib/font_manager.py:281: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
      "  'Matplotlib is building the font cache using fc-list. '\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.mlab as mlab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Line Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEKCAYAAAD9xUlFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4xLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvAOZPmwAAIABJREFUeJzt3Xd4VHXa//H3TYfQISAt9E4AIYBt7QUVQRRXH8vaUdddfdbfClgQxV52V/exIOq6qKurS+iKWFDsKKgkAULvvRMI6ffvjxmyMQaYQCaTST6v68qVKd85c58cmM+cdh9zd0RERAAqRboAEREpOxQKIiKST6EgIiL5FAoiIpJPoSAiIvkUCiIikk+hICIi+RQKIiKST6EgIiL5qkS6gOJq3Lixt2nTJtJliIhElfnz529399gjjYu6UGjTpg3z5s2LdBkiIlHFzNaEMk6bj0REJJ9CQURE8ikUREQkn0JBRETyKRRERCRfWEPBzOqb2UQzSzWzxWZ2YqHnzcz+bmbLzSzJzPqEsx4RETm8cB+S+hzwobsPM7NqQK1Cz58PdAz+DABeCv4WEZEICNuagpnVBU4FXgNw9yx3311o2BDgDQ/4DqhvZs3CVZOISDTKzs3jxc+Xs2Bd4Y/QkhfOzUftgG3A62b2k5m9amYxhca0ANYVuL8++NgvmNlwM5tnZvO2bdsWvopFRMqYlA17uPiFr3nqwyXMTNkc9vcLZyhUAfoAL7n78cB+YFShMVbE6/xXD7iPd/cEd0+IjT3iWdoiIlEvIzuXp2elMuSFr9myN5OXrurDqPO7hP19w7lPYT2w3t3nBu9P5NehsB5oVeB+S2BjGGsSESnz5q3eyYjEJFZu289lfVty/4XdqFeraqm8d9hCwd03m9k6M+vs7kuAs4BFhYZNA/5gZv8msIN5j7tvCldNIiJl2b7MHJ7+MJU3vltD83o1eeOG/pzaqXS3joT76KM/Av8KHnm0ErjezG4FcPdxwAfABcByIB24Psz1iIiUSXOWbuPeScls3HOAa09sw93ndSameun3LA3rO7r7z0BCoYfHFXjegdvDWYOISFm2Oz2Lh2csJvHH9bSPjeE/t5xIQpuGEasn6lpni4iUFzOTNzF66kJ2p2fxhzM68IczO1CjauWI1qRQEBEpZVv3ZvDA1IV8uHAzPVrUZcIN/ejevF6kywIUCiIipcbd+c/89TwyYxEZOXmMHNiFm3/TliqVy04bOoWCiEgpWLcznXsnJ/Plsu30b9OQJy6Np11s7UiX9SsKBRGRMMrNc974djVPz1qCAQ8P6c5VA1pTqVJR5+5GnkJBRCRMlm9NY2RiMvPX7OL0zrE8OjSeFvVrRrqsw1IoiIiUsOzcPF6es4K/f7qcWtUr87fLe3Fx7xaYlc21g4IUCiIiJSh5/R7unriA1M1pXNizGQ8N7k7j2tUjXVbIFAoiIiUgIzuXZz9ZxitfrqRRTDVevqYv53U/LtJlFZtCQUTkGM1duYNRk5JZtX0/lye04t4Lu1KvZuk0sCtpCgURkaOUlpHNUx8u4c3v1tCqYU3+ddMATu7QONJlHROFgojIUfgsdSv3TU5m094MbjylLf/v3E7Uqhb9H6nRPwciIqVo5/4sHp6xiMk/baBjk9ok3nYSfeIaRLqsEqNQEBEJgbvzfvImxkxdyJ4D2dxxVkduP6M91atEtoFdSVMoiIgcwZa9Gdw/JYWPF22hZ8t6vHXTALo2qxvpssJCoSAicgjuznvz1vHI+4vJysnj3gu6cMPJZauBXUlTKIiIFGHtjnRGTUrimxU7GNC2IU9e2pM2jWMiXVbYKRRERArIzXNe/3oVz3y0hCqVKvHY0Hiu6NeqzDawK2kKBRGRoKVb0hgxMYmf1+3mzC5NeHRoD5rVK9sN7EqaQkFEKrysnDxe+nwFz3+2jDo1qvLcFb0Z3Kt5VDSwK2kKBRGp0Bas283IxCRSN6cxpHdzHhjUjUZR1MCupCkURKRCOpCVy98+WcqrX66kSZ0avPq7BM7u1jTSZUWcQkFEKpxvV+xg1KQk1uxI58oBcYw6vwt1a0RnA7uSplAQkQpjb0Y2j3+Qyjvfr6V1o1q8ffMATmof3Q3sSlpYQ8HMVgNpQC6Q4+4JhZ4/HZgKrAo+NMndx4azJhGpmD5dvIX7JqewNS2D4ae2409nd6JmtfLVoqIklMaawhnuvv0wz3/p7oNKoQ4RqYB27MvkoemLmLZgI12Oq8PL1/SlV6v6kS6rzNLmIxEpl9ydaQs28tD0RaRlZPOnsztx2+ntqVal/LaoKAnhDgUHPjIzB1529/FFjDnRzBYAG4E/u/vCwgPMbDgwHCAuLi6c9YpIObBpzwHun5zCp6lb6d2qPk8N60mnpnUiXVZUCHconOzuG82sCfCxmaW6+xcFnv8RaO3u+8zsAmAK0LHwRIJhMh4gISHBw1yziESpvDznnR/W8vgHqeTk5XH/hV25/uS2VK4gLSpKQlhDwd03Bn9vNbPJQH/giwLP7y1w+wMze9HMGh9hH4SIyK+s3r6fUZOS+G7lTk5q34gnLulJXKNakS4r6oQtFMwsBqjk7mnB2+cCYwuNOQ7Y4u5uZv2BSsCOcNUkIuVPTm4e//h6FX/5aCnVqlTiyUvj+W1CqwrZoqIkhHNNoSkwObhgqgBvu/uHZnYrgLuPA4YBt5lZDnAAuMLdtXlIREKSunkvIycmsWD9Hs7p1pRHLu5B07o1Il1WVAtbKLj7SqBXEY+PK3D7eeD5cNUgIuVTZk4uL3y2ghc/W069mlV5/srjuTC+mdYOSoAOSRWRqPLj2l2MnJjEsq37GHp8Cx4Y1I0GMdUiXVa5oVAQkaiQnpXDXz5ayj++XsVxdWvw+nX9OKNLk0iXVe4oFESkzPt6+XZGTUpi3c4DXHNCa0YM7EwdNbALC4WCiJRZew5k8/gHi/n3D+to2ziGd4efwIB2jSJdVrmmUBCRMumjhZu5f0oKO/Zncetp7fnfsztSo6oa2IWbQkFEypRtaZk8OH0h7ydtomuzurx2bT/iW9aLdFkVhkJBRMoEd2fKzxt4aPoi0jNz+fO5nbjltPZUrawGdqVJoSAiEbdh9wHum5zM50u20Scu0MCuQxM1sIsEhYKIRExenvOvuWt4YmYqeQ5jLurG705sowZ2EaRQEJGIWLltH6MSk/l+9U5+07Exjw2Np1VDNbCLNIWCiJSqnNw8XvlyFX/7ZCk1qlTi6WE9Gda3pVpUlBEKBREpNYs27mVE4gJSNuzlvO5NeXhID5qogV2ZolAQkbDLyM7l+dnLGTdnBfVrVeOlq/pwfnyzSJclRVAoiEhYzV+zkxETk1ixbT+X9mnJ6EFdqV9LDezKKoWCiITF/swcnp61hAnfrqZ5vZpMuKE/p3WKjXRZcgQKBREpcV8s3cY9k5LZuOcAvzuhNXcP7ELt6vq4iQZaSiJSYvakZ/Pw+4uYOH897WJjeO+WE+nXpmGky5JiUCiISIn4MGUTo6cuZOf+LH5/envuOEsN7KKRQkFEjsnWtAzGTF3IzJTNdG9el9ev60ePFmpgF60UCiJyVNydxB838PCMRRzIzmXEwM7c/Jt2amAX5RQKIlJs63amc+/kZL5ctp1+bRrwxKU9aR9bO9JlSQlQKIhIyPLynDe+Xc1Ts5ZgwNgh3bl6QGsqqYFduaFQEJGQLN+6j1GJScxbs4tTO8Xy2NAetGygBnblTVhDwcxWA2lALpDj7gmFnjfgOeACIB24zt1/DGdNIlI82bl5jP9iJc99soxa1Svzl8t6cUmfFmpgV06VxprCGe6+/RDPnQ90DP4MAF4K/haRMiBlwx5GTExi0aa9XBjfjAcHdye2TvVIlyVhdMRQMLPLgA/dPc3M7gf6AI+U0Df6IcAb7u7Ad2ZW38yaufumEpi2iByljOxcnvt0GeO/WEnDmGqMu7ovA3scF+mypBSEsqYw2t3/Y2anAOcBzxD6N3oHPjIzB1529/GFnm8BrCtwf33wMYWCSIT8sHonIycmsXL7fn6b0JL7LuhGvVpVI12WlJJQQiE3+PtC4CV3n2pmD4Y4/ZPdfaOZNQE+NrNUd/+iwPNFbZT0wg+Y2XBgOEBcXFyIby0ixbEvM4enPkzljW/X0LJBTd66cQCndGwc6bKklIUSChvM7GXgbOBJM6sOhHR2irtvDP7eamaTgf5AwVBYD7QqcL8lsLGI6YwHxgMkJCT8KjRE5Nh8vmQr901OYeOeA9xwclv+37mdiFEDuwoplA/33wKzgIHuvhtoCNx9pBeZWYyZ1Tl4GzgXSCk0bBrwOws4Adij/QkipWfX/izueu9nrnv9B2pWq8zEW0/igYu6KRAqsMMueTOrBHzv7j0OPhb80A7lg7spMDl42FoV4G13/9DMbg1OZxzwAYHDUZcTOCT1+qOZCREpHnfng+TNjJmWwu70bO44swO3n9mB6lXUwK6iO2wouHuemS0wszh3X1ucCbv7SqBXEY+PK3DbgduLM10ROTZb92Zw/5QUPlq0hfgW9XjjhgF0a1430mVJGRHKOmIzYKGZfQ/sP/iguw8OW1UiUuLcnf/MW8/D7y8iKyePe87vwo2ntKWKGthJAaGEwkNhr0JEwmrdznTumZTMV8u3079tQ564JJ52amAnRThiKLj7HDNrDXR090/MrBagDY8iUSA3z5nwzWqenrWEypWMRy7uwZX949TATg4plDOabyZwjkBDoD2Bk8vGAWeFtzQRORbLtqQxIjGJn9bu5ozOsTw6NJ7m9WtGuiwp40LZfHQ7gfML5gK4+7LgyWgiUgZl5eQxbs4Knp+9nJjqlXn28t4M6d1cDewkJKGEQqa7Zx38B2VmVSjirGMRibyk9bsZMTGJ1M1pXNSrOWMu6kbj2mpgJ6ELJRTmmNm9QE0zOwf4PTA9vGWJSHFkZOfyt4+X8sqXK4mtU51XfpfAOd2aRrosiUKhhMIo4EYgGbiFwAlnr4azKBEJ3XcrdzAqMYnVO9L5n/6tGHV+V+rVVAM7OTqhHH2UZ2YTCOxTcGBJ8KQzEYmgtIxsnpiZyr/mriWuYS3evmkAJ3VQAzs5NqEcfXQhgaONVhDoatrWzG5x95nhLk5EijY7dQv3TU5hy94MbjqlLXed24la1dSvSI5dKP+K/kLg6mnLAcysPfA+oFAQKWU792cxdvpCpvy8kU5Na/PiVSdxfFyDSJcl5UgoobD1YCAErQS2hqkeESmCuzM9aRMPTltIWkY2d57VkdvP6EC1KmpRISXrkKFgZpcEby40sw+A9wjsU7gM+KEUahMRYPOeQAO7TxZvoVfLejw5bABdjlMDOwmPw60pXFTg9hbgtODtbYDWV0XCzN359w/reOz9xWTn5XHfBV254ZS2VFaLCgmjQ4aCu+vaBiIRsmbHfkYlJvPtyh2c0K4hT1zSkzaNYyJdllQAoRx91Bb4I9Cm4Hi1zhYpebl5zutfr+KZj5ZQtVIlHhsazxX9WqmBnZSaUHY0TwFeI3AWc154yxGpuJZsDjSwW7BuN2d1acIjQ3vQrJ4a2EnpCiUUMtz972GvRKSCysrJ48XPl/PCZ8upU6Mqf/+f47moZzM1sJOICCUUnjOzMcBHQObBB939x7BVJVJB/LxuNyMnJrFkSxpDejdnzEXdaRhTLdJlSQUWSijEA9cAZ/LfzUcevC8iR+FAVi5//XgJr321iiZ1avDatQmc1VUN7CTyQgmFoUA7d88KdzEiFcE3K7YzKjGZtTvTuXJAHKPO70LdGmpgJ2VDKKGwAKiPzmIWOSZ7M7J5/INU3vl+La0b1eKdm0/gxPaNIl2WyC+EEgpNgVQz+4Ff7lPQIakiIfpk0Rbum5LMtrRMhp/ajj+d3Yma1XSpcyl7QgmFMWGvQqSc2rEvkwenL2L6go10Oa4O469JoFer+pEuS+SQQrmewpxjeQMzqwzMAza4+6BCz10HPA1sCD70vLvrAj4S9dydaQs28uC0hezLzOGuczpx62nt1cBOyrxQzmhO47/XZK4GVAX2u3uoHbnuBBYDhxr/rrv/IcRpiZR5G3cf4P4pKcxO3UrvVvV5alhPOjWtE+myREISyprCL/41m9nFQP9QJm5mLYELgUeBu46mQJFokZfnvPPDWh7/IJXcPGf0oG5cd1IbNbCTqFLsSzW5+xQzGxXi8GeBEcDhviZdamanAkuBP7n7uuLWJBJpq7bvZ1RiEnNX7eTkDo14fGhP4hrVinRZIsUWyuajSwrcrQQk8N/NSYd73SACF+iZb2anH2LYdOAdd880s1uBCRRxUpyZDQeGA8TFxR3prUVKTU5uHq99tYq/fryUalUq8eSl8fw2oZVaVEjUMvfDf76b2esF7uYAq4FX3P2w5y2Y2eMEzoTOAWoQ2Kcwyd2vPsT4ysBOd693uOkmJCT4vHnzDluzSGlYvGkvIxOTSFq/h3O6NeWRi3vQtG6NSJclUiQzm+