{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Errori standard\n",
    "\n",
    "Vediamo alcuni errori comuni che ho riscontrato durante la lezione di laboratorio."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Consideriamo il programma che prende in input un numero e ne calcola il doppio.\n",
    "\n",
    "```python\n",
    "a = int(input(\"Immetti il numero a: \"))\n",
    "a2 = (2 * a)\n",
    "print = (\"Il doppio di a è\", a2)  # ERRORE\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parentesi di troppo\n",
    "Le parentesi nella riga\n",
    "\n",
    "```python\n",
    "a2 = (2 * a)\n",
    "```\n",
    "\n",
    "non sono un errore, ma non sono neanche necessarie. Meglio senza:\n",
    "\n",
    "```python\n",
    "a2 = 2 * a\n",
    "```\n",
    "\n",
    "Notare che invece le parentesi in tutti gli altri posti sono obbligatorie."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Un uguale di troppo\n",
    "\n",
    "L'uguale nella riga\n",
    "```python\n",
    "print = (\"Il doppio di a è\", a2)  # ERRORE\n",
    "```\n",
    "è sbagliato. Non genera un messaggio di errore, ma non stampa nulla. Python pensa che voi vogliate definire una variabile che si chiamata `print`, ed esegue l'assegnamento. Da quel momento in poi, la funzione `print` smetterà di funzionare perché è stata ridefinita da questa uguaglianza."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Spazi fuori posto\n",
    "\n",
    "In linea di massimo Python è piuttosto liberare nel dove mettere gli spazi. Ad esempio:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ciao Mondo\n",
      "Ciao Mondo\n"
     ]
    }
   ],
   "source": [
    "print(\"Ciao\",\"Mondo\")\n",
    "print   (    \"Ciao\"   ,   \"Mondo\"  )  # fa esattamente la stessa cosa dell'istruzione sopra"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tuttavia, il Python è estremamente pignolo per ciò che riguarda gli spazi a inizio di una riga: non è possibile aggiungere spazi liberamente, l'istruzione deve partire esattamente dalla prima colonna (almeno per ora, poi vedremo che questi spazi iniziali hanno un significato)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndentationError",
     "evalue": "unexpected indent (1663876427.py, line 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  Cell \u001b[0;32mIn[3], line 2\u001b[0;36m\u001b[0m\n\u001b[0;31m    print(\"Mondo\")   # Questa print da errore perché non inizia nella prima colonna\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m unexpected indent\n"
     ]
    }
   ],
   "source": [
    "print(\"Ciao\")\n",
    "  print(\"Mondo\")   # Questa print da errore perché non inizia nella prima colonna"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ancora sui numeri in virgola mobile (tipo `float`)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Notazione scientifica"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Questo è un numero in virgola mobile nella notazione standard."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.5"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "4.5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Per scrivere numeri in virgola mobile, si può usare la notazione scientifica. In questa notazione il carattere `E` (o anche `e`) è una abbreviazione di *per 10 elevato a*. Ricordiamo che moltiplicare un numero per $10^i$ vuol dire spostare la virgola di i posti a destra (se positivo) o a sinistra (se i è negativo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23000.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2.3E4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.012"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "-1.2E-2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se un numero è molto grande, Python usa la notazione scientifica per visualizzarcelo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.2676506002282294e+30"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2.0 ** 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1267650600228229401496703205376"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Notare che il risultato di prima è solo una approssimazione. Quello vero si può ottenere usando i numeri interi.\n",
    "2 ** 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problemi con l'uso dei numeri `float`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se un numero è troppo grande, non è possibile rappresentarlo con un dato di tipo float, e si genera un errore.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "OverflowError",
     "evalue": "(34, 'Numerical result out of range')",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mOverflowError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_1508067/3207207499.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;36m10.0\u001b[0m \u001b[0;34m**\u001b[0m \u001b[0;36m400\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mOverflowError\u001b[0m: (34, 'Numerical result out of range')"
     ]
    }
   ],
   "source": [
    "10.0 ** 400"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Le operazioni arimetiche danno talvolta un risultato sbagliato. Questo perché il tipo `float` rappresenta i numeri in base 2, e nella base 2 anche numeri che normalmente sono semplici, come 0.1, diventano periodici."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Questo calcolo resistuisce il valore corretto\n",
