Modul 5 - Modules, packages, string and list methods, and exceptions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Module\n",
    "klik untuk [Open in colab](https://colab.research.google.com/github/sykrn/py-dts/blob/master/Modul%205%20-%20Modules,%20packages,%20string%20and%20list%20methods,%20and%20exceptions.ipynb) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Computer code has a tendency to grow. \n",
    "\n",
    "Growing code is in fact a growing problem.\n",
    "\n",
    "A larger code always means tougher maintenance. Searching for bugs is always easier where the code is smaller\n",
    "\n",
    "If you want such a software project to be completed successfully, you have to have the means allowing you to:\n",
    "- divide all the tasks among the developers;\n",
    "- join all the created parts into one working whole.\n",
    "\n",
    "For example, a certain project can be divided into two main parts:\n",
    "\n",
    "- the user interface (the part that communicates with the user using widgets and a graphical screen)\n",
    "- the logic (the part processing data and producing results)\n",
    "\n",
    "Each of these parts can be (most likely) divided into smaller ones, and so on. Such a process is often called __decomposition__."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to make use of a module?\n"
   ]
  },
  {
   "attachments": {
    "module.PNG": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![module.PNG](attachment:module.PNG)\n",
    "\n",
    "- pengguna (__user__) :  Menggunakan module yang sudah ada\n",
    "- penyedia (__supplier__) : Membuat module baru"
   ]
  },
  {
   "attachments": {
    "module%20name.PNG": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "a module is identified by its __name__\n",
    "\n",
    "Each module consists of entities (like a book consists of chapters). These entities can be functions, variables, constants, classes, and objects. \n",
    "\n",
    "![module%20name.PNG](attachment:module%20name.PNG)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing a module"
   ]
  },
  {
   "attachments": {
    "import.PNG": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing a module is done by an instruction named `import`\n",
    "\n",
    "the clause contains:\n",
    "\n",
    "- the `import` keyword;\n",
    "- the `name` of the module which is subject to import.\n",
    "\n",
    "![import.PNG](attachment:import.PNG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math, sys"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## namespace"
   ]
  },
  {
   "attachments": {
    "namespace.PNG": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A namespace is a space (understood in a non-physical context) in which some names exist and the names don't conflict with each other (i.e., there are not two different objects of the same name).\n",
    "\n",
    "![namespace.PNG](attachment:namespace.PNG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy\n",
    "import math\n",
    "import scipy\n",
    "\n",
    "print(math.pi)\n",
    "print(math.e)\n",
    "print(numpy.pi)\n",
    "print(scipy.pi)\n",
    "#note: pi inside the module won't be affected by pi that we declare in main program"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from math import pi,e\n",
    "\n",
    "print(pi)\n",
    "print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The instruction consists of the following elements:\n",
    "\n",
    "- the `from` keyword;\n",
    "- the `name of the module` to be (selectively) imported;\n",
    "- the `import` keyword;\n",
    "- the `name` or `list of names` of the entity/entities which are being imported into the namespace."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## override nilai sin dan pi\n",
    "from math import sin, pi\n",
    "\n",
    "print(sin(pi/2))\n",
    "\n",
    "pi = 3.14\n",
    "\n",
    "def sin(x):\n",
    "    if 2 * x == pi:\n",
    "        return 0.99999999\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "print(sin(pi/2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mengimport semua modul"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from math import *\n",
    "\n",
    "print(tan(0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nama dari entitas digantikan dengan asterisk tunggal `*`\n",
    "\n",
    "`*` merupakan instruksi untuk meng-import semua entitas yang ada"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aliasing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Untuk nama file yang akan di `import` kan dapat dilakukan proses `aliasing`\n",
    "\n",
    "`Aliasing` menyebabkan `modul` diidentifikasi dengan `nama yang berbeda` dari `aslinya`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`import` module `as` alias\n",
    "\n",
    "`as` merupakan kata kunci untuk melakukan `aliasing`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jika kita ingin merename `math`, dengan `m` dapat dilakukan dengan cara sebagai berikut. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math as m\n",
    "\n",
    "print(m.pi)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Note__ : after successful execution of an aliased import, the original module name becomes inaccessible and must not be used."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__from__ `module` __import__ `name` __as__ `alias`\n",
    "\n",
    "__from__ `module` __import__ `n` __as__ `a`, `m` __as__ `b`, `o` __as__ `c`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from math import pi as PI, sin as sine\n",
    "\n",
    "print(sine(PI/2))\n",
    "\n",
    "dir(math)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with standard modules\n",
    "\n",
    "__dir__(`module`)\n",
    "\n",
    "The function returns an alphabetically sorted list containing all entities' names available in the module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "a = math.pow(2,3)\n",
    "print(a)\n",
    "\n",
    "for name in dir(math):\n",
    "    print(name, end=\"\\t\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  `math` module"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start with a quick preview of some of the functions provided by the math module.\n",
    "\n",
    "The first group of the math's functions are connected with trigonometry:\n",
    "\n",
    "- `sin(x)` → the sine of x;\n",
    "- `cos(x)` → the cosine of x;\n",
    "- `tan(x)` → the tangent of x.\n",
    "\n",
    "Here are also their inversed versions:\n",
    "\n",
    "- `asin(x)` → the arcsine of x;\n",
    "- `acos(x)` → the arccosine of x;\n",
    "- `atan(x)` → the arctangent of x.\n",
    "\n",
    "`x` is a radian\n",
    "\n",
