numpy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NUMPY LIBRARY"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NumPy is a Python library used for working with arrays.\n",
    "\n",
    "It also has functions for working in domain of linear algebra, fourier transform, and matrices.\n",
    "\n",
    "NumPy was created in 2005 by Travis Oliphant. It is an open source project and you can use it freely.\n",
    "\n",
    "NumPy stands for Numerical Python.\n",
    "\n",
    "NumPy arrays are stored at one continuous place in memory unlike lists, so processes can access and manipulate them very efficiently.\n",
    "\n",
    "This behavior is called locality of reference in computer science.\n",
    "\n",
    "This is the main reason why NumPy is faster than lists. Also it is optimized to work with latest CPU architectures.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating an Array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NumPy is used to work with arrays. The array object in NumPy is called ndarray.\n",
    "\n",
    "We can create a NumPy ndarray object by using the `array()` function."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 1-D array\n",
    "An array that has 0-D arrays as its elements is called uni-dimensional or 1-D array.\n",
    "\n",
    "These are the most common and basic arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4 5]\n",
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "import numpy as np    ## Create an alias with the as keyword while importing\n",
    "\n",
    "arr=np.array([1,2,3,4,5])\n",
    "\n",
    "print(arr)\n",
    "print(type(arr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2-D array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An array that has 1-D arrays as its elements is called a 2-D array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3]\n",
      " [4 5 6]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "arr = np.array([[1, 2, 3], [4, 5, 6]])\n",
    "\n",
    "print(arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3-D array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An array that has 2-D arrays (matrices) as its elements is called 3-D array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[1 2 3]\n",
      "  [4 5 6]]\n",
      "\n",
      " [[1 2 3]\n",
      "  [4 5 6]]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "arr = np.array([[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]]])\n",
    "\n",
    "print(arr)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dimensions of an Array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NumPy Arrays provides the `ndim` attribute that returns an integer that tells us how many dimensions the array have."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "a = np.array(42)\n",
    "b = np.array([1, 2, 3, 4, 5])\n",
    "c = np.array([[1, 2, 3], [4, 5, 6]])\n",
    "d = np.array([[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]]])\n",
    "\n",
    "print(a.ndim)\n",
    "print(b.ndim)\n",
    "print(c.ndim)\n",
    "print(d.ndim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Higher Dimensional Arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An array can have any number of dimensions.\n",
    "\n",
    "When the array is created, you can define the number of dimensions by using the `ndmin` argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[[[1 2 3 4]]]]]\n",
      "number of dimensions : 5\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "arr = np.array([1, 2, 3, 4], ndmin=5)\n",
    "\n",
    "print(arr)\n",
    "print('number of dimensions :', arr.ndim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this array the innermost dimension (5th dim) has 4 elements, the 4th dim has 1 element that is the vector, the 3rd dim has 1 element that is the matrix with the vector, the 2nd dim has 1 element that is 3D array and 1st dim has 1 element that is a 4D array."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Array Indexiing\n",
    "The indexes in NumPy arrays start with 0, meaning that the first element has index 0, and the second has index 1 etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "7\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "arr = np.array([1, 2, 3, 4])\n",
    "\n",
    "print(arr[0])\n",
    "print(arr[1])\n",
    "print(arr[2] + arr[3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To access elements from 2-D arrays we can use comma separated integers representing the dimension and the index of the element.\n",
    "\n",
    "Think of 2-D arrays like a table with rows and columns, where the dimension represents the row and the index represents the column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2nd element on 1st row:  2\n",
      "5th element on 2nd row:  10\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "arr = np.array([[1,2,3,4,5], [6,7,8,9,10]])\n",
    "\n",
    "print('2nd element on 1st row: ', arr[0, 1])\n",
    "print('5th element on 2nd row: ', arr[1, 4])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To access elements from 3-D arrays we can use comma separated integers representing the dimensions and the index of the element."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "arr = np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]])\n",
    "\n",
    "print(arr[0, 1, 2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "arr[0, 1, 2] prints the value 6.\n",
    "\n",
    "And this is why:\n",
    "\n",
    "The first number represents the first dimension, which contains two arrays:\n",
    "[[1, 2, 3], [4, 5, 6]]\n",
    "and:\n",
    "[[7, 8, 9], [10, 11, 12]]\n",
    "Since we selected 0, we are left with the first array:\n",
    "[[1, 2, 3], [4, 5, 6]]\n",
    "\n",
    "The second number represents the second dimension, which also contains two arrays:\n",
    "[1, 2, 3]\n",
    "and:\n",
    "[4, 5, 6]\n",
    "Since we selected 1, we are left with the second array:\n",
    "[4, 5, 6]\n",
    "\n",
    "The third number represents the third dimension, which contains three values:\n",
    "4\n",
    "5\n",
    "6\n",
    "Since we selected 2, we end up with the third value:\n",
    "6\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Negative Indexing\n",
    "Use negative indexing to access an array from the end."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last element from 2nd dim:  10\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "arr = np.array([[1,2,3,4,5], [6,7,8,9,10]])\n",
    "\n",
    "print('Last element from 2nd dim: ', arr[1, -1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Slicing \n",
    "Slicing in python means taking elements from one given index to another given index.\n",
    "\n",
    "We pass slice instead of index like this: [start:end].\n",
    "\n",
    "We can also define the step, like this: [start:end:step].\n",
    "\n",
    "If we don't pass start its considered 0\n",
    "\n",
    "If we don't pass end its considered length of array in that dimension\n",
    "\n",
    "If we don't pass step its considered 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 3 4 5]\n",
      "[5 6 7]\n",
      "[1 2 3 4]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "arr = np.array([1, 2, 3, 4, 5, 6, 7])\n",
    "\n",
    "print(arr[1:5])\n",
    "print(arr[4:])\n",
    "print(arr[:4])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Negative Slicing\n",
    "Use the minus operator to refer to an index from the end:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5 6]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "arr = np.array([1, 2, 3, 4, 5, 6, 7])\n",
    "\n",
    "print(arr[-3:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 4]\n",
      "[1 3 5 7]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "arr = np.array([1, 2, 3, 4, 5, 6, 7])\n",
    "\n",
    "print(arr[1:5:2])  ## use of step\n",
    "print(arr[::2])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Slicing 2-D array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7 8 9]\n",
      "[3 8]\n",
      "[[2 3 4]\n",
      " [7 8 9]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "arr = np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]])\n",
    "\n",
    "print(arr[1, 1:4])\n",
    "print(arr[0:2, 2])\n",
    "print(arr[0:2, 1:4])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Types \n",
    "NumPy has some extra data types, and refer to data types with one character, like i for integers, u for unsigned integers etc.\n",
    "- i : integer\n",
    "- b : boolean\n",
    "- u : unsigned integer\n",
    "- f : float\n",
    "- c : complex float \n",
    "- m : timedelta\n",
    "- M : datetime\n",
    "- O : object \n",
    "- S : string \n",
    "- U : unicode string \n",
    "- V : fixed chunk of memory for other type (void)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking the Datatype\n",
    "The NumPy array object has a property called `dtype` that returns the data type of the array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int32\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([1, 2, 3, 4])\n",
    "\n",
    "print(arr.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<U6\n"
     ]
    }
   ],
   "source": [
    "arr = np.array(['apple', 'banana', 'cherry'])\n",
    "\n",
    "print(arr.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creating arrays with a defined datatype\n",
    "We use the `array()` function to create arrays, this function can take an optional argument: `dtype` that allows us to define the expected data type of the array elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[b'1' b'2' b'3' b'4']\n",
      "|S1\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([1, 2, 3, 4], dtype='S')\n",
    "\n",
    "print(arr)\n",
    "print(arr.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Converting datatype on existing arrays\n",
    "The best way to change the data type of an existing array, is to make a copy of the array with the `astype()` method.\n",
    "\n",
    "The `astype()` function creates a copy of the array, and allows you to specify the data type as a parameter.\n",
    "\n",
    "The data type can be specified using a string, like `'f'` for float, `'i'` for integer etc. or you can use the data type directly like `float` for float and `int` for integer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3]\n",
      "int32\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([1.1, 2.1, 3.1])\n",
    "\n",
    "newarr = arr.astype('i')\n",
    "\n",
    "print(newarr)\n",
    "print(newarr.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3]\n",
      "int32\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([1.1, 2.1, 3.1])\n",
    "\n",
    "newarr = arr.astype(int)  ## float to int\n",
    "\n",
    "print(newarr)\n",
    "print(newarr.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ True False  True]\n",
      "bool\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([1, 0, 3])\n",
    "\n",
    "newarr = arr.astype(bool)  ## integer to bool\n",
    "\n",
    "print(newarr)\n",
    "print(newarr.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## COPY Vs VIEW \n",
    "The main difference between a copy and a view of an array is that the copy is a new array, and the view is just a view of the original array.\n",
    "\n",
    "The copy owns the data and any changes made to the copy will not affect original array, and any changes made to the original array will not affect the copy.\n",
    "\n",
    "The view does not own the data and any changes made to the view will affect the original array, and any changes made to the original array will affect the view."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### COPY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[42  2  3  4  5]\n",
      "[1 2 3 4 5]\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([1, 2, 3, 4, 5])\n",
    "x = arr.copy()\n",
    "arr[0] = 42\n",
    "\n",
    "print(arr)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### VIEW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[42  2  3  4  5]\n",
      "[42  2  3  4  5]\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([1, 2, 3, 4, 5])\n",
    "x = arr.view()\n",
    "arr[0] = 42\n",
    "\n",
    "print(arr)\n",
    "print(x)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Make changes in VIEW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[31  2  3  4  5]\n",
      "[31  2  3  4  5]\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([1, 2, 3, 4, 5])\n",
    "x = arr.view()\n",
    "x[0] = 31\n",
    "\n",
    "print(arr)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check if arrays owns its data\n",
    "As mentioned above, copies owns the data, and views does not own the data, but how can we check this?\n",
    "\n",
    "Every NumPy array has the attribute `base` that returns `None` if the array owns the data.\n",
    "\n",
    "Otherwise, the `base`  attribute refers to the original object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n",
      "[1 2 3 4 5]\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([1, 2, 3, 4, 5])\n",
    "\n",
    "x = arr.copy()\n",
    "y = arr.view()\n",
    "\n",
    "print(x.base)\n",
    "print(y.base)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Array Shape\n",
    "The shape of an array is the number of elements in each dimension.\n",
    "NumPy arrays have an attribute called `shape` that returns a tuple with each index having the number of corresponding elements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 4)\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])\n",
    "\n",
    "print(arr.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[[[1 2 3 4]]]]]\n",
      "shape of array : (1, 1, 1, 1, 4)\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([1, 2, 3, 4], ndmin=5)\n",
    "\n",
    "print(arr)\n",
    "print('shape of array :', arr.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### What does the shape tuple represent \n",
    "Integers at every index tells about the number of elements the corresponding dimension has.\n",
    "\n",
    "In the example above at index-4 we have value 4, so we can say that 5th ( 4 + 1 th) dimension has 4 elements."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Array Reshaping\n",
    "Reshaping means changing the shape of an array.\n",
    "\n",
    "The shape of an array is the number of elements in each dimension.\n",
    "\n",
    "By reshaping we can add or remove dimensions or change number of elements in each dimension."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reshape from 1-D to 2-D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1  2  3]\n",
      " [ 4  5  6]\n",
      " [ 7  8  9]\n",
      " [10 11 12]]\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])\n",
    "\n",
    "newarr = arr.reshape(4, 3)\n",
    "\n",
    "print(newarr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reshape from 1-D to 3-D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[ 1  2]\n",
      "  [ 3  4]\n",
      "  [ 5  6]]\n",
      "\n",
      " [[ 7  8]\n",
      "  [ 9 10]\n",
      "  [11 12]]]\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])\n",
    "\n",
    "newarr = arr.reshape(2, 3, 2)\n",
    "\n",
    "print(newarr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- we can reshape inito any shape as long as the elements required for reshaping are equal in both shapes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4 5 6 7 8]\n"
     ]
    }
   ],
   "source": [
    "## Returned array is copy or view?\n",
    "\n",
    "arr = np.array([1, 2, 3, 4, 5, 6, 7, 8])\n",
    "\n",
    "print(arr.reshape(2, 4).base)  ## It will be a view"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Unknown Dimension \n",
    "You are allowed to have one \"unknown\" dimension.\n",
    "\n",
    "Meaning that you do not have to specify an exact number for one of the dimensions in the `reshape` method.\n",
    "\n",
    "Pass `-1` as the value, and NumPy will calculate this number for you."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[1 2]\n",
      "  [3 4]]\n",
      "\n",
      " [[5 6]\n",
      "  [7 8]]]\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([1, 2, 3, 4, 5, 6, 7, 8])\n",
    "\n",
    "newarr = arr.reshape(2, 2, -1)\n",
    "\n",
    "print(newarr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Flattening the arrays\n",
    "Flattening array means converting a multidimensional array into a 1D array.\n",
    "We can use `reshape(-1)` to do this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4 5 6]\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([[1, 2, 3], [4, 5, 6]])\n",
    "\n",
    "newarr = arr.reshape(-1)\n",
    "\n",
    "print(newarr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Array Iterating \n",
    "Iterating means going through elements one by one.\n",
    "\n",
    "As we deal with multi-dimensional arrays in numpy, we can do this using basic `for` loop of python.\n",
    "\n",
    "If we iterate on a 1-D array it will go through each element one by one."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Iterating 1-D array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([1, 2, 3])\n",
    "\n",
    "for x in arr:\n",
    "  print(x)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Iterating 2-D array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3]\n",
      "[4 5 6]\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([[1, 2, 3], [4, 5, 6]])\n",
    "\n",
    "for x in arr:\n",
