{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python Basics (2024-01-29 - 2024-01-31)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = 42.666"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42.666"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = [1, 2, 'drei']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 'drei']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tuple unpacking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "a, b, c = 1, 2, 'drei'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'drei'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = b = c = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Swap two varibles?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = 1\n",
    "b = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp = a\n",
    "a = b\n",
    "b = tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 1)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a, b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = 1\n",
    "b = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "a, b = b, a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 1)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a, b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assignment Details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "139818180670696"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0x7f29f4fdf4e8'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hex(id(a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "139818180670696"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = 666"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "139817897762928"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "139818180670696"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id(b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Datatypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Integers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "i = 2**32 - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0xffffffff'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hex(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0b11111111111111111111111111111111'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bin(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0b100000000000000000000000000000000'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bin(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "i = 2**64 - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0b1111111111111111111111111111111111111111111111111111111111111111'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bin(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0b10000000000000000000000000000000000000000000000000000000000000000'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bin(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "10**100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "139817896921728"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "139817466913504"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = 16\n",
    "b = 0x10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0x10'"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hex(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0x10 + 16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = '16'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'16'"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int('0x10', 16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3//2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3%2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "10%3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "12%3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "10%4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2+3*4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2+(3*4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(2+3)*4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "10%3*2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(10%3)*2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "10%(3*2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on built-in function round in module builtins:\n",
      "\n",
      "round(number, ndigits=None)\n",
      "    Round a number to a given precision in decimal digits.\n",
      "\n",
      "    The return value is an integer if ndigits is omitted or None.  Otherwise\n",
      "    the return value has the same type as the number.  ndigits may be negative.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(round)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.142"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(3.142334567876545678765456787654, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classes? Struct? Cell?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = {\n",
    "    'x': 1,\n",
    "    'y': 2,\n",
    "    'z': 3,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p['x']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "class point:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = point()\n",
    "p.x = 1\n",
    "p.y = 2\n",
    "p.z = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "__main__.point"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "class point:\n",
    "    def __init__(self, x, y, z):\n",
    "        self.x = x\n",
    "        self.y = y\n",
    "        self.z = z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = point(1, 2, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "import collections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "point = collections.namedtuple('point', ('x', 'y', 'z'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = point(1, 2, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "__main__.point"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Boolean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = True\n",
    "b = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a and b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a == b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a ^ b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a or b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compound Datatypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### List"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "l = [1, 2.0, 'drei']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2.0\n",
      "drei\n"
     ]
    }
   ],
   "source": [
    "for elem in l:\n",
    "    print(elem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "l.append(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2.0, 'drei', 4]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "l.append([1,2,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2.0, 'drei', 4, [1, 2, 3]]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "l.extend([4, 5, 6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2.0, 'drei', 4, [1, 2, 3], 4, 5, 6]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "l += [7, 8, 'neun']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2.0, 'drei', 4, [1, 2, 3], 4, 5, 6, 7, 8, 'neun']"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l.pop(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 'drei', 4, [1, 2, 3], 4, 5, 6, 7, 8, 'neun']"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 'drei', 4, [1, 2, 3], 4, 5, 6, 7, 8, 'neun']"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'neun'"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l[9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'IndexError'> list index out of range\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    l[10]\n",