    "These functions take one argument (mind the domains) and return a measure of an angle in radians."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To effectively operate on angle measurements, the math module provides you with the following entities:\n",
    "\n",
    "- `pi` → a constant with a value that is an approximation of π;\n",
    "- `radians(x)` → a function that converts x from degrees to radians;\n",
    "- `degrees(x)` → acting in the other direction (from radians to degrees)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from math import pi, radians, degrees, sin, cos, tan, asin\n",
    "\n",
    "ad = 90\n",
    "\n",
    "ar = radians(ad)\n",
    "print(ar)\n",
    "ad = degrees(ar)\n",
    "print(ad)\n",
    "\n",
    "print(ad == 90.)\n",
    "print(ar == pi / 2.)\n",
    "print(sin(ar) / cos(ar) == tan(ar))\n",
    "print(asin(sin(ar)) == ar)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another group of the math's functions is formed by functions which are connected with exponentiation:\n",
    "\n",
    "- `e` → a constant with a value that is an approximation of Euler's number (e)\n",
    "- `exp(x)` → finding the value of ex;\n",
    "- `log(x)` → the natural logarithm of x\n",
    "- `log(x, b)` → the logarithm of x to base b\n",
    "- `log10(x)` → the decimal logarithm of x (more precise than log(x, 10))\n",
    "- `log2(x)` → the binary logarithm of x (more precise than log(x, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from math import e, exp, log\n",
    "\n",
    "print(pow(e, 1) == exp(log(e)))\n",
    "print(pow(2, 2) == exp(2 * log(2)))\n",
    "print(log(e, e) == exp(0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Built-in function\n",
    "\n",
    "Note: the pow() function:\n",
    "\n",
    "`pow(x, y)` → finding the value of xy (mind the domains)\n",
    "\n",
    "This is a built-in function, and doesn't have to be imported."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The last group consists of some general-purpose functions like:\n",
    "\n",
    "- ceil(x) → the ceiling of x (the smallest integer greater than or equal to x)\n",
    "- floor(x) → the floor of x (the largest integer less than or equal to x)\n",
    "- trunc(x) → the value of x truncated to an integer (__be careful__ - it's __not an equivalent__ either of ceil or floor)\n",
    "- factorial(x) → returns x! (x has to be an integral and not a negative)\n",
    "- hypot(x, y) → returns the length of the hypotenuse of a right-angle triangle with the leg lengths equal to x and y (the same as sqrt(pow(x, 2) + pow(y, 2)) but more precise)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It demonstrates the fundamental differences between `ceil()`, `floor()` and `trunc()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from math import ceil, floor, trunc\n",
    "\n",
    "x = 1.4\n",
    "y = 2.6\n",
    "\n",
    "print(floor(x), floor(y))\n",
    "print(floor(-x), floor(-y))\n",
    "print(ceil(x), ceil(y))\n",
    "print(ceil(-x), ceil(-y))\n",
    "print(trunc(x), trunc(y))\n",
    "print(trunc(-x), trunc(-y))"
   ]
  },
  {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `random` Module\n",
    "\n",
    "![random.PNG](attachment:random.PNG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It delivers some mechanisms allowing you to operate with __pseudorandom numbers__.\n",
    "\n",
    "__pseudo -__ :  the numbers generated by the modules may look random in the sense that you cannot predict their subsequent values, but don't forget that they all are calculated using very refined algorithms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from random import random\n",
    "\n",
    "for i in range(5):\n",
    "    print(random())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want integer random values, one of the following functions would fit better:\n",
    "\n",
    "- randrange(end)\n",
    "- randrange(beg, end)\n",
    "- randrange(beg, end, step)\n",
    "- randint(left, right)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from random import randrange, randint\n",
    "\n",
    "print(randrange(200), end=' ')\n",
    "print(randrange(50, 100), end=' ')\n",
    "print(randrange(50, 200, 10), end=' ')\n",
    "print(randint(5, 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is what we got in one of the launches:\n",
    "\n",
    "__`9,4,5,4,5,8,9,4,8,4,`__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's a function named in a very suggestive way - choice:\n",
    "\n",
    "choice(sequence)\n",
    "sample(sequence, elements_to_choose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from random import choice, sample\n",
    "\n",
    "lst = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
    "siswa = ['Ani','Budi','Cakra','Desi']\n",
    "print(choice(siswa))\n",
    "print(sample(lst, 5))\n",
    "print(sample(lst, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from random import randint\n",
    "\n",
    "for i in range(10):\n",
    "    print(randint(1, 10), end=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from platform import platform\n",
    "\n",
    "print(platform())\n",
    "print(platform(1))\n",
    "print(platform(0, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from platform import machine\n",
    "\n",
    "print(machine())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from platform import processor\n",
    "\n",
    "print(processor())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can read about all standard Python modules here: https://docs.python.org/3/py-modindex.html."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Package"
   ]
  },
  {
   "attachments": {
    "pacakge.PNG": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![pacakge.PNG](attachment:pacakge.PNG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- __a module is a kind of container filled with functions__ - you can pack as many functions as you want into one module and distribute it across the world;\n",
    "- of course, it's generally __a good idea not to mix functions with different application areas__ within one module\n",
    "- making many modules may cause a little mess - sooner or later you'll want to __group your modules__ exactly in the same way as you've previously grouped functions \n",
    "- __package__; in the world of modules, a package plays a similar role to a folder/directory in the world of files."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Membuat modul\n",
    "\n",
    "Pertama,kita membuat 2 file dengan nama aritmatika.py dan main.py\n",
    "\n",
    "Langkah:\n",
    "\n",
    "artimatika.py:\n",
    "\n",
    "- Buka python IDLE\n",
    "- Klik __file__ dan pilih __new file__\n",