    "  print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([[1, 2, 3], [4, 5, 6]])\n",
    "\n",
    "for x in arr:\n",
    "  for y in x:\n",
    "    print(y)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Iterating 3-D arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3]\n",
      " [4 5 6]]\n",
      "[[ 7  8  9]\n",
      " [10 11 12]]\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]])\n",
    "\n",
    "for x in arr:\n",
    "  print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]])\n",
    "\n",
    "for x in arr:\n",
    "  for y in x:\n",
    "    for z in y:\n",
    "      print(z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Iterating arrays using `nditer()`\n",
    "- The function `nditer()` is a helping function that can be used from very basic to very advanced iterations.\n",
    "\n",
    "In basic for loops, iterating through each scalar of an array we need to use n for loops which can be difficult to write for arrays with very high dimensionality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])\n",
    "\n",
    "for x in np.nditer(arr):\n",
    "  print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Iterating array with different data types\n",
    "- We can use `op_dtypes` argument and pass it the expected datatype to change the datatype of elements while iterating.\n",
    "\n",
    "NumPy does not change the data type of the element in-place (where the element is in array) so it needs some other space to perform this action, that extra space is called buffer, and in order to enable it in `nditer()` we pass flags=['buffered']."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b'1'\n",
      "b'2'\n",
      "b'3'\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([1, 2, 3])\n",
    "\n",
    "for x in np.nditer(arr, flags=['buffered'], op_dtypes=['S']):\n",
    "  print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "3\n",
      "5\n",
      "7\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])\n",
    "\n",
    "for x in np.nditer(arr[:, ::2]):   ## different step size\n",
    "  print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Enumerated Iteration using `ndenumerate()`\n",
    "- Enumeration means mentioning sequence number of somethings one by one.\n",
    "\n",
    "Sometimes we require corresponding index of the element while iterating, the `ndenumerate()` method can be used for those usecases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0,) 1\n",
      "(1,) 2\n",
      "(2,) 3\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([1, 2, 3])\n",
    "\n",
    "for idx, x in np.ndenumerate(arr):\n",
    "  print(idx, x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, 0) 1\n",
      "(0, 1) 2\n",
      "(0, 2) 3\n",
      "(0, 3) 4\n",
      "(1, 0) 5\n",
      "(1, 1) 6\n",
      "(1, 2) 7\n",
      "(1, 3) 8\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])  ## 2-D array\n",
    "\n",
    "for idx, x in np.ndenumerate(arr):\n",
    "  print(idx, x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Array Join \n",
    "Joining means putting contents of two or more arrays in a single array.\n",
    "\n",
    "In SQL we join tables based on a key, whereas in NumPy we join arrays by axes.\n",
    "\n",
    "We pass a sequence of arrays that we want to join to the `concatenate()` function, along with the axis. If axis is not explicitly passed, it is taken as 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4 5 6]\n"
     ]
    }
   ],
   "source": [
    "arr1 = np.array([1, 2, 3])\n",
    "\n",
    "arr2 = np.array([4, 5, 6])\n",
    "\n",
    "arr = np.concatenate((arr1, arr2))\n",
    "\n",
    "print(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 5 6]\n",
      " [3 4 7 8]]\n"
     ]
    }
   ],
   "source": [
    "arr1 = np.array([[1, 2], [3, 4]])\n",
    "\n",
    "arr2 = np.array([[5, 6], [7, 8]])\n",
    "\n",
    "arr = np.concatenate((arr1, arr2), axis=1)\n",
    "\n",
    "print(arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### joining arrays using stack function \n",
    "Stacking is same as concatenation, the only difference is that stacking is done along a new axis.\n",
    "\n",
    "We can concatenate two 1-D arrays along the second axis which would result in putting them one over the other, ie. stacking.\n",
    "\n",
    "We pass a sequence of arrays that we want to join to the `stack()` method along with the axis. If axis is not explicitly passed it is taken as 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 4]\n",
      " [2 5]\n",
      " [3 6]]\n"
     ]
    }
   ],
   "source": [
    "arr1 = np.array([1, 2, 3])\n",
    "\n",
    "arr2 = np.array([4, 5, 6])\n",
    "\n",
    "arr = np.stack((arr1, arr2), axis=1)\n",
    "\n",
    "print(arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Stacking along rows\n",
    "- `hstack()` : to stack along rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4 5 6]\n"
     ]
    }
   ],
   "source": [
    "arr1 = np.array([1, 2, 3])\n",
    "\n",
    "arr2 = np.array([4, 5, 6])\n",
    "\n",
    "arr = np.hstack((arr1, arr2))\n",
    "\n",
    "print(arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### stacking along columns\n",
    "- `vstack()` :  to stack along columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3]\n",
      " [4 5 6]]\n"
     ]
    }
   ],
   "source": [
    "arr1 = np.array([1, 2, 3])\n",
    "\n",
    "arr2 = np.array([4, 5, 6])\n",
    "\n",
    "arr = np.vstack((arr1, arr2))\n",
    "\n",
    "print(arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### stacking along height(depth)\n",
    "- `dstack()` : to stack along height, which is the same as depth."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[1 4]\n",
      "  [2 5]\n",
      "  [3 6]]]\n"
     ]
    }
   ],
   "source": [
    "arr1 = np.array([1, 2, 3])\n",
    "\n",
    "arr2 = np.array([4, 5, 6])\n",
    "\n",
    "arr = np.dstack((arr1, arr2))\n",
    "\n",
    "print(arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Array splitting \n",
    "- Splitting is reverse operation of Joining.\n",
    "\n",
    "Joining merges multiple arrays into one and Splitting breaks one array into multiple.\n",
    "\n",
    "We use `array_split()` for splitting arrays, we pass it the array we want to split and the number of splits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([1, 2]), array([3, 4]), array([5, 6])]\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([1, 2, 3, 4, 5, 6])\n",
    "\n",
    "newarr = np.array_split(arr, 3)  ## split array in 3 parts\n",
    "\n",
    "print(newarr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([1, 2]), array([3, 4]), array([5]), array([6])]\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([1, 2, 3, 4, 5, 6])\n",
    "\n",
    "newarr = np.array_split(arr, 4)  ## split array in 4 parts\n",
    "\n",
    "print(newarr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([[1, 2],\n",
      "       [3, 4]]), array([[5, 6],\n",
      "       [7, 8]]), array([[ 9, 10],\n",
      "       [11, 12]])]\n"
     ]
    }
   ],
   "source": [
    "## split the 2-D array into three 2-D arrays\n",
    "\n",
    "arr = np.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12]])\n",
    "\n",
    "newarr = np.array_split(arr, 3)   \n",
    "\n",
    "print(newarr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([[ 1],\n",
      "       [ 4],\n",
      "       [ 7],\n",
      "       [10],\n",
      "       [13],\n",
      "       [16]]), array([[ 2],\n",
      "       [ 5],\n",
      "       [ 8],\n",
      "       [11],\n",
      "       [14],\n",
      "       [17]]), array([[ 3],\n",
      "       [ 6],\n",
      "       [ 9],\n",
      "       [12],\n",
      "       [15],\n",
      "       [18]])]\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12], [13, 14, 15], [16, 17, 18]])\n",
    "\n",
    "newarr = np.hsplit(arr, 3)\n",
    "\n",
    "print(newarr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Searching Arrays\n",
    "You can search an array for a certain value, and return the indexes that get a match.\n",
    "\n",
    "To search an array, use the `where()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([3, 5, 6], dtype=int64),)\n"
     ]
    }
   ],
   "source": [
    "## find the indexes where the value is 4\n",
    "arr = np.array([1, 2, 3, 4, 5, 4, 4])\n",
    "\n",
    "x = np.where(arr == 4)\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([1, 3, 5, 7], dtype=int64),)\n"
     ]
    }
   ],
   "source": [
    "## indexes where the values are even\n",
    "arr = np.array([1, 2, 3, 4, 5, 6, 7, 8])\n",
    "\n",
    "x = np.where(arr%2 == 0)\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Search Sorted\n",
    "There is a method called `searchsorted()` which performs a binary search in the array, and returns the index where the specified value would be inserted to maintain the search order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([6, 7, 8, 9])\n",
    "\n",
    "x = np.searchsorted(arr, 7)\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### search from right side \n",
    "- `side='right'`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([6, 7, 8, 9])\n",
    "\n",
    "x = np.searchsorted(arr, 7, side='right')\n",
    "\n",
    "print(x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3]\n"
     ]
    }
   ],
   "source": [
    "## multiple values\n",
    "arr = np.array([1, 3, 5, 7])\n",
    "\n",
    "x = np.searchsorted(arr, [2, 4, 6])\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sorting Arrays\n",
    "Sorting means putting elements in an ordered sequence.\n",
    "\n",
    "Ordered sequence is any sequence that has an order corresponding to elements, like numeric or alphabetical, ascending or descending.\n",
    "\n",
    "The NumPy ndarray object has a function called `sort()`, that will sort a specified array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3]\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([3, 2, 0, 1])\n",
    "\n",
    "print(np.sort(arr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['apple' 'banana' 'cherry']\n"
     ]
    }
   ],
   "source": [
    "arr = np.array(['banana', 'cherry', 'apple'])\n",
    "\n",
    "print(np.sort(arr))  ## sort the array alphabetically"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False  True  True]\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([True, False, True])\n",
    "\n",
    "print(np.sort(arr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### sorting a 2D array "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2 3 4]\n",
      " [0 1 5]]\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([[3, 2, 4], [5, 0, 1]])\n",
    "\n",
    "print(np.sort(arr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filtering Arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[41 43]\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([41, 42, 43, 44])\n",
    "\n",
    "x = [True, False, True, False]\n",
    "\n",
    "newarr = arr[x]\n",
    "\n",
    "print(newarr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The example above will return [41, 43], why?\n",
    "\n",
    "Because the new array contains only the values where the filter array had the value True, in this case, index 0 and 2."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creating the filter array "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False, False, True, True]\n",
      "[43 44]\n"
     ]
    }
   ],
   "source": [
    "## create a filter array that will return only values higher than 42\n",
    "arr = np.array([41, 42, 43, 44])\n",
    "\n",
    "# Create an empty list\n",
    "filter_arr = []\n",
    "\n",
    "# go through each element in arr\n",
    "for element in arr:\n",
    "  # if the element is higher than 42, set the value to True, otherwise False:\n",
    "  if element > 42:\n",
    "    filter_arr.append(True)\n",
    "  else:\n",
    "    filter_arr.append(False)\n",
    "\n",
    "newarr = arr[filter_arr]\n",
    "\n",
    "print(filter_arr)\n",
    "print(newarr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False, True, False, True, False, True, False]\n",
      "[2 4 6]\n"
     ]
    }
   ],
   "source": [
    "## create a filter array that will return only even elements from the original\n",
    "## array\n",
    "arr = np.array([1, 2, 3, 4, 5, 6, 7])\n",
    "\n",
    "# Create an empty list\n",
    "filter_arr = []\n",
    "\n",
    "# go through each element in arr\n",
    "for element in arr:\n",
    "  # if the element is completely divisble by 2, set the value to True, otherwise False\n",
    "  if element % 2 == 0:\n",
    "    filter_arr.append(True)\n",
    "  else:\n",
    "    filter_arr.append(False)\n",
    "\n",
    "newarr = arr[filter_arr]\n",
    "\n",
    "print(filter_arr)\n",
    "print(newarr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### create a filter directly from array "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False False  True  True]\n",
      "[43 44]\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([41, 42, 43, 44])\n",
    "\n",
    "filter_arr = arr > 42\n",
    "\n",
    "newarr = arr[filter_arr]\n",
    "\n",
    "print(filter_arr)\n",
    "print(newarr)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Number\n",
    "Random number does NOT mean a different number every time. Random means something that can not be predicted logically."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pseudo Random and True Random\n",
    "Computers work on programs, and programs are definitive set of instructions. So it means there must be some algorithm to generate a random number as well.\n",
    "\n",
    "If there is a program to generate random number it can be predicted, thus it is not truly random.\n",
    "\n",
    "Random numbers generated through a generation algorithm are called pseudo random.\n",
    "\n",
    "Can we make truly random numbers?\n",
    "\n",
    "Yes. In order to generate a truly random number on our computers we need to get the random data from some outside source. This outside source is generally our keystrokes, mouse movements, data on network etc.\n",
    "\n",
    "We do not need truly random numbers, unless it is related to security (e.g. encryption keys) or the basis of application is the randomness (e.g. Digital roulette wheels)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11\n"
     ]
    }
   ],
   "source": [
    "from numpy import random    ## generate random number\n",
    "\n",
    "x = random.randint(100)    ## random() : generate random number\n",
    "\n",
    "print(x)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### generate random float \n",
    "- rand() : generate float number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.35406902608202917\n"
     ]
    }
   ],
   "source": [
    "x = random.rand()\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Data Distribution\n",
    "Data Distribution is a list of all possible values, and how often each value occurs.\n",
    "- choice(): allow us to specify the probability for each value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7 7 7 3 7 7 7 7 7 5 7 7 5 7 7 7 5 5 5 7 5 5 7 5 7 5 7 7 7 5 7 7 7 7 7 5 7\n",
      " 3 3 7 5 7 5 3 7 7 7 7 7 5 3 5 7 7 5 5 5 7 5 7 5 7 5 7 5 7 7 5 7 5 7 7 5 3\n",
      " 7 7 5 5 5 7 5 7 7 5 5 3 5 5 7 7 7 7 7 5 5 7 7 3 7 7]\n"
     ]
    }
   ],
   "source": [
    "x = random.choice([3, 5, 7, 9], p=[0.1, 0.3, 0.6, 0.0], size=(100))  ##The probability for the value to be 3 is set to be 0.1\n",
    "\n",
    "print(x)                                                             ##The probability for the value to be 5 is set to be 0.3\n",
    "\n",
    "                                                                     ##The probability for the value to be 7 is set to be 0.6\n",
    "\n",
    "                                                                     ##The probability for the value to be 9 is set to be 0\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Random Permutation \n",
    "- shuffle() : changing arrangement of elements in-place. i.e. in the array itself.\n",
    "- permutation() : returns a re-arranged array (and leaves the original array un-changed)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5 3 4 1 2]\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([1, 2, 3, 4, 5])\n",
    "\n",
    "random.shuffle(arr)\n",
    "\n",
    "print(arr)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3 2 5 1 4]\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([1, 2, 3, 4, 5])\n",
    "\n",
    "print(random.permutation(arr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Seaborn Module \n",
    "Seaborn is a library that uses Matplotlib underneath to plot graphs. It will be used to visualize random distributions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Install Seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: seaborn in c:\\users\\shubh\\anaconda3\\lib\\site-packages (0.12.2)\n",
      "Requirement already satisfied: numpy!=1.24.0,>=1.17 in c:\\users\\shubh\\anaconda3\\lib\\site-packages (from seaborn) (1.26.0)\n",
      "Requirement already satisfied: pandas>=0.25 in c:\\users\\shubh\\anaconda3\\lib\\site-packages (from seaborn) (1.5.3)\n",