/uCUcaF8o+haO6roK73wPcEyzmdODPhQPBzJq5+6bg3cEEdkiLlGmZObm8MHs5L36+gno1q/L8lcdzYbwa2En5EMrmo1jgZn59PYUbjuYNzWwsMM/dpwF3mNlgAmsTO4HrjmaaIqXlx7W7GDkxiWVb93HJ8S0YPagbDdTATsqRUDYffQN8CcwHcg8+7u6J4S2taNp8JJGQnpXDM7OW8vo3q2hWtwaPXhLPGZ2bRLoskZCV2OYjoJa7jyyBmkSi0lfLtnPP5CTW7TzANSe0ZsTAztRRAzspp0IJhRlmdoG7fxD2akTKkD0Hsnn0/UW8N289bRvH8O7wExjQTg3spHwLJRTuBO41s0wgGzDAi3FGs0jUmbVwM6OnpLBjfxa3ntae/z27IzWqqoGdlH/FPqNZpDzblpbJg9MW8n7yJro2q8tr1/YjvuVhj5IWKVeKfUazSHnk7kz+aQNjZywiPTOXP5/biVtOa0/VympgJxWLQkEqvA27D3Df5GQ+X7KNPnGBBnYdmmgFWSomhYJUWHl5zr/mruGJmak48OBF3bjmRDWwk4otpFAws1OAju7+evBkttruviq8pYmEz4pt+xiVmMQPq3fxm46NeWxoPK0aqoGdSChnNI8h0ASvM/A6gespvAWcHN7SREpeTm4e479cybOfLKNGlUo8Pawnw/q2VIsKkaBQ1hSGAscDPwK4+0Yz0wZXiToLN+5hZGISKRv2cl73pjw8pAdN1MBO5BdCCYUsd3czcwAziwlzTSIlKiM7l/+bvYxxc1bSoFY1XrqqD+fHN4t0WSJlUiih8J6ZvQzUN7ObgRuAV8JblkjJmL9mJyMmJrFi234u7dOS0YO6Ur+WGtiJHEooJ689Y2bnAHsJ7Fd4wN0/DntlIsdgf2YOT89awoRvV9O8Xk0m3NCf0zrFRroskTIvpKOP3P1jM5t7cLyZNXT3nWGtTOQofbF0G/dMSmbjngP87oTW3D2wC7Wr6+hrkVCEcvTRLcBY4ACQR7D3EdAuvKWJFM/u9CweeX8xE+evp11sDO/dciL92jSMdFkiUSWUr09/Brq7+/ZwFyNytGYmb2L01IXsSs/i96e3546z1MBO5GiEEgorgPRwFyJyNLamZTBm6kJmpmymW7O6/PP6fvRooQZ2IkcrlFC4B/gmuE8h8+CD7n5H2KoSOQJ3Z+L89Tzy/mIOZOdy93mdGX5qOzWwEzlGoYTCy8BsIJnAPgWRiFq3M517Jyfz5bLtJLRuwBOX9qRDk9qRLkukXAglFHLc/a6wVyJyBHl5zhvfruapWUswYOyQ7lw9oDWV1MBOpMSEEgqfmdlwYDq/3HykQ1Kl1CzfmsbIxGTmr9nFqZ1ieWxoD1o2UAM7kZIWSihcGfx9T4HHdEiqlIrs3DzGf7GS5z5ZRs1qlfnLZb24pE8LNbATCZNQzmhuWxqFiBSWsmEPIyYmsWjTXi6IP46HBvcgtk71SJclUq6FcvJaVeA24NTgQ58DL7t7dhjrkgosIzuX5z5dxvgvVtIwphrjru7DwB5qYCdSGkLZfPQSgWsovBi8f03wsZtCeQMzqwzMAza4+6BCz1UH3gD6AjuAy919dUiVS7n0/aqdjEpMYuX2/VzWtyX3X9iNerWqRroskQojlFDo5+69CtyfbWYLivEedwKLgbpFPHcjsMvdO5jZFcCTwOXFmLaUE/syc3hyZipvfreGlg1q8uaN/flNRzWwEyltoZzpk2tm7Q/eMbN2QG4oEzezlsCFwKuHGDIEmBC8PRE4y7QHscL5bMlWzv3rHN6au4brT27DrP89VYEgEiGhrCncTeCw1JUEmuG1Bq4PcfrPAiOAQ12prQWwDsDdc8xsD9AIUJ+lCmDX/iwenrGIST9toEOT2ky89ST6tm4Q6bJEKrRQjj761Mw6EriWggGp7p55hJdhZoOAre4+38xOP9Swot6yiGkNB4YDxMXFHemtpYxzdz5I3syYaSnsTs/mD2d04I9ndaB6FTWwE4m0I24+MrPLgGrungRcBLxjZn1CmPbJwGAzWw38GzjTzN4qNGY90Cr4PlWAesCvTopz9/HunuDuCbGx2qwQzbbszeCWN+dz+9s/0qxeTab94RT+fF5nBYJIGRHKPoXR7p5mZqcA5xHYB/DSkV7k7ve4e0t3bwNcAcx296sLDZsGXBu8PSw45ldrChL93J13f1jL2X+dw5yl2xh1fhcm//4kujUv6vgDEYmUUPYpHNypfCHwkrtPNbMHj/