    "0.2 - 0.1 - 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2.7755575615628914e-17"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ma questo no!!! Il risultato dovrebbe essere 0, ma non lo è\n",
    "0.3 - 0.1 - 0.1 - 0.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Funzioni"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La funzione `abs` restituisce il valore assoluto di un numero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "54"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abs(54)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "54"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abs(-54)"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quando il numero di argomenti forniti ad una funzione non è corretto, si genera un errore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "abs() takes exactly one argument (2 given)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_1508067/2085501094.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m54\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m23\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: abs() takes exactly one argument (2 given)"
     ]
    }
   ],
   "source": [
    "abs(54, 23)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Kernel.raw_input() takes from 1 to 2 positional arguments but 3 were given",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_1508067/3040375140.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Immetti a\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"ciao\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: Kernel.raw_input() takes from 1 to 2 positional arguments but 3 were given"
     ]
    }
   ],
   "source": [
    "input(\"Immetti a\", \"ciao\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Stessa cosa avviene se il numero di argomenti forniti è corretto, ma non il tipo. Ad esempio, non ha senso calcolare il valore assoluto di una stringa."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "bad operand type for abs(): 'str'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_1508067/2833784633.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"ciao\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: bad operand type for abs(): 'str'"
     ]
    }
   ],
   "source": [
    "abs(\"ciao\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alcune funzioni accettano un numero di argomenti variabile. Sappiamo già che `print` prende un numero qualunque di argomenti. Consideriamo adesso la funzione `round`. Se gli viene passato un solo argomento, calcola l'intero più vicino, e il risultato è un `int`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(56.13273)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "57"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(56.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se invece forniamo due argomenti, il secondo dice quante cifre dopo la virgola vogliamo conservare, e il risultato è un `float`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56.13"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(56.13273, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56.14"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(56.139, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Per avere spiegazioni sull'usa di una funzione, si può usare l'istruzione `help`. In realtà il termine *istruzione* è improprio: `help`, così come `print` e `input`, sono funzioni, esattamente come `abs` e `round`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on built-in function abs in module builtins:\n",
      "\n",
      "abs(x, /)\n",
      "    Return the absolute value of the argument.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(abs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "markdown",
   "metadata": {},
   "source": [
    "In alternativa, è possibile consultare il sito on-line della [documentazione Python](https://docs.python.org/3/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Infine, le funzioni `min` e `max` prendono un numero arbitrario di argomenti, e restituiscono il valore minimo o il massimo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-10"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(6, 2, -10, 99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(6, 2, -10, 99)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Funzioni, moduli e librerie"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Le funzioni viste fin'ora in Python sono dette *funzioni built-in*, o anche *funzioni predefinite*. Un elenco di tutte le funzioni built-in si trova qui: https://docs.python.org/3/library/functions.html. Python ha tantissime altre funzioni, che però fanne parte di *moduli*. Potete pensare ad un modulo come una raccolta di funzioni (e di tipi definiti dall'utente, anche se di questo è ancora presto per parlarne). Quando una funzione fa parte di un modulo, non è possibile usarla immediatamente, ma deve essere prima importata. Ad esempio, la funzione `sqrt` che calcola la radice quadrata non funziona direttamente. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'sqrt' 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/tmp/ipykernel_1508067/1103852951.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'sqrt' is not defined"