    "except Exception as e:\n",
    "    print(type(e), e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "l.insert(0, 'anfang')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['anfang', 1, 'drei', 4, [1, 2, 3], 4, 5, 6, 7, 8, 'neun']"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "l.insert(1, 'vor eins')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['anfang', 'vor eins', 1, 'drei', 4, [1, 2, 3], 4, 5, 6, 7, 8, 'neun']"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1000 in l"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Iteration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "anfang\n",
      "vor eins\n",
      "1\n",
      "drei\n",
      "4\n",
      "[1, 2, 3]\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "neun\n"
     ]
    }
   ],
   "source": [
    "for elem in l:\n",
    "    print(elem)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = {\n",
    "    'one': 1,\n",
    "    'two': 2,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d['one']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'KeyError'> 'three'\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    d['three']\n",
    "except Exception as e:\n",
    "    print(type(e), e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "d['three'] = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d['three']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "d['four'] = 'vier'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'one': 1, 'two': 2, 'three': 3, 'four': 'vier'}"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "d['four'] = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "one\n",
      "two\n",
      "three\n",
      "four\n"
     ]
    }
   ],
   "source": [
    "for elem in d:\n",
    "    print(elem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "one\n",
      "two\n",
      "three\n",
      "four\n"
     ]
    }
   ],
   "source": [
    "for elem in d.keys():\n",
    "    print(elem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "for elem in d.values():\n",
    "    print(elem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('one', 1)\n",
      "('two', 2)\n",
      "('three', 3)\n",
      "('four', 4)\n"
     ]
    }
   ],
   "source": [
    "for elem in d.items():\n",
    "    print(elem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "del d['one']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'one' in d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'two' in d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = {1, 2, 'drei'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "set"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2 in s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "s.add(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "4 in s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "s.remove(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "4 in s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "s1 = {1, 2, 3, 4}\n",
    "s2 = {3, 4, 5, 6}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1, 2, 3, 4, 5, 6}"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 | s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{3, 4}"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 & s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1, 2, 5, 6}"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 ^ s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "s.add('fuenf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1, 2, 'drei', 'fuenf'}"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ``while`` Loops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "number = 0\n",
    "while number < 5:\n",
    "    print(number)\n",
    "    number = number + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5050\n"
     ]
    }
   ],
   "source": [
    "summe = 0\n",
    "number = 1\n",
    "while number <= 100:\n",
    "    summe = summe + number\n",
    "    number = number + 1\n",
    "print(summe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5050"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(range(1, 101))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ``range()``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "for num in range(5):\n",
    "    print(num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "for num in range(0, 5):\n",
    "    print(num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "for num in range(3, 5):\n",
    "    print(num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "4\n",
      "6\n",
      "8\n"
     ]
    }
   ],
   "source": [
    "for num in range(2, 10, 2):\n",
    "    print(num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "4\n",
      "6\n",
      "8\n"
     ]
    }
   ],
   "source": [
    "num = 2\n",
    "while num < 10:\n",
    "    print(num)\n",
    "    num += 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Wuerfeln"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random.randrange(1, 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fuck 4\n"
     ]
    }
   ],
   "source": [
    "n_tries = 1\n",
    "MAX_TRIES = 3\n",
    "while n_tries <= MAX_TRIES:\n",
    "    eyes = random.randrange(1, 7)\n",
    "    if eyes == 6:\n",
    "        print(f'yay after %d tries' % n_tries)\n",
    "        break\n",
    "    n_tries += 1\n",
    "else:\n",
    "    print('fuck', n_tries)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Primeness, while"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 is a divisor\n",
      "not prime\n"
     ]
    }
   ],
   "source": [
    "number = 9\n",
    "\n",
    "divisor_candidate = 2\n",
    "while divisor_candidate < number:\n",
    "    if number % divisor_candidate == 0:\n",
    "        print(divisor_candidate, 'is a divisor')\n",
    "        print('not prime')\n",
    "        break\n",
    "    divisor_candidate += 1\n",
    "else:\n",
    "    print('prime')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ``for``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Caro\n",
      "Johanna\n",
      "Eva\n",
      "Jörg\n"
     ]
    }
   ],
   "source": [
    "for name in ['Caro', 'Johanna', 'Eva', 'Jörg']:\n",
    "    print(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = {\n",
    "    'one': 1,\n",
    "    'two': 2,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "one\n",
      "two\n"
     ]
    }
   ],
   "source": [
    "for elem in d:\n",
    "    print(elem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "two\n",
      "one\n"
     ]
    }
   ],
   "source": [
    "s = { 'one', 'two', 3 }\n",
    "for elem in s:\n",
    "    print(elem)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "C-like iteration: index-based"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "array = [ 1, 2, 3, 4 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "del i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(array)):\n",
    "    print(array[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "for elem in array:\n",