YNzWwsMM/dpwGvAW+a2XICawhXHO10pexauyOdeyYn8fXyHfRv25AnLomnXawa2ImURaGEwgYzexk4G3gyeG5BsfoTu/vnBE56w90fKPB4BnBZcaYl0SM3z/nnN6t5ZtYSKlcyHrm4B1f2j1MDO5EyLJRQ+C0wEHjG3XebWTMCRySJHNKyLWmMSEzip7W7Ob1zLI8Njad5/ZqRLktEjiCUo4/SgUkF7m8CNoWzKIleWTl5jJuzgudnLyememWevbw3Q3o3VwM7kSgRypqCSEiS1u9mxMQkUjenMahnMx4c3J3GtdXATiSaKBTkmB3IyuXZT5byypcraVy7OuOv6cu53Y+LdFkichQUCnJMvlu5g1GJSazekc7/9G/FqPO7Uq+mGtiJRCuFghyVtIxsnpiZyr/mriWuYS3evmkAJ3VoHOmyROQYKRSk2GanbuG+ySls2ZvBTae05a5zO1Grmv4piZQH+p8sIdu5P4ux0xcy5eeNdGxSmxdvO4nj49TATqQ8USjIEbk705M28eC0hew9kM2dZ3Xk92e0V78ikXJIoSCHtXlPBvdPSeaTxVvp1bIeT948gC7HqV+RSHmlUJAiuTv//mEdj72/mOy8PO67oCs3nNKWympRIVKuKRTkV9bs2M+oxGS+XbmDE9o15IlLetKmcUykyxKRUqBQkHy5ec7rX6/imY+WULVSJR4bGs8V/VqpgZ1IBaJQEACWbA40sFuwbjdndWnCI0N70KyeGtiJVDQKhQouKyePFz5bzoufL6dOjao8d0VvBvdSAzuRikqhUIH9vG43IyYuYOmWfQzp3ZwHBnWjkRrYiVRoCoUK6EBWLn/5aAn/+HoVTerU4LVrEzira9NIlyUiZYBCoYL5ZsV2RiUms3ZnOlcOiGPU+V2oW0MN7EQkQKFQQezNyObxDxbzzvfraN2oFu/cfAIntm8U6bJEpIxRKFQAnyzawn1TktmWlsnwU9vxp7M7UbOaWlSIyK8pFMqx7fsyeWj6IqYv2EiX4+ow/poEerWqH+myRKQMUyiUQ+7O1J838tD0hezLzOGuczpx62ntqValUqRLE5EyTqFQzmzcfYD7p6QwO3UrvVvV56lhPenUtE6kyxKRKKFQKCfy8py3v1/LEzNTyc1zRg/qxnUntVEDOxEplrCFgpnVAL4AqgffZ6K7jyk05jrgaWBD8KHn3f3VcNVUXq3avp9RiUnMXbWTkzs04vGhPYlrVCvSZYlIFArnmkImcKa77zOzqsBXZjbT3b8rNO5dd/9DGOsot3Jy83jtq1X89eOlVKtSiScvjee3Ca3UokJEjlrYQsHdHdgXvFs1+OPher+KZtHGvYxMTCJ5wx7O6daURy7uQdO6NSJdlohEubDuUzCzysB8oAPwgrvPLWLYpWZ2KrAU+JO7rwtnTdEuMyeX52cv56XPV1C/VlVeuLIPF8Qfp7UDESkRYQ0Fd88FeptZfWCymfVw95QCQ6YD77h7ppndCkwAziw8HTMbDgwHiIuLC2fJZdr8NbsYmZjE8q37uOT4Fowe1I0GMdUiXZaIlCMW2MpTCm9kNgbY7+7PHOL5ysBOd693uOkkJCT4vHnzwlFimZWelcPTs5bwz29W06xuDR69JJ4zOjeJdFkiEkXMbL67JxxpXDiPPooFst19t5nVBM4Gniw0ppm7bwreHQwsDlc90eqrZdsZNSmJ9bsOcM0JrRkxsDN11MBORMIknJuPmgETgmsAlYD33H2GmY0F5rn7NOAOMxsM5AA7gevCWE9U2ZOezaMfLOK9eetp2ziGd4efwIB2amAnIuFVapuPSkpF2Hz0YcpmRk9NYef+LIaf2o47z+pIjapqYCciRy/im4+k+LalZfLgtIW8n7yJrs3q8o9r+xHf8rC7WERESpRCoQxwdyb9uIGxMxZxICuXu8/rzPBT21G1shrYiUjpUihE2IbdB7h3UjJzlm6jT1yggV2HJmpgJyKRoVCIkLw85625a3hyZioOPHhRN645UQ3sRCSyFAoRsGLbPkYlJvHD6l38pmNjHhsaT6uGamAnIpGnUChF2bl5vPLlSp79ZBk1qlTi6WE9Gda3pVpUiEiZoVAoJSkb9jAyMYmFG/cysPtxjL24O03qqIGdiJQtCoUwy8jO5f9mL2PcnJU0qFWNl67qw/nxzSJdlohIkRQKYTRv9U5GJCaxctt+Lu3TktGDulK/lhrYiUjZpVAIg/2ZgQZ2E75dTfN6NZlwQ39O6xQb6bJERI