    "- Simpan file dengan nama __aritmatika.py__\n",
    "\n",
    "main.py:\n",
    "\n",
    "- Buka python IDLE\n",
    "- Klik __file__ dan pilih __new file__\n",
    "- Simpan file dengan __main.py__\n",
    "\n",
    "Note: __Kedua file disimpan dalam satu folder yang sama.__\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# aritmatika.py\n",
    "\n",
    "def tambah(a,b):\n",
    "    return a+b\n",
    "\n",
    "def kurang(a,b):\n",
    "    return a-b\n",
    "\n",
    "def kali(a,b):\n",
    "    return a*b\n",
    "\n",
    "def bagi(a,b):\n",
    "    return a/b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import aritmatika"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=aritmatika.tambah(3,4)\n",
    "b=aritmatika.kurang(3,4)\n",
    "c=aritmatika.kali(3,4)\n",
    "d=aritmatika.bagi(3,4)\n",
    "\n",
    "print(a)\n",
    "print(b)\n",
    "print(c)\n",
    "print(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from aritmatika import tambah\n",
    "\n",
    "a=tambah(10,3)\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Package"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Buatlah folder dengan nama LATIHAN_PYTHON\n",
    "2. Dalam folder LATIHAN_PYTHON, buatlah folder dengan nama latihan_package, file main.py, dan file `__init__.py` \n",
    "3. Dalam latihan_package, buatlah 2 file, dengan nama alpha.py dan beta.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#alpha.py\n",
    "# def alphaSatu():\n",
    "# \tprint(\"alpha   Satu\")\n",
    "\n",
    "# def alphaDua():\n",
    "# \tprint(\"alphaDua\")\n",
    "\n",
    "#beta.py\n",
    "# def betaSatu():\n",
    "# \tprint(\"betaSatu\")\n",
    "\n",
    "# def betaDua():\n",
    "# \tprint(\"betaDua\")\n",
    "\n",
    "#main.py\n",
    "\n",
    "# import latihan_package.alpha as a\n",
    "# import latihan_package.beta as b\n",
    "\n",
    "# a.alphaSatu()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cara mengakses package yang dibuat, copy dan paste code dalam file main.py\n",
    "\n",
    "import os\n",
    "\n",
    "os.chdir(r\"E:\\CTA\\DIGITAL TALENT\\digital-talent\\2019\\LATIHAN_PYTHON\")\n",
    "\n",
    "import latihan_package.alpha as a, latihan_package.beta as b\n",
    "\n",
    "a.alphaSatu()\n",
    "b.betaSatu()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir(r\"E:\\CTA\\DIGITAL TALENT\\digital-talent\\2019\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.getcwd()"
   ]
  },
  {
   "attachments": {
    "pacakgetree.PNG": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Konsep package\n",
    "\n",
    "![pacakgetree.PNG](attachment:pacakgetree.PNG)\n",
    "\n",
    "`packages`, like modules, may require initialization.\n",
    "\n",
    "Python expects that there is a file with a very unique name inside the package's folder:`__init__.py.`\n",
    "\n",
    "The content of the file is executed when any of the package's modules is imported. \n",
    "\n",
    "If you don't want any special initializations, you can leave the __file empty__, but you mustn't omit it.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir(r\"E:\\CTA\\DIGITAL TALENT\\digital-talent\\2019\")\n",
    "\n",
    "import latihan_package.alpha as a\n",
    "a.alphaSatu()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import alpha\n",
    "\n",
    "alpha.alphaSatu()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Errors, failures, and other "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kesalahan merupakan hal yang sering terjadi dalam proses pembuatan pemrograman. \n",
    "\n",
    "Sebab terjadinya kesalahan:\n",
    "- Kesalahan dalam penulisan kode, sehingga kode tidak dapat dijalankan sama sekali\n",
    "- Kesalahan yang terjadi ketika program sedang di eksekusi\n",
    "\n",
    "Dua buah cara yang dapat digunakan untuk memeriksa suatu kesalahan:\n",
    "- menggunakan blok `try....except`\n",
    "- menggunakan statement `assert`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Eksepsi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Merupakan kesalahan yang terjadi pada saat proses eksekusi program, dan akan mengacaukan alur dari perintah-perintah normal yang terdapat di dalam program."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pada kode di bawah masih memungkinkan terjadi kesalahan, yaitu:\n",
    "- user menginputkan string, dan\n",
    "- user menginputkan bilangan negatif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "x = float(input(\"Enter x: \"))\n",
    "y = math.sqrt(x)\n",
    "\n",
    "print(\"The square root of\", x, \"equals to\", y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = float(input(\"Enter x: \"))\n",
    "y = math.sqrt(x)\n",
    "\n",
    "print(\"The square root of\", X, \"equals to\", y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "value = 1\n",
    "value /= 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`NameError`, `ValueError`, `ZeroDvisionError` merupakan eksepsi untuk mengatasi kesalahan-kesalahan yang terjadi seperti contoh diatas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kedua aktivitas ini disebut __memunculkan(raising) eksepsi__. Kita dapat mengatakan bahwa Python selalu memunculkan eksepsi (atau bahwa eksepsi telah dimunculkan) ketika ia tidak tahu apa yang harus dilakukan dengan kode Anda. Yang menyebabkan:\n",
    "\n",
    "- eksepsi mengharapkan ada sebuah perintah yang dapat menangani atau mencegah terjadinya kesalahan\n",
    "- jika tidak ada perintah untuk menangani atau mencegah terjadinya kesalahan tersebut, python akan menghentikan atau __terminated__ program, sehingga akan muncul pesan __error__\n",
    "- jika kesalahan dapat ditangani, python akan melanjutkan pada kode program selanjutnya"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `try .... except`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "firstNumber = int(input(\"Enter the first number: \"))\n",
    "secondNumber = int(input(\"Enter the second number: \"))\n",
    "\n",
    "if secondNumber != 0:\n",
    "    print(firstNumber / secondNumber)\n",
    "else:\n",
    "    print(\"This operation cannot be done.\")\n",
    "\n",
    "print(\"THE END.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cara penanganan kesalahan seperti kode diatas dapat digunakan, tetapi memiliki kelemahan karena kode program dapat menjadi sangat kompleks dan besar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Untuk menangani ekspesi, python memanfaatkan blok yang disebut dengan `try...except`, dengan bentuk umum:\n",
    "\n",
    "___try___:\n",
    "    kode \n",
    "    .....\n",
    "___except TipeEksekusi___:\n",
    "    penanganan kesalahan\n",
    "    \n",
    "    \n",
    "    \n",
