index.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "instrumental-jumping",
   "metadata": {},
   "source": [
    "# Some basics regarding Python and Jupyter"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "magnetic-violation",
   "metadata": {},
   "source": [
    "For a similar, but more extensive tour see:\n",
    "https://github.com/jakevdp/WhirlwindTourOfPython"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "behavioral-disposition",
   "metadata": {},
   "source": [
    "## Jupyter Evaluation and Kernel State"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "played-calgary",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "interesting-seating",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "olympic-compatibility",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'y' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-9063a9f0e032>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'y' is not defined"
     ]
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "better-dollar",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "applicable-charleston",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "catholic-immunology",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y * 3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "equivalent-delhi",
   "metadata": {},
   "source": [
    "now try to restart your kernel!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "needed-inventory",
   "metadata": {},
   "source": [
    "more on this topic: https://realpython.com/jupyter-notebook-introduction/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "functional-astronomy",
   "metadata": {},
   "source": [
    "## Types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "derived-crawford",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 + 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "diverse-simple",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.3333333333333335"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "10 / 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "aggregate-devices",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(1 + 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "stretch-privilege",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(10 / 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "sought-colleague",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type('hello')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "finished-statistics",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"hello\" == 'hello'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "chronic-burke",
   "metadata": {},
   "source": [
    "more on this topic: https://realpython.com/python-data-types/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "above-probe",
   "metadata": {},
   "source": [
    "## Containers and Data Structures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "english-auction",
   "metadata": {},
   "outputs": [],
   "source": [
    "# this is a comment"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "established-syndrome",
   "metadata": {},
   "source": [
    "### Lists and Loops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "animal-amendment",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "squares = []\n",
    "for i in range(10):\n",
    "    squares.append(i ** 2)\n",
    "squares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "breathing-rapid",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m \u001b[0msquares\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobject\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m/\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m Append object to the end of the list.\n",
       "\u001b[0;31mType:\u001b[0m      builtin_function_or_method\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "?squares.append"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "colored-secret",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(squares)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fifteen-zoning",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "squares[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "domestic-madness",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4, 9, 16]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "squares[2:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "cordless-stack",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "25 in squares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "engaging-texture",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "30 in squares"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "absolute-fetish",
   "metadata": {},
   "source": [
    "### List Comprehensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "asian-cream",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[i ** 2 for i in range(10)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "legendary-friendship",
   "metadata": {},
   "source": [
    "### Mutability and Type Safety"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "handed-senior",
   "metadata": {},
   "outputs": [],
   "source": [
    "squares[1] = 'what?'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "disturbed-valuable",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 'what?', 4, 9, 16, 25, 36, 49, 64, 81]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "squares"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "severe-raise",