5IoVDC5izdxr2Tktm45wDXntiGu8/rTEx1/ZlFJDro06qE7E7P4uEZi0n8cT3tYmP4zy0nktCmYaTLEhEpFoVCCZiZvInRUxeyKz2L289ozx/PVAM7EYlOCoVjsHVvBg9MXciHCzfTvXkV4t/vAAAJ/klEQVRdJtzQj+7N1cBORKKXQuEouDsT56/n4RmLyMjJY8TAztz8GzWwE5Hop1AopnU707l3cjJfLttOvzYNeOLSnrSPrR3pskRESoRCIUS5ec4b367m6VlLMODhId25akBrKqmBnYiUIwqFECzfmsbIxGTmr9nFaZ1ieXRoD1o2UAM7ESl/FAqHkZ2bx8tzVvD3T5dTq3pl/vrbXgw9voUa2IlIuaVQOISUDXu4e2ISizft5cL4Zjw4uDuxdapHuiwRkbBSKBSSkZ3Ls58s45UvV9Iwphrjru7LwB7HRbosEZFSoVAo4PtVOxmVmMTK7fu5PKEV917QlXq1qka6LBGRUqNQANIysnnqwyW8+d0aWjaoyVs3DuCUjo0jXZaISKmr8KHw2ZKt3DcpmU17M7jh5Lb8+bxO1KpW4f8sIlJBhe3Tz8xqAF8A1YPvM9HdxxQaUx14A+gL7AAud/fV4aqpoF37s3h4xiIm/bSBDk1qM/HWk+jbukFpvLWISJkVzq/EmcCZ7r7PzKoCX5nZTHf/rsCYG4Fd7t7BzK4AngQuD2NNuDvvJ29izNSF7DmQzR1nduD2MztQvYoa2ImIhC0U3N2BfcG7VYM/XmjYEODB4O2JwPNmZsHXlrgtezMYPSWFjxZtIb5FPd66aQBdm9UNx1uJiESlsG48N7PKwHygA/CCu88tNKQFsA7A3XPMbA/QCNhe0rV8lrqVO/79E1k5edxzfhduPKUtVdTATkTkF8IaCu6eC/Q2s/rAZDPr4e4pBYYUdWrwr9YSzGw4MBwgLi7uqGpp2ziGPnENeHBwd9o2jjmqaYiIlHel8lXZ3XcDnwMDCz21HmgFYGZVgHrAziJeP97dE9w9ITb26K513KZxDBNu6K9AEBE5jLCFgpnFBtcQMLOawNlAaqFh04Brg7eHAbPDtT9BRESOLJybj5oBE4L7FSoB77n7DDMbC8xz92nAa8CbZracwBrCFWGsR0REjiCcRx8lAccX8fgDBW5nAJeFqwYRESkeHX4jIiL5FAoiIpJPoSAiIvkUCiIikk+hICIi+SzaTgsws23AmqN8eWPC0EIjQjQvZVN5mZfyMh+geTmotbsf8ezfqAuFY2Fm89w9IdJ1lATNS9lUXualvMwHaF6KS5uPREQkn0JBRETyVbRQGB/pAkqQ5qVsKi/zUl7mAzQvxVKh9imIiMjhVbQ1BREROYxyGQpm9g8z22pmKYd43szs72a23MySzKxPadcYihDm43Qz22NmPwd/HihqXFlgZq3M7DMzW2xmC83sziLGlPnlEuJ8RMVyMbMaZva9mS0IzstDRYypbmbvBpfJXDNrU/qVHlmI83KdmW0rsFxuikStoTCzymb2k5nNKOK58C4Tdy93P8CpQB8g5RDPXwDMJHDltxOAuZGu+Sjn43RgRqTrDHFemgF9grfrAEuBbtG2XEKcj6hYLsG/c+3g7arAXOCEQmN+D4wL3r4CeDfSdR/DvFwHPB/pWkOcn7uAt4v6dxTuZVIu1xTc/QuKuIJbAUOANzzgO6C+mTUrnepCF8J8RA133+TuPwZvpwGLCVyju6Ayv1xCnI+oEPw77wverRr8KbyTcQgwIXh7InCWmRV1Gd2ICnFeooKZtQQuBF49xJCwLpNyGQohaAGsK3B/PVH6Hxs4MbjKPNPMuke6mFAEV3ePJ/BtrqCoWi6HmQ+IkuUS3EzxM7AV+NjdD7lM3D0H2AM0Kt0qQxPCvABcGtw0OdHMWpVyiaF6FhgB5B3i+bAuk4oaCkWlajR+q/iRwKnrvYD/A6ZEuJ4jMrPaQCLwv+6+t/DTRbykTC6XI8xH1CwXd891995AS6C/mfUoNCRqlkkI8zIdaOPuPYFP+O+37TLDzAYBW919/uGGFfFYiS2TihoK64GC3xJaAhsjVMtRc/e9B1eZ3f0DoKqZNY5wWYdkZlUJfJD+y90nFTEkKpbLkeYj2pYLgLvvBj4HBhZ6Kn+ZmFkVoB5lfJPmoebF3Xe4e2bw7itA31IuLRQnA4PNbDXwb+BMM3ur0JiwLpOKGgrTgN8Fj3Y5Adjj7psiXVRxmdlxB7clmll/AstzR2SrKlqwzteAxe