    "Jika kita mempunyaki kode-kode \"mencurigakan\" yang mungkin dapat menimpilkan eksepsi,kita perlu menyimpan kode tersebut pada blok __try__. \n",
    "\n",
    "Ketika kode berjalan normal, kode pada bagian __except__ tidak akan dieksekusi. Sebaliknya jika terjadi kesalahan, maka eksekusi kode di bagian __try__ akan dihentikan, dan program akan mengeksekusi pada bagian __except__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    firstNumber = int(input(\"Enter the first number: \"))\n",
    "    secondNumber = int(input(\"Enter the second number: \"))\n",
    "    print(firstNumber / secondNumber)\n",
    "except:\n",
    "    print(\"This operation cannot be done.\")\n",
    "\n",
    "print(\"THE END.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    print(\"1\")\n",
    "    x = 1 / 0\n",
    "    print(\"2\")\n",
    "except:\n",
    "    print(\"Oh dear, something went wrong...\")\n",
    "\n",
    "print(\"3\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Block `try ...  except` dapat digunakan untuk menangani lebih dari satu eksepsi, dengan menggunkan bentuk umum: \n",
    "\n",
    "try:\n",
    "    :\n",
    "except exc1:\n",
    "    :\n",
    "except exc2:\n",
    "    :\n",
    "except:\n",
    "    :\n",
    "    \n",
    "Satu atau beberapa statement yang terdapat dalam blok `try` dapat menimbulkan lebih dari satu tipe ekspesi. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    x = int(input(\"Enter a number: \"))\n",
    "    y = 1 / x\n",
    "    print(y)\n",
    "except ZeroDivisionError:\n",
    "    print(\"You cannot divide by zero, sorry.\")\n",
    "except ValueError:\n",
    "    print(\"You must enter an integer value.\")\n",
    "except:\n",
    "    print(\"Oh dear, something went wrong...\")\n",
    "\n",
    "print(\"THE END.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Percobaan:\n",
    "- Inputkan bilangan integer (e.g., 5)\n",
    "- Inputkan 0\n",
    "- Inputkan data non-integer\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Built-in Exception"
   ]
  },
  {
   "attachments": {
    "exceptions.PNG": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python 3 menyediakan 63 built-in exceptions dan semuanya membentuk pohon hirarki\n",
    "\n",
    "![exceptions.PNG](attachment:exceptions.PNG)\n",
    "\n",
    "\n",
    "- ZeroDivisionError merupakan spesial kasus dari kelas eksepsi ArithmeticError;\n",
    "- ArithmeticError merupakan spesial kasus dari kelas eksepsi Exception;\n",
    "- Exception merupakan spesial kasus dari kelas eksepsi BaseException;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    y = 1 / 0\n",
    "except ZeroDivisionError:\n",
    "    print(\"Oooppsss...\")\n",
    "\n",
    "print(\"THE END.\")\n",
    "\n",
    "# Ubah ZeroDivisionError dengan ArithmeticError, Exception dan BaseException"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jika eksepsi muncul di dalam fungsi, maka eksepsi itu dapat ditangani dengan dua cara:\n",
    "- di dalam fungsi\n",
    "- di luar fungsi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def badFun(n):\n",
    "    try:\n",
    "        return 1 / n\n",
    "    except ArithmeticError:\n",
    "        print(\"Arithmetic Problem!\")\n",
    "    return None\n",
    "\n",
    "badFun(0)\n",
    "\n",
    "print(\"THE END.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def badFun(n):\n",
    "    return 1 / n\n",
    "\n",
    "try:\n",
    "    badFun(0)\n",
    "except ArithmeticError:\n",
    "    print(\"What happened? An exception was raised!\")\n",
    "\n",
    "print(\"THE END.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `Raise`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Eksepsi tertentu dapat kita panggil secara pakasa dengan menggunakan perintah `raise`, meskipun sebenarnya ada kejadian yang menyebabkan jenis kesalahan tersebut"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def badFun(n):\n",
    "    raise ZeroDivisionError"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`raise` digunakan untuk memanggil secara paksa dari suatu eksepsi, meskipun tidak ada kejadian yang menyebabkan jenis kesalahan tersebut."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def badFun(n):\n",
    "    1/n\n",
    "    raise ZeroDivisionError\n",
    "\n",
    "try:\n",
    "    badFun(0)\n",
    "except ArithmeticError:\n",
    "    print(\"What happened? An error?\")\n",
    "\n",
    "print(\"THE END.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def badFun(n):\n",
    "    try:\n",
    "        return n / 0\n",
    "    except:\n",
    "        print(\"I did it again!\")\n",
    "        raise\n",
    "\n",
    "try:\n",
    "    badFun(1)\n",
    "except ArithmeticError:\n",
    "    print(\"I see!\")\n",
    "\n",
    "print(\"THE END.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pemanggilan `raise` tanpa nama eksepsi hanya dapat dilakukan di dalam bagian `except`\n",
    "\n",
    "Dari kode di atas `ZeroDivisionError` muncul sebanyak dua kali, yaitu:\n",
    "- di dalam `try`\n",
    "- di bagian`except` di dalam fungsi\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assert\n",
    "\n",
    "`assert expression`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fungsi assertion:\n",
    "\n",
    "- Assert akan mengevaluasi ekspresi\n",
    "- Jika ekspresi bernilai True atau nilai numerik bukan nol, atau string tidak kosong, atau nilai lain yang berbeda dari `None` tidak akan di eksekusi\n",
    "- Jika selain itu akan muncul eksepsi `AssertionError`\n",
    "\n",
    "Penggunaan assertion:\n",
    "- kita dapat menggunakan assertion jika kita ingin kode yang dibuat benar-benar aman dari data kita belum yakin kebenarannya\n",
    "- mengamankan kode dari hasil yang tidak valid\n",
    "- assertion merupakan pelengkap `exception`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "x = float(input(\"Enter a number: \"))\n",
    "assert x >= 0.0\n",
    "\n",
    "x = math.sqrt(x)\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Strings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Computers store characters as numbers\n",
    "\n",
    "Every character used by a computer corresponds to a unique number, and vice versa\n",
    "\n",
    "Some of these characters are called whitespaces, while others are named control characters, because their purpose is to control input/output devices.\n",
    "\n",
    "ASCII (short for American Standard Code for Information Interchange) is the most widely used, and you can assume that nearly all modern devices (like computers, printers, mobile phones, tablets, etc.) use that code.\n",
    "\n",
    "The code provides space for 256 different characters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## I18N\n",