   "metadata": {},
   "source": [
    "### Lists in the Python API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "silver-paris",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 'an example'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "atlantic-dance",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['an', 'example']"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "adjustable-approval",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmaxsplit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Return a list of the words in the string, using sep as the delimiter string.\n",
       "\n",
       "sep\n",
       "  The delimiter according which to split the string.\n",
       "  None (the default value) means split according to any whitespace,\n",
       "  and discard empty strings from the result.\n",
       "maxsplit\n",
       "  Maximum number of splits to do.\n",
       "  -1 (the default value) means no limit.\n",
       "\u001b[0;31mType:\u001b[0m      builtin_function_or_method\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "?x.split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "alive-merchandise",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Object `'an example'.split` not found.\n"
     ]
    }
   ],
   "source": [
    "?'an example'.split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "complicated-large",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['__add__',\n",
       " '__class__',\n",
       " '__contains__',\n",
       " '__delattr__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattribute__',\n",
       " '__getitem__',\n",
       " '__getnewargs__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__init__',\n",
       " '__init_subclass__',\n",
       " '__iter__',\n",
       " '__le__',\n",
       " '__len__',\n",
       " '__lt__',\n",
       " '__mod__',\n",
       " '__mul__',\n",
       " '__ne__',\n",
       " '__new__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__rmod__',\n",
       " '__rmul__',\n",
       " '__setattr__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__subclasshook__',\n",
       " 'capitalize',\n",
       " 'casefold',\n",
       " 'center',\n",
       " 'count',\n",
       " 'encode',\n",
       " 'endswith',\n",
       " 'expandtabs',\n",
       " 'find',\n",
       " 'format',\n",
       " 'format_map',\n",
       " 'index',\n",
       " 'isalnum',\n",
       " 'isalpha',\n",
       " 'isascii',\n",
       " 'isdecimal',\n",
       " 'isdigit',\n",
       " 'isidentifier',\n",
       " 'islower',\n",
       " 'isnumeric',\n",
       " 'isprintable',\n",
       " 'isspace',\n",
       " 'istitle',\n",
       " 'isupper',\n",
       " 'join',\n",
       " 'ljust',\n",
       " 'lower',\n",
       " 'lstrip',\n",
       " 'maketrans',\n",
       " 'partition',\n",
       " 'replace',\n",
       " 'rfind',\n",
       " 'rindex',\n",
       " 'rjust',\n",
       " 'rpartition',\n",
       " 'rsplit',\n",
       " 'rstrip',\n",
       " 'split',\n",
       " 'splitlines',\n",
       " 'startswith',\n",
       " 'strip',\n",
       " 'swapcase',\n",
       " 'title',\n",
       " 'translate',\n",
       " 'upper',\n",
       " 'zfill']"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "twenty-angel",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['this', 'morning'], ['and', 'yesterday', 'evening']]"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[x.split() for x in 'this morning, and yesterday evening'.split(',')]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "informational-spank",
   "metadata": {},
   "source": [
    "### Tuples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "behavioral-avenue",
   "metadata": {},
   "outputs": [],
   "source": [
    "greek_alphabet = ('alpha', 'beta', 'gamma')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "solid-favor",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'beta'"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "greek_alphabet[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "liquid-graduate",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'tuple' object does not support item assignment",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-137-3b5f2ecda26d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgreek_alphabet\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'rho'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
     ]
    }
   ],
   "source": [
    "greek_alphabet[1] = 'rho'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "conceptual-ordinary",
   "metadata": {},
   "source": [
    "### Dicts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "swiss-directory",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Peru'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_to_country = {\n",
    "    'Lima': 'Peru',\n",
    "    'Kinshasa': 'Democratic Republic of the Congo',\n",
    "    'Lisbon': 'Portugal'\n",
    "}\n",
    "\n",
    "# alternative Syntax:\n",
    "# city_to_country = dict(Lima='Peru', Kinshasa='Democratic Republic of the Congo')\n",
    "\n",
    "city_to_country['Lima']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "aerial-blond",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Hanoi'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-17-4e84bd810d49>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcity_to_country\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Hanoi'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m: 'Hanoi'"