    "    print(elem)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ``else``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "needle found\n"
     ]
    }
   ],
   "source": [
    "haystack = ['straw', 'mouse', 'straw', 'needle', 'straw']\n",
    "for item in haystack:\n",
    "    if item == 'needle':\n",
    "        print('needle found')\n",
    "        break\n",
    "else:\n",
    "    print(\"couldn't find needle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "couldn't find needle\n"
     ]
    }
   ],
   "source": [
    "haystack = ['straw', 'mouse', 'straw', 'straw']\n",
    "for item in haystack:\n",
    "    if item == 'needle':\n",
    "        print('needle found')\n",
    "        break\n",
    "else:\n",
    "    print(\"couldn't find needle\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The ``range`` function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "numbers = [0, 1, 2, 3, 4]\n",
    "for elem in numbers:\n",
    "    print(elem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "for elem in range(5):\n",
    "    print(elem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "for elem in range(0, 5, 1):\n",
    "    print(elem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "5\n",
      "8\n"
     ]
    }
   ],
   "source": [
    "for elem in range(2, 9, 3):\n",
    "    print(elem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "for elem in range(2, 10):\n",
    "    print(elem)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "a, b = 1, 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    }
   ],
   "source": [
    "if a < b:\n",
    "    print(b)\n",
    "else:\n",
    "    print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "c, d = 33, 12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "33\n"
     ]
    }
   ],
   "source": [
    "if c < d:\n",
    "    print(d)\n",
    "else:\n",
    "    print(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "def maximum(lhs, rhs):\n",
    "    if lhs < rhs:\n",
    "        max = rhs\n",
    "    else:\n",
    "        max = lhs\n",
    "    return max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "a, b = 1, 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = maximum(a, b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    }
   ],
   "source": [
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "def foo(bar):\n",
    "    bar = 666"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "foo(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "function"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(maximum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = maximum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "function"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a(1, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sequences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "l = ['prime-function.py', '1', '2', '53', '57', '9']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1', '2', '53', '57', '9']"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l[1:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1', '2']"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l[1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1', '2', '53', '57', '9']"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l[1:] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Misc Crap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ipaddress"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "addr = ipaddress.ip_address('192.168.1.1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IPv4Address('192.168.1.1')"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "addr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "addr.is_loopback"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "addr = ipaddress.ip_address('127.0.0.1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "addr.is_loopback"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'127.0.0.1'"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "addr.exploded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "das ist keine gueltige IP addresse\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    addr = ipaddress.ip_address('111')\n",
    "except ValueError:\n",
    "    print('das ist keine gueltige IP addresse')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sequential Datatyoes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 5, 10, 15, 20, 25]"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[i*5 for i in range(6)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "v = [1, 2, 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3]"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v*5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "vector = numpy.array([1,2,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5, 10, 15])"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vector*5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'    '"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "' '*4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "text = 'Hello World'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'IndexError'> string index out of range\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    text[20]\n",
    "except Exception as e:\n",
    "    print(type(e), e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'d'"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'H'"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text[-11]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'IndexError'> string index out of range\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    text[-12]\n",
    "except Exception as e:\n",
    "    print(type(e), e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "a_tuple = (1, 'one', 1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'n'"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_tuple[1][1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References, (Im)mutability, And Memory Management"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0x7f29f4fdf4e8'"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hex(id(a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sys\n",
    "sys.getsizeof(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = [1,2,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "del a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "139818180670696"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "139818180670696"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "a += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "139818180670728"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = (1,2,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'AttributeError'> 'tuple' object has no attribute 'append'\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    t.append(4)\n",
    "except Exception as e:\n",
    "    print(type(e), e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "l1 = [1,2,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "l2 = l1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "139817467481600"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id(l1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "139817467481600"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id(l2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "l1.append(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "139817467481600"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id(l1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "139817467481600"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id(l2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4]"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4]"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4]"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "def modifyit(seq):\n",