     ]
    }
   ],
   "source": [
    "sqrt(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La seguente istruzione importa la funzione `sqrt` dal modulo `main`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from math import sqrt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Una volta importata, potete utilizzare la funzione."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sqrt(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La funzione `log`, che fa sempre parte del modulo `math`, siccome non è stata importata non si può utilizzare. Un elenco di tutte le funzioni del modulo `math` lo trovate su https://docs.python.org/3/library/math.html#module-math"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'log' 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/tmp/ipykernel_1508067/4269932452.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'log' is not defined"
     ]
    }
   ],
   "source": [
    "log(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ovviamente mi basta importare anche la funzione `log`, per poterla usare. È possibile anche importare più funzioni contemporaneamente, elencandole."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importa le funzioni log (logaritmo naturale) e log2 (logaritmo in base 2)\n",
    "from math import log, log2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0794415416798357"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log2(8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "È possibile anche importare tutte le funzioni di un modulo, usando l'asterisco come nome di funzione, ad esempio:\n",
    "```python\n",
    "from math import *\n",
    "```\n",
    "Tuttavia, questa cosa è di solito sconsigliata perché si perde il controllo di quale e quante funzioni sono definite."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In alternativa, invece di importare una funzione, posso importare un modulo. In tal caso, posso usare tutte le funzioni di quel modulo, ma devo par precedere il nome della funzione dal nome del modulo, separati da un punto."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math # Importa il modulo math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'sin' 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/tmp/ipykernel_1508067/3469024889.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Non posso usare la funzione sin perché non l'ho importate esplicitamente\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0msin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'sin' is not defined"
     ]
    }
   ],
   "source": [
    "# Non posso usare la funzione sin perché non l'ho importate esplicitamente\n",
    "sin(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1411200080598672"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ma posso usarla scrivendo math.sin, perché ho importato il modulo math\n",
    "math.sin(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0\n",
      "2.0\n"
     ]
    }
   ],
   "source": [
    "# Notare che in questo momento posso usare sqrt sia direttamente, che passando per il modulo math.\n",
    "print(sqrt(4))\n",
    "print(math.sqrt(4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Oltre al modulo `math`, Python mette a disposizione tantissimi altri moduli, la cui domentazioni si trova qui: https://docs.python.org/3/library/index.html. Tutti questi moduli costituiscono quella che si chiama la *libreria stadard* di Python. In generale, una libreria è una collezione di moduli collegati tra di loro che implementano assieme una qualche funzionalità (quindi, in sotanza, una libreria è una collezione di moduli, e un modulo a sua volta è una collezione di funzioni e tipi definiti dall'utente). La libreria standard è l'insieme di tutti i moduli a disposizione del programmatore Python una volta installato.\n",
    "\n",
    "È possibile aggiungere a Python altre librerie per utilizzare funzionali non presenti nella libreria standard, ma questo lo vedremo in una lezione successiva."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Esercizio R2.3**\n",
    "\n",
    "Scrivere in Python le seguenti formule matematiche:\n",
    "  - $s=s_0 + v_0 t + \\frac{1}{2}gt^2$\n",
    "  - $c=\\sqrt{a^2 + b^2 -2 ab \\cos \\gamma}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Soluzioni**\n",
    "\n",
    "Per la prima espressione, le seguenti sono tutte più o meno equivalenti:\n",
    "\n",
    "```python\n",
    "s = s0 + v0*t + 0.5*g*(t**2) \n",
    "s = s0 + v0*t + g*(t**2)/ 2\n",
    "s = s0 + v0*t + 1/2*g*(t**2) \n",
    "```\n",
    "\n",
    "Per la seconda espressione, questa è la versione Python più ovvia. Notare che, in Python, è obbligatorio mettere le parentesi attorno all'argomento della funzione `cos`, mentre in matematichese non è necessario.\n",
    "\n",
    "```python\n",