      "Requirement already satisfied: matplotlib!=3.6.1,>=3.1 in c:\\users\\shubh\\anaconda3\\lib\\site-packages (from seaborn) (3.7.1)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\shubh\\anaconda3\\lib\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (1.0.5)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\shubh\\anaconda3\\lib\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (0.11.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\shubh\\anaconda3\\lib\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (4.25.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\shubh\\anaconda3\\lib\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (1.4.4)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\shubh\\anaconda3\\lib\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (23.0)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\shubh\\anaconda3\\lib\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (9.4.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\shubh\\anaconda3\\lib\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (3.0.9)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\shubh\\anaconda3\\lib\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\shubh\\anaconda3\\lib\\site-packages (from pandas>=0.25->seaborn) (2022.7)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\shubh\\anaconda3\\lib\\site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.1->seaborn) (1.16.0)\n",
      "Requirement already satisfied: seaborn in c:\\users\\shubh\\anaconda3\\lib\\site-packages (0.12.2)\n",
      "Requirement already satisfied: numpy!=1.24.0,>=1.17 in c:\\users\\shubh\\anaconda3\\lib\\site-packages (from seaborn) (1.26.0)\n",
      "Requirement already satisfied: pandas>=0.25 in c:\\users\\shubh\\anaconda3\\lib\\site-packages (from seaborn) (1.5.3)\n",
      "Requirement already satisfied: matplotlib!=3.6.1,>=3.1 in c:\\users\\shubh\\anaconda3\\lib\\site-packages (from seaborn) (3.7.1)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\shubh\\anaconda3\\lib\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (1.0.5)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\shubh\\anaconda3\\lib\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (0.11.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\shubh\\anaconda3\\lib\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (4.25.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\shubh\\anaconda3\\lib\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (1.4.4)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\shubh\\anaconda3\\lib\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (23.0)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\shubh\\anaconda3\\lib\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (9.4.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\shubh\\anaconda3\\lib\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (3.0.9)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\shubh\\anaconda3\\lib\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\shubh\\anaconda3\\lib\\site-packages (from pandas>=0.25->seaborn) (2022.7)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\shubh\\anaconda3\\lib\\site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.1->seaborn) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "! pip install seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Distplots \n",
    "Distplot stands for distribution plot, it takes as input an array and plots a curve corresponding to the distribution of points in the array."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### import matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### plotting a displot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shubh\\AppData\\Local\\Temp\\ipykernel_23132\\2628693116.py:1: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot([0, 1, 2, 3, 4, 5])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot([0, 1, 2, 3, 4, 5])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shubh\\AppData\\Local\\Temp\\ipykernel_23132\\1287324351.py:1: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot([0, 1, 2, 3, 4, 5], hist=False)  ### without histogram\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot([0, 1, 2, 3, 4, 5], hist=False)  ### without histogram\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normal(Gaussian) Distribution\n",
    "The Normal Distribution is one of the most important distributions.\n",
    "\n",
    "It is also called the Gaussian Distribution after the German mathematician Carl Friedrich Gauss.\n",
    "\n",
    "It fits the probability distribution of many events, eg. IQ Scores, Heartbeat etc.\n",
    "\n",
    "Use the `random.normal()` method to get a Normal Data Distribution.\n",
    "\n",
    "It has three parameters:\n",
    "\n",
    "- `loc` - (Mean) where the peak of the bell exists.\n",
    "\n",
    "- `scale` - (Standard Deviation) how flat the graph distribution should be.\n",
    "\n",
    "- `size` - The shape of the returned array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.7382595   0.16565892 -0.89668772]\n",
      " [ 0.50380209  0.37921605 -0.90327301]]\n"
     ]
    }
   ],
   "source": [
    "from numpy import random\n",
    "\n",
    "x = random.normal(size=(2, 3))\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.00687037 -0.63510791  1.4152999 ]\n",
      " [ 4.53391775 -0.35848785 -0.20694105]]\n"
     ]
    }
   ],
   "source": [
    "#### Generate a random normal distribution of size 2x3 with mean at 1 and standard deviation of 2\n",
    "\n",
    "x = random.normal(loc=1, scale=2, size=(2, 3))\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### visualization of normal distribution "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shubh\\AppData\\Local\\Temp\\ipykernel_23132\\4119326688.py:1: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(random.normal(size=1000), hist=False)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(random.normal(size=1000), hist=False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Binomial Distribution\n",
    "Binomial Distribution is a Discrete Distribution.\n",
    "\n",
    "It describes the outcome of binary scenarios, e.g. toss of a coin, it will either be head or tails.\n",
    "\n",
    "It has three parameters:\n",
    "\n",
    "`n` - number of trials.\n",
    "\n",
    "`p` - probability of occurence of each trial (e.g. for toss of a coin 0.5 each).\n",
    "\n",
    "`size` - The shape of the returned array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7 3 5 5 8 5 5 6 5 4]\n"
     ]
    }
   ],
   "source": [
    "x = random.binomial(n=10, p=0.5, size=10)\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shubh\\AppData\\Local\\Temp\\ipykernel_23132\\1729804619.py:2: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(random.binomial(n=10, p=0.5, size=1000), hist=True, kde=False)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#### visualization of binomial distribution \n",
    "sns.distplot(random.binomial(n=10, p=0.5, size=1000), hist=True, kde=False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shubh\\AppData\\Local\\Temp\\ipykernel_23132\\3895629782.py:2: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(random.normal(loc=50, scale=5, size=1000), hist=False, label='normal')\n",
      "C:\\Users\\shubh\\AppData\\Local\\Temp\\ipykernel_23132\\3895629782.py:3: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(random.binomial(n=100, p=0.5, size=1000), hist=False, label='binomial')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### difference between normal and binomial distribution\n",
    "sns.distplot(random.normal(loc=50, scale=5, size=1000), hist=False, label='normal')\n",
    "sns.distplot(random.binomial(n=100, p=0.5, size=1000), hist=False, label='binomial')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Poisson Distribution \n",
    "Poisson Distribution is a Discrete Distribution.\n",
    "\n",
    "It estimates how many times an event can happen in a specified time. e.g. If someone eats twice a day what is the probability he will eat thrice?\n",
    "\n",
    "It has two parameters:\n",
    "\n",
    "`lam` - rate or known number of occurrences e.g. 2 for above problem.\n",
    "\n",
    "`size` - The shape of the returned array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 4 2 2 4 0 3 4 2 6]\n"
     ]
    }
   ],
   "source": [
    "## Generate a random 1x10 distribution for occurrence 2\n",
    "x = random.poisson(lam=2, size=10)\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shubh\\AppData\\Local\\Temp\\ipykernel_23132\\3774698850.py:2: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(random.poisson(lam=2, size=1000), kde=False)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### visualization of poisson distribution \n",
    "sns.distplot(random.poisson(lam=2, size=1000), kde=False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shubh\\AppData\\Local\\Temp\\ipykernel_23132\\789305511.py:3: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(random.normal(loc=50, scale=7, size=1000), hist=False, label='normal')\n",
      "C:\\Users\\shubh\\AppData\\Local\\Temp\\ipykernel_23132\\789305511.py:4: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(random.poisson(lam=50, size=1000), hist=False, label='poisson')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### difference between normal and poisson \n",
    "\n",
    "sns.distplot(random.normal(loc=50, scale=7, size=1000), hist=False, label='normal')\n",
    "sns.distplot(random.poisson(lam=50, size=1000), hist=False, label='poisson')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shubh\\AppData\\Local\\Temp\\ipykernel_23132\\2301095882.py:2: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(random.binomial(n=1000, p=0.01, size=1000), hist=False, label='binomial')\n",
      "C:\\Users\\shubh\\AppData\\Local\\Temp\\ipykernel_23132\\2301095882.py:3: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(random.poisson(lam=10, size=1000), hist=False, label='poisson')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## differnce between binomial and poisson \n",
    "sns.distplot(random.binomial(n=1000, p=0.01, size=1000), hist=False, label='binomial')\n",
    "sns.distplot(random.poisson(lam=10, size=1000), hist=False, label='poisson')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Uniform Distribution \n",
    "Used to describe probability where every event has equal chances of occuring.\n",
    "\n",
    "E.g. Generation of random numbers.\n",
    "\n",
    "It has three parameters:\n",
    "\n",
    "`a`- lower bound - default 0 .0.\n",
    "\n",
    "`b `- upper bound - default 1.0.\n",
    "\n",
    "`size` - The shape of the returned array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.33939148 0.0625063  0.48419414]\n",
      " [0.40296082 0.77644977 0.36152377]]\n"
     ]
    }
   ],
   "source": [
    "x = random.uniform(size=(2, 3))\n",
    "\n",
    "print(x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shubh\\AppData\\Local\\Temp\\ipykernel_23132\\1499926064.py:1: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(random.uniform(size=1000), hist=False)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(random.uniform(size=1000), hist=False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Distribution\n",
    "Logistic Distribution is used to describe growth.\n",
    "\n",
    "Used extensively in machine learning in logistic regression, neural networks etc.\n",
    "\n",
    "It has three parameters:\n",
    "\n",
    "`loc` - mean, where the peak is. Default 0.\n",
    "\n",
    "`scale` - standard deviation, the flatness of distribution. Default 1.\n",
    "\n",
    "`size` - The shape of the returned array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.52093163 14.36027028 -0.81422674]\n",
      " [-3.30084951 -1.8585685  -0.02470648]]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "x = random.logistic(loc=1, scale=2, size=(2, 3))\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shubh\\AppData\\Local\\Temp\\ipykernel_23132\\3861040944.py:1: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(random.logistic(size=1000), hist=False)\n"
     ]
    },
    {
     "data": {
      "image/png": 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AMCjCH75qL2mLsbNZI6KgUcqRW96AY63jnIg8EQMQEdF1rN1fY+ICJa3DEfzUXrhzeCQArglEno0BiIjoOkcLqwG41/ifa1m3xvjyWDH0RpPE1RBJgwGIiOgaJrPAidauodGxQRJX4xiTB4Qi3F+NmsYW7DpbLnU5RJJgACIiukZOWR0aDCb4qhQYEO4ndTkOoZDLcH/rlHh2g5GnYgAiIrrG0dYB0CNjAqGQu+4O8Ddj7Qbbda4MVQ0Giash6nsMQERE17Ct/+OGA6CvNTjSH8n9AtBiEvj3ca4JRJ6HAYiI6BpH3XAF6M48MMbSCrSZW2OQB2IAIiJq1aA34vyVOgDuOQX+eveOjoZCLsOxwhpcKKuXuhyiPsUARETU6vjlWpgFEKXVICJAI3U5Dhfqp8atg8IAAFuzORiaPAsDEBFRq2OXawC47/o/HbEOht56pAhmM7fGIM/BAERE1Mo6A8yTAtCMoeHw13ihuLYZP+RWSl0OUZ9hACIiauWOO8DfjEapwOyRUQAsK0MTeQoGICIiAKW1zSjVNUMhl2FEjHvtAH8z94yMBgB8c6oULSazxNUQ9Q0GICIiXN3/a1CEP3xU7rUD/M1MSAxGqJ8KNY0t2H+hQupyiPoEAxAREYBsW/eXZ7X+AICXQo5ZyZZusP8cL5G4GqK+wQBERATPHAB9Les4oO2nSrlDPHkEBiAi8ngms8CJIvfeAf5mxicEIyJAjbpmI/adZzcYuT8GICLyeGdLdWg0mOCn9nLbHeBvRi6X4a4RllYg7g1GnoABiIg8Xla+ZQD0mDj33gH+Zu5uDUDfni2DwcjZYOTeGICIyOMdzrMEoHHxwRJXIq2xcUEI9bN0g3FRRHJ3DEBE5PEO51UBAMYleOb4Hyu5XIY7hkUAsAyGJnJnDEBE5NGKa5pQXGtZANFTZ4Bda+ZwSwDacfoK9wYjt8YAREQe7XDr+J9hUQHwVXvWAogdSUsKhb/aC+V1emS3Lg5J5I4YgIjIo2W1dn+lxHt295eVykuO24aGAwC2n7oicTVEjsMAREQe7ZB1ALSHj/+51szhkQAs44CEYDcYuScGICLyWPV6I86W6gBwBti1pg0Kg0ohR35lIy6WN0hdDpFDMAARkcfKLqiGWQAxQd6I1GqkLsdp+Kq9MLG/JRDuOlsmcTVEjsEAREQe68dLlvE/4xPY+nO924ZYxgF9e5bjgMg9MQARkcc6mGsJQBMTGYCuZw1Ah/OqoWtukbgaIvtjACIij9TcYsLRwhoAwMT+IdIW44TiQ3zRP8wXRrPg5qjklhiAiMgjZRfUwGAyI9xfjYQQH6nLcUozWluBvuM4IHJDDEBE5JEOXrLsdTWxfwhkMs/dAPVGprcGoD3ny7gqNLkdBiAi8kgc/3Nz4xOC4a/2QkW9AceLaqUuh8iuGICIyOPojSYcKbAsgDipPwNQZ5QKOdIGWMZH7c8pl7gaIvtiACIij3OssBZ6oxmhfiokhflJXY5TmzIwDACwL4cDocm9MAARkcc5mGsZ/zMhMZjjf25i6oBQAMCRgmo06I0SV0NkPwxARORxMlsD0CROf7+p+BAfxAR5o8UkbAtHErkDBiAi8ijNLSYczreM/0lLCpW4Gucnk8kwdaDlc2I3GLkTBiAi8ihZ+dUwGM2ICFAjKcxX6nJcwpQBlnFA+y9wIDS5DwYgIvIo31+wtGJMTgrl+J8uSksKgUwGnL9Sjyu6ZqnLIbILBiAi8ijfX7SM/0kbwO6vrgryVWFEPy0AdoOR+2AAIiKPUdvUghOXawAAkwdwAHR3TG4NjJmtAZLI1TEAEZHHOJhbCbMA+of6IkrrLXU5LsW6YrZ1CxEiV8cAREQe40Br68Vkdn9127iEYCjkMlyubsLl6kapyyHqNQYgIvIYtgHQ7P7qNj+1F5JbxwFZ91EjcmUMQETkEYprmpBTVg+5DEjtzxagnrDum8ZuMHIHDEBE5BH2tW7mOTo2EFofpcTVuKZJiZaWs4NcEZrcAAMQEXmEPectAeiWQWESV+K6xiUEQS4D8isbUVLbJHU5RL3CAEREbs9oMtvWr5nGANRj/holxwGR22AAIiK3d+xyDeqajQj0UWJkTKDU5bg0TocndyF5AHr33XeRmJgIjUaDlJQU7Nu3r9NzS0pK8Mgjj2Dw4MGQy+VYunRpu3PWrVsHmUzW7qu5mcu3E3mqPecs3V9TBoRCIef2F70xkeOAyE1IGoA2bdqEpUuX4oUXXkB2djamTp2KWbNmoaCgoMPz9Xo9wsLC8MILL2DUqFGdXjcgIAAlJSVtvjQajaPeBhE5uT2t3V8c/9N7KfFBAIDc8gZUNxgkroao5yQNQCtXrsTChQuxaNEiDB06FKtWrUJsbCxWr17d4fkJCQl46623MH/+fGi12k6vK5PJEBkZ2eaLiDxTVYMBx1u3v+D4n94L8lUhKcwXAHCkoFriaoh6TrIAZDAYkJWVhfT09DbH09PTceDAgV5du76+HvHx8YiJicHs2bORnZ19w/P1ej10Ol2bLyJyD/svVEAIYEikPyIC2BJsD9ZWoMP5DEDkuiQLQBUVFTCZTIiIiGhzPCIiAqWlpT2+7pAhQ7Bu3Tp8+eWX2LBhAzQaDSZPnoycnJxOn/P6669Dq9XavmJjY3v8+kTkXKzjf9j9ZT/j4i0DobMYgMiFST4IWiZrOyBRCNHuWHdMmjQJv/jFLzBq1ChMnToVn376KQYNGoR33nmn0+csX74ctbW1tq/CwsIevz4ROQ8hBPa2LoDI7i/7GdvaAnSssAYGo1niaoh6xkuqFw4NDYVCoWjX2lNWVtauVag35HI5xo8ff8MWILVaDbVabbfXJCLncKakDuV1engrFRiXECR1OW4jKcwXgT5K1DS24FRxLcbE8bMl1yNZC5BKpUJKSgoyMjLaHM/IyEBaWprdXkcIgaNHjyIqKspu1yQi12Bt/UlNCoHaSyFxNe5DJpMhpTX0sBuMXJWkXWDLli3Dhx9+iI8++ghnzpzBs88+i4KCAixZsgSApWtq/vz5bZ5z9OhRHD16FPX19SgvL8fRo0dx+vRp2+N/+tOfsH37duTm5uLo0aNYuHAhjh49arsmEXkO2/ifgdz81N5SEhiAyLVJ1gUGAHPnzkVlZSVeffVVlJSUIDk5Gdu2bUN8fDwAy8KH168JNGbMGNvvs7Ky8MknnyA+Ph55eXkAgJqaGjz++OMoLS2FVqvFmDFjsHfvXkyYMKHP3hcRSa9Bb8ThfMtifdMGh0tcjfuxtgAdzq/u9dhNIinIhBBC6iKcjU6ng1arRW1tLQICAqQuh4h6YOfpK1i0/jBig72x9/+bzh/QdtbcYkLyy9thNAt8//xt6BfoLXVJRN36+S35LDAiIkew7f4+MIzhxwE0SgWGRPkDAI4W1EhbDFEPMAARkdsRQmD3+TIAwK3s/nKYUa0byx5rXWmbyJUwABGR28mtaEBhVRNUCjnSkkKkLsdtjY4NBMAWIHJNDEBE5HZ2t87+Gp8YBF+1pHM93Jo1AJ0oqoXRxAURybUwABGR29l9rrX7axC7vxwpKcwPfmovNLWYcP5KvdTlEHULAxARuZUmgwkHL1mmv986mNtfOJJcLsPIGC0A4GhhjbTFEHUTAxARuZUfcithMJoRrdVgQLif1OW4PWs32DEGIHIxDEBE5Fas3V/TBodz+nsfGGUdCM0ARC6GAYiI3Mru1vV/2P3VN8a0BqDzZXWo1xulLYaoGxiAiMht5FU0IL+yEUqFDJMHcP+vvhAeoEGUVgMhgBOXa6Uuh6jLGICIyG1Yu7/GxQfDj9Pf+4x1IPSpYgYgch0MQETkNtj9JY3kaEsAOlnEAESugwGIiNxCc4sJmRcrAQDTGID6VHJrC9AJBiByIT0KQJcuXbJ3HUREvfJDbiX0RjMiAzQYHOEvdTkexdoClFvRgAYOhCYX0aMANGDAAEyfPh0ff/wxmpub7V0TEVG37bmm+4vT3/tWmL8aEQFqCAGcKdFJXQ5Rl/QoAB07dgxjxozBb3/7W0RGRuKJJ57Ajz/+aO/aiIi6bM85jv+RkrUViN1g5Cp6FICSk5OxcuVKFBUVYe3atSgtLcWUKVMwfPhwrFy5EuXl5fauk4ioUwWVjcitaICXXIY0Tn+XRHI/60BotgCRa+jVIGgvLy/cf//9+PTTT7FixQpcvHgRzz33HGJiYjB//nyUlJTYq04iok7tPm+Z/j42PggBGqXE1XgmawDiVHhyFb0KQIcPH8aTTz6JqKgorFy5Es899xwuXryI7777DkVFRZgzZ4696iQi6hS7v6SX3C8AAJBTVo/mFpPE1RDdXI9WClu5ciXWrl2Lc+fO4a677sL69etx1113QS635KnExES8//77GDJkiF2LJSK6XnOLCQdap7/fOihc4mo8V2SABqF+KlTUG3CmRIcxcUFSl0R0Qz1qAVq9ejUeeeQRFBQU4IsvvsDs2bNt4ccqLi4Oa9assUuRRESdOZRXhaYWEyIC1BgaxenvUpHJZBhuXRCxmOOAyPn1qAUoIyMDcXFx7UKPEAKFhYWIi4uDSqXCggUL7FIkEVFndrd2f00bxOnvUkvuF4A958txijPByAX0qAUoKSkJFRUV7Y5XVVUhMTGx10UREXXV1fV/2P0ltRHWmWAcCE0uoEcBSAjR4fH6+npoNJpeFURE1FWXqxtxoaweCjl3f3cG1i6wc6V10Bs5EJqcW7e6wJYtWwbA0tf70ksvwcfHx/aYyWTCwYMHMXr0aLsWSETUmf05lpbo0bGB0Hpz+rvUYoK8ofVWorapBTlX6m1T44mcUbcCUHZ2NgBLC9CJEyegUqlsj6lUKowaNQrPPfecfSskIurE/guWADSFrT9OQSaTYUQ/LfZfqMDJoloGIHJq3QpAu3btAgA89thjeOuttxAQEOCQooiIbsZsFrbd36cMZAByFsP7BVgCEMcBkZPr0SywtWvX2rsOIqJuOVtah8oGA3xVCoyODZS6HGp1dU8wToUn59blAPTAAw9g3bp1CAgIwAMPPHDDc7ds2dLrwoiIbuT71u6vCYnBUCp6tag92ZF1JtiZEh1aTGb+3ZDT6nIA0mq1tjU2tFr26xKRtKzjfzj7y7nEBfvAX+2FOr0RF8vrMSSSQyXIOXU5AF3b7cUuMCKSksFoxo+XqgBw/I+zkctlGBYdgIOXqnCySMcARE6rR22TTU1NaGxstP05Pz8fq1atwo4dO+xWGBFRZ7ILqtHUYkKonwqDI7j9hbOxLYjIFaHJifUoAM2ZMwfr168HANTU1GDChAl48803MWfOHKxevdquBRIRXc86/ictKZTbXzgh6/T3EwxA5MR6FICOHDmCqVOnAgA+//xzREZGIj8/H+vXr8fbb79t1wKJiK7H9X+c2/BoS7fX2RIdzOaOdw4gklqPAlBjYyP8/S3Nzjt27MADDzwAuVyOSZMmIT8/364FEhFdq665BccuW1oWJnP8j1PqH+YHjVKOBoMJ+VWNN38CkQR6FIAGDBiAL774AoWFhdi+fTvS09MBAGVlZVwckYgc6mBuFUxmgcRQX/QL9Ja6HOqAQi7D4NbBz6eLuR4QOaceBaCXXnoJzz33HBISEjBx4kSkpqYCsLQGjRkzxq4FEhFda79t/E+IxJXQjQyLsgSgU1wRmpxUj1aC/slPfoIpU6agpKQEo0aNsh2fMWMG7r//frsVR0R0ve85/sclDGsdB3S6hC1A5Jx6FIAAIDIyEpGRkW2OTZgwodcFERF1pkzXjJyyeshkQCpbgJyadSA0u8DIWfUoADU0NOAvf/kLvv32W5SVlcFsNrd5PDc31y7FERFd6/uLltaf5GgtAn1UEldDNzIk0h8yGVBWp0d5nR5h/mqpSyJqo0cBaNGiRdizZw/mzZuHqKgorsNBRH1if45l93duf+H8fFReSAz1RW55A06X6DDNP0zqkoja6FEA+vrrr/Gf//wHkydPtnc9REQdEkJw/I+LGRYVYAlAxTpMG8QARM6lR7PAgoKCEBwcbO9aiIg6lVvRgFJdM1RecoxLCJK6HOqC4dGWFaE5EJqcUY8C0J///Ge89NJLbfYDIyJyJGvrz7j4IGiUComroa6wzgTjVHhyRj3qAnvzzTdx8eJFREREICEhAUqlss3jR44csUtxRERW+3MsAYjjf1yHdS2gSxUNaDQY4aPq8cRjIrvr0d1433332bkMIqLOGU1mZOZaBkBz/I/rCPNXI9xfjbI6Pc6W1mFsHLsuyXn0KAC9/PLL9q6DiKhTJ4t1qGs2IkDjZdtpnFzDsOgAlJ0rx+liHQMQOZUejQECgJqaGnz44YdYvnw5qqqqAFi6voqKiuxWHBERcHX8T2pSCBRyLrvhSq5uicGB0ORcetQCdPz4cdx+++3QarXIy8vD4sWLERwcjK1btyI/Px/r16+3d51E5MGs43/Y/eV6uCUGOasetQAtW7YMjz76KHJycqDRaGzHZ82ahb1799qtOCKiJoMJWfnVADgA2hVZp8KfLdHBaDLf5GyivtOjAHTo0CE88cQT7Y7369cPpaWlvS6KiMjqcH4VDCYzorUaJIb6Sl0OdVN8sA98VArojWZcqmiQuhwimx4FII1GA52ufXPmuXPnEBbG1T6JyH72t47/SRsQym13XJBcLsPQKHaDkfPpUQCaM2cOXn31VbS0tAAAZDIZCgoK8Pzzz+PBBx+0a4FE5Nm4/YXrsw6E5s7w5Ex6FIDeeOMNlJeXIzw8HE1NTZg2bRoGDBgAf39/vPbaa/aukYg8VHWDwTZ7KG1AiMTVUE8Nj+ZMMHI+PZoFFhAQgP3792PXrl3IysqC2WzG2LFjcfvtt9u7PiLyYJm5lRACGBzhj3B/zc2fQE7p2plgQgh2ZZJT6HYAMpvNWLduHbZs2YK8vDzIZDIkJiYiMjKSNzYR2dXV8T9s/XFlgyL8oZDLUNVgwBWdHpFahlmSXre6wIQQuPfee7Fo0SIUFRVhxIgRGD58OPLz8/Hoo4/i/vvvd1SdROSBOP7HPWiUCgwI8wMAnC7hxqjkHLrVArRu3Trs3bsX3377LaZPn97mse+++w733Xcf1q9fj/nz59u1SCLyPIVVjcivbIRCLsPE/mwBcnXDogNw7kodThXpcNuQCKnLIepeC9CGDRvwhz/8oV34AYDbbrsNzz//PP75z3/arTgi8lwHLlpaf0bHBsJPzV3EXd0wToUnJ9OtAHT8+HHceeednT4+a9YsHDt2rNdFERHtv2DZ/Z2rP7sHbolBzqZbAaiqqgoREZ03XUZERKC6urrXRRGRZzObBQ5w/I9bsbYA5Vc2QtfcInE1RN0MQCaTCV5enTdFKxQKGI3GXhdFRJ7t3JU6VDYY4KNSYHRsoNTlkB0E+aoQ3Tr762xJncTVEHVzELQQAo8++ijUanWHj+v1ersURUSezTr7a0JiMFRePVqvlZzQsOgAFNc243RxLSYkBktdDnm4bgWgBQsW3PQczgAjot7az+4vtzQsKgA7z5RxHBA5hW4FoLVr1zqqDiIiAIDBaMbB3CoAHADtboZFawFwSwxyDpK3Lb/77rtITEyERqNBSkoK9u3b1+m5JSUleOSRRzB48GDI5XIsXbq0w/M2b96MYcOGQa1WY9iwYdi6dauDqicieztaWIOmFhNC/VQYHOEvdTlkR9Y9wXKu1MNgNEtcDXk6SQPQpk2bsHTpUrzwwgvIzs7G1KlTMWvWLBQUFHR4vl6vR1hYGF544QWMGjWqw3MyMzMxd+5czJs3D8eOHcO8efPw8MMP4+DBg458K0RkJ9bur9SkUMjl3FrHncQEecNf7QWDyYyL5fVSl0MeTtIAtHLlSixcuBCLFi3C0KFDsWrVKsTGxmL16tUdnp+QkIC33noL8+fPh1ar7fCcVatW4Y477sDy5csxZMgQLF++HDNmzMCqVasc+E6IyF6ubn/B1Z/djUwmw1DuDE9OQrIAZDAYkJWVhfT09DbH09PTceDAgR5fNzMzs901Z86cecNr6vV66HS6Nl9E1PfqmltwtLAGAMf/uCtrN9hpBiCSmGQBqKKiAiaTqd3CihERESgtLe3xdUtLS7t9zddffx1ardb2FRsb2+PXJ6Ke+/FSFUxmgYQQH8QE+UhdDjnA1S0xuCkqSUvyQdAyWds+fiFEu2OOvuby5ctRW1tr+yosLOzV6xNRz1jH/6Sx9cdtDbumBUgIIXE15Mkk22EwNDQUCoWiXctMWVnZDbfbuJnIyMhuX1OtVne6uCMR9Z3vuf6P2xsY7g+lQgZdsxGXq5sQG8yWPpKGZC1AKpUKKSkpyMjIaHM8IyMDaWlpPb5uampqu2vu2LGjV9ckIscr0zXj/JV6yGRAan8OgHZXKi85BoZbljfggogkJclagABg2bJlmDdvHsaNG4fU1FR88MEHKCgowJIlSwBYuqaKioqwfv1623OOHj0KAKivr0d5eTmOHj0KlUqFYcOGAQCeeeYZ3HLLLVixYgXmzJmDf/3rX9i5cyf279/f5++PiLruwEXL7u/J0VoE+aokroYcaVh0AE6X6HC6WIeZwyOlLoc8lKQBaO7cuaisrMSrr76KkpISJCcnY9u2bYiPjwdgWfjw+jWBxowZY/t9VlYWPvnkE8THxyMvLw8AkJaWho0bN+KPf/wjXnzxRSQlJWHTpk2YOHFin70vIuq+q+N/2Prj7qwDoTkVnqQkExyF1o5Op4NWq0VtbS0CAgKkLofI7QkhkPaX71BS24z1v5yAWwaFSV0SOdDB3ErM/eAH9Av0xvfP3yZ1OeRGuvPzW/JZYEREF8vrUVLbDLWXnLuEewDrYohFNU2oaTRIXA15KgYgIpLc3vOW7q8JicHQKBUSV0OOFqBRIjbYGwAXRCTpMAARkeT25ZQDAKYO5PR3T3F1QUQGIJIGAxARSUpvNOGH3CoAwNSBHPvjKYZHW/ZzZAsQSYUBiIgklZVfjaYWE0L91BgS6S91OdRHOBOMpMYARESS2pdjGf9zy8DQXm+DQ64juZ+lBSinrA5NBpPE1ZAnYgAiIknZxv8M4vgfTxIRoEaYvxpmwY1RSRoMQEQkmcp6PU4WWbpAJnP/L48ik8kwsrUV6FghAxD1PQYgIpKMdfXnoVEBCPfXSFwN9bWRMYEAgBNFDEDU9xiAiEgy+68Z/0OeZ2SMpQXo+OUaaQshj8QARESSEELYBkBz+rtnGtEagHIrGlDX3CJxNeRpGICISBIXyupRqrNsfzEuIUjqckgCoX5q9Av0hhCwjQUj6isMQEQkib053P6CgBGtA6FPFNVIWwh5HAYgIpKEdfr7Lez+8mgjbOOAOBCa+hYDEBH1Ocv2F5UAuP6PpxvVOhOMAYj6GgMQEfW5rLxqNLeYEeavxuAIbn/hyaxdYAVVjahuMEhcDXkSBiAi6nO7zpUBsHR/cfsLz6b1USIx1BcAcIzT4akPMQARUZ/79qwlAN02JFziSsgZjIkNBABkF9RIWgd5FgYgIupTeRUNyC1vgJdcxvE/BAAYExcIADhSUC1tIeRRGICIqE9919r6Mz4hGAEapcTVkDMYE2dZB+poYQ3MZiFxNeQpGICIqE9Zx//MGMruL7IYEukPjVKOumYjcivqpS6HPAQDEBH1mXq90Tb9fTrH/1ArL4XctjHqkfwaSWshz8EARER9Zn9OBVpMAgkhPujfOvOHCLg6Dii7kOOAqG8wABFRn/nu7BUAltYfTn+na42JtYwD4kww6isMQETUJ8xmgV3nLNtfzBgSIXE15GzGtrYAnbtSh3q9UdpiyCMwABFRnzhZXIvyOj18VQpMSAyWuhxyMuEBGtvO8McKa6QuhzwAAxAR9Qnr9PcpA0Oh8uK3HmpvbLylG+xwHscBkePxuxAR9QlrAGL3F3VmQoIlAB3Kq5K4EvIEDEBE5HBldc223b5vHRImcTXkrMa3do0eKahGi8kscTXk7hiAiMjhdp+1DH4eGaNFuL9G4mrIWQ0K94fWW4lGgwmninVSl0NujgGIiBzO2v01fTAXP6TOyeUyjG/tBvvxUqXE1ZC7YwAiIodqbjFhb46lBYi7v9PNWGcI/niJA6HJsRiAiMih9uVUoNFgQrRWg5ExWqnLISc3ITEEgGUgNDdGJUdiACIih/r6ZAkAYGZyJFd/ppsaHh0Ab6UCtU0tyCnjxqjkOAxAROQwLSYzdp62bH9x5/BIiashV6BUyJESz3FA5HgMQETkMJkXK6FrNiLUT4VxCVz9mbrGOg7oh0tcD4gchwGIiBzm65OlAIA7hkVCIWf3F3VNapJlHFDmxUqOAyKHYQAiIocwmQUyTlsC0Kxkdn9R142ODYSvSoGqBgPOlHI9IHIMBiAicojDeVWoqDcgQOOFSf1DpC6HXIhSIcfE1nvm+wsVEldD7ooBiIgc4ptTltaf24dFcPNT6rbJA0IBAPsvcCA0OQa/KxGR3QkhsL11/A9nf1FPTGkNQD9eqkRzi0niasgdMQARkd0du1yL4tpm+KgUuGUQNz+l7hsU4YdQPzWaW8w4UsBVocn+GICIyO6+aW39mT4kHBqlQuJqyBXJZDJMGcBxQOQ4DEBEZFdCCHzTuvozu7+oN6YMtLQe7s9hACL7YwAiIrs6d6UOeZWNUHnJMZ2bn1IvWMcBHS+qRWW9XuJqyN0wABGRXf3nuKX155aBofBTe0lcDbmySK0Gw6ICIASw+1y51OWQm2EAIiK7EULgX0eLAQD3jIqWuBpyBzOGWloRvztbJnEl5G4YgIjIbo4W1qCgqhHeSgXuGBYhdTnkBm5r7Ubde74cBqNZ4mrInTAAEZHdWFt/0odHwEfF7i/qvVExgQj1U6FOb8ThPG6OSvbDAEREdmE0mfHv1vE/c0az+4vsQy6XYfpgSyvQzjPsBiP7YQAiIrvIzK1ERb0eQT5KTB3IxQ/JfqzjgL49ewVCcHd4sg8GICKyC2v3110joqBU8FsL2c+UgWFQKmTIr2zExfJ6qcshN8HvUkTUa80tJtvqz/eN6SdxNeRu/NReSEuyrAn09YlSiashd8EARES9tutsGer1RvQL9EZKXJDU5ZAbuntEFADgPydKJK6E3AUDEBH12hdHiwBY1v6Ry2USV0PuKH14BLzkMpwtrUMuu8HIDhiAiKhXaptasOusZZVezv4iRwn0USE1ybI56tcn2Q1GvccARES9sv1kKQwmMwZF+GFIpL/U5ZAbs3WDHWc3GPUeAxAR9cq/jlm6v+aM7geZjN1f5DjpwyOhkMtwukSHvIoGqcshF8cAREQ9VlzThAMXKwEA93LvL3KwYF8V0lq7wf59vFjiasjVMQARUY9tOXIZQgATE4MRG+wjdTnkAayb7G7NLuKiiNQrDEBE1CNCCHyedRkA8NC4WImrIU8xKzkSai85LpY34PjlWqnLIRfGAEREPXIorxp5lY3wVSlw14hIqcshD+GvUWLmcMv9tuXIZYmrIVfGAEREPfLZ4UIAwN0jo7jzO/WpB8ZaVhv/6ngJDEazxNWQq2IAIqJua9AbbSvysvuL+tqUAaEI9VOjqsGAPefLpS6HXBQDEBF127YTJWg0mJAQ4oNx8dz6gvqWl0KO+1oX3dycxW4w6hnJA9C7776LxMREaDQapKSkYN++fTc8f8+ePUhJSYFGo0H//v3x3nvvtXl83bp1kMlk7b6am5sd+TaIPMpnrT90fpISw7V/SBIPpsQAAHaeuYLyOr3E1ZArkjQAbdq0CUuXLsULL7yA7OxsTJ06FbNmzUJBQUGH51+6dAl33XUXpk6diuzsbPzhD3/Ab37zG2zevLnNeQEBASgpKWnzpdFo+uItEbm9/MoG/HipCjIZ8MDYGKnLIQ81NCoAo2MDYTQLbOZgaOoBSQPQypUrsXDhQixatAhDhw7FqlWrEBsbi9WrV3d4/nvvvYe4uDisWrUKQ4cOxaJFi/DLX/4Sb7zxRpvzZDIZIiMj23wRkX1Yp75PGRCK6EBviashT/azCZbxZxt/LOCaQNRtkgUgg8GArKwspKentzmenp6OAwcOdPiczMzMdufPnDkThw8fRktLi+1YfX094uPjERMTg9mzZyM7O/uGtej1euh0ujZfRNSeySxsYy44+JmkNntkNPzUXsirbERmbqXU5ZCLkSwAVVRUwGQyISIios3xiIgIlJZ2vNNvaWlph+cbjUZUVFQAAIYMGYJ169bhyy+/xIYNG6DRaDB58mTk5OR0Wsvrr78OrVZr+4qN5Td2oo4cuFiB4tpmBGi8kD4s4uZPIHIgX7UX7m0dDL3xx0KJqyFXI/kg6OsHUAohbjiosqPzrz0+adIk/OIXv8CoUaMwdepUfPrppxg0aBDeeeedTq+5fPly1NbW2r4KC/kPiagj1h8y946OhkapkLgaIuBn4+MAAN+cLEVFPQdDU9dJFoBCQ0OhUCjatfaUlZW1a+WxioyM7PB8Ly8vhISEdPgcuVyO8ePH37AFSK1WIyAgoM0XEbVVXqfH9lOWf3+PTIiXuBoiixExWoyKDYTBZMbGHzueQEPUEckCkEqlQkpKCjIyMtocz8jIQFpaWofPSU1NbXf+jh07MG7cOCiVyg6fI4TA0aNHERUVZZ/CiTzUZ1mFMJoFRscGYlg0/5NAzuPRNEsg/78f8tFi4srQ1DWSdoEtW7YMH374IT766COcOXMGzz77LAoKCrBkyRIAlq6p+fPn285fsmQJ8vPzsWzZMpw5cwYfffQR1qxZg+eee852zp/+9Cds374dubm5OHr0KBYuXIijR4/arklE3Wc2C2xo/d/1zyfGSVwNUVt3jYhCqJ8aV3RXWymJbkbSDXzmzp2LyspKvPrqqygpKUFycjK2bduG+HhLmi8pKWmzJlBiYiK2bduGZ599Fv/4xz8QHR2Nt99+Gw8++KDtnJqaGjz++OMoLS2FVqvFmDFjsHfvXkyYMKHP3x+Ru9h3oQKFVU3w13hh9shoqcshakPtpcAjE+Pw9rc5WPd9Hu9R6hKZ4OIJ7eh0Omi1WtTW1nI8EBGAx9cfxo7TV/BoWgJeuXe41OUQtXNF14zJf/kORrPAv389Bcn9tFKXRBLozs9vyWeBEZFzu6JrxrdnywCw+4ucV0SABneNsIz1XHcgT9piyCUwABHRDW06VAiTWWBCQjAGRvhLXQ5RpxakJQAAvjxWjEpOiaebYAAiok6ZzMI2tfgRtv6QkxsbF4iRMVoYjGZsPMT13OjGGICIqFO7z5WhuLYZQT5K3JnMPfXIuclkMixITQAA/F8mp8TTjTEAEVGnPjloaf35SUoMV34mlzB7VBRC/VQo1TVzSjzdEAMQEXWosKoRu85ZBj//bAK7v8g1qL0UeKT1fv1w3yXuEk+dYgAiog6tz8yDWQBTB4aif5if1OUQddm81ASoFHIcLaxBVn611OWQk2IAIqJ2GvRG2yDSX05OlLgaou4J81fjgbH9AAAf7M2VuBpyVgxARNTOluwi1DUbkRjqi2mDwqQuh6jbFk21BPeMM1eQW14vcTXkjBiAiKgNs1lg3feXAAALUuMhl8skroio+waE+2PGkHAIAazZf0nqcsgJMQARURv7L1TgYnkD/NReeDAlRupyiHps8S39AQCfZ13mwojUDgMQEbWxtrX156FxMfDXKCWuhqjnJiYGY2SMFnqjGf/3Q77U5ZCTYQAiIpvc8nrsOlcOmQy2BeWIXJVMJsPjra1A6zPz0dxikrgiciYMQERksz7T8r/k2waHIyHUV+JqiHrvzuGRiAnyRlWDAZ9nXZa6HHIiDEBEBADQNbfgs8OWqe+PTk6QthgiO/FSyLFwimVG2Jr9l2Ayc2FEsmAAIiIAwGeHL6PBYMKAcD9MGRAqdTlEdvPwuFhovZW4VNGAr0+WSF0OOQkGICJCi8mMj1qnCj82OQEyGae+k/vwVXvhsdZWzb9/d4HbYxAABiAiAvDv48UoqmlCqJ8KD47l1HdyP4+mJcBP7YWzpXX49kyZ1OWQE2AAIvJwQgi8v8eyXcBjkxO56zu5pUAfFealxgMA3tnFViBiACLyeLvPleNsaR18VQr8YmK81OUQOczCKYnQKOU4VliD/RcqpC6HJMYAROThVu+5CAB4ZGIctD5c+JDcV6ifGj+bEAcAeOe7CxJXQ1JjACLyYEcKqvHjpSooFTIsnNJf6nKIHO7xW/pDpZDjx0tVOJhbKXU5JCEGICIP9n5r6899o/shUquRuBoix4vSeuMn4ywD/f++i61AnowBiMhDXSyvx47TVwAAT0xj6w95jl9NS4JCLsO+nAocK6yRuhySCAMQkYf6YE8uhADuGBaBAeH+UpdD1Gdig31w3+h+AIBVO89LXA1JhQGIyAMV1zRha3YRAGAJW3/IA/36tgFQyGXYda4ch/KqpC6HJMAAROSB/r7rAgwmMyb1D0ZKfLDU5RD1uYRQXzw8LhYA8NdvznJdIA/EAETkYQqrGvHpIcump8vuGCxxNUTSeWbGQKi95DiUV43d58qlLof6GAMQkYf5+3cXYDQLTB0YigmJbP0hzxWp1WBBWgIAYMU3Z7lTvIdhACLyIHkVDfj8yGUAwNLbB0lcDZH0fjUtCQEayx5hnx4ulLoc6kMMQEQe5O3vcmAyC9w6OAwp8UFSl0MkuSBfle0/A29sPwddc4vEFVFfYQAi8hAXy+vxRevMr2V3sPWHyGpeajySwnxR2WDAO9/mSF0O9REGICIP8dbOHJgFcPvQCIyMCZS6HCKnoVTI8cfZwwAA6w7k4fyVOokror7AAETkAc5fqcNXx4sBAEtvHyhxNUTOZ/rgcNw+NBwtJoHnNx/ngGgPwABE5AHe2H4OQgB3Do9Ecj+t1OUQOaU/35cMP7UXjhTU4OMf8qUuhxyMAYjIzf2QW4kdp69AIZfht+kc+0PUmSitN35/p2VtrL9+cxaXqxslrogciQGIyI2ZzQKv/ecMAOCn42MxMIJ7fhHdyM8nxmN8QhAaDCYs3XgURpNZ6pLIQRiAiNzYv44V4URRLfzUXniWM7+Ibkoul+HNh0bDX+2Fw/nVWLWTs8LcFQMQkZtq0Bvx12/OAQB+dWsSQv3UEldE5BriQnzw3w+MAAD8Y/cF7M+pkLgicgQGICI39c53F1BS24yYIG8snJIodTlELuWeUdH42YRYCAE89ckRXCyvl7oksjMGICI3dKGsHh/uywUAvHLPcGiUCokrInI9L98zHGPjAlHb1ILH1h5CRb1e6pLIjhiAiNyMEAKvfHkKRrPAjCHhuH1YhNQlEbkkjVKB/50/DnHBPiioasTCdYdQ28StMtwFAxCRm9l8pAj7L1RA5SXHy/cMl7ocIpcW4qfG2sfGI9BHiWOXa/HzD39AdYNB6rLIDhiAiNzIFV0zXv3qFADLis9xIT4SV0Tk+pLC/LBh8SSE+KpwskiHn37wAwqruEaQq2MAInITQgi8sPUEdM1GjIzR4vGp/aUuichtDI0KwKYnUhERoMa5K3W49+/7OTvMxTEAEbmJz7MuY+eZMigVMvzPT0bBS8F/3kT2NCDcD1uenIwR/bSobmzB/I8OYsU3Z9HcYpK6NOoBfockcgMXyurx0r+sXV+DMDiSKz4TOUK/QG98tiQVD6XEwCyA1bsvYtZb+7DnfDmE4AaqroQBiMjFNbeY8PQnR9DUYkJaUgiWTEuSuiQit6ZRKvA/D43CB/NSEBGgxqWKBiz46Ef89IMfcDC3kkHIRcgE/6ba0el00Gq1qK2tRUBAgNTlEHVKCIHlW05g46FChPiq8PUzUxEeoJG6LCKPUdvUgre/zcH//ZAPg9Gyb9jw6AAsSE3AnSMiEaBRSlyhZ+nOz28GoA4wAJGrWLP/Ev7879OQyYB1j03AtEFhUpdE5JGKa5rw910XsDnrMvStQUjlJcetg8Jwz6hozBgaDh+Vl8RVuj8GoF5iACJXsOtsGRb+v0MwC+CPdw/FIs76IpJcdYMBmw4X4rPDhbhY3mA77q1UYPqQMKQPi8T0weHQ+rBlyBEYgHqJAYic3eG8Ksz/6Ec0Gkz46fhYvP7ACMhkMqnLIqJWQgicLa3Dv48X46tjJSi4Zt0gL7kME/sHI31YJO4YFoHoQG8JK3UvDEC9xABEzuz45Rr8/H8Pok5vxNSBoVizYDxUXpzPQOSshBA4UVSLHaeuIOP0FZy7Utfm8eR+AZgzqh/uG9MPYf5qiap0DwxAvcQARM7qSEE1Hltr2Y9oQmIw/t9jE+Ct4kanRK4kr6IBGactYehQfhWsP4W95DLcOjgcD42LwW1DwqHkWl7dxgDUSwxA5Iy+PXMFT31yBM0tZoyJC8T/LZwIPzUHVRK5ssp6Pb45VYrPDl/G0cIa2/EwfzV+OTkRP58Ux5lk3cAA1EsMQORMhBBY+30eXtt2BiazwK2Dw/CPR8bCl+GHyK3kXKnD51mXsflIESrq9QAAf7UXfj4pHr+ckoBwfy5xcTMMQL3EAETOol5vxO83H8d/jpcAAH6SEoPXHxjBpnEiN2YwmvHlsWK8v+cicsrqAVim1P90fCyemj4AEVzrq1MMQL3EAETOIPNiJX63+RgKq5rgJZfhj3cPxYK0BM72IvIQZrPAt2fL8N6ei8jKrwYAqL3k+PnEePzq1iQOmO4AA1AvMQCRlGobW/DGjnP4vx/yAVj2Hnr7Z6OREh8scWVEJJUDFyvwt4zzOJRnCULeSgXmp8XjiVuSEOyrkrg658EA1EsMQCSFFpMZnx4uxBvbz6G6sQUA8LMJsfjDXUPhz0GQRB5PCIF9ORV4M+M8jrUOmPZVKfDY5EQsntqfiyuCAajXGICoL7WYzNh6pAh/33XBtljaoAg/vHLPcKQNCJW4OiJyNkIIfHe2DCszzuNUsQ4A4K/xwqIp/fHYlASPnjXGANRLDEDUFwxGM7YcuYx/7L6AwqomAEConwpPTR+AX0yK50BnIrohIQS2n7qCVTvP42ypZXFFrbcSj9/SH4+mJXjkTFEGoF5iACJHKqppwoaDBdh0uBDldZaprqF+aiyZ1h8/nxjPhQ2JqFvMZoFtJ0vwt4zztv3Hgn1VWDKtP+ZNSvCo7ykMQL3EAET2ZjCasS+nHJ8cLMCuc2Uwt/6rC/dX44lpSXhkQpxHfZMiIvszmQW+PFaEt3bmIK/S0p0e4qvCA2P74aFxsRgU4S9xhY7HANRLDEBkD00GE/bmlOObk6XYeeYK6pqNtscmDwjBzyfG445hEezqIiK7MprM2JJdhLe/zcHl6ibb8ZExWjyUEoNZI6IQ6ueeU+hdKgC9++67+J//+R+UlJRg+PDhWLVqFaZOndrp+Xv27MGyZctw6tQpREdH43e/+x2WLFnS5pzNmzfjxRdfxMWLF5GUlITXXnsN999/f5drYgByPUII1DS24HJ1Eyrq9WgwGNGoN1l+NZigkMugUsihVsqh9lLAR6VAqJ8aoX4qhPqr4a/26vX6Ok0GE86U6nA4rwoHc6tw4GIlmlpMtsdD/dSYMzoaj0yMQ1KYX2/fMhHRDbWYzNh9rhyfZxXi2zNlMJqv/rgfHh2AWwaF4ZaBYUiJD3KbDZVdJgBt2rQJ8+bNw7vvvovJkyfj/fffx4cffojTp08jLi6u3fmXLl1CcnIyFi9ejCeeeALff/89nnzySWzYsAEPPvggACAzMxNTp07Fn//8Z9x///3YunUrXnrpJezfvx8TJ07sUl3uHICEEGg0mFCvN8JoFpABkMkAGWRQyGXwVSvgrVQ43WJ7QghU1BtwuboRRTVNuFzdhKLqpjZ/bjSYbn6hTqi95JZA5K9GqK8KQb4qBPkoEeijQqCPEkp56zcHGSADoDeaUdNoQEW9AYVVjcirbMCligaYr/vX1C/QG3cmR+LO5EiMjQuCQu5cnysReYbKej3+dbQYW7OLcKKots1jGqUcQ6MCkBytRXK/AAyP1iIpzM8lu+VdJgBNnDgRY8eOxerVq23Hhg4divvuuw+vv/56u/N///vf48svv8SZM2dsx5YsWYJjx44hMzMTADB37lzodDp8/fXXtnPuvPNOBAUFYcOGDV2qy9kCkBACeqMZjQYTGg1GNBlMaDRYWjfqmo2obzairrkF9XrLn+v01uMtll+tx1vPuf6H9PVkMsBX5QVftaL1Vy/4qb3gr/GCv0YJf40XAq75vb9GiQDva/6s9oKXQg6FXAYvuazNrwDQYhIwmMxoMZphMJlR12xEbVMLdE0t0DW3oKrBgFJdM67UNuOKTo8rumYU1TRBbzTf9LMK81cjIkBtq9tHZWntMQvLOBy90QS90Yz6ZiMqGwwor9OjXm+86XW7KtRPjdGxWkxMDEFqUgiGRwc4XZgkIs9WXqfH/gvl2HOuHPtyKlDZYOjwvDB/NWKDvBEb7IN+gd4I8VMjpPU/iCG+KvhrvOCtsvyn2VupgJcTdOd35+e3ZHPkDAYDsrKy8Pzzz7c5np6ejgMHDnT4nMzMTKSnp7c5NnPmTKxZswYtLS1QKpXIzMzEs88+2+6cVatWdVqLXq+HXq+3/bm21pKOdTpdd97STZ2/osMrX56GySxgEgJms7D93tT6e+sxsxAwmgVaTGY0t5hvGlq6Sy4DFHI5AAEhAAHLADorXTNg33ffezKZZdBwtNYb0YEaRAd6X/OlQZTWGxpl9//H0txiQkWdHhUNelTWG1DdYEB1kwE1jS2oaWxBbZMBZmEJotZPSKmQI9BbiSBfNfoFaRAT6INB4X4Iu26Pnrq6Oju8cyIi+1EDmJEUgBlJATCb++NSZQPOluhwprTO8muJDrVNRlzRN+JKRTUOd/G6SoUcGqUM3koFFHI55HJAIZNBLpdBIbP8J1gmk9mODY7ww5/mJNv1vVl/bnelbUeyAFRRUQGTyYSIiIg2xyMiIlBaWtrhc0pLSzs832g0oqKiAlFRUZ2e09k1AeD111/Hn/70p3bHY2Nju/p2qI8USF0AERHZxdcAVjno2nV1ddBqtTc8R/JVkq7vHhBC3LDLoKPzrz/e3WsuX74cy5Yts/3ZbDajqqoKISEh3e6+0Ol0iI2NRWFhoVN0n0mJn8VV/Cyu4mdxFT+Ltvh5XMXP4qrufBZCCNTV1SE6Ovqm15UsAIWGhkKhULRrmSkrK2vXgmMVGRnZ4fleXl4ICQm54TmdXRMA1Go11Oq2UwIDAwO7+lY6FBAQ4PE3rRU/i6v4WVzFz+IqfhZt8fO4ip/FVV39LG7W8mMl2YgllUqFlJQUZGRktDmekZGBtLS0Dp+Tmpra7vwdO3Zg3LhxUCqVNzyns2sSERGR55G0C2zZsmWYN28exo0bh9TUVHzwwQcoKCiwreuzfPlyFBUVYf369QAsM77+/ve/Y9myZVi8eDEyMzOxZs2aNrO7nnnmGdxyyy1YsWIF5syZg3/961/YuXMn9u/fL8l7JCIiIucjaQCaO3cuKisr8eqrr6KkpATJycnYtm0b4uPjAQAlJSUoKLg67DUxMRHbtm3Ds88+i3/84x+Ijo7G22+/bVsDCADS0tKwceNG/PGPf8SLL76IpKQkbNq0qctrAPWWWq3Gyy+/3K5LzRPxs7iKn8VV/Cyu4mfRFj+Pq/hZXOWoz0LylaCJiIiI+pr0qxYRERER9TEGICIiIvI4DEBERETkcRiAiIiIyOMwAHXTa6+9hrS0NPj4+HS6WGJBQQHuuece+Pr6IjQ0FL/5zW9gMHS82ZyVXq/Hr3/9a4SGhsLX1xf33nsvLl++7IB34Bi7d++GTCbr8OvQoUOdPu/RRx9td/6kSZP6sHLHSUhIaPfert/77npCCLzyyiuIjo6Gt7c3br31Vpw6daqPKnaMvLw8LFy4EImJifD29kZSUhJefvnlm/6bcJd7491330ViYiI0Gg1SUlKwb9++G56/Z88epKSkQKPRoH///njvvff6qFLHev311zF+/Hj4+/sjPDwc9913H86dO3fD53T2feXs2bN9VLVjvPLKK+3eU2Rk5A2f4673RUffJ2UyGZ566qkOz7fnPSH5VhiuxmAw4KGHHkJqairWrFnT7nGTyYS7774bYWFh2L9/PyorK7FgwQIIIfDOO+90et2lS5fiq6++wsaNGxESEoLf/va3mD17NrKysqBQdH+Dz76WlpaGkpKSNsdefPFF7Ny5E+PGjbvhc++8806sXbvW9meVSuWQGqXw6quvYvHixbY/+/n53fD8v/71r1i5ciXWrVuHQYMG4b/+679wxx134Ny5c/D393d0uQ5x9uxZmM1mvP/++xgwYABOnjyJxYsXo6GhAW+88cYNn+vq98amTZuwdOlSvPvuu5g8eTLef/99zJo1C6dPn0ZcXFy78y9duoS77roLixcvxscff4zvv/8eTz75JMLCwtos9+GK9uzZg6eeegrjx4+H0WjECy+8gPT0dJw+fRq+vr43fO65c+farAAcFhbm6HIdbvjw4di5c6ftzzf6Pu/O98WhQ4dgMplsfz558iTuuOMOPPTQQzd8nl3uCUE9snbtWqHVatsd37Ztm5DL5aKoqMh2bMOGDUKtVova2toOr1VTUyOUSqXYuHGj7VhRUZGQy+Xim2++sXvtfcFgMIjw8HDx6quv3vC8BQsWiDlz5vRNUX0sPj5e/O1vf+vy+WazWURGRoq//OUvtmPNzc1Cq9WK9957zwEVSuevf/2rSExMvOE57nBvTJgwQSxZsqTNsSFDhojnn3++w/N/97vfiSFDhrQ59sQTT4hJkyY5rEaplJWVCQBiz549nZ6za9cuAUBUV1f3XWF94OWXXxajRo3q8vmedF8888wzIikpSZjN5g4ft+c9wS4wO8vMzERycnKbjdhmzpwJvV6PrKysDp+TlZWFlpYWpKen245FR0cjOTkZBw4ccHjNjvDll1+ioqICjz766E3P3b17N8LDwzFo0CAsXrwYZWVlji+wj6xYsQIhISEYPXo0XnvttRt2+1y6dAmlpaVt7gO1Wo1p06a57H3QmdraWgQHB9/0PFe+NwwGA7Kystr8fQJAenp6p3+fmZmZ7c6fOXMmDh8+jJaWFofVKoXa2loA6NJ9MGbMGERFRWHGjBnYtWuXo0vrEzk5OYiOjkZiYiJ++tOfIjc3t9NzPeW+MBgM+Pjjj/HLX/7yphuR2+OeYACys9LS0nYbrwYFBUGlUrXbpPXa56hUKgQFBbU5HhER0elznN2aNWswc+ZMxMbG3vC8WbNm4Z///Ce+++47vPnmmzh06BBuu+026PX6PqrUcZ555hls3LgRu3btwtNPP41Vq1bhySef7PR869/19fePK98HHbl48SLeeecd25Y3nXH1e6OiogImk6lbf58dff+IiIiA0WhERUWFw2rta0IILFu2DFOmTEFycnKn50VFReGDDz7A5s2bsWXLFgwePBgzZszA3r17+7Ba+5s4cSLWr1+P7du343//939RWlqKtLQ0VFZWdni+p9wXX3zxBWpqam74H2e73hO9bkNyAy+//LIAcMOvQ4cOtXlOZ11gixcvFunp6e2OK5VKsWHDhg5f/5///KdQqVTtjt9+++3iiSee6NmbspOefDaFhYVCLpeLzz//vNuvV1xcLJRKpdi8ebO93oJd9eTzsPr8888FAFFRUdHh499//70AIIqLi9scX7RokZg5c6bd30tv9eSzKCoqEgMGDBALFy7s9us5+71xvaKiIgFAHDhwoM3x//qv/xKDBw/u8DkDBw4U//3f/93m2P79+wUAUVJS4rBa+9qTTz4p4uPjRWFhYbefO3v2bHHPPfc4oCrp1NfXi4iICPHmm292+Lin3Bfp6eli9uzZ3X5eT+8JDoIG8PTTT+OnP/3pDc9JSEjo0rUiIyNx8ODBNseqq6vR0tLSLsFf+xyDwYDq6uo2rUBlZWWS72Lfk89m7dq1CAkJwb333tvt14uKikJ8fDxycnK6/dy+0Jt7xTqD6cKFCwgJCWn3uHUWSGlpKaKiomzHy8rKOr13pNTdz6K4uBjTp0+3bXzcXc5+b1wvNDQUCoWiXWvPjf4+IyMjOzzfy8urw3vGFf3617/Gl19+ib179yImJqbbz580aRI+/vhjB1QmHV9fX4wYMaLTe9sT7ov8/Hzs3LkTW7Zs6fZze3pPMADB8o0qNDTULtdKTU3Fa6+9hpKSEtsPsR07dkCtViMlJaXD56SkpECpVCIjIwMPP/wwAMtGsCdPnsRf//pXu9TVU939bIQQWLt2LebPnw+lUtnt16usrERhYWGbAOBMenOvZGdnA0Cn7y0xMRGRkZHIyMjAmDFjAFj6xPfs2YMVK1b0rGAH6s5nUVRUhOnTpyMlJQVr166FXN793ndnvzeup1KpkJKSgoyMDNx///224xkZGZgzZ06Hz0lNTcVXX33V5tiOHTswbty4Hv17ciZCCPz617/G1q1bsXv3biQmJvboOtnZ2S5zD3SVXq/HmTNnMHXq1A4fd+f7wmrt2rUIDw/H3Xff3e3n9vie6HabkYfLz88X2dnZ4k9/+pPw8/MT2dnZIjs7W9TV1QkhhDAajSI5OVnMmDFDHDlyROzcuVPExMSIp59+2naNy5cvi8GDB4uDBw/aji1ZskTExMSInTt3iiNHjojbbrtNjBo1ShiNxj5/j72xc+dOAUCcPn26w8cHDx4stmzZIoQQoq6uTvz2t78VBw4cEJcuXRK7du0Sqampol+/fkKn0/Vl2XZ34MABsXLlSpGdnS1yc3PFpk2bRHR0tLj33nvbnHft5yGEEH/5y1+EVqsVW7ZsESdOnBA/+9nPRFRUlEt/HtZur9tuu01cvnxZlJSU2L6u5Y73xsaNG4VSqRRr1qwRp0+fFkuXLhW+vr4iLy9PCCHE888/L+bNm2c7Pzc3V/j4+Ihnn31WnD59WqxZs0YolcoedSc7m1/96ldCq9WK3bt3t7kHGhsbbedc/3n87W9/E1u3bhXnz58XJ0+eFM8//7wA4DLdoJ357W9/K3bv3i1yc3PFDz/8IGbPni38/f098r4QQgiTySTi4uLE73//+3aPOfKeYADqpgULFnQ41mHXrl22c/Lz88Xdd98tvL29RXBwsHj66adFc3Oz7fFLly61e05TU5N4+umnRXBwsPD29hazZ88WBQUFffjO7ONnP/uZSEtL6/RxAGLt2rVCCCEaGxtFenq6CAsLE0qlUsTFxYkFCxa45Pu+XlZWlpg4caLQarVCo9GIwYMHi5dfflk0NDS0Oe/az0MIy1T4l19+WURGRgq1Wi1uueUWceLEiT6u3r7Wrl3b6Riha7nrvfGPf/xDxMfHC5VKJcaOHdtm2veCBQvEtGnT2py/e/duMWbMGKFSqURCQoJYvXp1H1fsGJ3dA9fe/9d/HitWrBBJSUlCo9GIoKAgMWXKFPGf//yn74u3s7lz54qoqCihVCpFdHS0eOCBB8SpU6dsj3vSfSGEENu3bxcAxLlz59o95sh7QiaEEN1vNyIiIiJyXZwGT0RERB6HAYiIiIg8DgMQEREReRwGICIiIvI4DEBERETkcRiAiIiIyOMwABEREZHHYQAiIiIij8MARERERB6HAYiIiIg8DgMQEREReRwGICIiIvI4/z/pDgeyH4fcEQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(random.logistic(size=1000), hist=False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shubh\\AppData\\Local\\Temp\\ipykernel_23132\\1766656502.py:2: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(random.normal(scale=2, size=1000), hist=False, label='normal')\n",