     ]
    }
   ],
   "source": [
    "city_to_country['Hanoi']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "right-practitioner",
   "metadata": {},
   "outputs": [],
   "source": [
    "city_to_country['Hanoi'] = 'Vietnam'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bigger-sunrise",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Vietnam'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_to_country['Hanoi']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "white-lingerie",
   "metadata": {},
   "source": [
    "### Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "polyphonic-region",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries = {'Peru', 'Democratic Republic of the Congo', 'Portugal'}\n",
    "\n",
    "# alternative Syntax:\n",
    "# countries = set('Peru', 'Democratic Republic of the Congo', 'Portugal')\n",
    "\n",
    "'Peru' in countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "helpful-surfing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'Luxembourg' in countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "coupled-uruguay",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Add an element to a set.\n",
       "\n",
       "This has no effect if the element is already present.\n",
       "\u001b[0;31mType:\u001b[0m      method_descriptor\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "?set.add"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "vocal-turkish",
   "metadata": {},
   "outputs": [],
   "source": [
    "countries.add('Luxembourg')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "phantom-advisory",
   "metadata": {},
   "source": [
    "countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "fourth-belle",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Peru', 'Portugal', 'Luxembourg', 'Democratic Republic of the Congo']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(countries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "buried-intention",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0, 16, 25, 36, 4, 49, 64, 81, 9, 'what?'}"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(squares)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "royal-syria",
   "metadata": {},
   "source": [
    "more on this topic: https://realpython.com/python-data-structures/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "available-bench",
   "metadata": {},
   "source": [
    "# String Formatting and Interpolation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "prompt-server",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'an apple'"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'an' + ' ' + 'apple'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "offensive-craps",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'an apple'"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "' '.join(['an', 'apple'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "respected-harbor",
   "metadata": {},
   "outputs": [],
   "source": [
    "some_year = 2525"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "numeric-spectrum",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "can only concatenate str (not \"int\") to str",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-214-cb7942e5f6b1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m'in the year '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0msome_year\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: can only concatenate str (not \"int\") to str"
     ]
    }
   ],
   "source": [
    "'in the year ' + some_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "super-contractor",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'in the year 2525'"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'in the year ' + str(some_year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "emotional-galaxy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'in the year 2525'"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'in the year %d' % some_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "latest-nightlife",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'in the year 2525'"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f'in the year {some_year}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "sorted-diana",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'in the 25th century, the BETA corporation was founded.'"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f'in the {some_year // 100}th century, the {greek_alphabet[1].upper()} corporation was founded.'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "burning-galaxy",
   "metadata": {},
   "source": [
    "# Files, Scopes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ready-marks",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('example.txt', 'r') as f:\n",
    "    text = f.read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "mexican-portable",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Tyger Tyger, burning bright,\\nIn the forests of the night;\\nWhat immortal hand or eye,\\nCould frame thy fearful symmetry?\\n\\n- William Blake\\n'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "rental-bunch",
   "metadata": {},
   "source": [
    "more on this topic: https://realpython.com/read-write-files-python/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "color-philosophy",
   "metadata": {},
   "source": [