    "\n",
    "The software I18N is a standard in present times. Each program has to be written in a way that enables it to be used all around the world, among different cultures, languages and alphabets.\n",
    "\n",
    "### Code points and code pages\n",
    "\n",
    "A code point is a number which makes a character. For example, 32 is a code point which makes a space in ASCII encoding. We can say that standard ASCII code consists of 128 code points.\n",
    "\n",
    "A code page is a standard for using the upper 128 code points to store specific national characters. \n",
    "\n",
    "For example, the code point 200 makes Č (a letter used by some Slavic languages) when utilized by the ISO/IEC 8859-2 code page, and makes Ш (a Cyrillic letter) when used by the ISO/IEC 8859-5 code page."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unicode\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Code pages helped the computer industry to solve I18N issues for some time, but it soon turned out that they would not be a permanent solution.\n",
    "\n",
    "Unicode assigns unique (unambiguous) characters (letters, hyphens, ideograms, etc.) to more than a million code points. The first 128 Unicode code points are identical to ASCII, and the first 256 Unicode code points are identical to the ISO/IEC 8859-1 code page (a code page designed for western European languages)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Nature of Strings in Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python's strings are immutable sequences.\n",
    "\n",
    "The `len()` function used for strings returns a number of characters contained by the arguments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example 1\n",
    "word = 'by'\n",
    "print(len(word))\n",
    "\n",
    "# Example 2\n",
    "empty = ''\n",
    "print(len(empty))\n",
    "\n",
    "# Example 3\n",
    "i_am = 'I\\'m'\n",
    "print(i_am)\n",
    "print(len(i_am))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Multiline strings`\n",
    "\n",
    "The string starts with three apostrophes, not one. The same tripled apostrophe is used to terminate it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "multiLine = '''Line #1  \n",
    "disini string\n",
    "lagi\n",
    "Line #2'''\n",
    "\n",
    "print(len(multiLine))\n",
    "\n",
    "# The missing character is simply invisible - it's a whitespace."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "multiLine = \"\"\"Line #1\n",
    "Line #2\"\"\"\n",
    "\n",
    "print(len(multiLine))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "multiLine = 'Line #1\n",
    "Line #2'\n",
    "\n",
    "print(len(multiLine))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Operations on strings\n",
    "\n",
    "In general, strings can be:\n",
    "\n",
    "- concatenated (joined) (`+`)\n",
    "    The + operator used against two or more strings produces a new string containing all the characters from its arguments\n",
    "- replicated. (`*`)\n",
    "    The * operator needs a string and a number as arguments; in this case, the order doesn't matter - you can put the number before the string, or vice versa, the result will be the same\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "str1 = 'a'\n",
    "str2 = 'b'\n",
    "\n",
    "print(str1 + str2)\n",
    "print(str2 + str1)\n",
    "print(5 * 'halo ')\n",
    "print('b' * 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Operations on strings: ord()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to know a specific character's ASCII/UNICODE code point value, you can use a function named ord() (as in ordinal)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Demonstrating the ord() function\n",
    "\n",
    "ch1 = 'a' \n",
    "ch2 = ' ' # space\n",
    "\n",
    "print(ord(ch1))\n",
    "print(ord(ch2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Operations on strings: chr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function takes a code point and returns its character."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Demonstrating the chr() function\n",
    "\n",
    "print(chr(97))\n",
    "print(chr(945))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 'a'\n",
    "chr(ord(x)) == x\n",
    "x = 90\n",
    "ord(chr(x)) == x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Strings as sequences: indexing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Python's strings are sequences.__\n",
    "\n",
    "Strings aren't lists, but __you can treat them like lists in many particular cases__."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# If you want to access any of a string's characters, you can do it using indexing,\n",
    "# Indexing strings\n",
    "\n",
    "exampleString = 'CIKARANG'\n",
    "vokal = ['a','i','u','e','o']\n",
    "for i in range(len(exampleString)):\n",
    "    if exampleString[i].lower() in vokal:\n",
    "        continue\n",
    "    print(exampleString[i], end=\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Strings as sequences: iterating\n",
    "\n",
    "__Iterating through the strings works__, too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Iterating through a string\n",
    "\n",
    "exampleString = 'silly walks'\n",
    "\n",
    "for ch in exampleString:\n",
    "    print(ch, end='*')\n",
    "\n",
    "print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Slices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Slices\n",
    "\n",
    "alpha = \"BPPTIK CIKARANG\"\n",
    "\n",
    "print(alpha[1::2])\n",
    "\n",
    "# print(alpha[1:3])\n",
    "# print(alpha[3:])\n",
    "# print(alpha[:3])\n",
    "print(alpha[3:-2])\n",
    "# print(alpha[-3:4])\n",
    "# print(alpha[::2])\n",
    "# print(alpha[1::2])"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "The `in` and `not in` operators\n",
    "\n",
    "The `in` operator checks if its left argument (a string) can be found anywhere within the right argument (another string).\n",
    "\n",
    "The result of the check is simply `True` or `False`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "alphabet = 'abcdefghijklmnopqrstuvwxyz'\n",
    "\n",
    "print(\"f\" in alphabet)\n",
    "print(\"F\" in alphabet)\n",
    "print(\"1\" in alphabet)\n",
    "print(\"ghi\" in alphabet)\n",
    "print(\"Xyz\" in alphabet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "alphabet = \"abcdefghijklmnopqrstuvwxyz\"\n",
    "\n",
    "print(\"f\" not in alphabet)\n",
    "print(\"F\" not in alphabet)\n",
    "print(\"1\" not in alphabet)\n",
    "print(\"ghi\" not in alphabet)\n",