    "    seq.append(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "modifyit(l1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 5]"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Miscellaneous String Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = 'abc'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.isalpha()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'123'.isdigit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'ABC'.isupper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = 'mississippi'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.count('ss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.find('ss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [],
   "source": [
    "pos = s.find('ss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.find('ss', pos+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.find('x')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.index('ss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'ValueError'> substring not found\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    s.index('x')\n",
    "except Exception as e:\n",
    "    print(type(e), e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = 'file.csv'\n",
    "filename.endswith('.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [],
   "source": [
    "line = 'one:two:three'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['one', 'two', 'three']"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "line.split(':')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [],
   "source": [
    "a, b, c = line.split(':')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'one'"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['one', 'two:three']"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "line.split(':', 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [],
   "source": [
    "elems = ['one', 'two', 'three']        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'one:two:three'"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "':'.join(elems)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = ' \\t \\n       sjdgosi    gfosvbsisvbsok  \\r     '"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'sjdgosi    gfosvbsisvbsok'"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'sjdgosi    gfosvbsisvbsok  \\r     '"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.lstrip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' \\t \\n       sjdgosi    gfosvbsisvbsok'"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.rstrip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = '        '"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(s.strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = '       xx   '"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'xx'"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'       xx   '"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ``os``, Und Einlesen Von Directories"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ``os.listdir()``, ``os.path.join()``, ``os.sep``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [],
   "source": [
    "files = os.listdir('data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['file1.csv', 'file2.csv', 'nochwas.txt']"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [],
   "source": [
    "csv_files = []\n",
    "for f in files:\n",
    "    if f.endswith('.csv'):\n",
    "        csv_files.append(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['file1.csv', 'file2.csv']"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csv_files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Das ganze mehr PYTHONIC, bitte!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [],
   "source": [
    "csv_files = [os.path.join('data', f) for f in os.listdir('data') if f.endswith('.csv')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['data/file1.csv', 'data/file2.csv']"
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csv_files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ``os.walk()``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('data', ['subdir1', 'subdir2'], ['file1.csv', 'file2.csv', 'nochwas.txt'])\n",
      "('data/subdir1', [], ['file1.csv', 'file2.csv'])\n",
      "('data/subdir2', [], ['file1.csv', 'file2.csv'])\n"
     ]
    }
   ],
   "source": [
    "for elem in os.walk('data'):\n",
    "    print(elem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [],
   "source": [
    "csv_files = []\n",
    "for dir, subdirs, files in os.walk('data'):\n",
    "    for f in files:\n",
    "        if f.endswith('.csv'):\n",
    "            csv_files.append(os.path.join(dir, f))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['data/file1.csv',\n",
       " 'data/file2.csv',\n",
       " 'data/subdir1/file1.csv',\n",
       " 'data/subdir1/file2.csv',\n",
       " 'data/subdir2/file1.csv',\n",
       " 'data/subdir2/file2.csv']"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csv_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/'"
      ]
     },
     "execution_count": 329,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.sep"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ``pathlib``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pathlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [],
   "source": [
    "mein_dir = 'data'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [],
   "source": [
    "mein_csv_file = os.path.join(mein_dir,'file1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'data/file1.csv'"
      ]
     },
     "execution_count": 339,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mein_csv_file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Path?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [],
   "source": [
    "mein_dir = pathlib.Path('data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pathlib.PosixPath"
      ]
     },
     "execution_count": 343,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(mein_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('data/subdir/file1.csv')"
      ]
     },
     "execution_count": 345,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mein_dir / 'subdir' / 'file1.csv'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Iteration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "for elem in range(5):\n",
    "    print(elem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "for elem in [0, 1, 2, 3, 4]:\n",
    "    print(elem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = range(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "range"
      ]
     },
     "execution_count": 357,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "range(0, 3)"
      ]
     },
     "execution_count": 358,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "for elem in r:\n",
    "    print(elem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = range(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {},