    "# Modules"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "annoying-checkout",
   "metadata": {},
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "arranged-invention",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 're' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-80-b80d3ea14515>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mre\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 're' is not defined"
     ]
    }
   ],
   "source": [
    "re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "yellow-russia",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "focal-mustang",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 're' from '/opt/miniconda3/envs/nteract/lib/python3.7/re.py'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "re"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "induced-prairie",
   "metadata": {},
   "source": [
    "### Using a Module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "unknown-hopkins",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<re.Match object; span=(10, 13), match='365'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "re.search(r'[0-9]+', 'there are 365 days in a year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "frequent-treasurer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m \u001b[0mre\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msearch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpattern\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Scan through string looking for a match to the pattern, returning\n",
       "a Match object, or None if no match was found.\n",
       "\u001b[0;31mFile:\u001b[0m      /opt/miniconda3/envs/nteract/lib/python3.7/re.py\n",
       "\u001b[0;31mType:\u001b[0m      function\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "?re.search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "conscious-studio",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "re.Match"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = re.search(r'[0-9]+', 'there are 365 days in a year')\n",
    "type(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "killing-kruger",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mType:\u001b[0m        Match\n",
       "\u001b[0;31mString form:\u001b[0m <re.Match object; span=(10, 13), match='365'>\n",
       "\u001b[0;31mFile:\u001b[0m        /opt/miniconda3/envs/nteract/lib/python3.7/re.py\n",
       "\u001b[0;31mDocstring:\u001b[0m  \n",
       "The result of re.match() and re.search().\n",
       "Match objects always have a boolean value of True.\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "?m"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "equipped-bumper",
   "metadata": {},
   "source": [
    "### Imports and Local Names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "annoying-adelaide",
   "metadata": {},
   "outputs": [],
   "source": [
    "import collections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "given-investing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter()"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = collections.Counter()\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "negative-figure",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "senior-frost",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = Counter()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "incorporate-baltimore",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mInit signature:\u001b[0m \u001b[0mCounter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m     \n",
       "Dict subclass for counting hashable items.  Sometimes called a bag\n",
       "or multiset.  Elements are stored as dictionary keys and their counts\n",
       "are stored as dictionary values.\n",
       "\n",
       ">>> c = Counter('abcdeabcdabcaba')  # count elements from a string\n",
       "\n",
       ">>> c.most_common(3)                # three most common elements\n",
       "[('a', 5), ('b', 4), ('c', 3)]\n",
       ">>> sorted(c)                       # list all unique elements\n",
       "['a', 'b', 'c', 'd', 'e']\n",
       ">>> ''.join(sorted(c.elements()))   # list elements with repetitions\n",
       "'aaaaabbbbcccdde'\n",
       ">>> sum(c.values())                 # total of all counts\n",
       "15\n",
       "\n",
       ">>> c['a']                          # count of letter 'a'\n",
       "5\n",
       ">>> for elem in 'shazam':           # update counts from an iterable\n",
       "...     c[elem] += 1                # by adding 1 to each element's count\n",
       ">>> c['a']                          # now there are seven 'a'\n",
       "7\n",
       ">>> del c['b']                      # remove all 'b'\n",
       ">>> c['b']                          # now there are zero 'b'\n",
       "0\n",
       "\n",
       ">>> d = Counter('simsalabim')       # make another counter\n",
       ">>> c.update(d)                     # add in the second counter\n",
       ">>> c['a']                          # now there are nine 'a'\n",
       "9\n",
       "\n",
       ">>> c.clear()                       # empty the counter\n",
       ">>> c\n",
       "Counter()\n",
       "\n",
       "Note:  If a count is set to zero or reduced to zero, it will remain\n",
       "in the counter until the entry is deleted or the counter is cleared:\n",
       "\n",
       ">>> c = Counter('aaabbc')\n",
       ">>> c['b'] -= 2                     # reduce the count of 'b' by two\n",