    "print(\"Xyz\" not in alphabet)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Python strings are immutable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- It doesn't allow you to use the `del` instruction to remove anything from a string.\n",
    "- You can do with del and a string is to remove the string as a whole.\n",
    "- Python strings don't have the `append()` method\n",
    "- The `insert()` method is __illegal__, too"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "alphabet = \"abcdefghijklmnopqrstuvwxyz\"\n",
    "\n",
    "del alphabet[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "alphabet = \"abcdefghijklmnopqrstuvwxyz\"\n",
    "\n",
    "alphabet.append(\"A\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "alphabet = \"abcdefghijklmnopqrstuvwxyz\"\n",
    "\n",
    "alphabet.insert(0, \"A\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Operations on strings: min()\n",
    "\n",
    "The function finds the __minimum element__ of the sequence passed as an argument.\n",
    "\n",
    "There is one condition - the sequence (string, list, it doesn't matter) cannot be empty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Demonstrating min() - Example 1\n",
    "print(min(\"aAbByYzZ\"))\n",
    "\n",
    "\n",
    "# Demonstrating min() - Examples 2 & 3\n",
    "t = 'The Knights Who Say \"Ni!\"'\n",
    "print('[' + min(t) + ']')\n",
    "\n",
    "t = [0, 1, 2]\n",
    "print(min(t))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Operations on strings: max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A function named `max()` finds the maximum element of the sequence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Demonstrating max() - Example 1\n",
    "print(max(\"aAbByYzZ\"))\n",
    "\n",
    "\n",
    "# Demonstrating max() - Examples 2 & 3\n",
    "t = 'The Knights Who Say \"Ni!\"'\n",
    "print('[' + max(t) + ']')\n",
    "\n",
    "t = [0, 1, 2]\n",
    "print(max(t))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Operations on strings: the `index()` method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `index()` method (it's a method, not a function) searches the sequence from the beginning, in order to find the first element of the value specified in its argument.\n",
    "\n",
    "The element searched for must occur in the sequence - its absence will cause a ValueError exception.\n",
    "\n",
    "The method returns the index of the first occurrence of the argument which means that the lowest possible result is 0, while the highest is the length of argument decremented by 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Demonstrating the index() method\n",
    "print(\"aAbByYzZaA\".index(\"b\"))\n",
    "print(\"aAbByYzZaA\".index(\"Z\"))\n",
    "print(\"aAbByYzZaA\".index(\"A\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Operations on strings: the list() function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The list() function takes its argument (a string) and creates a new list containing all the string's characters, one per list element.\n",
    "\n",
    "`list()` is able to create a new list from many other entities (e.g., from tuples and dictionaries)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Operations on strings: the count() method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The count() method counts all occurrences of the element inside the sequence. \n",
    "\n",
    "The absence of such elements doesn't cause any problems."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Demonstrating the list() function\n",
    "print(list(\"abcabc\"))\n",
    "\n",
    "# Demonstrating the count() method\n",
    "print(\"abcabc\".count(\"b\"))\n",
    "print('abcabc'.count(\"d\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# String Method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The capitalize() method \n",
    "\n",
    "The capitalize() method creates a new string filled with characters taken from the source string, but it tries to modify them in the following way:\n",
    "\n",
    "- if the first character inside the string is a letter,  it will be converted to upper-case\n",
    "- all remaining letters from the string will be converted to lower-case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"terima kasih selamat malam\".capitalize())\n",
    "print('ALPHA'.capitalize())\n",
    "print(' Alpha'.capitalize())\n",
    "print('123'.capitalize())\n",
    "print(\"αβγδ\".capitalize())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The center() method\n",
    "\n",
    "The `center()` method makes a copy of the original string, trying to center it inside a field of a specified width\n",
    "\n",
    "The centering is actually done by adding some spaces before and after the string."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('[' + 'Beta'.center(2) + ']')\n",
    "print('[' + 'Beta'.center(8) + ']')\n",
    "print('[' + 'Beta'.center(20) + ']')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The two-parameter variant of center() makes use of the character from the second argument, instead of a space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('[' + 'gamma'.center(20, '*') + ']')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The endswith() method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `endswith()` method checks if the given string ends with the specified argument and returns `True` or `False`, depending on the check result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = \"zeta\"\n",
    "print(t.endswith(\"a\"))\n",
    "print(t.endswith(\"A\"))\n",
    "print(t.endswith(\"et\"))\n",
    "print(t.endswith(\"eta\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Demonstrating the endswith() method\n",
    "if \"epsilon\".endswith(\"on\"):\n",
    "    print(\"yes\")\n",
    "else:\n",
    "    print(\"no\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The find() method\n",
    "\n",
    "The find() method is similar to index(), it looks for a substring and returns the index of first occurrence of this substring, but:\n",
    "\n",
    " it doesn't generate an error for an argument containing a non-existent substring\n",
    " \n",
    " it works with strings only\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = 'theta'\n",
    "print(t.find('eta'))\n",
    "print(t.find('et'))\n",
    "print(t.find('the'))\n",
    "print(t.find('ha'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('kappa'.find('a', 2))\n",
    "\n",
    "# proses pencarian dimulai dari indeks ke dua, proses pencarian akan berhenti jika sudah ditemukan huruf 'a'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "txt = \"\"\"A variation of the ordinary lorem ipsum\n",
    "text has been used in typesetting since the 1960s \n",
