{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d9f819b9",
   "metadata": {
    "tags": [
     "hide-output",
     "scroll-output"
    ]
   },
   "source": [
    "# NumPy: n-dimensional arrays\n",
    "- Sum of elements and store in sum_of_matrix variable\n",
    "- Find max/min element in matrix and store in max_value/min_value variable\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c74880a5",
   "metadata": {
    "tags": [
     "hide-output",
     "scroll-output"
    ]
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "34acfbec",
   "metadata": {
    "tags": [
     "hide-output",
     "scroll-output"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50 9 0\n"
     ]
    }
   ],
   "source": [
    "matrix = np.array([[1, 5, 3], [9, 2, 6], [4, 8, 7], [0, 3, 2]])\n",
    "sum_of_matrix = np.sum(matrix)\n",
    "max_value, min_value = np.max(matrix), np.min(matrix)\n",
    "print(sum_of_matrix, max_value, min_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "72d76c18",
   "metadata": {
    "tags": [
     "hide-output",
     "scroll-output"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9 0\n"
     ]
    }
   ],
   "source": [
    "matrix = np.array([[1, 5, 3], [9, 2, 6], [4, 8, 7], [0, 3, 2]])\n",
    "max_idx = np.unravel_index(np.argmax(matrix), matrix.shape)\n",
    "min_idx = np.unravel_index(np.argmin(matrix), matrix.shape)\n",
    "max_value = matrix[max_idx]\n",
    "min_value = matrix[min_idx]\n",
    "print(max_value, min_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c94ba47",
   "metadata": {
    "tags": [
     "hide-output",
     "scroll-output"
    ]
   },
   "source": [
    "## Create Numpy array"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac905469",
   "metadata": {
    "tags": [
     "hide-output",
     "scroll-output"
    ]
   },
   "source": [
    "### From List"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25cbec63",
   "metadata": {
    "tags": [
     "hide-output",
     "scroll-output"
    ]
   },
   "source": [
    "- #### Create 1D array from list `[1, 2, 3, 4, 5]`\n",
    "- #### Create 2D array of shape 2x3 from list `[[1, 2, 3], [4, 5, 6]]`\n",
    "- #### Create 3D array of shape 2x2x3 from list `[[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bb79d1bc",
   "metadata": {
    "tags": [
     "hide-output",
     "scroll-output"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "list_1d:  [1 2 3 4 5]\n",
      "list_2d:  [[1 2 3]\n",
      " [4 5 6]]\n",
      "list_3d:  [[[ 1  2  3]\n",
      "  [ 4  5  6]]\n",
      "\n",
      " [[ 7  8  9]\n",
      "  [10 11 12]]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "list_1d = np.array([1, 2, 3, 4, 5])\n",
    "list_2d = np.array([[1, 2, 3], [4, 5, 6]])\n",
    "list_3d = np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]])\n",
    "print('list_1d: ', list_1d)\n",
    "print('list_2d: ', list_2d)\n",
    "print('list_3d: ', list_3d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7bc4dcf3",
   "metadata": {
    "tags": [
     "hide-output",
     "scroll-output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(dtype('int64'), (2, 2, 3), 3)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_3d.dtype, list_3d.shape, list_3d.ndim"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "143f3e70",
   "metadata": {
    "tags": [
     "hide-output",
     "scroll-output"
    ]
   },
   "source": [
    "## Array Creation Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bc1e5a5",
   "metadata": {},
   "source": [
    "### np.zeros\n",
    "\n",
    "`np.zeros(shape, dtype=float, order='C', *, like=None)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7f3c4ec5",
   "metadata": {
    "tags": [
     "hide-output",
     "scroll-output"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 0.]\n",
      " [0. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "zeros_matrix_2x3 = np.zeros((2,3))\n",
    "print(zeros_matrix_2x3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8667cb1d",
   "metadata": {
    "tags": [
     "hide-output",
     "scroll-output"
    ]
   },
   "source": [
    "### np.ones\n",
    "\n",
    "`np.ones(shape, dtype=None, order='C', *, like=None)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f1d8fcb4",
   "metadata": {
    "tags": [
     "hide-output",
     "scroll-output"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 1. 1.]\n",
      " [1. 1. 1.]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "ones_matrix_2x3 = np.ones((2,3))\n",
    "print(ones_matrix_2x3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3aeb0a51",
   "metadata": {
    "tags": [
     "hide-output",
     "scroll-output"
    ]
   },
   "source": [
    "### np.full\n",
    "\n",
    "`np.full(shape, fill_value, dtype=None, order='C', *, like=None)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3b3f7ac4",
   "metadata": {
    "tags": [
     "hide-output",
     "scroll-output"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[9 9 9]\n",
      " [9 9 9]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "full_of_9_matrix = np.full((2,3), 9)\n",
    "print(full_of_9_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04acedab",
   "metadata": {
    "tags": [
     "hide-output",
     "scroll-output"
    ]
   },
   "source": [
    "### np.arange\n",
    "\n",
    "`np.arange([start, ]stop, [step, ]dtype=None, *, like=None)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1d770e41",
   "metadata": {
    "tags": [
     "hide-output",
     "scroll-output"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3 4]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "arange_numpy = np.arange(start=0, stop=5, step=1)\n",
    "print(arange_numpy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0df6e4fc",
   "metadata": {
    "tags": [
     "hide-output",
     "scroll-output"
    ]
   },
   "source": [
    "### np.eye\n",
    "\n",
    "`np.eye(N, M=None, k=0, dtype=<class 'float'>, order='C', *, like=None)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "28203cc5",