7+10MMK/PLJZT5iJblYmaxZlY/eLsmcDaQWmjYNODa4O1hwGwP7uEsS0KZl0L7pwYT2B9Uprj7Pe7e0t3bENiJPNvdry40LKzLJGzXaI4kM3uHwBEgjc1sPTCGwI4n3H0c8AGBI12WA+nA9ZGp9PBCmI9hwG1mlgMcAK4oi/9hg04GrgGSg9t9Ae4F4iCqlkso8xEty6UZMMHMKhMIrvfcfYaZjQXmufs0AgH4ppktJ/Bt9IrIlXtYoczLHWY2GMghMC/XRazaYirNZaIzmkVEJF9F3XwkIiJFUCiIiEg+hYKIiORTKIiISD6FgoiI5FMoiJQRZvZPMxsW6TqkYlMoiJQDwePzRY6ZQkGikpnFmNn7waZzKWZ2efDxs4J96JMtcD2K6sHHV5vZY2b2rZnNM7M+ZjbLzFaY2a0Fpnu3mf0QbJr2q578wTH7zOzR4Ht/Z2ZNg4//4pu+me0L/j7dzOaY2XtmttTMnjCzqyzQ/z/ZzNoXmPzZZvZlcNyg4Osrm9nTBeq6pcB0PzOztwmcTFfk30SkOBQKEq0GAhvdvZe79wA+NLMawD+By909nsAZ+7cVeM06dz8R+DI4bhiB6zaMBTCzc4GOQH+gN9DXzE4t4r1jgO+CDe++AG4Ood5ewJ1APIEzoju5e38C7ZH/WGBcG+A0Aq2TxwXn6UYCLT/6Af2Am82sbXB8f+A+d+9W1N8khLpEfkGhINEqmcC36ifN7DfuvgfoDKxy96XBMRMIXKjooGkFXjvX3dPcfRuQEeybc27w5ycCnU67EAiJwrKAg1fEmk/gg/xIfgheiyETWAF8VKCWgq9/z93z3H0ZsDJYw7kEekL9TKBNd6MCdX3v7qsO8zcRKZZy2ftIyj93X2pmfQn0SnrczD7ivx/6h3KwQ2ZegdsH71ch0JL4cXd/+QjTyS7QyyiX//4/yiH4RSvYEK9aEe9d+P0Pvnf+rBV6Lw/W9Ud3n1XwCTM7HdifP7CIv4m7jz3CvIj8gtYUJCqZWXMg3d3fAp4hcNnSVKCNmXUIDrsGmFOMyc4CbrDAtRIwsxZm1qQYr1/Nf9sxDyHYvLCYLjOzSsH9DO2AJcG6brNAy27MrJOZxRR+4SH+JiLFojUFiVbxwNNmlgdkA7e5e4aZXQ/8xwJ95n8AxoU6QXf/yMy6At8GO1/vA64mcCWvULwCTDWz74FPKfAtvhiWEAiypsCtwXl6lcAmph+DayDbgIuLeO2v/iZH8f5SwalLqoiI5NPmIxERyadQEBGRfAoFERHJp1AQEZF8CgUREcmnUBARkXwKBRERyadQEBGRfP8fsklmNKXIJg8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3c9f9c7320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Line plot\n",
    "plt.plot([1,2,3,4],[3,4,5,6])\n",
    "plt.xlabel('some numbers')\n",
    "plt.ylabel('some numbers')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3c9bfda128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Adding elements to line plots\n",
    "t = np.arange(0.0, 2.0, 0.01) # Generate equally space numbers between 0 and 2\n",
    "s = 1 + np.sin(2*np.pi*t)  # Apply sin function to the random numbers\n",
    "plt.plot(t, s)\n",
    "\n",
    "plt.xlabel('time (s)')\n",
    "plt.ylabel('voltage (mV)')\n",
    "plt.title('About as simple as it gets, folks')\n",
    "plt.grid(True)\n",
    "plt.savefig(\"test.png\") # Save a plot. Check the directory\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bar Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3c9bfdfd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = [3, 10, 7, 5, 3, 4.5, 6, 8.1]\n",