   "outputs": [],
   "source": [
    "it = iter(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "range_iterator"
      ]
     },
     "execution_count": 364,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(it)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 366,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(it)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 367,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(it)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 368,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(it)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'StopIteration'> \n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    next(it)\n",
    "except StopIteration as e:\n",
    "    print(type(e), e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [],
   "source": [
    "l = [0, 1, 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {},
   "outputs": [],
   "source": [
    "it = iter(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list_iterator"
      ]
     },
     "execution_count": 373,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(it)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 374,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(it)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 375,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(it)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 376,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(it)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'StopIteration'> \n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    next(it)\n",
    "except StopIteration as e:\n",
    "    print(type(e), e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {},
   "outputs": [],
   "source": [
    "def null_eins_zwei():\n",
    "    print('start, yield 0')\n",
    "    yield 0\n",
    "    print('yield 1')\n",
    "    yield 1\n",
    "    print('yield 2')\n",
    "    yield 2\n",
    "    print('end')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {},
   "outputs": [],
   "source": [
    "seq = null_eins_zwei()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<generator object null_eins_zwei at 0x7f296befbc40>"
      ]
     },
     "execution_count": 386,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {},
   "outputs": [],
   "source": [
    "it = iter(seq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start, yield 0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 388,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(it)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yield 1\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 389,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(it)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yield 2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 390,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(it)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'StopIteration'> \n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    next(it)\n",
    "except StopIteration as e:\n",
    "    print(type(e), e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# More About Lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "metadata": {},
   "outputs": [],
   "source": [
    "l = [2, 3, 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "metadata": {},
   "outputs": [],
   "source": [
    "l.append(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 3, 5, 100]"
      ]
     },
     "execution_count": 395,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {},
   "outputs": [],
   "source": [
    "l.extend([2, 1, 6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 3, 5, 100, 2, 1, 6]"
      ]
     },
     "execution_count": 397,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = {500, 300, 234}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "234\n",
      "500\n",
      "300\n"
     ]
    }
   ],
   "source": [
    "for elem in s:\n",
    "    print(elem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "metadata": {},
   "outputs": [],
   "source": [
    "l.extend(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 3, 5, 100, 2, 1, 6, 234, 500, 300]"
      ]
     },
     "execution_count": 401,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "metadata": {},
   "outputs": [],
   "source": [
    "l.extend(range(3, 15, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 3, 5, 100, 2, 1, 6, 234, 500, 300, 3, 6, 9, 12]"
      ]
     },
     "execution_count": 403,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "metadata": {},
   "outputs": [],
   "source": [
    "l.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 2, 3, 3, 5, 6, 6, 9, 12, 100, 234, 300, 500]"
      ]
     },
     "execution_count": 405,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 408,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random.randrange(1, 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "metadata": {},
   "outputs": [],
   "source": [
    "random.shuffle(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[12, 6, 2, 5, 100, 3, 6, 234, 3, 300, 500, 9, 1, 2]"
      ]
     },
     "execution_count": 410,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4, 5, 6, 3, 2, 5, 6, 2, 2, 5]"
      ]
     },
     "execution_count": 418,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[random.randrange(1,7) for _ in range(10)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 422,
   "metadata": {},
   "outputs": [],
   "source": [
    "l.reverse()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 1, 9, 500, 300, 3, 234, 6, 3, 100, 5, 2, 6, 12]"
      ]
     },
     "execution_count": 423,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 424,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = 'abc'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[12, 6, 2, 5, 100, 3, 6, 234, 3, 300, 500, 9, 1, 2]"
      ]
     },
     "execution_count": 427,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(reversed(l))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2]"
      ]
     },
     "execution_count": 428,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(range(3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# More About Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = {2, 1, 3}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict"
      ]
     },
     "execution_count": 431,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 432,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "set"
      ]
     },
     "execution_count": 433,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "metadata": {},
   "outputs": [],
   "source": [
    "s.add(500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 435,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{500}"
      ]
     },
     "execution_count": 435,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "metadata": {},
   "outputs": [],
   "source": [
    "s.add(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{100, 500}"
      ]
     },
     "execution_count": 437,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "metadata": {},
   "outputs": [],
   "source": [
    "s.add(200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 439,
   "metadata": {},
   "outputs": [],
   "source": [