       ">>> c.most_common()                 # 'b' is still in, but its count is zero\n",
       "[('a', 3), ('c', 1), ('b', 0)]\n",
       "\u001b[0;31mInit docstring:\u001b[0m\n",
       "Create a new, empty Counter object.  And if given, count elements\n",
       "from an input iterable.  Or, initialize the count from another mapping\n",
       "of elements to their counts.\n",
       "\n",
       ">>> c = Counter()                           # a new, empty counter\n",
       ">>> c = Counter('gallahad')                 # a new counter from an iterable\n",
       ">>> c = Counter({'a': 4, 'b': 2})           # a new counter from a mapping\n",
       ">>> c = Counter(a=4, b=2)                   # a new counter from keyword args\n",
       "\u001b[0;31mFile:\u001b[0m           /opt/miniconda3/envs/nteract/lib/python3.7/collections/__init__.py\n",
       "\u001b[0;31mType:\u001b[0m           type\n",
       "\u001b[0;31mSubclasses:\u001b[0m     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "?Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "universal-permit",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter as MyCounter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "blank-mayor",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = MyCounter()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "departmental-booth",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "incorrect-appraisal",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Entity</th>\n",
       "      <th>Currency</th>\n",
       "      <th>AlphabeticCode</th>\n",
       "      <th>NumericCode</th>\n",
       "      <th>MinorUnit</th>\n",
       "      <th>WithdrawalDate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AFGHANISTAN</td>\n",
       "      <td>Afghani</td>\n",
       "      <td>AFN</td>\n",
       "      <td>971.0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ÅLAND ISLANDS</td>\n",
       "      <td>Euro</td>\n",
       "      <td>EUR</td>\n",
       "      <td>978.0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ALBANIA</td>\n",
       "      <td>Lek</td>\n",
       "      <td>ALL</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ALGERIA</td>\n",
       "      <td>Algerian Dinar</td>\n",
       "      <td>DZD</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AMERICAN SAMOA</td>\n",
       "      <td>US Dollar</td>\n",
       "      <td>USD</td>\n",
       "      <td>840.0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>ZIMBABWE</td>\n",
       "      <td>Zimbabwe Dollar (new)</td>\n",
       "      <td>ZWN</td>\n",
       "      <td>942.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2006-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>ZIMBABWE</td>\n",
       "      <td>Zimbabwe Dollar</td>\n",
       "      <td>ZWR</td>\n",
       "      <td>935.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2009-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>ZZ01_Gold-Franc</td>\n",
       "      <td>Gold-Franc</td>\n",
       "      <td>XFO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2006-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>ZZ02_RINET Funds Code</td>\n",
       "      <td>RINET Funds Code</td>\n",
       "      <td>XRE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1999-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>ZZ05_UIC-Franc</td>\n",
       "      <td>UIC-Franc</td>\n",
       "      <td>XFU</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>441 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Entity               Currency AlphabeticCode  NumericCode  \\\n",
       "0              AFGHANISTAN                Afghani            AFN        971.0   \n",
       "1            ÅLAND ISLANDS                   Euro            EUR        978.0   \n",
       "2                  ALBANIA                    Lek            ALL          8.0   \n",
       "3                  ALGERIA         Algerian Dinar            DZD         12.0   \n",
       "4           AMERICAN SAMOA              US Dollar            USD        840.0   \n",
       "..                     ...                    ...            ...          ...   \n",
       "436               ZIMBABWE  Zimbabwe Dollar (new)            ZWN        942.0   \n",
       "437               ZIMBABWE        Zimbabwe Dollar            ZWR        935.0   \n",
       "438        ZZ01_Gold-Franc             Gold-Franc            XFO          NaN   \n",
       "439  ZZ02_RINET Funds Code       RINET Funds Code            XRE          NaN   \n",
       "440         ZZ05_UIC-Franc              UIC-Franc            XFU          NaN   \n",
       "\n",
       "    MinorUnit WithdrawalDate  \n",
       "0           2            NaN  \n",
       "1           2            NaN  \n",
       "2           2            NaN  \n",
       "3           2            NaN  \n",
       "4           2            NaN  \n",
       "..        ...            ...  \n",
       "436       NaN        2006-09  \n",
       "437       NaN        2009-06  \n",
       "438       NaN        2006-10  \n",
       "439       NaN        1999-11  \n",
       "440       NaN        2013-11  \n",
       "\n",
       "[441 rows x 6 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(\"example.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "recent-empty",
   "metadata": {},
   "source": [
    "import name conventions:\n",
    "\n",
    "* numpy &rarr; np\n",
    "* matplotlib.pyplot &rarr; plt\n",