      "C:\\Users\\shubh\\AppData\\Local\\Temp\\ipykernel_23132\\1766656502.py:3: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(random.logistic(size=1000), hist=False, label='logistic')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## difference between logical and normal distribution\n",
    "sns.distplot(random.normal(scale=2, size=1000), hist=False, label='normal')\n",
    "sns.distplot(random.logistic(size=1000), hist=False, label='logistic')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multinomial Distribution\n",
    "Multinomial distribution is a generalization of binomial distribution.\n",
    "\n",
    "It describes outcomes of multi-nomial scenarios unlike binomial where scenarios must be only one of two. e.g. Blood type of a population, dice roll outcome.\n",
    "\n",
    "It has three parameters:\n",
    "\n",
    "`n`- number of possible outcomes (e.g. 6 for dice roll).\n",
    "\n",
    "`pvals` - list of probabilties of outcomes (e.g. [1/6, 1/6, 1/6, 1/6, 1/6, 1/6] for dice roll).\n",
    "\n",
    "`size` - The shape of the returned array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 0 2 2 1]\n"
     ]
    }
   ],
   "source": [
    "x = random.multinomial(n=6, pvals=[1/6, 1/6, 1/6, 1/6, 1/6, 1/6])\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exponential Distribution\n",
    "Exponential distribution is used for describing time till next event e.g. failure/success etc.\n",
    "\n",
    "It has two parameters:\n",
    "\n",
    "`scale` - inverse of rate ( see lam in poisson distribution ) defaults to 1.0.\n",
    "\n",
    "`size` - The shape of the returned array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[8.02107448 5.61662317 3.41547052]\n",
      " [5.37314244 3.02035979 4.57950269]]\n"
     ]
    }
   ],
   "source": [
    "x = random.exponential(scale=2, size=(2, 3))\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shubh\\AppData\\Local\\Temp\\ipykernel_23132\\3584495776.py:1: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(random.exponential(size=1000), hist=False)\n"
     ]
    },
    {
     "data": {
      "image/png": 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38e6770r5bfg052Jiqc64cYoM0SApPAiG2iYcKq3D3PFRktZDRES+S9Jws3DhQhiNRqxZswYVFRXIyMjAtm3bkJycDACoqKjocuaN1WrFihUrUFZWhsDAQKSnp+Pjjz/GzTffLNW34POc28ClXG/jNDUpDIbaJhQZGG6IiGjwBNHPuhWazWbo9XqYTCauvwHwq4++xSu7z2PpvDFYOX+CpLW8uvs8nv7oW8wbH4VXH5wuaS1ERORdBvL7W/L2CyQtqbeBd+Zcd3OwtA52u19lbiIiciOGGz/XsaBY2jU3AJAWp4NG5egQfs7YKHU5RETkoxhu/Jxz5CYyRC1xJUCAUoFJ7a0YDpRckrgaIiLyVQw3fs7YYAUARHrBgmKg03k3PKmYiIgGieHGj7W02lBvaQPg2IrtDaa1r7vhyA0REQ0Ww40fq2lwTEmplQrotJJ34gDQMXJz6mI9GtqDFxER0UAw3PixmvYpqYgQdZ/9vIZbjE6LkWGBsIvA4Qt1UpdDREQ+iOHGj9W4FhN7x5SU05T2qSn2mSIiosFguPFjxkbv2SnV2dTEMABAkYHrboiIaOAYbvyYc1rK20ZupiU71t0cMNTBzw7QJiIiN2C48WOuM268ZBu4U3q8DmqlArWNVhhqm6Quh4iIfAzDjR9z7paKCPauaSmNSon0kY6+IVx3Q0REA8Vw48ec4cYbOoJ3NzXROTXFdTdERDQwDDd+zOila26AjiaahTzMj4iIBojhxo85R268MdxkjXKM3ByvMKO+pVXiaoiIyJcw3PipVpsdl5ocoSHCy7aCA0CcPhCJ4Y7D/A5w3Q0REQ0Aw42fqm10TEkpBGBEkPeFGwC4elQ4AGDfuVqJKyEiIl/CcOOnnNvAw4M1UCq8o/VCd9Pbw81ehhsiIhoAhhs/ZWx0Lib2zlEbAJie4gg3By/UwdJmk7gaIiLyFQw3fsrZV8obt4E7pUQGIzJEDWubHYcvmKQuh4iIfATDjZ/y1gP8OhMEwbXuhlNTRETUXww3fsqbt4F3xnBDREQDxXDjp1xNM714WgroWHdTWHIJNjubaBIR0ZUx3PgpXxm5SYvTIVSjQoOlDccrzFKXQ0REPoDhxk85R2688QC/zpQKAdOSHacVc2qKiIj6g+HGT7maZnr5yA3QMTW17zzDDRERXRnDjR+y20XXCcXePi0FdISbvedqIYpcd0NERJfHcOOH6ppbXYtzw714K7jTpAQ91CoFjI1WnK1plLocIiLycgw3fqi20TElFapVQa3y/n8FNColpiSGAWCfKSIiujLv/81Gbmd0Lib2gVEbJ/aZIiKi/mK48UPO9TYRPrDexulq57obLiomIqIrYLjxQ86mmb6w3sYpM3kEFAJw4VIzyuuapS6HiIi8GMONH3KN3PhQuAnRqJAerwfAqSkiIro8hhs/ZGw/48aXRm4A4JrRjqmpPWeMEldCRETejOHGD/nitBQAZI+NBADsPlsjcSVEROTNGG78UMeCYt8KN1ePCodKIaC0thmltU1Sl0NERF6K4cYP1bpGbnxntxTgWHczuf28G05NERFRXxhu/JDRBxcUO2WPiQAA7D7DqSkiIuodw42fEUURl3x0zQ0AzHSFGyP7TBERUa8YbvyMubkNbT7UV6q7aUkjoFYpUFVvwZlq9pkiIqKeGG78jLG9r1SIRgVtgFLiagZOG6BEVvIIAMAeTk0REVEvGG78TK0PT0k5ZXeamiIiIuqO4cbP+OoZN53NHOM472bPWSPsdq67ISKirhhu/IwvdgTvblKCHsFqJeqaWnG80ix1OURE5GUYbvxMbaNvtl7oLECpwPQUtmIgIqLeSR5uNmzYgJSUFGi1WmRmZmLHjh39et6uXbugUqkwZcoUzxYoM65pKR87nbi7Wc5WDAw3RETUjaThZuvWrVi+fDlWr16NoqIizJkzB/Pnz4fBYLjs80wmExYvXozrr79+mCqVD1/sCN4b53k335w1otVml7gaIiLyJpKGm3Xr1uGhhx7CkiVLkJaWhvXr1yMxMRF5eXmXfd7DDz+Me+65BzNnzhymSuXDV1svdJcWq0NYUAAarTYcKTNJXQ4REXkRycKN1WpFYWEhcnJyulzPycnB7t27+3zeyy+/jDNnzuDpp5/u1/tYLBaYzeYuD38mhwXFAKBQCJg52jF6w3U3RETUmWThpqamBjabDTExMV2ux8TEoLKystfnFBcXY+XKlXj99dehUqn69T5r166FXq93PRITE4dcuy/z1Y7gvXGed7PrNA/zIyKiDpIvKBYEocvnoij2uAYANpsN99xzD379619j/Pjx/X79VatWwWQyuR6lpaVDrtlXiaIoi0P8nJzn3ewvuYSWVpvE1RARkbfo3/CHB0RGRkKpVPYYpamqquoxmgMA9fX12L9/P4qKivDoo48CAOx2O0RRhEqlwmeffYbrrruux/M0Gg00Gt9eX+IuDZY2WNsX30b4+JobABgTFYx4vRblphbsPVeLueOjpC6JiIi8gGQjN2q1GpmZmcjPz+9yPT8/H9nZ2T3u1+l0OHLkCA4ePOh6LF26FKmpqTh48CBmzJgxXKX7LOd6m8AAJQLVvtdXqjtBEDB7nGP0ZkdxtcTVEBGRt5Bs5AYAcnNzsWjRImRlZWHmzJnYuHEjDAYDli5dCsAxpVRWVoYtW7ZAoVAgIyOjy/Ojo6Oh1Wp7XKfeyaH1QndzxkXh7f0XsKOY626IiMhB0nCzcOFCGI1GrFmzBhUVFcjIyMC2bduQnJwMAKioqLjimTfUf3JaTOw0a2wkBAE4UVmPKnMLonVaqUsiIiKJCaIo+lXnQbPZDL1eD5PJBJ1OJ3U5w2rrPgN+8e4R/FdqFF55YLrU5bjN9/6yE4cvmLDursm4Y1qC1OUQEZEHDOT3t+S7pWj4yHFaCgDmuNbdcGqKiIgYbvxKrUwO8Otu9ljHLqkdxTWw2/1qIJKIiHrBcONHOtbc+P428M6mJYchSK1ETYMFJyrrpS6HiIgkxnDjR+Q6LaVRKXFNeysGbgknIiKGGz8il47gvXGuu9nJVgxERH6P4caPyKn1QnfOcPPNuVq2YiAi8nMMN35CFEXUNFgAyKP1QndjokIQp9fC2mbH3nO1UpdDREQSYrjxE01WGyxtjr5S4TI6xM9JEIROW8K57oaIyJ8x3PgJ55SUWqVAsAz6SvVmzriOLeFEROS/BhVuzp075+46yMOMnRYTC4IgcTWe0b0VAxER+adBhZuxY8fi2muvxWuvvYaWFv4S8QW1jY71NnJcTOwUHqxGRrweAHdNERH5s0GFm0OHDmHq1Kn42c9+htjYWDz88MPYu3evu2sjNzI2yHenVGdsxUBERIMKNxkZGVi3bh3Kysrw8ssvo7KyErNnz0Z6ejrWrVuH6mou6PQ2zjU3kTI7nbi7zutu/KwnLBERtRvSgmKVSoXbb78db7/9Np577jmcOXMGK1asQEJCAhYvXoyKigp31UlDJOczbjpjKwYiIhpSuNm/fz8eeeQRxMXFYd26dVixYgXOnDmDL774AmVlZbjtttvcVScNkVxbL3SnUSkxIyUcALeEExH5q0GFm3Xr1mHixInIzs5GeXk5tmzZgpKSEjzzzDNISUnBrFmz8Le//Q0HDhxwd700SHJuvdAdt4QTEfk31WCelJeXhwcffBAPPPAAYmNje70nKSkJL7300pCKI/cxNsh/t5TT3PFdWzFoA+R5rg8REfVuUOEmPz8fSUlJUCi6DvyIoojS0lIkJSVBrVbjvvvuc0uRNHSuc25keDpxd85WDBWmFuw9V4u546OkLomIiIbRoKalxowZg5qankP+tbW1SElJGXJR5H4dC4rlvVsKcLRimD2WrRiIiPzVoMJNX1tsGxoaoNVqh1QQuV9Lqw1NVkenbH+YlgKAOeO57oaIyF8NaFoqNzcXgOP/jJ966ikEBQW5vmaz2fDNN99gypQpbi2Qhs45JRWgFKDTDmom0ufM6dSKodLUglg9QzcRkb8Y0G+6oqIiAI6RmyNHjkCt7hgFUKvVmDx5MlasWOHeCmnIajudTizXvlLdjQhWY1JCGA6V1qHgVDXuujpR6pKIiGiYDCjcfPnllwCABx54AH/605+g0+k8UhS5l9HVV0r+6206mzc+CodK67C9mOGGiMifDGrNzcsvv8xg40P86Yybzua1r7vZWVwDm52tGIiI/EW/R27uuOMOvPLKK9DpdLjjjjsue+9777035MLIffyl9UJ3kxP00GlVMDW34tCFOkxLGiF1SURENAz6HW70er1rvYZer/dYQeR+/tJ6oTuVUoE546Lw8ZEKbD9ZzXBDROQn+h1uXn755V4/Ju/nPJ3Y36alAMdpxR8fqUBBcTWeuHG81OUQEdEwGNSam+bmZjQ1Nbk+Lykpwfr16/HZZ5+5rTByH9eamxD/WlAMwHU68aHSOlxq/+dARETyNqhwc9ttt2HLli0AgLq6OkyfPh0vvPACbrvtNuTl5bm1QBq6mgb/nJYCgDh9IFJjQmEXgZ2neaAfEZE/GFS4OXDgAObMmQMAeOeddxAbG4uSkhJs2bIFf/7zn91aIA2dc+Qm0g/6SvXG2Uiz4BRbMRAR+YNBhZumpiaEhoYCAD777DPccccdUCgUuOaaa1BSUuLWAmno/KkjeG/mjY8GABQUV/fZOoSIiORjUOFm7Nix+OCDD1BaWop///vfyMnJAQBUVVXx/Bsv09JqQ2N7Xyl/XHMDAFmjRkAboMBFswUnL9ZLXQ4REXnYoMLNU089hRUrVmDUqFGYMWMGZs6cCcAxijN16lS3FkhD4499pbrTBigxc3QEAGD7SU5NERHJ3aDCzfe//30YDAbs378fn376qev69ddfjz/+8Y9uK46Gzh/7SvXGuWtqO9fdEBHJ3qD/Vz42NhaxsbFdrk2fPn3IBZF7+Wtfqe6crRj2n7+ERksbgjX+OYpFROQPBvUnfGNjI373u9/h888/R1VVFex2e5evnz171i3F0dAZG/x7p5RTSmQwEsMDUVrbjK/PGnF9WozUJRERkYcMKtwsWbIE27dvx6JFixAXF+fX0x3ezl/7SnUnCALmjovC698YUHCqmuGGiEjGBhVuPvnkE3z88ceYNWuWu+shN6tpdLZe8O9pKcAxNfX6NwauuyEikrlBLSgeMWIEwsPD3V0LeYBzQXGEn09LAcDMMRFQKQScNzahxNgodTlEROQhgwo3v/nNb/DUU0916S9F3sm5Fdwfm2Z2F6oNQGayozM4TysmIpKvQU1LvfDCCzhz5gxiYmIwatQoBAQEdPn6gQMH3FIcDZ2Ra266mJcahW/O1WL7qWosmjlK6nKIiMgDBhVuFixY4OYyyFOcrRf89XTi7uaOi8LvPz2J3WeMsLbZoVYNavCSiIi82KDCzdNPP+3uOshDajkt1cVVcTpEhmhQ02DB/pJaZI+JlLokIiJys0H/b2tdXR02bdqEVatWoba2FoBjOqqsrMxtxdHQNFttaHL1lWK4AQCFQsDccY5Aw11TRETyNKhwc/jwYYwfPx7PPfccnn/+edTV1QEA3n//faxatcqd9dEQOE8nVisVCOGJvC7zUh2nFRecqpG4EiIi8oRBhZvc3Fzcf//9KC4uhlardV2fP38+CgoKBvRaGzZsQEpKCrRaLTIzM7Fjx44+7925cydmzZqFiIgIBAYGYsKECexldRnGTtvAedBih9ljIyEIwPEKM6rMLVKXQ0REbjaocLNv3z48/PDDPa6PHDkSlZWV/X6drVu3Yvny5Vi9ejWKioowZ84czJ8/HwaDodf7g4OD8eijj6KgoADHjx/Hk08+iSeffBIbN24czLchezyduHcRIRpMHKkHABQUc/SGiEhuBhVutFotzGZzj+snT55EVFRUv19n3bp1eOihh7BkyRKkpaVh/fr1SExMRF5eXq/3T506FXfffTfS09MxatQo/OhHP8JNN9102dEef1bDnVJ9mscu4UREsjWocHPbbbdhzZo1aG1tBeDo22MwGLBy5Urceeed/XoNq9WKwsJC5OTkdLmek5OD3bt39+s1ioqKsHv3bsybN6/PeywWC8xmc5eHv+BOqb7NbQ83O4qrYbOLEldDRETuNKhw8/zzz6O6uhrR0dFobm7GvHnzMHbsWISGhuK3v/1tv16jpqYGNpsNMTFdGxjGxMRccWorISEBGo0GWVlZWLZsGZYsWdLnvWvXroVer3c9EhMT+1WfHPB04r5NTQxDqFaFuqZWHCkzSV0OERG50aC20Oh0OuzcuRNffvklCgsLYbfbMW3aNNxwww0Dfq3uC11FUbzi4tcdO3agoaEBX3/9NVauXImxY8fi7rvv7vXeVatWITc31/W52Wz2m4DjXFAczm3gPaiUCsweG4lPjlZi+8lqTEkMk7okIiJykwGHG7vdjldeeQXvvfcezp8/D0EQkJKSgtjY2H4FE6fIyEgolcoeozRVVVU9RnO6S0lJAQBMnDgRFy9exK9+9as+w41Go4FG459rTpxbwSPZEbxXc8dH4ZOjlSgorsbjN4yTuhwiInKTAU1LiaKI733ve1iyZAnKysowceJEpKeno6SkBPfffz9uv/32fr+WWq1GZmYm8vPzu1zPz89Hdnb2gGqyWCz9vt+fcLfU5TnX3RQZLsHU1CpxNURE5C4DGrl55ZVXUFBQgM8//xzXXnttl6998cUXWLBgAbZs2YLFixf36/Vyc3OxaNEiZGVlYebMmdi4cSMMBgOWLl0KwDGlVFZWhi1btgAAXnzxRSQlJWHChAkAHOfePP/883jssccG8m34jc7n3FBPI8MCMTY6BKerGrDzdA1umRQndUlEROQGAwo3b775Jn75y1/2CDYAcN1112HlypV4/fXX+x1uFi5cCKPRiDVr1qCiogIZGRnYtm0bkpOTAQAVFRVdzryx2+1YtWoVzp07B5VKhTFjxuB3v/tdr2fu+DtRFF3TUhGclurTvPFROF3VgIJT1Qw3REQyIYii2O99sLGxsfj0008xZcqUXr9eVFSE+fPnD+ggv+FmNpuh1+thMpmg0+mkLsdjGi1tSH/63wCAb399E4LZfqFX209V477NexGr02LPqut4kjMRkZcayO/vAa25qa2tvexi35iYGFy6dGkgL0ke4lxvo1EpEKRWSlyN95qREg6NSoFKcwuKqxqkLoeIiNxgQOHGZrNBpep7BECpVKKtrW3IRdHQOU8njgzRcDTiMrQBSlwzOgIAsP0kTysmIpKDAc1ViKKI+++/v8+t1dy15D24U6r/5o6PwvZT1SgorsaP546WuhwiIhqiAYWb++6774r39HcxMXkWd0r137zxUfgNgG/O1qLJ2oYgNdcnERH5sgH9Kf7yyy97qg5yMyNHbvptTFQwEkYE4sKlZuw+bcQNV13+EEkiIvJug+otRd7P2GnNDV2eIAi4NjUaAPDlySqJqyEioqFiuJEprrkZmOsmOMLNVyerMYDTEYiIyAsx3MhUDTuCD8g1oyOgUSlQVtfMLeFERD6O4Uamahs5LTUQgWolZo5xbAn/8gSnpoiIfBnDjUw5d0txWqr/nOtuvmC4ISLyaQw3MuToK8Wt4APlDDf7Sy7B3MIu4UREvorhRoYaLG2wttkBsGnmQCRFBGFMVDBsdhE7i2ukLoeIiAaJ4UaGnDulgtRKBLKv1IC4toRzaoqIyGcx3MhQDdfbDJpzS/iXJ6tht3NLOBGRL2K4kaFa13obTkkNVNaocASrlahpsODbcrPU5RAR0SAw3MiQ83RinnEzcGqVArPHRQLgacVERL6K4UaGjDzAb0g6pqYYboiIfBHDjQy5Wi9wG/ig/Ff7ouKDpXWuUTAiIvIdDDcy5GqayW3ggxKj0+KqOB1EEdh+qlrqcoiIaIAYbmTIyKaZQ+acmvqcW8KJiHwOw40MOVsv8HTiwbvhqhgAwPaT1bC02SSuhoiIBoLhRoZcW8E5LTVok0bqER2qQYOlDXvOGKUuh4iIBoDhRmYcfaXat4Jz5GbQFAoBN7aP3uQfuyhxNURENBAMNzJTb2lDq81xsi7X3AxN53DD04qJiHwHw43M1NQ7Rm1CNCpoA9hXaihmjolAiEaFqnoLDpeZpC6HiIj6ieFGZpx9paJCud5mqDQqJealRgEA8o9VSlwNERH1F8ONzFS3j9xEcr2NW+S0T0199i3X3RAR+QqGG5mprm8BwJEbd/mv1GioFAKKqxpwrqZR6nKIiKgfGG5kxjUtxY7gbqEPDMA1oyMAcGqKiMhXMNzITMe0FMONu3BLOBGRb2G4kZnq9r5SnJZyH+dpxftLLqGGjTSJiLwew43M1DDcuN3IsEBkjHQ00vziOHtNERF5O4YbmeG0lGfcmBYLAPiM626IiLwew42MiKLIkRsPyUl3TE3tKK5Bk7VN4mqIiOhyGG5kxNTc6mq9wL5S7jUhNhRJ4UGwtNnxxQlOTREReTOGGxlxTknpAwOgUbH1gjsJgoBbJsUBAD4+XCFxNUREdDkMNzLiDDeckvKMWyY6ws0XJ6rQaOHUFBGRt2K4kRHnNnC2XvCM9HgdRkU4pqY+59QUEZHXYriRkY6RG63ElchT56mp/ztULnE1RETUF4YbGXG2XuDIjefcMjEeAPDVqWrUt7RKXA0REfWG4UZGuObG89LiQjE6MhjWNjvbMRAReSmGGxlxtV7gAX4eIwgCvjfFMXrzflGZxNUQEVFvGG5kpMZ5OjFHbjxqwZSRAIBdp2tQZW6RuBoiIuqO4UZGOHIzPEZFBmNaUhjsIvARFxYTEXkdhhuZsNlFGNvDTTRHbjzu9mkJAID3DnBqiojI20gebjZs2ICUlBRotVpkZmZix44dfd773nvv4cYbb0RUVBR0Oh1mzpyJf//738NYrfe61GSFXQQEAQgP5m4pT/vuxDgEKAUcqzDjZGW91OUQEVEnkoabrVu3Yvny5Vi9ejWKioowZ84czJ8/HwaDodf7CwoKcOONN2Lbtm0oLCzEtddei1tvvRVFRUXDXLn3ce6UCg9SQ6WUPLPK3ohgNf4rNRoA8N6BCxJXQ0REnQmiKIpSvfmMGTMwbdo05OXlua6lpaVhwYIFWLt2bb9eIz09HQsXLsRTTz3Vr/vNZjP0ej1MJhN0Ot2g6vZGBaeqsXjzXqTGhOLfT8yVuhy/8OnRSix9rRCRIRrsWXUdAhgqiYg8ZiC/vyX709hqtaKwsBA5OTldrufk5GD37t39eg273Y76+nqEh4f3eY/FYoHZbO7ykKMaZ+uFUE5JDZfr06IRGaJBTYMFnx/nmTdERN5CsnBTU1MDm82GmJiYLtdjYmJQWVnZr9d44YUX0NjYiLvuuqvPe9auXQu9Xu96JCYmDqlub3XR7Ag3MTq2XhguAUoFfpDlWFj85t5SiashIiInycfRBUHo8rkoij2u9ebNN9/Er371K2zduhXR0dF93rdq1SqYTCbXo7RUnr+ELraft8JwM7x+eLUjLBcUV+PCpSaJqyEiIkDCcBMZGQmlUtljlKaqqqrHaE53W7duxUMPPYS3334bN9xww2Xv1Wg00Ol0XR5y5Ao33AY+rJIjgpE9JgKiCLy9T57BmYjI10gWbtRqNTIzM5Gfn9/len5+PrKzs/t83ptvvon7778fb7zxBm655RZPl+kzOHIjnR9OTwIAbN1filabXeJqiIhI0mmp3NxcbNq0CZs3b8bx48fxxBNPwGAwYOnSpQAcU0qLFy923f/mm29i8eLFeOGFF3DNNdegsrISlZWVMJlMUn0LXsO55iaa4WbY3ZQeg6hQDS6aLdh2pELqcoiI/J6k4WbhwoVYv3491qxZgylTpqCgoADbtm1DcnIyAKCioqLLmTd/+9vf0NbWhmXLliEuLs71ePzxx6X6FryCKIqoqneM3MTqGW6Gm0alxKJrHP/Obt55DhKerkBERJD4nBspyPGcG2ODBZnP/AcAcOqZ+VCrJF8n7ndqGizI/t0XsLbZ8e5PspGZPELqkoiIZMUnzrkh93FOSUUEqxlsJBIZosGCKfEAgM27zklcDRGRf+NvQhm42D4lxfU20npwdgoAx8nFpbXcFk5EJBWGGxmocu2U4jZwKU2I1WHOuEjY7CI2fHVG6nKIiPwWw40MOKelYjlyI7nHrx8HAHinsBRldc0SV0NE5J8YbmSg0sxpKW+RNSoc2WMi0GoTkffVaanLISLySww3MsBpKe/iHL15e98FVJg4ekNENNwYbmTA1TQzlCM33mDG6AjMSAmH1WbHH/NPSV0OEZHfYbiRAbZe8D6/mD8BAPDPwgv4tpwnaBMRDSeGGx/XZrOjpqF95EbPaSlvMS1pBL43OR6iCDzzf8d5ajER0TBiuPFxNQ1W2EVAqRAQEcxw403++zup0KgU2HPWiPxjF6Uuh4jIbzDc+DjnlFRUiAZKhSBxNdRZwoggLJnjONjv1/86hgZLm8QVERH5B4YbH3eRO6W82rJrxyIxPBBldc147pMTUpdDROQXGG583EWecePVgtQqPHfHJADAP74uwddnjRJXREQkfww3Po6nE3u/7LGRuHt6EgDgv985DFNzq8QVERHJG8ONj3ON3IRyWsqbrbp5AkaGBcJQ24Sf//MQd08REXkQw42PqzA5wk1cWKDEldDl6LQByPvRNKiVCnx27CL+vuOs1CUREckWw42PK29vzhgfxmkpbzcpIQxP3XoVAOC5T0/iyxNVEldERCRPDDc+TBRFV+fpkRy58Qn3zkjC9zMTYLOLeOT1AygyXJK6JCIi2WG48WG1jVZY2uwAgFg9R258gSAIWHvHRMwdH4XmVhsefGUfii/WS10WEZGsMNz4sPK69gP8QjXQqJQSV0P9FaBUIO/eaZicoMelplYs3Pg1jpax/xQRkbsw3PiwMtd6G05J+ZpgjQqvPDAdkxL0qG204u6NX2Pf+VqpyyIikgWGGx9W7lpvwykpXzQiWI3Xl8zA9FHhqLe04d6/f4N3Cy9IXRYRkc9juPFhrp1Seo7c+KpQbQBefXA6bkqPgdVmx8/+eQjPbjuONptd6tKIiHwWw40PKzdxWkoOAtVK5N2biceuGwsA2FhwFvf8/RvXAY1ERDQwDDc+rKx9QTHDje9TKAT8LCcVL94zDSEaFfaer8XNf9qBHcXVUpdGRORzGG58WDnPuJGdWybF4V+PzUZanA7GRisWb96LdfmnYLOzXQMRUX8x3PgoS5sN1fWOppk8nVheUiKD8f4j2bh7ehJEEfjz58VYvPkb18+biIguj+HGR100OX7RaVQKhAerJa6G3E0boMTaOybijwsnIzBAiV2njbj5zzuw54xR6tKIiLwew42P6tx2QRAEiashT7l9agL+9dgsjI8JQXW9Bfdu+hovfnkadk5TERH1ieHGR5XzAD+/MTY6FB8sm4U7pyXALgJ/+PdJPPjqPtS3tEpdGhGRV2K48VHsBu5fgtQqvHDXZPz++5OgUSnw1clq/OCve1z/HhARUQeGGx/FM278011ZiXhnaTaiQjU4UVmPBS/uwolKs9RlERF5FYYbH8UzbvzXxAQ93n8kG+NjQlBVb8EPN36NIxfYeJOIyInhxkfxjBv/ljAiCP98OBtTEsNQ19SKe/7+NQpL2HiTiAhguPFJoijiwqUmAAw3/kwfFIDXlszA9BRH4837N+/jCA4RERhufFJVvQUtrXYoFQJGjmC48WchGhVefWC6q7P4os3f4GRlvdRlERFJiuHGB5UYHaM28WFaBCj5I/R3gWolXro/C5Pbp6gWvfQNd1ERkV/jb0YfVGJsBAAkhwdLXAl5i1BtAF594GrXIuMHX+E5OETkvxhufJBz5CY5IkjiSsibhAWpsfn+q13bxJe9UYRWm13qsoiIhh3DjQ8qqWW4od4ljAjC5vuuRmCAEgWnqvHUh0chimzVQET+heHGBxnap6WSOC1FvZiYoMf/3j0VCgF4c28p/rr9rNQlERENK4YbH8SRG7qSG66KwVPfvQoA8NynJ7DtSIXEFRERDR+GGx9jampFXZNjoSjDDV3O/bNS8MCsUQCA3LcP4mgZz8AhIv/AcONjSmodU1JRoRoEqVUSV0PebvXNaZg7PgotrXb8vy37UV1vkbokIiKPY7jxMa6dUuEctaErUykV+N+7p2J0VDDKTS14+B/7YWmzSV0WEZFHSR5uNmzYgJSUFGi1WmRmZmLHjh193ltRUYF77rkHqampUCgUWL58+fAV6iUM7ettkjglRf2kDwzApsVZ0GlVOGCowy/f4w4qIpI3ScPN1q1bsXz5cqxevRpFRUWYM2cO5s+fD4PB0Ov9FosFUVFRWL16NSZPnjzM1XqH8zWOaalREdwpRf03OioEL947DUqFgHcPXMCmHeekLomIyGMkDTfr1q3DQw89hCVLliAtLQ3r169HYmIi8vLyer1/1KhR+NOf/oTFixdDr9cPc7XegTulaLDmjIvCk7ekAQDWfnIcX56okrgiIiLPkCzcWK1WFBYWIicnp8v1nJwc7N69223vY7FYYDabuzx8maF9zU0S19zQINyfPQo/vDoRdhH46ZtFOF3FJptEJD+ShZuamhrYbDbExMR0uR4TE4PKykq3vc/atWuh1+tdj8TERLe99nBrabWh0twCAEjmtBQNgiAIWHNbhquL+EOv7kdto1XqsoiI3EryBcWCIHT5XBTFHteGYtWqVTCZTK5HaWmp2157uDkXE4dqVBgRFCBxNeSr1CoF8n40DSPDAlFibMKDr+xDk7VN6rKIiNxGsnATGRkJpVLZY5Smqqqqx2jOUGg0Guh0ui4PX1V8sQEAMCY6xK0BkPxPRIgGrz54NfSBAThYWofH3ihCG5tsEpFMSBZu1Go1MjMzkZ+f3+V6fn4+srOzJarKu5266FgfMT4mROJKSA7GRodi8/1Z0KgU+PxEFZ78gFvEiUgeJJ2Wys3NxaZNm7B582YcP34cTzzxBAwGA5YuXQrAMaW0ePHiLs85ePAgDh48iIaGBlRXV+PgwYM4duyYFOUPu+IqZ7gJlbgSkovM5HBXk8239pVi/X+KpS6JiGjIJD2/f+HChTAajVizZg0qKiqQkZGBbdu2ITk5GYDj0L7uZ95MnTrV9XFhYSHeeOMNJCcn4/z588NZuiSc01JjozlyQ+6Tkx6L3yzIwOr3j+JPnxcjMlSDRdckS10WEdGgCaKfjUObzWbo9XqYTCafWn9jbbPjqqc+RZtdxO6V1yE+LFDqkkhm1n12En/+4jQA4DcLMhhwiMirDOT3t+S7pah/zhsb0WYXEaJRIU6vlbockqEnbhyPH89JAQD8zwdH8counmJMRL6J4cZHdJ6S4k4p8gRBEPDLm9Pw8LzRAIBf/esYNu04K3FVREQDx3DjI7hTioaDIAhY+Z0JWHbtGADAMx8fx8aCMxJXRUQ0MAw3PsK5U2pcNHdKkWcJgoAVOan46XVjAQDPbjuB5z49Abvdr5bnEZEPY7jxEc5pqXEcuaFhIAgCcnNSsSJnPAAg76szyH37IKxtPOiPiLwfw40PsLbZca6mEQAwjmfc0DB69Lpx+P33J0GlEPDBwXLct3kvTM2tUpdFRHRZDDc+oPNOqXjulKJhdldWIjbffzWC1UrsOWvED/66G+V1zVKXRUTUJ4YbH8CdUiS1ueOj8PbSmYgO1eDUxQYseHEXDpXWSV0WEVGvGG58wLEKEwAglVNSJKH0eD3eXzYL42NCUFVvwV1/24OPD1dIXRYRUQ8MNz7g8AVHuJmYoJe4EvJ3I8MC8e5PsnFtahQsbXYse+MA/vx5MRtuEpFXYbjxcqIousLN5IQwaYshAhCqDcCm+67Gg7Mcpxmvyz+F5VsPoqXVJnFlREQODDdersTYBFNzK9QqBVJjOS1F3kGpEPDUrVfh2dsnQqUQ8OHBctz9969RXW+RujQiIoYbb3foQh0A4Ko4HdQq/rjIu9wzIwlbHpwOnVaFIkMdFry4C8crzFKXRUR+jr8tvdyhUueUFNfbkHfKHhuJD5bNQkpkMMrqmvH9vN34/PhFqcsiIj/GcOPlDreP3EziehvyYqOjQvD+I9nIHhOBRqsNS7bsx6YdZ7nQmIgkwXDjxdpsdhwtbx+5SeTIDXm3sCA1Xn1wOu6engRRdDTdXPXeEbZsIKJhx3DjxYqrGtDSakeIRoXRkewpRd4vQKnAs7dn4H++exUUAvDWvlIs3vwN6pqsUpdGRH6E4caLOaekMkbqoFDwZGLyDYIg4KHZKdh0XxZCNCp8fbYWC17chTPVDVKXRkR+guHGix1ynm+TGCZtIUSDcN2EGLz7k2yMDAvEeWMTbn9xF3YW10hdFhH5AYYbL7bvXC0AYCrDDfmo1NhQfPjoLGQmj4C5pQ33vbwXr31dInVZRCRzDDdeqtLUguKqBggCMHN0pNTlEA1aZIgGry+ZgdunjoTNLuLJD47i1//6Fm02LjQmIs9guPFSu047hu8njdRDHxQgcTVEQ6MNUGLdXZPx85tSAQAv7zqPJVv2w9zSKnFlRCRHDDdeyhluZo3lqA3JgyAIWHbtWOTdOw3aAAW+OlmN21/chZOV9VKXRkQyw3DjhURRxM72cDOb4YZkZv7EOPzz4WzE6rQ4U92I7/1lJ97aa+CBf0TkNgw3Xuh0VQOq6i3QqBSYljxC6nKI3G5igh4f/3Q25o2PgqXNjpXvHcGPt+xHlblF6tKISAYYbryQc9Rmeko4tAFKiash8oyIEA1evv9qrJo/AWqlAv85XoWc9QV4a68BdjtHcYho8BhuvBDX25C/UCgEPDxvDP712Gykx+tQ19SKle8dwe0bdqGwpFbq8ojIRzHceJmWVhv2nDECAGaNYbgh/5AaG4oPls3Ck7ekIUSjwqELJtyZtwf3bd6LA4ZLUpdHRD5GJXUB1NVXJ6vQaLUhXq9FerxO6nKIhk2AUoElc0bje5PjsS7/FP5ZeAHbT1Vj+6lqTE7QY9HMUZifEYtgDf/YIqLLE0Q/26JgNpuh1