    "s.remove(500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 440,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "200 in s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n",
      "200\n"
     ]
    }
   ],
   "source": [
    "for elem in s:\n",
    "    print(elem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "metadata": {},
   "outputs": [],
   "source": [
    "s1 = {1, 2, 3, 4}\n",
    "s2 = {3, 4, 5, 6}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{3, 4}"
      ]
     },
     "execution_count": 443,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1& s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1, 2, 3, 4, 5, 6}"
      ]
     },
     "execution_count": 444,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 | s2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# More About Dictionaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 445,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = {\n",
    "    'one': 1,\n",
    "    'two': 2,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "metadata": {},
   "outputs": [],
   "source": [
    "d['three'] = 'drei'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'one': 1, 'two': 2, 'three': 'drei'}"
      ]
     },
     "execution_count": 447,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "metadata": {},
   "outputs": [],
   "source": [
    "del d['three']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 450,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 450,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'three' not in d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 452,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'KeyError'> 'three'\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    d['three']\n",
    "except Exception as e:\n",
    "    print(type(e), e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 453,
   "metadata": {},
   "outputs": [],
   "source": [
    "v = d.get('one')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 454,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 455,
   "metadata": {},
   "outputs": [],
   "source": [
    "v = d.get('three')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "print(v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 461,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "v = d.get('three')\n",
    "if v is not None:\n",
    "    print(v)\n",
    "else:\n",
    "    v = 3\n",
    "    print(v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 462,
   "metadata": {},
   "outputs": [],
   "source": [
    "v = d.get('three', 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 464,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 464,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 465,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "v = d.get('three')\n",
    "if v is not None:\n",
    "    print(v)\n",
    "else:\n",
    "    d['three'] = 3\n",
    "    print(d['three'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 466,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 466,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v = d.setdefault('four', 4)\n",
    "v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 467,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'one': 1, 'two': 2, 'three': 3, 'four': 4}"
      ]
     },
     "execution_count": 467,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dictionary Iteration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 468,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "one\n",
      "two\n",
      "three\n",
      "four\n"
     ]
    }
   ],
   "source": [
    "for elem in d:\n",
    "    print(elem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 469,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 469,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'one' in d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 471,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "one\n",
      "two\n",
      "three\n",
      "four\n"
     ]
    }
   ],
   "source": [
    "for elem in d.keys():\n",
    "    print(elem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 472,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "for elem in d.values():\n",
    "    print(elem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 474,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('one', 1)\n",
      "('two', 2)\n",
      "('three', 3)\n",
      "('four', 4)\n"
     ]
    }
   ],
   "source": [
    "for elem in d.items():\n",
    "    print(elem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 475,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "key:one, value:1\n",
      "key:two, value:2\n",
      "key:three, value:3\n",
      "key:four, value:4\n"
     ]
    }
   ],
   "source": [
    "for elem in d.items():\n",
    "    k = elem[0]\n",
    "    v = elem[1]\n",
    "    print(f'key:{k}, value:{v}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 476,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "key:one, value:1\n",
      "key:two, value:2\n",
      "key:three, value:3\n",
      "key:four, value:4\n"
     ]
    }
   ],
   "source": [
    "for k, v in d.items():\n",
    "    print(f'key:{k}, value:{v}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 477,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 477,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 478,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'one': 1, 'two': 2, 'three': 3, 'four': 4}"
      ]
     },
     "execution_count": 478,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 479,
   "metadata": {},
   "outputs": [],
   "source": [
    "d['one'] = 'eins'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 480,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'one': 'eins', 'two': 2, 'three': 3, 'four': 4}"
      ]
     },
     "execution_count": 480,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 482,
   "metadata": {},
   "outputs": [],
   "source": [
    "other = { 'three': 'drei', 'four': 'vier', 'five': 'fuenf'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 483,
   "metadata": {},
   "outputs": [],
   "source": [
    "d.update(other)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 484,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'one': 'eins', 'two': 2, 'three': 'drei', 'four': 'vier', 'five': 'fuenf'}"
      ]
     },
     "execution_count": 484,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 485,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict"
      ]
     },
     "execution_count": 485,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 486,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{}"
      ]
     },
     "execution_count": 486,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 487,
   "metadata": {},
   "outputs": [],
   "source": [
    "kvpairs = [('eins', 1), ('zwei', 2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 488,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'eins': 1, 'zwei': 2}"
      ]
     },
     "execution_count": 488,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict(kvpairs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ``numpy``, ``scikit-learn``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 492,
   "metadata": {},
   "outputs": [],
   "source": [