    "or earlier, when it was popularized by advertisements \n",
    "for Letraset transfer sheets. It was introduced to \n",
    "the Information Age in the mid-1980s by the Aldus Corporation, \n",
    "which employed it in graphics and word-processing templates\n",
    "for its desktop publishing program PageMaker (from Wikipedia)\"\"\"\n",
    "\n",
    "fnd = txt.find('the')\n",
    "while fnd != -1:\n",
    "    print(fnd)\n",
    "    fnd = txt.find('the', fnd + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('kappa'.find('a', 1, 4))\n",
    "print('kappa'.find('a', 2, 4))\n",
    "\n",
    "# argumen ketiga menunjuk ke indeks pertama yang tidak akan dipertimbangkan selama pencarian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Demonstrating the find() method\n",
    "print(\"Eta\".find(\"ta\"))\n",
    "print(\"Eta\".find(\"mma\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The isalnum() method\n",
    "\n",
    "The parameterless method named isalnum() checks if the string contains only digits or alphabetical characters (letters), and returns True or False according to the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Demonstrating the isalnum() method\n",
    "print('lambda30'.isalnum())\n",
    "print('lambda'.isalnum())\n",
    "print('30'.isalnum())\n",
    "print('@'.isalnum())\n",
    "print('lambda_30'.isalnum())\n",
    "print(''.isalnum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The isalpha() method\n",
    "\n",
    "The isalpha() method is more specialized - it's interested in letters only.\n",
    "\n",
    "### The isdigit() method\n",
    "\n",
    "In turn, the isdigit() method looks at digits only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example 1: Demonstrating the isapha() method\n",
    "print(\"Moooo\".isalpha())\n",
    "print('Mu40'.isalpha())\n",
    "\n",
    "# Example 2: Demonstrating the isdigit() method\n",
    "print('2018'.isdigit())\n",
    "print(\"Year2019\".isdigit())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The islower() method\n",
    "\n",
    "The islower() method is a fussy variant of isalpha()\n",
    "\n",
    "It accepts lower-case letters only.\n",
    "\n",
    "### The isspace() method\n",
    "\n",
    "The isspace() method identifies whitespaces only\n",
    "\n",
    "### The isupper() method\n",
    "The isupper() method is the upper-case version of islower()\n",
    "\n",
    "It concentrates on upper-case letters only."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example 1: Demonstrating the islower() method\n",
    "print(\"Moooo\".islower())\n",
    "print('moooo'.islower())\n",
    "\n",
    "# Example 2: Demonstrating the isspace() method\n",
    "print(' \\n '.isspace())\n",
    "print(\" \".isspace())\n",
    "print(\"mooo mooo mooo\".isspace())\n",
    "\n",
    "# Example 3: Demonstrating the isupper() method\n",
    "print(\"Moooo\".isupper())\n",
    "print('moooo'.isupper())\n",
    "print('MOOOO'.isupper())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The join() method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Demonstrating the join() method\n",
    "print(\" \".join([\"omicron\", \"pi\", \"rho\"]))\n",
    "\n",
    "# the join() method is invoked from within a string containing a comma\n",
    "# the join's argument is a list containing three strings;\n",
    "# the method returns a new string."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The lower() method\n",
    "\n",
    "The lower() method makes a copy of a source string, replaces all upper-case letters with their lower-case counterparts, and returns the string as the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Demonstrating the lower() method\n",
    "print(\"SiGmA=60\".lower())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The lstrip() method\n",
    "\n",
    "The parameterless lstrip() method returns a newly created string formed from the original one by removing all leading whitespaces.\n",
    "\n",
    "The one-parameter lstrip() method, removes all characters enlisted in its argument"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = \"    tes\"\n",
    "print(s)\n",
    "print(s.lstrip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Demonstrating the lstrip() method\n",
    "print(\"[\" + \" tau \".lstrip() + \"]\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"www.ugm.com\".lstrip(\"w\"))\n",
    "print(\"pythoninstitute.org\".lstrip(\"institute\"))\n",
    "print(\"annisa maulida\".lstrip())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The replace() method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The two-parameter replace() method returns a copy of the original string in which all occurrences of the first argument have been replaced by the second argument\n",
    "\n",
    "The three-parameter replace() variant uses the third argument (a number) to limit the number of replacements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Demonstrating the replace() method\n",
    "print(\"www.netacad.com\".replace(\"netacad.com\", \"pythoninstitute.org\"))\n",
    "print(\"This is it!\".replace(\"is\", \"are\"))\n",
    "print(\"Apple juice\".replace(\"juice\", \"\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"This is it!\".replace(\"is\", \"are\", 1))\n",
    "print(\"This is it!\".replace(\"is\", \"are\", 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The rfind() method\n",
    "\n",
    "Start their searches from the end of the string\n",
    "\n",
    "hence the prefix `r`, for `right`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Demonstrating the rfind() method\n",
    "print(\"tau tau tau\".rfind(\"ta\"))\n",
    "print(\"tau tau tau\".rfind(\"ta\", 9))\n",
    "print(\"tau tau tau\".rfind(\"ta\", 3, 9))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The rstrip() method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Two variants of the rstrip() method do nearly the same as lstrips, but affect the opposite side of the string."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Demonstrating the rstrip() method\n",
    "print(\"[\" + \" upsilon \".rstrip() + \"]\")\n",
    "print(\"cisco.com\".rstrip(\".com\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The split() method\n",
    "\n",
    "The split() method does what it says - it splits the string and builds a list of all detected substrings.\n",
    "\n",
    "The method assumes that the substrings are delimited by whitespaces "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Demonstrating the split() method\n",
    "# print(\"phi       chi\\npsi\".split())\n",
    "\n",
    "s = \"naik delman istimewa. kududuk dimuka. duduk disamping pak kusir\"\n",