    "* pandas &rarr; pd\n",
    "* tensorflow &rarr; tf\n",
    "* ..."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dominican-metabolism",
   "metadata": {},
   "source": [
    "more on these topics:\n",
    "* https://realpython.com/python-modules-packages/\n",
    "* https://realpython.com/regex-python/\n",
    "* https://realpython.com/regex-python-part-2/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "thick-friendly",
   "metadata": {},
   "source": [
    "# Functions and Lambdas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "banner-married",
   "metadata": {},
   "outputs": [],
   "source": [
    "def name_items(n, name):\n",
    "    if n < 10:\n",
    "        return 'few'\n",
    "    else:\n",
    "        return 'many'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "spiritual-assignment",
   "metadata": {},
   "outputs": [],
   "source": [
    "name_items(5, 'apple')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "radio-advertising",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def draw_circle(hatch=None, color='green'):\n",
    "    # also see: https://matplotlib.org/stable/gallery/color/named_colors.html\n",
    "    circle = plt.Circle((0, 0), 0.75, facecolor=color, hatch=hatch)\n",
    "    plt.gca().add_patch(circle)\n",
    "    plt.axis('scaled')\n",
    "    \n",
    "draw_circle()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "absolute-eligibility",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_circle('/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "unique-arrangement",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_circle('//', 'red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "latin-segment",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_circle(color='#FF8020')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "quantitative-block",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "args = {\n",
    "    'color': 'yellow'\n",
    "}\n",
    "\n",
    "draw_circle(**args)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "round-rhythm",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "args = ['..']\n",
    "\n",
    "draw_circle(*args)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "adult-auckland",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-20, 3, 4, 7, 10]"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted([10, 7, 3, -20, 4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "religious-relations",
   "metadata": {},
   "outputs": [],
   "source": [
    "cities = [\n",
    "    ('New York City', 20870000),\n",
    "    ('Karachi', 15400000),\n",
    "    ('Buenos Aires', 16157000)\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "discrete-endorsement",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Buenos Aires', 16157000),\n",
       " ('Karachi', 15400000),\n",
       " ('New York City', 20870000)]"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(cities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "experienced-momentum",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Karachi', 15400000),\n",
       " ('Buenos Aires', 16157000),\n",
       " ('New York City', 20870000)]"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_population(x):\n",
    "    return x[1]\n",
    "\n",
    "sorted(cities, key=get_population)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "educational-insight",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Karachi', 15400000),\n",
       " ('Buenos Aires', 16157000),\n",
       " ('New York City', 20870000)]"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(cities, key=lambda x: x[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "immediate-reader",
   "metadata": {},
   "source": [
    "more on this topic:\n",
    "* https://realpython.com/defining-your-own-python-function/\n",
    "* https://realpython.com/python-lambda/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "unavailable-affect",
   "metadata": {},
   "source": [
    "# Interactivity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "furnished-breast",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c7942bac6ce54872b8427364ddac6e40",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=50, description='x', min=1), Output()), _dom_classes=('widget-interact',…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import math\n",
    "from ipywidgets import interact\n",
    "interact(lambda x: math.sqrt(x), x=(1, 100));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "gross-generation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot([10, 15, 10, 5, 7, 20]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "adaptive-jacob",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.cm\n",
    "import random\n",
    "import math\n",
    "\n",
    "def plot_f(a, b):\n",
    "    plt.plot([math.sin(t * 0.1 * a) + math.cos(t * 0.1 * b) for t in range(0, 100)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "manufactured-architecture",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bef58b3d463f432394ea7576c25ac328",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=2.5, description='a', max=5.0), FloatSlider(value=2.5, description='b'…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ipywidgets import interact\n",
    "interact(plot_f, a=(0.0, 5.0, 0.1), b=(0.0, 5.0, 0.1));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sought-tucson",
   "metadata": {},
   "source": [