+thMpmg03lfePjJa4X45GglHp47GqtuTpO6HCLJlBgb8eKXp/FBUTms7Qf+BQYoccNVMbh+QjSyx0YgOlQrcZVENFwG8vub4caLmFtakfXMf2Bts+Pjn85Gerxe6pKIJGdssOCtfaX45/5SnDc2dfnahNhQzBobiWtGR2Bygh7ROoYdIrliuLkMbw437xRewIp/HsKYqGD8J3ceBIGdwImcRFHE4QsmfHK0EjtPV+PbcjO6/+kVo9Ng4sgwTErQY0JsKFJjQ5E4IggKBf9bIvJ1A/n9zclrL/LhwTIAwG1TRjLYEHUjCAImJ4ZhcmIYgAmobbRi1+ka7CyuQVHpJZyuasBFswUXzRfxn+MXXc8LDFBiXEwIxkWHIjXW8ffkiCAkjAiCWsU9FURyxHDjJarrLdjdvkvqe5PjJa6GyPuFB6tx6+R43Nr+30ujpQ3HKsw4fMGEo2UmnLpYj+KqBjS32nD4ggmHL5i6PF8QgHh9IJLCgxCn1yIiRI3IEA0iQjQIDw6ANkDpeKiU0AYoEKBUQKkQOh6CAEX7xyqFAIXg+FghgP9zQiQxhhsvsWXPedjsIqYkhmFUZLDU5RD5nGCNClePCsfVo8Jd12x2ESXGRpy6WI9TFxtw8mI9zlQ1oMTYhOZWG8rqmlFW1+z2WtQqBSKD1YgM1SAi2BGaEsODMDoqGGOiQpASGcwDOok8iOHGC9S3tOLV3ecBAA/PHS1tMUQyolQIGB0VgtFRIfhORsd1URRR3WBBaW0TSoxNqKq3oKbeAmOjFTUNFlxqsqKl1Y6WVhtaWu2wtNrQarfDbgdsogjbFU5QtrbZUW5qQbmp93YSggAkjgjCxJF6TErQY3JiGDJG6hHCbe5EbsH/krzAG98YYG5pw+ioYNyUHit1OUSyJwgCokO1iA7VIjM5/MpP6EYURdhFx8iQXRTRZncEHrvd8XFLqw3GRiuMDRbUNFhQXW9BibEJZ6obcKa6EabmVhhqm2CobcLHRyraawLGR4dialIYpiWNwLTkMIyODOFiaKJBYLiRWEurDZt2ngMA/GTeGP5BRuQDBEGAUnCMDPUlMTyo1+uiKKK20YoTlfU4fMGEQ6V1OHyhDuWmFpy8WI+TF+vx1r5SAIA+MABTEjvCzpTEMIRqAzzyPRHJCcONxN74xoDqegvi9VrcNmWk1OUQkYcJgoCIEA1mjdV06R9XVd+Cg4Y6HDDU4YDhEg5fqIOpudV1SrPjuY7RHWfQSY3VYXxMCILU/KOcqDOecyOh0tom3LS+AE1WG357ewbunZEsaT1E5D1abXacqKjHAcMl16O0tufiZ0EAksKDkBoT2n62jyPwJEUEQaPiomWSDx7idxneEm5EUcSil/Zi5+kaTB8Vjrf+3zWckiKiy6qqb0FR+8jOt2VmnKg0o6bB2uu9CsExNTYmKgSjI4PbF1YHY3RUMKJCNNyuTj6Hh/j5gDf2GrDzdA00KgWe+/4kBhsiuqLoUC1uSo/tsvGgpsGCk5X1OFFZj5OVZpysrMfpqgY0Wm0oMTp2g33R7XVCtSqMjgrBmMhg1/b00VEhSI4I4hZ1kgWGGwl8ceIinv7wWwDAipxUpPBcGyIapMgQDSK7rd8RRRFV9RacqW7A2epG19/P1jTgwqVm1Le04VBpHQ6V1nV5LUEAEkYEYnRkSHvg6Qg/0aEc7SHfwWmpYfb1WSPu27wXljY7vjc5Hn9cOOWyOy6IiNyppdXm2pZ+1hl+ahpxtroB9S1tfT4vRKNyhJ32Ka6k8CDE6LSI02sRq9dyxIc8jmtuLkOqcCOKIl7/xoDf/N8xWNrsuCEtGnk/ykSAkr1tiEh6oiiipsHaMcpT3YCz7aHHUNuEK5xbiLCgAMTqHEHH0cZCjYhgNSKCNQh3fhziOLGZQYgGw6fW3GzYsAF/+MMfUFFRgfT0dKxfvx5z5szp8/7t27cjNzcX3377LeLj4/Hf//3fWLp06TBWPHDnahrx7LbjyD/maOZ33YRo/OWeaQw2ROQ1BEFAVKgGUaEaXDM6osvXLG02GIxNONM+tXW2uhFll5px0dyCClMLmlttqGtqRV1TK05U1l/xvYLVyvbAo2kPPWqEB2sQGaJGeLDjERmicX3MMEQDJWm42bp1K5YvX44NGzZg1qxZ+Nvf/ob58+fj2LFjSEpK6nH/uXPncPPNN+PHP/4xXnvtNezatQuPPPIIoqKicOedd0rwHfTN0mbDnjNGvFN4AduOVMAuAgFKAb/4zgQ8OCuFC4iJyGdoVEqMiwnFuJjQHl8TRRHmljZUmlpQaW5BpakZNQ1W1Laf0Ow4qbn980YLWm0iGq02NNY297q1vTchGlV7AOoIRB2jQe2jQ50CEbu9k6TTUjNmzMC0adOQl5fnupaWloYFCxZg7dq1Pe7/xS9+gY8++gjHjx93XVu6dCkOHTqEPXv29Os9PTUtVd/Sih3FNfi23ISjZWYUllxCg6Vj/vr6CdH4WU4qroqX9mwdIiKpiKKIektbe9ixuEJQbXtPr9r2IORsXVHbaEXblebDehGqVbmmv9QqR0f3AKWAAKUCaqUCKqUAAQKc66MFARDg/ATOjyAIjqtCx5dci6oF11/geq2O53W8niB0PB9Xuq/T+6LbPV1r6FpfR90CApQCNCoFNCrH965RKaAJUECtVLo+7vI1laL9Y2X71xz/jLxx8bhPTEtZrVYUFhZi5cqVXa7n5ORg9+7dvT5nz549yMnJ6XLtpptuwksvvYTW1lYEBPQ8ltxiscBisbg+N5lMABz/kNzpvLERSzfv7HItMkSN/0qNxg+vTsSEOJ1H3peIyNdEqIEItQrjRqgA9N6mAugYFapttOBSoxXGxlZcarKitsHq+Htjx98dH7fCZhdhsgDtf9TTIAWoFNCoBGiUjiCkVimgEByBSiEACmfIa/9cgOPvaP98RFAAXrw30601OX9/9mdMRrJwU1NTA5vNhpiYmC7XY2JiUFlZ2etzKisre72/ra0NNTU1iIuL6/GctWvX4te//nWP64mJiUOovn9KARQB+KPH34mIiMi7vPaIZ163vr4eer3+svdIvqC4+9CXKIqXHQ7r7f7erjutWrUKubm5rs/tdjtqa2sRERHhlcNunmY2m5GYmIjS0lLJ20/Q5fFn5Tv4s/Id/Fn5ju4/K1EUUV9fj/j4+Cs+V7JwExkZCaVS2WOUpqqqqsfojFNsbGyv96tUKkRERPT6HI1GA41G0+VaWFjY4AuXCZ1Ox/+wfQR/Vr6DPyvfwZ+V7+j8s7rSiI2TZEvK1Wo1MjMzkZ+f3+V6fn4+srOze33OzJkze9z/2WefISsrq9f1NkREROR/JN0vl5ubi02bNmHz5s04fvw4nnjiCRgMBte5NatWrcLixYtd9y9duhQlJSXIzc3F8ePHsXnzZrz00ktYsWKFVN8CEREReRlJ19wsXLgQRqMRa9asQUVFBTIyMrBt2zYkJycDACoqKmAwGFz3p6SkYNu2bXjiiSfw4osvIj4+Hn/+85+97owbb6bRaPD000/3mKoj78Ofle/gz8p38GflO4bys/K79gtEREQkbzzGkYiIiGSF4YaIiIhkheGGiIiIZIXhhoiIiGSF4cbPbNiwASkpKdBqtcjMzMSOHTukLom6Wbt2La6++mqEhoYiOjoaCxYswMmTJ6Uui65g7dq1EAQBy5cvl7oU6kNZWRl+9KMfISIiAkFBQZgyZQoKCwulLou6aWtrw5NPPomUlBQEBgZi9OjRWLNmDex2e79fg+HGj2zduhXLly/H6tWrUVRUhDlz5mD+/PldttuT9LZv345ly5bh66+/Rn5+Ptra2pCTk4PGxkapS6M+7Nu3Dxs3bsSkSZOkLoX6cOnSJcyaNQsBAQH45JNPcOzYMbzwwgs8sd4LPffcc/jrX/+Kv/zlLzh+/Dh+//vf4w9/+AP+93//t9+vwa3gfmTGjBmYNm0a8vLyXNfS0tKwYMECrF27VsLK6HKqq6sRHR2N7du3Y+7cuVKXQ900NDRg2rRp2LBhA5555hlMmTIF69evl7os6mblypXYtWsXR6t9wHe/+13ExMTgpZdecl278847ERQUhH/84x/9eg2O3PgJq9WKwsJC5OTkdLmek5OD3bt3S1QV9YfJZAIAhIeHS1wJ9WbZsmW45ZZbcMMNN0hdCl3GRx99hKysLPzgBz9AdHQ0pk6dir///e9Sl0W9mD17Nj7//HOcOnUKAHDo0CHs3LkTN998c79fQ/Ku4DQ8ampqYLPZejQljYmJ6dGMlLyHKIrIzc3F7NmzkZGRIXU51M1bb72FAwcOYN++fVKXQldw9uxZ5OXlITc3F7/85S+xd+9e/PSnP4VGo+nS5oek94tf/AImkwkTJkyAUqmEzWbDb3/7W9x99939fg2GGz8jCEKXz0VR7HGNvMejjz6Kw4cPY+fOnVKXQt2Ulpbi8ccfx2effQatVit1OXQFdrsdWVlZePbZZwEAU6dOxbfffou8vDyGGy+zdetWvPbaa3jjjTeQnp6OgwcPYvny5YiPj8d9993Xr9dguPETkZGRUCqVPUZpqqqqeozmkHd47LHH8NFHH6GgoAAJCQlSl0PdFBYWoqqqCpmZma5rNpsNBQUF+Mtf/gKLxQKlUilhhdRZXFwcrrrqqi7X0tLS8O6770pUEfXl5z//OVauXIkf/vCHAICJEyeipKQEa9eu7Xe44ZobP6FWq5GZmYn8/Pwu1/Pz85GdnS1RVdQbURTx6KOP4r333sMXX3yBlJQUqUuiXlx//fU4cuQIDh486HpkZWXh3nvvxcGDBxlsvMysWbN6HKlw6tQpV6Nm8h5NTU1QKLrGE6VSOaCt4By58SO5ublYtGgRsrKyMHPmTGzcuBEGgwFLly6VujTqZNmyZXjjjTfw4YcfIjQ01DXaptfrERgYKHF15BQaGtpjHVRwcDAiIiK4PsoLPfHEE8jOzsazzz6Lu+66C3v37sXGjRuxceNGqUujbm699Vb89re/RVJSEtLT01FUVIR169bhwQcf7P+LiORXXnzxRTE5OVlUq9XitGnTxO3bt0tdEnUDoNfHyy+/LHVpdAXz5s0TH3/8canLoD7861//EjMyMkSNRiNOmDBB3Lhxo9QlUS/MZrP4+OOPi0lJSaJWqxVHjx4trl69WrRYLP1+DZ5zQ0RERLLCNTdEREQkKww3REREJCsMN0RERCQrDDdEREQkKww3REREJCsMN0RERCQrDDdEREQkKww3REREJCsMN0RERCQrDDdEREQkKww3REREJCsMN0RERCQr/x+Ijj4fBo9CKgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(random.exponential(size=1000), hist=False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Chi Square Distribution \n",
    "Chi Square distribution is used as a basis to verify the hypothesis.\n",
    "\n",
    "It has two parameters:\n",
    "\n",
    "`df` - (degree of freedom).\n",
    "\n",
    "`size` - The shape of the returned array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.17012123 3.33106671 2.01480465]\n",
      " [1.58648646 0.06571704 0.0454426 ]]\n"
     ]
    }
   ],
   "source": [
    "x = random.chisquare(df=2, size=(2, 3))\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shubh\\AppData\\Local\\Temp\\ipykernel_23132\\2060280000.py:1: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(random.chisquare(df=1, size=1000), hist=False)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(random.chisquare(df=1, size=1000), hist=False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rayleigh Distribution \n",
    "Rayleigh distribution is used in signal processing.\n",
    "\n",
    "It has two parameters:\n",
    "\n",
    "`scale` - (standard deviation) decides how flat the distribution will be default 1.0.\n",
    "\n",
    "`size` - The shape of the returned array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[3.48816308 1.50055223 4.01876713]\n",
      " [2.47381552 3.97566838 6.79259993]]\n"
     ]
    }
   ],
   "source": [
    "x = random.rayleigh(scale=2, size=(2, 3))\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shubh\\AppData\\Local\\Temp\\ipykernel_23132\\3971468590.py:1: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(random.rayleigh(size=1000), hist=False)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(random.rayleigh(size=1000), hist=False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ufunc Numpy\n",
    "ufuncs are used to implement vectorization in NumPy which is way faster than iterating over elements.\n",
    "ufuncs also take additional arguments, like:\n",
    "\n",
    "`where` : boolean array or condition defining where the operations should take place.\n",
    "\n",
    "`dtype` : defining the return type of elements.\n",
    "\n",
    "`out` : output array where the return value should be copied."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is Vectorization?\n",
    "Converting iterative statements into a vector based operation is called vectorization.\n",
    "\n",
    "It is faster as modern CPUs are optimized for such operations.\n",
    "\n",
    "Add the Elements of Two Lists\n",
    "list 1: [1, 2, 3, 4]\n",
    "\n",
    "list 2: [4, 5, 6, 7]\n",
    "\n",
    "One way of doing it is to iterate over both of the lists and then sum each elements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5, 7, 9, 11]\n"
     ]
    }
   ],
   "source": [
    "x = [1, 2, 3, 4]\n",
    "y = [4, 5, 6, 7]\n",
    "z = []\n",
    "\n",
    "for i, j in zip(x, y):\n",
    "  z.append(i + j)\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 5  7  9 11]\n"
     ]
    }
   ],
   "source": [
    "x = [1, 2, 3, 4]\n",
    "y = [4, 5, 6, 7]\n",
    "z = np.add(x, y)\n",
    "\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create your own Ufunc \n",
    "To create your own ufunc, you have to define a function, like you do with normal functions in Python, then you add it to your NumPy ufunc library with the `frompyfunc()` method.\n",
    "\n",
    "The `frompyfunc()` method takes the following arguments:\n",
    "\n",
    "- `function` - the name of the function.\n",
    "- `inputs` - the number of input arguments (arrays).\n",
    "- `outputs` - the number of output arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6 8 10 12]\n"
     ]
    }
   ],
   "source": [
    "def myadd(x, y):\n",
    "  return x+y\n",
    "\n",
    "myadd = np.frompyfunc(myadd, 2, 1)\n",
    "\n",
    "print(myadd([1, 2, 3, 4], [5, 6, 7, 8]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ufunc'>\n",
      "<class 'numpy._ArrayFunctionDispatcher'>\n"
     ]
    }
   ],
   "source": [
    "print(type(np.add))  ## check if a function is usfunc\n",
    "print(type(np.concatenate)) ##type of another func"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple Arithmetic\n",
    "You could use arithmetic operators `+` `-` `*` `/` directly between NumPy arrays, but this section discusses an extension of the same where we have functions that can take any array-like objects e.g. lists, tuples etc. and perform arithmetic conditionally."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[30 32 34 36 38 40]\n"
     ]
    }
   ],
   "source": [
    "## Addition\n",
    "arr1 = np.array([10, 11, 12, 13, 14, 15])\n",
    "arr2 = np.array([20, 21, 22, 23, 24, 25])\n",
    "\n",
    "newarr = np.add(arr1, arr2)\n",
    "\n",
    "print(newarr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-10  -1   8  17  26  35]\n"
     ]
    }
   ],
   "source": [
    "## Substraction\n",
    "arr1 = np.array([10, 20, 30, 40, 50, 60])\n",
    "arr2 = np.array([20, 21, 22, 23, 24, 25])\n",
    "\n",
    "newarr = np.subtract(arr1, arr2)\n",
    "\n",
    "print(newarr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 200  420  660  920 1200 1500]\n"
     ]
    }
   ],
   "source": [
    "## Multiplication \n",
    "arr1 = np.array([10, 20, 30, 40, 50, 60])\n",
    "arr2 = np.array([20, 21, 22, 23, 24, 25])\n",
    "\n",
    "newarr = np.multiply(arr1, arr2)\n",
    "\n",
    "print(newarr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 3.33333333  4.          3.          5.         25.          1.81818182]\n"
     ]
    }
   ],
   "source": [
    "## Division \n",
    "arr1 = np.array([10, 20, 30, 40, 50, 60])\n",
    "arr2 = np.array([3, 5, 10, 8, 2, 33])\n",
    "\n",
    "newarr = np.divide(arr1, arr2)\n",
    "\n",
    "print(newarr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[      1000    3200000  729000000 -520093696       2500          0]\n"
     ]
    }
   ],
   "source": [
    "## Power\n",
    "arr1 = np.array([10, 20, 30, 40, 50, 60])\n",
    "arr2 = np.array([3, 5, 6, 8, 2, 33])\n",
    "\n",
    "newarr = np.power(arr1, arr2)\n",
    "\n",
    "print(newarr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1  6  3  0  0 27]\n"
     ]
    }
   ],
   "source": [
    "## Reamainder \n",
    "arr1 = np.array([10, 20, 30, 40, 50, 60])\n",
    "arr2 = np.array([3, 7, 9, 8, 2, 33])\n",
    "\n",
    "newarr = np.mod(arr1, arr2)\n",
    "\n",
    "print(newarr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([ 3,  2,  3,  5, 25,  1]), array([ 1,  6,  3,  0,  0, 27]))\n"
     ]
    }
   ],
   "source": [
    "## quotient and mod\n",
    "arr1 = np.array([10, 20, 30, 40, 50, 60])\n",
    "arr2 = np.array([3, 7, 9, 8, 2, 33])\n",
    "\n",
    "newarr = np.divmod(arr1, arr2)\n",
    "\n",
    "print(newarr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 1 2 3 4]\n"
     ]
    }
   ],
   "source": [
    "## Absolute value\n",
    "arr = np.array([-1, -2, 1, 2, 3, -4])\n",
    "\n",
    "newarr = np.absolute(arr)\n",
    "\n",
    "print(newarr)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rounding decimals\n",
    "There are primarily five ways of rounding off decimals in NumPy:\n",
    "\n",
    "- truncation\n",
    "- fix\n",
    "- rounding\n",
    "- floor\n",
    "- ceil"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Truncation\n",
    "Remove the decimals, and return the float number closest to zero. Use the `trunc()` and `fix()` functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-3.  3.]\n",
      "[-3.  3.]\n"
     ]
    }
   ],
   "source": [
    "arr1 = np.trunc([-3.1666, 3.6667])\n",
    "\n",
    "print(arr)\n",
    "\n",
    "arr2 = np.fix([-3.1666, 3.6667])\n",
    "\n",
    "print(arr)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Rounding \n",
    " The `around()` function increments preceding digit or decimal by 1 if >=5 else do nothing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.17\n"
     ]
    }
   ],
   "source": [
    "arr = np.around(3.1666, 2)  ## upto 2 decimal\n",
    " \n",
    "print(arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Floor\n",
    "The floor() function rounds off decimal to nearest lower integer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-4.  3.]\n"
     ]
    }
   ],
   "source": [
    "arr = np.floor([-3.1666, 3.6667])\n",
    "\n",
    "print(arr)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Ceil\n",
    "The ceil() function rounds off decimal to nearest upper integer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-3.  4.]\n"
     ]
    }
   ],
   "source": [
    "arr = np.ceil([-3.1666, 3.6667])\n",
    "\n",
    "print(arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logs\n",
    "- Log at Base 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.         1.         1.5849625  2.         2.32192809 2.5849625\n",
      " 2.80735492 3.         3.169925  ]\n"
     ]
    }
   ],
   "source": [
    "arr = np.arange(1, 10)\n",
    "\n",
    "print(np.log2(arr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Log at Base 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.         0.30103    0.47712125 0.60205999 0.69897    0.77815125\n",
      " 0.84509804 0.90308999 0.95424251]\n"
     ]
    }
   ],
   "source": [
    "arr = np.arange(1, 10)\n",
    "\n",
    "print(np.log10(arr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Log at Base e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.         0.69314718 1.09861229 1.38629436 1.60943791 1.79175947\n",
      " 1.94591015 2.07944154 2.19722458]\n"
     ]
    }
   ],
   "source": [
    "arr = np.arange(1, 10)\n",
    "\n",
    "print(np.log(arr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Log at any base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.7005483074552052\n"
     ]
    }
   ],
   "source": [
    "from math import log\n",
    "import numpy as np\n",
    "\n",
    "nplog = np.frompyfunc(log, 2, 1)  ## math.log() : in-built function\n",
    "\n",
    "print(nplog(100, 15))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 4 6]\n"
     ]
    }
   ],
   "source": [
    "arr1 = np.array([1, 2, 3])\n",
    "arr2 = np.array([1, 2, 3])\n",
    "\n",
    "newarr = np.add(arr1, arr2)\n",
    "\n",
    "print(newarr)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12\n"
     ]
    }
   ],
   "source": [
    "arr1 = np.array([1, 2, 3])\n",
    "arr2 = np.array([1, 2, 3])\n",
    "\n",
    "newarr = np.sum([arr1, arr2])\n",
    "\n",
    "print(newarr)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- summation over an axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6 6]\n"
     ]
    }
   ],
   "source": [
    "arr1 = np.array([1, 2, 3])\n",
    "arr2 = np.array([1, 2, 3])\n",
    "\n",
    "newarr = np.sum([arr1, arr2], axis=1) ## axis=1 will sum the numbers in each array\n",
    "\n",
    "print(newarr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### cumulative sum \n",
    "Cummulative sum means partially adding the elements in array.\n",
    "\n",
    "E.g. The partial sum of [1, 2, 3, 4] would be [1, 1+2, 1+2+3, 1+2+3+4] = [1, 3, 6, 10].\n",
    "\n",
    "Perfom partial sum with the `cumsum()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 3 6]\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([1, 2, 3])\n",
    "\n",
    "newarr = np.cumsum(arr)\n",
    "\n",
    "print(newarr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Product\n",
    "To find the product of the elements in an array, use the `prod()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([1, 2, 3, 4])\n",
    "\n",
    "x = np.prod(arr)\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40320\n"
     ]
    }
   ],
   "source": [
    "arr1 = np.array([1, 2, 3, 4])\n",
    "arr2 = np.array([5, 6, 7, 8])\n",
    "\n",
    "x = np.prod([arr1, arr2])\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  24 1680]\n"
     ]
    }
   ],
   "source": [
    "arr1 = np.array([1, 2, 3, 4])\n",
    "arr2 = np.array([5, 6, 7, 8])\n",
    "\n",
    "newarr = np.prod([arr1, arr2], axis=1)\n",
    "\n",
    "print(newarr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Cumulative product\n",
    "Cummulative product means taking the product partially.\n",
    "\n",
    "E.g. The partial product of [1, 2, 3, 4] is [1, 1*2, 1*2*3, 1*2*3*4] = [1, 2, 6, 24]\n",