    "import PIL.Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = PIL.Image.open('color-reduction/veggie.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 495,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=686x477>"
      ]
     },
     "execution_count": 495,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 496,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 499,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_array = numpy.array(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 502,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(477, 686, 4)"
      ]
     },
     "execution_count": 502,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_array.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One point in the middle of the cube"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 501,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([107,  68, 112, 255], dtype=uint8)"
      ]
     },
     "execution_count": 501,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_array[350,500]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 509,
   "metadata": {},
   "outputs": [],
   "source": [
    "rgb = img_array[:,:,:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 512,
   "metadata": {},
   "outputs": [],
   "source": [
    "alpha = img_array[:,:,3:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 518,
   "metadata": {},
   "outputs": [],
   "source": [
    "x, y, z = rgb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 519,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "477"
      ]
     },
     "execution_count": 519,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 520,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "686"
      ]
     },
     "execution_count": 520,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 521,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 521,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 531,
   "metadata": {},
   "outputs": [],
   "source": [
    "rgb_linear = rgb.reshape(((x*y), z))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 532,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[255, 255, 255],\n",
       "       [255, 255, 255],\n",
       "       [255, 255, 255],\n",
       "       ...,\n",
       "       [255, 255, 255],\n",
       "       [255, 255, 255],\n",
       "       [255, 255, 255]], dtype=uint8)"
      ]
     },
     "execution_count": 532,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rgb_linear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 533,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 534,
   "metadata": {},
   "outputs": [],
   "source": [
    "km = KMeans(n_clusters=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 535,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KMeans<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.cluster.KMeans.html\">?<span>Documentation for KMeans</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KMeans()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "KMeans()"
      ]
     },
     "execution_count": 535,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km.fit(rgb_linear)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 537,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 67.18423981,  37.51866902,  61.40779841],\n",
       "       [254.94278979, 254.7484102 , 254.52017749],\n",
       "       [121.71821931, 114.48415009,  25.61361798],\n",
       "       [226.10754686,  72.97606427,  41.93141702],\n",
       "       [225.5569347 , 182.5296816 , 135.4875877 ],\n",
       "       [122.85174112,  81.87099894, 132.85349982],\n",
       "       [241.35345614, 158.73043954,  14.19387197],\n",
       "       [156.7373525 ,  24.19961382,  27.95700493]])"
      ]
     },
     "execution_count": 537,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 540,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, ..., 1, 1, 1], dtype=int32)"
      ]
     },
     "execution_count": 540,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 543,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 543,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(km.labels_) == x*y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 544,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0, 1, 2, 3, 4, 5, 6, 7}"
      ]
     },
     "execution_count": 544,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(km.labels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 561,
   "metadata": {},
   "outputs": [],
   "source": [
    "#for l in km.labels_:\n",
    "#    rgb_linear[idx]??? = km.cluster_centers_[l]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So wie jetzt?!?!?!?!?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 547,
   "metadata": {},
   "outputs": [],
   "source": [
    "pixels = [\n",
    "    [2, 3, 4],\n",
    "    [1,2,3],\n",
    "    [4,5,6],\n",
    "    [45,23,3],\n",
    "]\n",
    "centers = [\n",
    "    [3,2,3],\n",
    "    [3,1,6],\n",
    "    [8,9,1],\n",
    "]\n",
    "labels = [0,2,1,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 552,
   "metadata": {},
   "outputs": [],
   "source": [
    "names = ['Joerg', 'Isi', 'Johanna']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 553,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Joerg\n",
      "Isi\n",
      "Johanna\n"
     ]
    }
   ],
   "source": [
    "for name in names:\n",
    "    print(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 555,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 Joerg\n",
      "1 Isi\n",
      "2 Johanna\n"
     ]
    }
   ],
   "source": [
    "for idx, name in enumerate(names):\n",
    "    print(idx, name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 569,
   "metadata": {},
   "outputs": [],
   "source": [
    "for idx, label in enumerate(labels):\n",
    "    pixels[idx] = centers[label]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 560,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[3, 2, 3], [8, 9, 1], [3, 1, 6], [3, 2, 3]]"
      ]
     },
     "execution_count": 560,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pixels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Weiter mit richtigen Bildern ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Austauschen der einzelenen Pixel auf ihr reduzierted Center. *Dabei wird das urspruengliche rgb veraendert, da rgb_linear durch reshape() nur ein alternativer View auf das gleiche Memory ist*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 562,
   "metadata": {},
   "outputs": [],
   "source": [
    "for idx, label in enumerate(km.labels_):\n",
    "    rgb_linear[idx] = km.cluster_centers_[label]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 563,
   "metadata": {},
   "outputs": [],
   "source": [
    "reduced_array = numpy.concatenate((rgb, alpha), axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 564,
   "metadata": {},
   "outputs": [],
   "source": [
    "reduced_img = PIL.Image.fromarray(reduced_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 567,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGBA size=686x477>"
      ]
     },
     "execution_count": 567,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reduced_img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 568,
   "metadata": {},
   "outputs": [],
   "source": [
    "reduced_img.save('color-reduction/reduced-veggie.png')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}