Part1_Numpy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NUMPY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Untuk mengimpor numpy\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.16.4'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Untuk mengetahui versi numpy\n",
    "np.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "add(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n",
      "\n",
      "Add arguments element-wise.\n",
      "\n",
      "Parameters\n",
      "----------\n",
      "x1, x2 : array_like\n",
      "    The arrays to be added.  If ``x1.shape != x2.shape``, they must be\n",
      "    broadcastable to a common shape (which may be the shape of one or\n",
      "    the other).\n",
      "out : ndarray, None, or tuple of ndarray and None, optional\n",
      "    A location into which the result is stored. If provided, it must have\n",
      "    a shape that the inputs broadcast to. If not provided or `None`,\n",
      "    a freshly-allocated array is returned. A tuple (possible only as a\n",
      "    keyword argument) must have length equal to the number of outputs.\n",
      "where : array_like, optional\n",
      "    Values of True indicate to calculate the ufunc at that position, values\n",
      "    of False indicate to leave the value in the output alone.\n",
      "**kwargs\n",
      "    For other keyword-only arguments, see the\n",
      "    :ref:`ufunc docs <ufuncs.kwargs>`.\n",
      "\n",
      "Returns\n",
      "-------\n",
      "add : ndarray or scalar\n",
      "    The sum of `x1` and `x2`, element-wise.\n",
      "    This is a scalar if both `x1` and `x2` are scalars.\n",
      "\n",
      "Notes\n",
      "-----\n",
      "Equivalent to `x1` + `x2` in terms of array broadcasting.\n",
      "\n",
      "Examples\n",
      "--------\n",
      ">>> np.add(1.0, 4.0)\n",
      "5.0\n",
      ">>> x1 = np.arange(9.0).reshape((3, 3))\n",
      ">>> x2 = np.arange(3.0)\n",
      ">>> np.add(x1, x2)\n",
      "array([[  0.,   2.,   4.],\n",
      "       [  3.,   5.,   7.],\n",
      "       [  6.,   8.,  10.]])\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Untuk mengetahui kegunaan suatu method\n",
    "print(np.info(np.add))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ARRAY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Membuat list pada python\n",
    "a = [1, 2, 3, 4]\n",
    "b = [5, 6, 7, 8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4, 5, 6, 7, 8]\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "can't multiply sequence by non-int of type 'list'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-6-8a0ca2a921ae>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# Operasi list\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: can't multiply sequence by non-int of type 'list'"
     ]
    }
   ],
   "source": [
    "# Operasi list\n",
    "print(a+b)\n",
    "print(a*b)\n",
    "\n",
    "# List tidak bisa dioperasikan secara matematis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 6  8 10 12]\n",
      "[ 5 12 21 32]\n"
     ]
    }
   ],
   "source": [
    "# Membuat array dengan numpy\n",
    "a_array = np.array(a)\n",
    "b_array = np.array(b)\n",
    "\n",
    "# Operasi array\n",
    "print(a_array + b_array)\n",
    "print(a_array * b_array)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LISTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v = np.array([1,2,3])\n",
    "\n",
    "v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Membuat matriks 3 kolom 2 baris\n",
    "m = np.array([[1,2,3],[4,5,6]])\n",
    "\n",
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mengecek Tipe Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(numpy.ndarray, numpy.ndarray)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mengecek tipe data array\n",
    "type(v), type(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = [1,2,3]\n",
    "\n",
    "type(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mengecek ukuran dan jumlah elemen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3,)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Untuk mengecek ukuran array\n",
    "v.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 3)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Untuk mengecek ukuran array\n",
    "# Ukuran adalah 2 baris 3 kolom\n",
    "m.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Untuk mengecek jumlah elemen\n",
    "m.size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mengubah tipe data dalam array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dtype untuk menihat tipe data dalam array\n",
    "m.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 2., 3.],\n",
       "       [2., 3., 4.]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dtype juga dapat digunakan untuk mengubah tipe data array\n",
    "m = np.array([[1,2,3],[2,3,4]], dtype = float)\n",
    "\n",
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Membuat matrix zero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Membuat zero matrix\n",
    "zero_matrix = np.zeros(5)\n",
    "zero_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0.],\n",
       "       [0., 0.],\n",
       "       [0., 0.],\n",
       "       [0., 0.],\n",
       "       [0., 0.]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zero_matrix2 = np.zeros((5,2))\n",
    "zero_matrix2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Membuat matrix ones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1., 1., 1.])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Membuat matrix ones\n",
    "ones_matrix = np.ones(5)\n",
    "ones_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1.]],\n",
       "\n",
       "       [[1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1.]],\n",
       "\n",
       "       [[1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1.]],\n",
       "\n",
       "       [[1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1.]],\n",
       "\n",
       "       [[1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1.]]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ones_matrix2 = np.ones((5,3,4))\n",
    "ones_matrix2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 3, 4)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ones_matrix2.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Membuat Matrix Tiga Dimensi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 1,  2,  3],\n",
       "        [ 4,  5,  6],\n",
       "        [ 7,  8,  9]],\n",
       "\n",
       "       [[10, 11, 12],\n",
       "        [13, 14, 15],\n",
       "        [16, 17, 18]]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tri_d = np.array([\n",
    "    [[1,2,3],        # 'Array' ini menunjukkan baris pertama\n",
    "     [4,5,6],        # Baris == kolom tiga dimensi\n",
    "     [7,8,9]],       # Kolom == kedalaman tiga dimensi\n",
    "    \n",
    "    [[10,11,12],     # Ini menunjukkan baris kedua\n",
    "     [13,14,15],\n",
    "     [16,17,18]]   \n",
    "])\n",
    "\n",
    "tri_d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EXERCISE 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Membuat matrix 4 baris 3 kolom dari list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3],\n",
       "       [ 4,  5,  6],\n",
       "       [ 7,  8,  9],\n",
       "       [10, 11, 12]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list1 = [[1,2,3], \n",
    "         [4,5,6],\n",
    "         [7,8,9],\n",
    "         [10,11,12]]\n",
    "matrix1= np.array(list1)\n",
    "matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Membuat matriks tertera"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2,  7, 12,  0],\n",
       "       [ 3,  9,  3,  4],\n",
       "       [ 4,  0,  1,  3]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix2 = np.array([[2,7,12,0],[3,9,3,4],[4,0,1,3]])\n",
    "matrix2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Membuat matrix dengan ukuran (size) 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1.],\n",
       "       [1., 1.],\n",
       "       [1., 1.],\n",
       "       [1., 1.],\n",
       "       [1., 1.]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix3 = np.ones((5,2))\n",
    "matrix3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Membuat matriks ones dengan 2 baris, 3 kolom, 3 kedalaman"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1., 1., 1.],\n",
       "        [1., 1., 1.],\n",
       "        [1., 1., 1.]],\n",
       "\n",
       "       [[1., 1., 1.],\n",
       "        [1., 1., 1.],\n",
       "        [1., 1., 1.]]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix4 = np.ones((2,3,3))\n",
    "matrix4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5. Membuat array tertera dari zero arrays dan for loops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2., 2., 2.],\n",
       "       [2., 2., 2.],\n",
       "       [2., 2., 2.],\n",
       "       [2., 2., 2.],\n",
       "       [2., 2., 2.]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix5 = np.zeros((5,3))\n",
    "for i in range(2):\n",
    "    matrix5 = matrix5 + np.ones((5,3))\n",
    "matrix5"
   ]
  }
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