    "a = s.split('.')\n",
    "print(a[-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The startswith() method\n",
    "\n",
    "The startswith() method is a mirror reflection of endswith() - it checks if a given string starts with the specified substring."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Demonstrating the startswith() method\n",
    "print(\"omega\".startswith(\"meg\"))\n",
    "print(\"omega\".startswith(\"om\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The strip() method\n",
    "\n",
    "The trip() method combines the effects caused by rstrip() and lstrip() - it makes a new string lacking all the leading and trailing whitespaces."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Demonstrating the strip() method\n",
    "print(\"[\" + \"   aleph   \".strip() + \"]\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The swapcase() method\n",
    "\n",
    "The swapcase() method makes a new string by swapping the case of all letters within the source string: lower-case characters become upper-case, and vice versa.\n",
    "\n",
    "### The title() method\n",
    "\n",
    "It changes every word's first letter to upper-case, turning all other ones to lower-case.\n",
    "\n",
    "### The upper() method\n",
    "\n",
    "The upper() method makes a copy of the source string, replaces all lower-case letters with their upper-case counterparts, and returns the string as the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Demonstrating the swapcase() method\n",
    "print(\"I know that I know nothing.\".swapcase())\n",
    "\n",
    "print()\n",
    "\n",
    "# Demonstrating the title() method\n",
    "print(\"I know that I know nothing. Part 1.\".title())\n",
    "\n",
    "print()\n",
    "\n",
    "# Demonstrating the upper() method\n",
    "print(\"I know that I know nothing. Part 2.\".upper())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lab : Mysplit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mysplit(strng):\n",
    "    strng = strng.strip()\n",
    "    start = 0\n",
    "    strng += \" \"\n",
    "    if len(strng)>0:\n",
    "        l = []\n",
    "        fnd = strng.find(\" \")\n",
    "        while fnd != -1:\n",
    "            l.append(strng[start:fnd])\n",
    "            start = fnd+1\n",
    "            fnd = strng.find(\" \", fnd + 1)\n",
    "            \n",
    "        return l\n",
    "        \n",
    "    else:\n",
    "        return []\n",
    "    \n",
    "    \n",
    "def mysplit2(strng):\n",
    "    strng = strng.strip()\n",
    "    l = []\n",
    "    w = \"\"\n",
    "    i = 0\n",
    "    strng += \" \"\n",
    "    for ch in strng: \n",
    "        if ch is \" \":            \n",
    "            l.append(w)\n",
    "            w = \"\"\n",
    "        else:\n",
    "            w += ch        \n",
    "        i += 1        \n",
    "    return l\n",
    "    \n",
    "    \n",
    "# \"To be or not to be, that is the question\".find(\" \")\n",
    "\n",
    "print(mysplit2(\"To be or not to be, that is the question\"))\n",
    "print(mysplit2(\"To be or not to be,that is the question\"))\n",
    "print(mysplit2(\"   \"))\n",
    "print(mysplit2(\" abc \"))\n",
    "print(mysplit2(\"\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"To be or not to be, that is the question\".strip().find(\" \")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## String in Action"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing strings\n",
    "\n",
    "Python's strings can be compared using the same set of operators which are in use in relation to numbers.\n",
    "\n",
    "It just compares code point values, character by character.\n",
    "\n",
    "- `==`\n",
    "- `!=`\n",
    "- `>`\n",
    "- `>=`\n",
    "- `<`\n",
    "- `<=`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('alpha' == 'alpha')\n",
    "print ('alpha' != 'Alpha')\n",
    "print ('alpha' < 'alphabet')\n",
    "# String comparison is always case-sensitive (upper-case letters are taken as lesser than lower-case).\n",
    "print ('beta' > 'Beta')\n",
    "\n",
    "# Even if a string contains digits only, it's still not a number.\n",
    "print('10' == '010')\n",
    "print('10' > '010')\n",
    "print('10' > '8')\n",
    "print('20' < '8')\n",
    "print('20' < '80')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apakah output dari kode program di bawah ini\n",
    "print('10' == 10)\n",
    "print('10' != 10)\n",
    "print('10' == 1)\n",
    "print('10' != 1)\n",
    "print('10' > 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sorting\n",
    "\n",
    "- The first is implemented as a function named sorted().\n",
    "   - The function takes one argument (a list) and returns a new list\n",
    "- The second function named `sort()`\n",
    "    - The second method affects the list itself - no new list is created"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Demonstrating the sorted() function\n",
    "firstGreek = ['omega', 'alpha', 'pi', 'gamma']\n",
    "firstGreek2 = sorted(firstGreek)\n",
    "\n",
    "print(firstGreek)\n",
    "print(firstGreek2)\n",
    "\n",
    "print()\n",
    "\n",
    "# Demonstrating the sort() method\n",
    "secondGreek = ['omega', 'alpha', 'pi', 'gamma']\n",
    "print(secondGreek)\n",
    "\n",
    "secondGreek.sort()\n",
    "print(secondGreek)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Strings vs. numbers\n",
    "\n",
    "How to convert a number (an integer or a float) into a string, and vice versa."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "itg = 13\n",
    "flt = 1.3\n",
    "si = str(itg)\n",
    "sf = str(flt)\n",
    "#print(type(si))\n",
    "print(si + ' ' + sf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Caesar cipher\n",
    "text = input(\"Enter your message: \")\n",
    "\n",
    "def doCipher(text):\n",
    "    cipher = ''\n",
    "    for char in text:\n",
    "        if not char.isalpha():\n",
    "            continue\n",
    "        char = char.upper()\n",
    "        code = ord(char) + 1\n",
    "        if code > ord('Z'):\n",
    "            code = ord('A')\n",
    "        cipher += chr(code)\n",
    "    return cipher\n",
    "\n",
    "def deCipher(text):\n",
    "    cipher = ''\n",
    "    for char in text:\n",
    "        if not char.isalpha():\n",
    "            continue\n",
    "        char = char.upper()\n",
    "        code = ord(char) - 1\n",
    "        if code < ord('A'):\n",
    "            code = ord('Z')\n",
    "        cipher += chr(code)\n",
    "    return cipher\n",
    "\n",
    "print(deCipher(doCipher(text)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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