    "x = range(len(y))\n",
    "width = 1/1.5\n",
    "plt.bar(x, y, width, color=\"blue\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scatter Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2095e50c9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 50\n",
    "# Generate random numbers\n",
    "x = np.random.rand(N)\n",
    "y = np.random.rand(N)\n",
    "colors = np.random.rand(N)\n",
    "area = np.pi * (15 * np.random.rand(N))**2  # 0 to 15 point radii\n",
    "\n",
    "plt.scatter(x, y, s=area, c=colors, alpha=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZgAAAEYCAYAAABhi+CNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJRhjjDGesARjjDHGE5ZgjDHGeOL/AaSSpcXz4qAwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2095de8be80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mu, sigma = 100, 15\n",
    "x = mu + sigma*np.random.randn(10000) # Generate random values with some distribution\n",
    "\n",
    "# the histogram of the data\n",
    "n, bins, patches = plt.hist(x, 50, normed=1, facecolor='green', alpha=0.75)\n",
    "\n",
    "# add a 'best fit' line\n",
    "y = mlab.normpdf( bins, mu, sigma)\n",
    "l = plt.plot(bins, y, 'r--', linewidth=1)\n",
    "\n",
    "plt.xlabel('Smarts')\n",
    "plt.ylabel('Probability')\n",
    "plt.title(r'$\\mathrm{Histogram\\ of\\ IQ:}\\ \\mu=100,\\ \\sigma=15$')\n",
    "plt.axis([40, 160, 0, 0.03])\n",
    "plt.grid(True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pie Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWQAAADuCAYAAAAOR30qAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8f8BHuf1zTjeQd0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2095fe95a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pie chart, where the slices will be ordered and plotted counter-clockwise:\n",
    "labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'\n",
    "sizes = [15, 30, 45, 10]\n",
    "explode = (0, 0.1, 0, 0)  # only \"explode\" the 2nd slice (i.e. 'Hogs')\n",
    "\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',\n",
    "        shadow=True, startangle=90)\n",
    "ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}