    "\n",
    "Perfom partial sum with the `cumprod()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[   5   30  210 1680]\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([5, 6, 7, 8])\n",
    "\n",
    "newarr = np.cumprod(arr)\n",
    "\n",
    "print(newarr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Differences\n",
    "A discrete difference means subtracting two successive elements.\n",
    "\n",
    "E.g. for [1, 2, 3, 4], the discrete difference would be [2-1, 3-2, 4-3] = [1, 1, 1]\n",
    "\n",
    "To find the discrete difference, use the `diff()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  5  10 -20]\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([10, 15, 25, 5])\n",
    "\n",
    "newarr = np.diff(arr)\n",
    "\n",
    "print(newarr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  5 -30]\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([10, 15, 25, 5])\n",
    "\n",
    "newarr = np.diff(arr, n=2) ## twice\n",
    "\n",
    "print(newarr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<PIL.TiffImagePlugin.TiffImageFile image mode=L size=200x200>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now, we often think about two dimensional arrays being made up of rows and columns, but you can also think\n",
    "# of these arrays as just a giant ordered list of numbers, and the *shape* of the array, the number of rows\n",
    "# and columns, is just an abstraction that we have for a particular purpose. Actually, this is exactly how\n",
    "# basic images are stored in computer environments.\n",
    "\n",
    "# Let's take a look at an example and see how numpy comes into play.\n",
    "# For this demonstration I'll use the python imaging library (PIL) and a function to display images in the\n",
    "# Jupyter notebook\n",
    "from PIL import Image\n",
    "from IPython.display import display\n",
    "\n",
    "# And let's just look at the image I'm talking about\n",
    "im = Image.open('chris.tiff')\n",
    "display(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(200, 200)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[118, 117, 118, ..., 103, 107, 110],\n",
       "       [113, 113, 113, ..., 100, 103, 106],\n",
       "       [108, 108, 107, ...,  95,  98, 102],\n",
       "       ...,\n",
       "       [177, 181, 182, ..., 193, 198, 192],\n",
       "       [178, 182, 183, ..., 193, 201, 189],\n",
       "       [178, 182, 184, ..., 193, 201, 187]], dtype=uint8)"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now, we can conver this PIL image to a numpy array\n",
    "array=np.array(im)\n",
    "print(array.shape)\n",
    "array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[255, 255, 255, ..., 255, 255, 255],\n",
       "       [255, 255, 255, ..., 255, 255, 255],\n",
       "       [255, 255, 255, ..., 255, 255, 255],\n",
       "       ...,\n",
       "       [255, 255, 255, ..., 255, 255, 255],\n",
       "       [255, 255, 255, ..., 255, 255, 255],\n",
       "       [255, 255, 255, ..., 255, 255, 255]])"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here we see that we have a 200x200 array and that the values are all uint8. The uint means that they are\n",
    "# unsigned integers (so no negative numbers) and the 8 means 8 bits per byte. This means that each value can\n",
    "# be up to 2*2*2*2*2*2*2*2=256 in size (well, actually 255, because we start at zero). For black and white\n",
    "# images black is stored as 0 and white is stored as 255. So if we just wanted to invert this image we could\n",
    "# use the numpy array to do so\n",
    "\n",
    "# Let's create an array the same shape\n",
    "mask=np.full(array.shape,255)\n",
    "mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[137, 138, 137, ..., 152, 148, 145],\n",
       "       [142, 142, 142, ..., 155, 152, 149],\n",
       "       [147, 147, 148, ..., 160, 157, 153],\n",
       "       ...,\n",
       "       [ 78,  74,  73, ...,  62,  57,  63],\n",
       "       [ 77,  73,  72, ...,  62,  54,  66],\n",
       "       [ 77,  73,  71, ...,  62,  54,  68]], dtype=uint8)"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now let's subtract that from the modified array\n",
    "modified_array=array-mask\n",
    "\n",
    "# And lets convert all of the negative values to positive values\n",
    "modified_array=modified_array*-1\n",
    "\n",
    "# And as a last step, let's tell numpy to set the value of the datatype correctly\n",
    "modified_array=modified_array.astype(np.uint8)\n",
    "modified_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=L size=200x200>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# And lastly, lets display this new array. We do this by using the fromarray() function in the python\n",
    "# imaging library to convert the numpy array into an object jupyter can render\n",
    "display(Image.fromarray(modified_array))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 400)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=L size=400x100>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cool. Ok, remember how I started this by talking about how we could just think of this as a giant array\n",
    "# of bytes, and that the shape was an abstraction? Well, we could just decide to reshape the array and still\n",
    "# try and render it. PIL is interpreting the individual rows as lines, so we can change the number of lines\n",
    "# and columns if we want to. What do you think that would look like?\n",
    "reshaped=np.reshape(modified_array,(100,400))\n",
    "print(reshaped.shape)\n",
    "display(Image.fromarray(reshaped))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Can't say I find that particularly flattering. By reshaping the array to be only 100 rows high but 400\n",
    "# columns we've essentially doubled the image by taking every other line and stacking them out in width. This\n",
    "# makes the image look more stretched out too.\n",
    "\n",
    "# This isn't an image manipulation course, but the point was to show you that these numpy arrays are really\n",
    "# just abstractions on top of data, and that data has an underlying format (in this case, uint8). But further,\n",
    "# we can build abstractions on top of that, such as computer code which renders a byte as either black or \n",
    "# white, which has meaning to people. In some ways, this whole degree is about data and the abstractions that\n",
    "# we can build on top of that data, from individual byte representations through to complex neural networks of\n",
    "# functions or interactive visualizations. Your role as a data scientist is to understand what the data means\n",
    "# (it's context an collection), and transform it into a different representation to be used for sensemaking."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Trying Numpy with datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here we have a very popular dataset on wine quality, and we are going to only look at red wines. The data\n",
    "# fields include: fixed acidity, volatile aciditycitric acid, residual sugar, chlorides, free sulfur dioxide,\n",
    "# total sulfur dioxidedensity, pH, sulphates, alcohol, quality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 7.4  ,  0.7  ,  0.   , ...,  0.56 ,  9.4  ,  5.   ],\n",
       "       [ 7.8  ,  0.88 ,  0.   , ...,  0.68 ,  9.8  ,  5.   ],\n",
       "       [ 7.8  ,  0.76 ,  0.04 , ...,  0.65 ,  9.8  ,  5.   ],\n",
       "       ...,\n",
       "       [ 6.3  ,  0.51 ,  0.13 , ...,  0.75 , 11.   ,  6.   ],\n",
       "       [ 5.9  ,  0.645,  0.12 , ...,  0.71 , 10.2  ,  5.   ],\n",
       "       [ 6.   ,  0.31 ,  0.47 , ...,  0.66 , 11.   ,  6.   ]])"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To load a dataset in Numpy, we can use the genfromtxt() function. We can specify data file name, delimiter\n",
    "# (which is optional but often used), and number of rows to skip if we have a header row, hence it is 1 here\n",
    "\n",
    "# The genfromtxt() function has a parameter called dtype for specifying data types of each column this\n",
    "# parameter is optional. Without specifying the types, all types will be casted the same to the more\n",
    "# general/precise type\n",
    "\n",
    "wines = np.genfromtxt(\"winequality-red.csv\", delimiter=\";\", skip_header=1)\n",
    "wines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "one integer 0 for slicing:  [7.4 7.8 7.8 ... 6.3 5.9 6. ]\n",
      "0 to 1 for slicing: \n",
      " [[7.4]\n",
      " [7.8]\n",
      " [7.8]\n",
      " ...\n",
      " [6.3]\n",
      " [5.9]\n",
      " [6. ]]\n"
     ]
    }
   ],
   "source": [
    "# Recall that we can use integer indexing to get a certain column or a row. For example, if we want to select\n",
    "# the fixed acidity column, which is the first coluumn, we can do so by entering the index into the array.\n",
    "# Also remember that for multidimensional arrays, the first argument refers to the row, and the second\n",
    "# argument refers to the column, and if we just give one argument then we'll get a single dimensional list\n",
    "# back.\n",
    "\n",
    "# So all rows combined but only the first column from them would be\n",
    "print(\"one integer 0 for slicing: \", wines[:, 0])\n",
    "# But if we wanted the same values but wanted to preserve that they sit in their own rows we would write\n",
    "print(\"0 to 1 for slicing: \\n\", wines[:, 0:1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is another great example of how the shape of the data is an abstraction which we can layer\n",
    "# intentionally on top of the data we are working with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7.4  , 0.7  , 0.   ],\n",
       "       [7.8  , 0.88 , 0.   ],\n",
       "       [7.8  , 0.76 , 0.04 ],\n",
       "       ...,\n",
       "       [6.3  , 0.51 , 0.13 ],\n",
       "       [5.9  , 0.645, 0.12 ],\n",
       "       [6.   , 0.31 , 0.47 ]])"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# If we want a range of columns in order, say columns 0 through 3 (recall, this means first, second, and\n",
    "# third, since we start at zero and don't include the training index value), we can do that too\n",
    "wines[:, 0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7.4  , 0.   , 0.076],\n",
       "       [7.8  , 0.   , 0.098],\n",
       "       [7.8  , 0.04 , 0.092],\n",
       "       ...,\n",
       "       [6.3  , 0.13 , 0.076],\n",
       "       [5.9  , 0.12 , 0.075],\n",
       "       [6.   , 0.47 , 0.067]])"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What if we want several non-consecutive columns? We can place the indices of the columns that we want into\n",
    "# an array and pass the array as the second argument. Here's an example\n",
    "wines[:, [0,2,4]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.6360225140712945"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can also do some basic summarization of this dataset. For example, if we want to find out the average\n",
    "# quality of red wine, we can select the quality column. We could do this in a couple of ways, but the most\n",
    "# appropriate is to use the -1 value for the index, as negative numbers mean slicing from the back of the\n",
    "# list. We can then call the aggregation functions on this data.\n",
    "wines[:,-1].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's take a look at another dataset, this time on graduate school admissions. It has fields such as GRE\n",
    "# score, TOEFL score, university rating, GPA, having research experience or not, and a chance of admission.\n",
    "# With this dataset, we can do data manipulation and basic analysis to infer what conditions are associated\n",
    "# with higher chance of admission. Let's take a look."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       (233, 312, 107, 2, 2.5, 3.5, 8.27, 0, 0.69),\n",
       "       (234, 304, 100, 2, 2.5, 3.5, 8.07, 0, 0.64),\n",
       "       (235, 330, 113, 5, 5. , 4. , 9.31, 1, 0.91),\n",
       "       (236, 326, 111, 5, 4.5, 4. , 9.23, 1, 0.88),\n",
       "       (237, 325, 112, 4, 4. , 4.5, 9.17, 1, 0.85),\n",
       "       (238, 329, 114, 5, 4.5, 5. , 9.19, 1, 0.86),\n",
       "       (239, 310, 104, 3, 2. , 3.5, 8.37, 0, 0.7 ),\n",
       "       (240, 299, 100, 1, 1.5, 2. , 7.89, 0, 0.59),\n",
       "       (241, 296, 101, 1, 2.5, 3. , 7.68, 0, 0.6 ),\n",
       "       (242, 317, 103, 2, 2.5, 2. , 8.15, 0, 0.65),\n",
       "       (243, 324, 115, 3, 3.5, 3. , 8.76, 1, 0.7 ),\n",
       "       (244, 325, 114, 3, 3.5, 3. , 9.04, 1, 0.76),\n",
       "       (245, 314, 107, 2, 2.5, 4. , 8.56, 0, 0.63),\n",
       "       (246, 328, 110, 4, 4. , 2.5, 9.02, 1, 0.81),\n",
       "       (247, 316, 105, 3, 3. , 3.5, 8.73, 0, 0.72),\n",
       "       (248, 311, 104, 2, 2.5, 3.5, 8.48, 0, 0.71),\n",
       "       (249, 324, 110, 3, 3.5, 4. , 8.87, 1, 0.8 ),\n",
       "       (250, 321, 111, 3, 3.5, 4. , 8.83, 1, 0.77),\n",
       "       (251, 320, 104, 3, 3. , 2.5, 8.57, 1, 0.74),\n",
       "       (252, 316,  99, 2, 2.5, 3. , 9.  , 0, 0.7 ),\n",
       "       (253, 318, 100, 2, 2.5, 3.5, 8.54, 1, 0.71),\n",
       "       (254, 335, 115, 4, 4.5, 4.5, 9.68, 1, 0.93),\n",
       "       (255, 321, 114, 4, 4. , 5. , 9.12, 0, 0.85),\n",
       "       (256, 307, 110, 4, 4. , 4.5, 8.37, 0, 0.79),\n",
       "       (257, 309,  99, 3, 4. , 4. , 8.56, 0, 0.76),\n",
       "       (258, 324, 100, 3, 4. , 5. , 8.64, 1, 0.78),\n",
       "       (259, 326, 102, 4, 5. , 5. , 8.76, 1, 0.77),\n",
       "       (260, 331, 119, 4, 5. , 4.5, 9.34, 1, 0.9 ),\n",
       "       (261, 327, 108, 5, 5. , 3.5, 9.13, 1, 0.87),\n",
       "       (262, 312, 104, 3, 3.5, 4. , 8.09, 0, 0.71),\n",
       "       (263, 308, 103, 2, 2.5, 4. , 8.36, 1, 0.7 ),\n",
       "       (264, 324, 111, 3, 2.5, 1.5, 8.79, 1, 0.7 ),\n",
       "       (265, 325, 110, 2, 3. , 2.5, 8.76, 1, 0.75),\n",
       "       (266, 313, 102, 3, 2.5, 2.5, 8.68, 0, 0.71),\n",
       "       (267, 312, 105, 2, 2. , 2.5, 8.45, 0, 0.72),\n",
       "       (268, 314, 107, 3, 3. , 3.5, 8.17, 1, 0.73),\n",
       "       (269, 327, 113, 4, 4.5, 5. , 9.14, 0, 0.83),\n",
       "       (270, 308, 108, 4, 4.5, 5. , 8.34, 0, 0.77),\n",
       "       (271, 306, 105, 2, 2.5, 3. , 8.22, 1, 0.72),\n",
       "       (272, 299,  96, 2, 1.5, 2. , 7.86, 0, 0.54),\n",
       "       (273, 294,  95, 1, 1.5, 1.5, 7.64, 0, 0.49),\n",
       "       (274, 312,  99, 1, 1. , 1.5, 8.01, 1, 0.52),\n",
       "       (275, 315, 100, 1, 2. , 2.5, 7.95, 0, 0.58),\n",
       "       (276, 322, 110, 3, 3.5, 3. , 8.96, 1, 0.78),\n",
       "       (277, 329, 113, 5, 5. , 4.5, 9.45, 1, 0.89),\n",
       "       (278, 320, 101, 2, 2.5, 3. , 8.62, 0, 0.7 ),\n",
       "       (279, 308, 103, 2, 3. , 3.5, 8.49, 0, 0.66),\n",
       "       (280, 304, 102, 2, 3. , 4. , 8.73, 0, 0.67),\n",
       "       (281, 311, 102, 3, 4.5, 4. , 8.64, 1, 0.68),\n",
       "       (282, 317, 110, 3, 4. , 4.5, 9.11, 1, 0.8 ),\n",
       "       (283, 312, 106, 3, 4. , 3.5, 8.79, 1, 0.81),\n",
       "       (284, 321, 111, 3, 2.5, 3. , 8.9 , 1, 0.8 ),\n",
       "       (285, 340, 112, 4, 5. , 4.5, 9.66, 1, 0.94),\n",
       "       (286, 331, 116, 5, 4. , 4. , 9.26, 1, 0.93),\n",
       "       (287, 336, 118, 5, 4.5, 4. , 9.19, 1, 0.92),\n",
       "       (288, 324, 114, 5, 5. , 4.5, 9.08, 1, 0.89),\n",
       "       (289, 314, 104, 4, 5. , 5. , 9.02, 0, 0.82),\n",
       "       (290, 313, 109, 3, 4. , 3.5, 9.  , 0, 0.79),\n",
       "       (291, 307, 105, 2, 2.5, 3. , 7.65, 0, 0.58),\n",
       "       (292, 300, 102, 2, 1.5, 2. , 7.87, 0, 0.56),\n",
       "       (293, 302,  99, 2, 1. , 2. , 7.97, 0, 0.56),\n",
       "       (294, 312,  98, 1, 3.5, 3. , 8.18, 1, 0.64),\n",
       "       (295, 316, 101, 2, 2.5, 2. , 8.32, 1, 0.61),\n",
       "       (296, 317, 100, 2, 3. , 2.5, 8.57, 0, 0.68),\n",
       "       (297, 310, 107, 3, 3.5, 3.5, 8.67, 0, 0.76),\n",
       "       (298, 320, 120, 3, 4. , 4.5, 9.11, 0, 0.86),\n",
       "       (299, 330, 114, 3, 4.5, 4.5, 9.24, 1, 0.9 ),\n",
       "       (300, 305, 112, 3, 3. , 3.5, 8.65, 0, 0.71),\n",
       "       (301, 309, 106, 2, 2.5, 2.5, 8.  , 0, 0.62),\n",
       "       (302, 319, 108, 2, 2.5, 3. , 8.76, 0, 0.66),\n",
       "       (303, 322, 105, 2, 3. , 3. , 8.45, 1, 0.65),\n",
       "       (304, 323, 107, 3, 3.5, 3.5, 8.55, 1, 0.73),\n",
       "       (305, 313, 106, 2, 2.5, 2. , 8.43, 0, 0.62),\n",
       "       (306, 321, 109, 3, 3.5, 3.5, 8.8 , 1, 0.74),\n",
       "       (307, 323, 110, 3, 4. , 3.5, 9.1 , 1, 0.79),\n",
       "       (308, 325, 112, 4, 4. , 4. , 9.  , 1, 0.8 ),\n",
       "       (309, 312, 108, 3, 3.5, 3. , 8.53, 0, 0.69),\n",
       "       (310, 308, 110, 4, 3.5, 3. , 8.6 , 0, 0.7 ),\n",
       "       (311, 320, 104, 3, 3. , 3.5, 8.74, 1, 0.76),\n",
       "       (312, 328, 108, 4, 4.5, 4. , 9.18, 1, 0.84),\n",
       "       (313, 311, 107, 4, 4.5, 4.5, 9.  , 1, 0.78),\n",
       "       (314, 301, 100, 3, 3.5, 3. , 8.04, 0, 0.67),\n",
       "       (315, 305, 105, 2, 3. , 4. , 8.13, 0, 0.66),\n",
       "       (316, 308, 104, 2, 2.5, 3. , 8.07, 0, 0.65),\n",
       "       (317, 298, 101, 2, 1.5, 2. , 7.86, 0, 0.54),\n",
       "       (318, 300,  99, 1, 1. , 2.5, 8.01, 0, 0.58),\n",
       "       (319, 324, 111, 3, 2.5, 2. , 8.8 , 1, 0.79),\n",
       "       (320, 327, 113, 4, 3.5, 3. , 8.69, 1, 0.8 ),\n",
       "       (321, 317, 106, 3, 4. , 3.5, 8.5 , 1, 0.75),\n",