    "more on this topic: https://ipywidgets.readthedocs.io/en/latest/examples/Using%20Interact.html"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "distinguished-separation",
   "metadata": {},
   "source": [
    "# Classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "criminal-edinburgh",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a car with 4 wheels and 2 doors'"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class Vehicle:\n",
    "    def number_of_wheels(self):\n",
    "        raise NotImplementedError()\n",
    "\n",
    "        \n",
    "class Car(Vehicle):\n",
    "    def __init__(self, doors=4):\n",
    "        self._doors = doors\n",
    "\n",
    "    def number_of_wheels(self):\n",
    "        return 4\n",
    " \n",
    "    def number_of_doors(self):\n",
    "        return self._doors\n",
    "\n",
    "\n",
    "my_car = Car(2)\n",
    "f'a car with {my_car.number_of_wheels()} wheels and {my_car.number_of_doors()} doors'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "moral-civilization",
   "metadata": {},
   "source": [
    "more on this topic: https://realpython.com/python3-object-oriented-programming/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "emotional-behalf",
   "metadata": {},
   "source": [
    "# Numpy, Tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "geological-better",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "informed-crazy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.array([1, 2, 3, 4])\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "incorrect-teaching",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0.02602794, 0.35301796, 0.2766494 , 0.79390885],\n",
       "        [0.25934136, 0.88756167, 0.74794007, 0.04301107]],\n",
       "\n",
       "       [[0.2427287 , 0.65559994, 0.18124693, 0.42844279],\n",
       "        [0.24387321, 0.78512089, 0.50368093, 0.9607215 ]],\n",
       "\n",
       "       [[0.77999966, 0.29633627, 0.55234431, 0.83077194],\n",
       "        [0.7461072 , 0.53708343, 0.27209902, 0.18406189]]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.random.random((3, 2, 4))\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "defined-throat",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 2, 4)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "integral-complex",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.2766494 , 0.79390885],\n",
       "       [0.18124693, 0.42844279],\n",
       "       [0.55234431, 0.83077194]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[:, 0, 2:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "eleven-leonard",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[3.05205588, 3.70603592, 3.55329879, 4.5878177 ],\n",
       "        [3.51868273, 4.77512334, 4.49588014, 3.08602213]],\n",
       "\n",
       "       [[3.4854574 , 4.31119988, 3.36249387, 3.85688557],\n",
       "        [3.48774642, 4.57024178, 4.00736186, 4.921443  ]],\n",
       "\n",
       "       [[4.55999932, 3.59267255, 4.10468862, 4.66154388],\n",
       "        [4.4922144 , 4.07416686, 3.54419804, 3.36812379]]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(x + 1.5) * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "arbitrary-update",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "arbitrary-context",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: id=0, shape=(3, 2, 4), dtype=float32, numpy=\n",
       "array([[[0.02602794, 0.35301796, 0.2766494 , 0.79390883],\n",
       "        [0.25934136, 0.8875617 , 0.74794006, 0.04301107]],\n",
       "\n",
       "       [[0.2427287 , 0.65559995, 0.18124694, 0.42844278],\n",
       "        [0.24387321, 0.7851209 , 0.50368094, 0.9607215 ]],\n",
       "\n",
       "       [[0.7799997 , 0.29633626, 0.5523443 , 0.8307719 ],\n",
       "        [0.7461072 , 0.53708345, 0.27209902, 0.1840619 ]]], dtype=float32)>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "tf.convert_to_tensor(x, np.float32)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "underlying-composer",
   "metadata": {},
   "source": [
    "more on this topic: https://realpython.com/numpy-tutorial/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bibliographic-suspect",
   "metadata": {},
   "source": [
    "# The End"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "introductory-education",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "def dot2(x):\n",
    "    return np.sum(x * x, axis=-1)\n",
    "\n",
    "def heart_sdf(p):\n",
    "    # see https://www.iquilezles.org/www/articles/distfunctions2d/distfunctions2d.htm\n",
    "    # see https://www.shadertoy.com/view/3tyBzV\n",
    "\n",
    "    p[:, 0] = np.abs(p[:, 0])\n",
    "    c3 = dot2(p - 0.5 * np.clip(p[:, 0] + p[:, 1], 0, None)[:, np.newaxis])\n",
    "    c1 = np.sqrt(dot2(p - np.array([0.25, 0.75]))) - np.sqrt(2.0) / 4.0\n",
    "    c2 = np.sqrt(np.minimum(dot2(p - np.array([0, 1])), c3)) * np.sign(p[:, 0] - p[:, 1])\n",
    "    return np.select([p[:, 0] + p[:, 1] > 1, True], [c1, c2])\n",
    "\n",
    "def plot_heart():\n",
    "    dxy = 1 / 100\n",
    "    grid = np.mgrid[-1:1:dxy, -0.5:1.5:dxy]\n",
    "    p = np.vstack(np.dstack(grid))\n",
    "    d = heart_sdf(p).reshape(grid.shape[-2], -1).T\n",
    "    plt.figure(figsize=(4, 4))\n",
    "    plt.contourf(d, cmap='RdYlBu', levels=10, vmin=-0.1, vmax=0.5)\n",
    "\n",
    "    \n",
    "plot_heart()"
   ]
  }
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