   "metadata": {
    "tags": [
     "hide-output",
     "scroll-output"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 0. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 0. 1.]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "ones_in_diagonal_zeros_elsewhere = np.eye(3)\n",
    "print(ones_in_diagonal_zeros_elsewhere)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6361e2b9",
   "metadata": {
    "tags": [
     "hide-output",
     "scroll-output"
    ]
   },
   "source": [
    "### np.random.random\n",
    "\n",
    "`np.random.random(size=None)`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da09f6ed",
   "metadata": {
    "tags": [
     "hide-output",
     "scroll-output"
    ]
   },
   "source": [
    "Return random floats in the half-open interval `[0.0, 1.0)`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "132dcc99",
   "metadata": {
    "tags": [
     "hide-output",
     "scroll-output"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.3511155  0.15105555 0.04231165]\n",
      " [0.38675743 0.85587757 0.31778496]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "my_arr = np.random.random((2,3))\n",
    "print(my_arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d482457",
   "metadata": {
    "tags": [
     "hide-output",
     "scroll-output"
    ]
   },
   "source": [
    "## Important NumPy functions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5725c25c",
   "metadata": {
    "tags": [
     "hide-output",
     "scroll-output"
    ]
   },
   "source": [
    "### np.clip\n",
    "\n",
    "`np.clip(a, a_min, a_max, out=None, **kwargs)`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df45bd17",
   "metadata": {
    "tags": [
     "hide-output",
     "scroll-output"
    ]
   },
   "source": [
    "Any values less than `a_min` are set to `a_min`, and any values greater than `a_max` are set to `a_max`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "98b90e1d",
   "metadata": {
    "tags": [
     "hide-output",
     "scroll-output"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3 5 8 8 3 6]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "arr = np.array([1,5,8,10,3,6])\n",
    "clipped_arr = np.clip(arr, a_min=3, a_max=8)\n",
    "print(clipped_arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9153e66",
   "metadata": {
    "tags": [
     "hide-output",
     "scroll-output"
    ]
   },
   "source": [
    "### np.concatenate\n",
    "\n",
    "`np.concatenate((a1, a2, ...), axis=0, out=None, dtype=None, casting=\"same_kind\")`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6610893b",
   "metadata": {
    "tags": [
     "hide-output",
     "scroll-output"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4 5 6]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "arr1 = np.array([1,2,3])\n",
    "arr2 = np.array([4,5,6])\n",
    "concatenated = np.concatenate((arr1, arr2))\n",
    "print(concatenated)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e71e1e4d",
   "metadata": {
    "tags": [
     "hide-output",
     "scroll-output"
    ]
   },
   "source": [
    "### Add more axes to the array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "acf89f32",
   "metadata": {
    "tags": [
     "hide-output",
     "scroll-output"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5, 1) [[1]\n",
      " [2]\n",
      " [3]\n",
      " [4]\n",
      " [5]]\n",
      "(1, 5) [[1 2 3 4 5]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "arr = np.array([1,2,3,4,5])\n",
    "new_arr1 = arr[:, np.newaxis]\n",
    "new_arr2 = arr[np.newaxis, :]\n",
    "print(new_arr1.shape, new_arr1)\n",
    "print(new_arr2.shape, new_arr2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bc75608",
   "metadata": {
    "tags": [
     "hide-output",
     "scroll-output"
    ]
   },
   "source": [
    "### np.vectorize\n",
    "\n",
    "`np.vectorize(pyfunc, otypes=None, doc=None, excluded=None, cache=False, signature=None)`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ce9b3fe",
   "metadata": {
    "tags": [
     "hide-output",
     "scroll-output"
    ]
   },
   "source": [
    "This function converts a Python function that operates on scalars into a vectorized function that can operate on NumPy arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "dda9ba9d",
   "metadata": {
    "tags": [
     "hide-output",
     "scroll-output"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 3.14159265 12.56637061 28.27433388 50.26548246]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "def calculate_circle_area(radius):\n",
    "    return np.pi * (radius**2)\n",
    "\n",
    "radius_values = np.array([1,2,3,4])\n",
    "vectorized_func = np.vectorize(calculate_circle_area)\n",
    "area_values = vectorized_func(radius_values)\n",
    "print(area_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d450695b",
   "metadata": {
    "tags": [
     "hide-output",
     "scroll-output"
    ]
   },
   "source": [
    "### np.where\n",
    "\n",
    "`np.where(condition, x=None, y=None, *, out=None)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a6853c80",
   "metadata": {
    "tags": [
     "hide-output",
     "scroll-output"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 0 0]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "arr = np.array([1,2,3,4,5])\n",
    "greater_than_3 = np.where(arr > 3, 0, arr)\n",
    "print(greater_than_3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "296b85ed",
   "metadata": {
    "tags": [
     "hide-output",
     "scroll-output"
    ]
   },
   "source": [
    "### flatten\n",
    "\n",
    "`<array>.flatten(order='C')`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "98a482cf",
   "metadata": {
    "tags": [
     "hide-output",
     "scroll-output"
    ]
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "arr = np.array([[1, 2, 3], [4, 5, 6]])\n",
    "flattened_arr = arr.flatten()"
   ]
  }
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