       "       (322, 323, 104, 3, 4. , 4. , 8.44, 1, 0.73),\n",
       "       (323, 314, 107, 2, 2.5, 4. , 8.27, 0, 0.72),\n",
       "       (324, 305, 102, 2, 2. , 2.5, 8.18, 0, 0.62),\n",
       "       (325, 315, 104, 3, 3. , 2.5, 8.33, 0, 0.67),\n",
       "       (326, 326, 116, 3, 3.5, 4. , 9.14, 1, 0.81),\n",
       "       (327, 299, 100, 3, 2. , 2. , 8.02, 0, 0.63),\n",
       "       (328, 295, 101, 2, 2.5, 2. , 7.86, 0, 0.69),\n",
       "       (329, 324, 112, 4, 4. , 3.5, 8.77, 1, 0.8 ),\n",
       "       (330, 297,  96, 2, 2.5, 1.5, 7.89, 0, 0.43),\n",
       "       (331, 327, 113, 3, 3.5, 3. , 8.66, 1, 0.8 ),\n",
       "       (332, 311, 105, 2, 3. , 2. , 8.12, 1, 0.73),\n",
       "       (333, 308, 106, 3, 3.5, 2.5, 8.21, 1, 0.75),\n",
       "       (334, 319, 108, 3, 3. , 3.5, 8.54, 1, 0.71),\n",
       "       (335, 312, 107, 4, 4.5, 4. , 8.65, 1, 0.73),\n",
       "       (336, 325, 111, 4, 4. , 4.5, 9.11, 1, 0.83),\n",
       "       (337, 319, 110, 3, 3. , 2.5, 8.79, 0, 0.72),\n",
       "       (338, 332, 118, 5, 5. , 5. , 9.47, 1, 0.94),\n",
       "       (339, 323, 108, 5, 4. , 4. , 8.74, 1, 0.81),\n",
       "       (340, 324, 107, 5, 3.5, 4. , 8.66, 1, 0.81),\n",
       "       (341, 312, 107, 3, 3. , 3. , 8.46, 1, 0.75),\n",
       "       (342, 326, 110, 3, 3.5, 3.5, 8.76, 1, 0.79),\n",
       "       (343, 308, 106, 3, 3. , 3. , 8.24, 0, 0.58),\n",
       "       (344, 305, 103, 2, 2.5, 3.5, 8.13, 0, 0.59),\n",
       "       (345, 295,  96, 2, 1.5, 2. , 7.34, 0, 0.47),\n",
       "       (346, 316,  98, 1, 1.5, 2. , 7.43, 0, 0.49),\n",
       "       (347, 304,  97, 2, 1.5, 2. , 7.64, 0, 0.47),\n",
       "       (348, 299,  94, 1, 1. , 1. , 7.34, 0, 0.42),\n",
       "       (349, 302,  99, 1, 2. , 2. , 7.25, 0, 0.57),\n",
       "       (350, 313, 101, 3, 2.5, 3. , 8.04, 0, 0.62),\n",
       "       (351, 318, 107, 3, 3. , 3.5, 8.27, 1, 0.74),\n",
       "       (352, 325, 110, 4, 3.5, 4. , 8.67, 1, 0.73),\n",
       "       (353, 303, 100, 2, 3. , 3.5, 8.06, 1, 0.64),\n",
       "       (354, 300, 102, 3, 3.5, 2.5, 8.17, 0, 0.63),\n",
       "       (355, 297,  98, 2, 2.5, 3. , 7.67, 0, 0.59),\n",
       "       (356, 317, 106, 2, 2. , 3.5, 8.12, 0, 0.73),\n",
       "       (357, 327, 109, 3, 3.5, 4. , 8.77, 1, 0.79),\n",
       "       (358, 301, 104, 2, 3.5, 3.5, 7.89, 1, 0.68),\n",
       "       (359, 314, 105, 2, 2.5, 2. , 7.64, 0, 0.7 ),\n",
       "       (360, 321, 107, 2, 2. , 1.5, 8.44, 0, 0.81),\n",
       "       (361, 322, 110, 3, 4. , 5. , 8.64, 1, 0.85),\n",
       "       (362, 334, 116, 4, 4. , 3.5, 9.54, 1, 0.93),\n",
       "       (363, 338, 115, 5, 4.5, 5. , 9.23, 1, 0.91),\n",
       "       (364, 306, 103, 2, 2.5, 3. , 8.36, 0, 0.69),\n",
       "       (365, 313, 102, 3, 3.5, 4. , 8.9 , 1, 0.77),\n",
       "       (366, 330, 114, 4, 4.5, 3. , 9.17, 1, 0.86),\n",
       "       (367, 320, 104, 3, 3.5, 4.5, 8.34, 1, 0.74),\n",
       "       (368, 311,  98, 1, 1. , 2.5, 7.46, 0, 0.57),\n",
       "       (369, 298,  92, 1, 2. , 2. , 7.88, 0, 0.51),\n",
       "       (370, 301,  98, 1, 2. , 3. , 8.03, 1, 0.67),\n",
       "       (371, 310, 103, 2, 2.5, 2.5, 8.24, 0, 0.72),\n",
       "       (372, 324, 110, 3, 3.5, 3. , 9.22, 1, 0.89),\n",
       "       (373, 336, 119, 4, 4.5, 4. , 9.62, 1, 0.95),\n",
       "       (374, 321, 109, 3, 3. , 3. , 8.54, 1, 0.79),\n",
       "       (375, 315, 105, 2, 2. , 2.5, 7.65, 0, 0.39),\n",
       "       (376, 304, 101, 2, 2. , 2.5, 7.66, 0, 0.38),\n",
       "       (377, 297,  96, 2, 2.5, 2. , 7.43, 0, 0.34),\n",
       "       (378, 290, 100, 1, 1.5, 2. , 7.56, 0, 0.47),\n",
       "       (379, 303,  98, 1, 2. , 2.5, 7.65, 0, 0.56),\n",
       "       (380, 311,  99, 1, 2.5, 3. , 8.43, 1, 0.71),\n",
       "       (381, 322, 104, 3, 3.5, 4. , 8.84, 1, 0.78),\n",
       "       (382, 319, 105, 3, 3. , 3.5, 8.67, 1, 0.73),\n",
       "       (383, 324, 110, 4, 4.5, 4. , 9.15, 1, 0.82),\n",
       "       (384, 300, 100, 3, 3. , 3.5, 8.26, 0, 0.62),\n",
       "       (385, 340, 113, 4, 5. , 5. , 9.74, 1, 0.96),\n",
       "       (386, 335, 117, 5, 5. , 5. , 9.82, 1, 0.96),\n",
       "       (387, 302, 101, 2, 2.5, 3.5, 7.96, 0, 0.46),\n",
       "       (388, 307, 105, 2, 2. , 3.5, 8.1 , 0, 0.53),\n",
       "       (389, 296,  97, 2, 1.5, 2. , 7.8 , 0, 0.49),\n",
       "       (390, 320, 108, 3, 3.5, 4. , 8.44, 1, 0.76),\n",
       "       (391, 314, 102, 2, 2. , 2.5, 8.24, 0, 0.64),\n",
       "       (392, 318, 106, 3, 2. , 3. , 8.65, 0, 0.71),\n",
       "       (393, 326, 112, 4, 4. , 3.5, 9.12, 1, 0.84),\n",
       "       (394, 317, 104, 2, 3. , 3. , 8.76, 0, 0.77),\n",
       "       (395, 329, 111, 4, 4.5, 4. , 9.23, 1, 0.89),\n",
       "       (396, 324, 110, 3, 3.5, 3.5, 9.04, 1, 0.82),\n",
       "       (397, 325, 107, 3, 3. , 3.5, 9.11, 1, 0.84),\n",
       "       (398, 330, 116, 4, 5. , 4.5, 9.45, 1, 0.91),\n",
       "       (399, 312, 103, 3, 3.5, 4. , 8.78, 0, 0.67),\n",
       "       (400, 333, 117, 4, 5. , 4. , 9.66, 1, 0.95)],\n",
       "      dtype=[('Serial_No', '<i4'), ('GRE_Score', '<i4'), ('TOEFL_Score', '<i4'), ('University_Rating', '<i4'), ('SOP', '<f8'), ('LOR', '<f8'), ('CGPA', '<f8'), ('Research', '<i4'), ('Chance_of_Admit', '<f8')])"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can specify data field names when using genfromtxt() to loads CSV data. Also, we can have numpy try and\n",
    "# infer the type of a column by setting the dtype parameter to None\n",
    "graduate_admission = np.genfromtxt('Admission_Predict.csv', dtype=None, delimiter=',', skip_header=1,\n",
    "                                   names=('Serial No','GRE Score', 'TOEFL Score', 'University Rating', 'SOP',\n",
    "                                          'LOR','CGPA','Research', 'Chance of Admit'))\n",
    "graduate_admission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(400,)"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Notice that the resulting array is actually a one-dimensional array with 400 tuples\n",
    "graduate_admission.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9.65, 8.87, 8.  , 8.67, 8.21])"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can retrieve a column from the array using the column's name for example, let's get the CGPA column and\n",
    "# only the first five values.\n",
    "graduate_admission['CGPA'][0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.86 , 3.548, 3.2  , 3.468, 3.284, 3.736, 3.28 , 3.16 , 3.2  ,\n",
       "       3.44 , 3.36 , 3.6  , 3.64 , 3.2  , 3.28 , 3.32 , 3.48 , 3.2  ,\n",
       "       3.52 , 3.4  ])"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Since the GPA in the dataset range from 1 to 10, and in the US it's more common to use a scale of up to 4,\n",
    "# a common task might be to convert the GPA by dividing by 10 and then multiplying by 4\n",
    "graduate_admission['CGPA'] = graduate_admission['CGPA'] /10 *4\n",
    "graduate_admission['CGPA'][0:20] #let's get 20 values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "219"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Recall boolean masking. We can use this to find out how many students have had research experience by\n",
    "# creating a boolean mask and passing it to the array indexing operator\n",
    "len(graduate_admission[graduate_admission['Research'] == 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "328.7350427350427\n",
      "302.2857142857143\n"
     ]
    }
   ],
   "source": [
    "# Since we have the data field chance of admission, which ranges from 0 to 1, we can try to see if students\n",
    "# with high chance of admission (>0.8) on average have higher GRE score than those with lower chance of\n",
    "# admission (<0.4)\n",
    "\n",
    "# So first we use boolean masking to pull out only those students we are interested in based on their chance\n",
    "# of admission, then we pull out only their GPA scores, then we print the mean values.\n",
    "print(graduate_admission[graduate_admission['Chance_of_Admit'] > 0.8]['GRE_Score'].mean())\n",
    "print(graduate_admission[graduate_admission['Chance_of_Admit'] < 0.4]['GRE_Score'].mean())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([(  1, 337, 118, 4, 4.5, 4.5, 3.86 , 1, 0.92),\n",
       "       (  6, 330, 115, 5, 4.5, 3. , 3.736, 1, 0.9 ),\n",
       "       ( 12, 327, 111, 4, 4. , 4.5, 3.6  , 1, 0.84),\n",
       "       ( 23, 328, 116, 5, 5. , 5. , 3.8  , 1, 0.94),\n",
       "       ( 24, 334, 119, 5, 5. , 4.5, 3.88 , 1, 0.95),\n",
       "       ( 25, 336, 119, 5, 4. , 3.5, 3.92 , 1, 0.97),\n",
       "       ( 26, 340, 120, 5, 4.5, 4.5, 3.84 , 1, 0.94),\n",
       "       ( 33, 338, 118, 4, 3. , 4.5, 3.76 , 1, 0.91),\n",
       "       ( 34, 340, 114, 5, 4. , 4. , 3.84 , 1, 0.9 ),\n",
       "       ( 35, 331, 112, 5, 4. , 5. , 3.92 , 1, 0.94),\n",
       "       ( 36, 320, 110, 5, 5. , 5. , 3.68 , 1, 0.88),\n",
       "       ( 44, 332, 117, 4, 4.5, 4. , 3.64 , 0, 0.87),\n",
       "       ( 45, 326, 113, 5, 4.5, 4. , 3.76 , 1, 0.91),\n",
       "       ( 46, 322, 110, 5, 5. , 4. , 3.64 , 1, 0.88),\n",
       "       ( 47, 329, 114, 5, 4. , 5. , 3.72 , 1, 0.86),\n",
       "       ( 48, 339, 119, 5, 4.5, 4. , 3.88 , 0, 0.89),\n",
       "       ( 49, 321, 110, 3, 3.5, 5. , 3.54 , 1, 0.82),\n",
       "       ( 71, 332, 118, 5, 5. , 5. , 3.856, 1, 0.94),\n",
       "       ( 72, 336, 112, 5, 5. , 5. , 3.904, 1, 0.96),\n",
       "       ( 73, 321, 111, 5, 5. , 5. , 3.78 , 1, 0.93),\n",
       "       ( 74, 314, 108, 4, 4.5, 4. , 3.616, 1, 0.84),\n",
       "       ( 82, 340, 120, 4, 5. , 5. , 3.8  , 1, 0.96),\n",
       "       ( 83, 320, 110, 5, 5. , 4.5, 3.688, 1, 0.92),\n",
       "       ( 84, 322, 115, 5, 4. , 4.5, 3.744, 1, 0.92),\n",
       "       ( 85, 340, 115, 5, 4.5, 4.5, 3.78 , 1, 0.94),\n",
       "       ( 98, 331, 120, 3, 4. , 4. , 3.584, 1, 0.86),\n",
       "       ( 99, 332, 119, 4, 5. , 4.5, 3.696, 1, 0.9 ),\n",
       "       (107, 329, 111, 4, 4.5, 4.5, 3.672, 1, 0.87),\n",
       "       (108, 338, 117, 4, 3.5, 4.5, 3.784, 1, 0.91),\n",
       "       (109, 331, 116, 5, 5. , 5. , 3.752, 1, 0.93),\n",
       "       (121, 335, 117, 5, 5. , 5. , 3.824, 1, 0.94),\n",
       "       (122, 334, 119, 5, 4.5, 4.5, 3.792, 1, 0.94),\n",
       "       (127, 323, 113, 3, 4. , 3. , 3.728, 1, 0.85),\n",
       "       (129, 326, 112, 3, 3.5, 3. , 3.64 , 1, 0.84),\n",
       "       (130, 333, 118, 5, 5. , 5. , 3.74 , 1, 0.92),\n",
       "       (131, 339, 114, 5, 4. , 4.5, 3.904, 1, 0.96),\n",
       "       (135, 333, 113, 5, 4. , 4. , 3.712, 1, 0.89),\n",
       "       (136, 314, 109, 4, 3.5, 4. , 3.508, 1, 0.82),\n",
       "       (141, 329, 110, 2, 4. , 3. , 3.66 , 1, 0.84),\n",
       "       (142, 332, 118, 2, 4.5, 3.5, 3.744, 1, 0.9 ),\n",
       "       (143, 331, 115, 5, 4. , 3.5, 3.776, 1, 0.92),\n",
       "       (144, 340, 120, 4, 4.5, 4. , 3.968, 1, 0.97),\n",
       "       (146, 320, 113, 2, 2. , 2.5, 3.456, 1, 0.81),\n",
       "       (148, 326, 114, 3, 3. , 3. , 3.644, 1, 0.83),\n",
       "       (149, 339, 116, 4, 4. , 3.5, 3.92 , 1, 0.96),\n",
       "       (151, 334, 114, 4, 4. , 4. , 3.772, 1, 0.93),\n",
       "       (152, 332, 116, 5, 5. , 5. , 3.712, 1, 0.94),\n",
       "       (153, 321, 112, 5, 5. , 5. , 3.624, 1, 0.86),\n",
       "       (165, 329, 111, 4, 4.5, 4. , 3.604, 1, 0.81),\n",
       "       (172, 334, 117, 5, 4. , 4.5, 3.628, 1, 0.89),\n",
       "       (173, 322, 110, 4, 4. , 5. , 3.652, 1, 0.86),\n",
       "       (174, 323, 113, 4, 4. , 4.5, 3.692, 1, 0.89),\n",
       "       (175, 321, 111, 4, 4. , 4. , 3.588, 1, 0.87),\n",
       "       (176, 320, 111, 4, 4.5, 3.5, 3.548, 1, 0.85),\n",
       "       (177, 329, 119, 4, 4.5, 4.5, 3.664, 1, 0.9 ),\n",
       "       (178, 319, 110, 3, 3.5, 3.5, 3.616, 0, 0.82),\n",
       "       (186, 327, 113, 4, 4.5, 4.5, 3.644, 1, 0.89),\n",
       "       (187, 317, 107, 3, 3.5, 3. , 3.472, 1, 0.84),\n",
       "       (188, 335, 118, 5, 4.5, 3.5, 3.776, 1, 0.93),\n",
       "       (189, 331, 115, 5, 4.5, 3.5, 3.744, 1, 0.93),\n",
       "       (190, 324, 112, 5, 5. , 5. , 3.632, 1, 0.88),\n",
       "       (191, 324, 111, 5, 4.5, 4. , 3.664, 1, 0.9 ),\n",
       "       (192, 323, 110, 5, 4. , 5. , 3.592, 1, 0.87),\n",
       "       (193, 322, 114, 5, 4.5, 4. , 3.576, 1, 0.86),\n",
       "       (194, 336, 118, 5, 4.5, 5. , 3.812, 1, 0.94),\n",
       "       (203, 340, 120, 5, 4.5, 4.5, 3.964, 1, 0.97),\n",
       "       (204, 334, 120, 5, 4. , 5. , 3.948, 1, 0.97),\n",
       "       (212, 328, 110, 4, 5. , 4. , 3.656, 1, 0.82),\n",
       "       (213, 338, 120, 4, 5. , 5. , 3.864, 1, 0.95),\n",
       "       (214, 333, 119, 5, 5. , 4.5, 3.912, 1, 0.96),\n",
       "       (215, 331, 117, 4, 4.5, 5. , 3.768, 1, 0.94),\n",
       "       (216, 330, 116, 5, 5. , 4.5, 3.744, 1, 0.93),\n",
       "       (217, 322, 112, 4, 4.5, 4.5, 3.704, 1, 0.91),\n",
       "       (218, 321, 109, 4, 4. , 4. , 3.652, 1, 0.85),\n",
       "       (219, 324, 110, 4, 3. , 3.5, 3.588, 1, 0.84),\n",
       "       (230, 324, 111, 4, 3. , 3. , 3.604, 1, 0.82),\n",
       "       (235, 330, 113, 5, 5. , 4. , 3.724, 1, 0.91),\n",
       "       (236, 326, 111, 5, 4.5, 4. , 3.692, 1, 0.88),\n",
       "       (237, 325, 112, 4, 4. , 4.5, 3.668, 1, 0.85),\n",
       "       (238, 329, 114, 5, 4.5, 5. , 3.676, 1, 0.86),\n",
       "       (246, 328, 110, 4, 4. , 2.5, 3.608, 1, 0.81),\n",
       "       (254, 335, 115, 4, 4.5, 4.5, 3.872, 1, 0.93),\n",
       "       (255, 321, 114, 4, 4. , 5. , 3.648, 0, 0.85),\n",
       "       (260, 331, 119, 4, 5. , 4.5, 3.736, 1, 0.9 ),\n",
       "       (261, 327, 108, 5, 5. , 3.5, 3.652, 1, 0.87),\n",
       "       (269, 327, 113, 4, 4.5, 5. , 3.656, 0, 0.83),\n",
       "       (277, 329, 113, 5, 5. , 4.5, 3.78 , 1, 0.89),\n",
       "       (283, 312, 106, 3, 4. , 3.5, 3.516, 1, 0.81),\n",
       "       (285, 340, 112, 4, 5. , 4.5, 3.864, 1, 0.94),\n",
       "       (286, 331, 116, 5, 4. , 4. , 3.704, 1, 0.93),\n",
       "       (287, 336, 118, 5, 4.5, 4. , 3.676, 1, 0.92),\n",
       "       (288, 324, 114, 5, 5. , 4.5, 3.632, 1, 0.89),\n",
       "       (289, 314, 104, 4, 5. , 5. , 3.608, 0, 0.82),\n",
       "       (298, 320, 120, 3, 4. , 4.5, 3.644, 0, 0.86),\n",
       "       (299, 330, 114, 3, 4.5, 4.5, 3.696, 1, 0.9 ),\n",
       "       (312, 328, 108, 4, 4.5, 4. , 3.672, 1, 0.84),\n",
       "       (326, 326, 116, 3, 3.5, 4. , 3.656, 1, 0.81),\n",
       "       (336, 325, 111, 4, 4. , 4.5, 3.644, 1, 0.83),\n",
       "       (338, 332, 118, 5, 5. , 5. , 3.788, 1, 0.94),\n",
       "       (339, 323, 108, 5, 4. , 4. , 3.496, 1, 0.81),\n",
       "       (340, 324, 107, 5, 3.5, 4. , 3.464, 1, 0.81),\n",
       "       (360, 321, 107, 2, 2. , 1.5, 3.376, 0, 0.81),\n",
       "       (361, 322, 110, 3, 4. , 5. , 3.456, 1, 0.85),\n",
       "       (362, 334, 116, 4, 4. , 3.5, 3.816, 1, 0.93),\n",
       "       (363, 338, 115, 5, 4.5, 5. , 3.692, 1, 0.91),\n",
       "       (366, 330, 114, 4, 4.5, 3. , 3.668, 1, 0.86),\n",
       "       (372, 324, 110, 3, 3.5, 3. , 3.688, 1, 0.89),\n",
       "       (373, 336, 119, 4, 4.5, 4. , 3.848, 1, 0.95),\n",
       "       (383, 324, 110, 4, 4.5, 4. , 3.66 , 1, 0.82),\n",
       "       (385, 340, 113, 4, 5. , 5. , 3.896, 1, 0.96),\n",
       "       (386, 335, 117, 5, 5. , 5. , 3.928, 1, 0.96),\n",
       "       (393, 326, 112, 4, 4. , 3.5, 3.648, 1, 0.84),\n",
       "       (395, 329, 111, 4, 4.5, 4. , 3.692, 1, 0.89),\n",
       "       (396, 324, 110, 3, 3.5, 3.5, 3.616, 1, 0.82),\n",
       "       (397, 325, 107, 3, 3. , 3.5, 3.644, 1, 0.84),\n",
       "       (398, 330, 116, 4, 5. , 4.5, 3.78 , 1, 0.91),\n",
       "       (400, 333, 117, 4, 5. , 4. , 3.864, 1, 0.95)],\n",
       "      dtype=[('Serial_No', '<i4'), ('GRE_Score', '<i4'), ('TOEFL_Score', '<i4'), ('University_Rating', '<i4'), ('SOP', '<f8'), ('LOR', '<f8'), ('CGPA', '<f8'), ('Research', '<i4'), ('Chance_of_Admit', '<f8')])"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take a moment to reflect here, do you understand what is happening in these calls?\n",
    "\n",
    "# When we do the boolean masking we are left with an array with tuples in it still, and numpy holds underneath\n",
    "# this a list of the columns we specified and their name and indexes\n",
    "graduate_admission[graduate_admission['Chance_of_Admit'] > 0.8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.7106666666666666\n",
      "3.0222857142857142\n"
     ]
    }
   ],
   "source": [
    "# Let's also do this with GPA\n",
    "print(graduate_admission[graduate_admission['Chance_of_Admit'] > 0.8]['CGPA'].mean())\n",
    "print(graduate_admission[graduate_admission['Chance_of_Admit'] < 0.4]['CGPA'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hrm, well, I guess one could have expected this. The GPA and GRE for students who have a higher chance of\n",
    "# being admitted, at least based on our cursory look here, seems to be higher."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So that's a bit of a whirlwing tour of numpy, the core scientific computing library in python. Now, you're going to see a lot more of this kind of discussion, as the library we'll be focusing on in this course is pandas, which is built on top of numpy. Don't worry if it didn't all sink in the first time, we're going to dig in to most of these topics again with pandas. However, it's useful to know that many of the functions and capabilities of numpy are available to you within pandas."
   ]
  }
 ],
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