    "c = sqrt(a**2 + b**2 - 2*a*b*cos(gamma))\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notare che in Python i nomi delle variabili possono contenre anche caratteri di altri alfabeti. Ad esempio, se siete in grado in qualche modo di scrivere il carattere ɣ (gamma), e il come fare dipende dal vostro sistema operativo, potete scrivere:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ɣ = 2\n",
    "ɣ + 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Esercizio R2.4**\n",
    "\n",
    "Scrivere in matematichese il seguente assegnamento in Python:\n",
    "\n",
    "```python\n",
    "z = sqrt(x*x + y*y)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Soluzioni**\n",
    "\n",
    "$z = \\sqrt{x^2 + y^2}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Esercizio R2.9**\n",
    "\n",
    "Individuare due errori (uno a tempo di esecuzione, uno logico) del seguente programma:\n",
    "```python\n",
    "from math import sqrt\n",
    "x = 2\n",
    "y = 4\n",
    "print(\"Il prodotto di\", x, \"e\", y, \"è\", x + y)\n",
    "print(\"La radice della loro differenza è\", sqrt(x - y))\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Soluzioni**\n",
    "\n",
    "  - La prima istruzione `print` stampa la somma di `x` ed `y`, ma a giudicare da quello che c'è scritto nelle stringhe esplicative, dovrebbe stampare il loro prodotto.\n",
    "  - La seconda istruzione `print` causa un errore a tempo di esecuzione perché `x - y` è uguale a -2, che è negativo, e come sappiamo non si può calcolare la radice quadrata di un numero negativo (almeno se non vogliamo andare a finire nel territorio dei numeri complessi.)"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Esercizio**\n",
    "\n",
    "Scrivere un programma che prende in input base e altezza di un rettangolo, e calcola la lunghezza delal diagonale.\n",
    "\n",
    "**Soluzione**\n",
    "\n",
    "Per la soluzione consultare il `programma_231003_1_diagonale_rettangolo.py`. Per calcolare la diagonale data base e altezza si usa il teorema di Pitagora:\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convenzioni stilistiche"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Python è bene seguire alcune convenzioni che, sebbene non obbligatorie, rendono più leggibili i programmi. Queste convenzioni sono descritte in dettaglio nella [Style Guide for Python Code](https://peps.python.org/pep-0008/). Ne riassumo qui gli aspetti più significativi.\n",
    "\n",
    "Alcune convenzioni riguardano gli spazi:\n",
    "\n",
    "  1. Mettere sempre uno spazio prima e dopo l'istruzione di assegnamento `=`. \n",
    "     - ~~`x=1`~~\n",
    "     - `x = 1`\n",
    "  2. È possibile mettere spazi prima e dopo gli operatori binari (`+`, `-`, `*`, etc...) per rendere una espressione più leggibile.\n",
    "  3. Non mettere mai spazi tra il nome di una funzione e la parentesi aperta.\n",
    "     - ~~`abs (34)`~~\n",
    "     - `abs(34)`\n",
    "  4. Non mettere mai spazi prima o dopo il punto che separa il nome del modulo dal nome di una funzione.\n",
    "     - ~~`math . sin(3)`~~\n",
    "     - `math.sin(3)`\n",
    "  \n",
    "Altre riguardano il nome delle variabili:\n",
    "\n",
    "  1. I nomi delle variabili sono tutte in lettere maiuscole.\n",
    "     - ~~`Pippo`~~\n",
    "     - ~~`PiPPo`~~\n",
    "     - `pippo`\n",
    "  2. Se il nome è formato da più parole, separare le parole dal carattere sottolineatura `_`.\n",
    "     - ~~`baseminore`~~\n",
    "     - ~~`base minore`~~ (questa è proprio sbagliata, uno spazio non può far parte del nome di una variabile)\n",
    "     - `base_minore`\n",
    "\n",
    "Questa convenzione per i nomi delle variabili prende il nome di *snake case*. Una alternativa spesso usata in altri linguaggi, ad esempio Java, è il *camel case*, che differisce per il punto 2:\n",
    "  \n",
    "  2. Se il nome è formato da più parole, scrivere le parole attaccate ma iniziare tutte tranne la prima con una lettera maiuscola\n",
    "     - ~~`baseminore`~~\n",
    "     - ~~`base minore`~~\n",
    "     - `baseMinore`\n",
    "\n",
    "Il vostro libro vi consiglia impropriamente di usare il camel case anche per Python: non fatelo.\n",
    "\n",
    "Infine, un caso particolari sono le variabili che contengono i cosidetti *numeri magici* o altre costanti significative per il programma. In questo caso, per rendere evidente che si tratta di valori costanti, si utilizza sempre la convenzione dello snake case, ma la variabile si scrive tutta in maiuscolo.\n",
    "  - ~~`pi_greco`~~\n",
    "  - ~~`piGreco`~~\n",
    "  - `PI_GRECO`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stringhe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Le stringhe sono sequenze di caratteri. È possibile scrivere un valore di tipo stringa racchiundendo i caratteri che compongono la sequenza tra virgolette doppie o singole (apici)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ciao sono io'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"ciao sono io\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ciao sono io'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'ciao sono io'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Un caso particolare è la *stringa vuota*, ovvero quella che non contiene nessun carattere, che si scrive come una coppia di virgolette o apici consecutive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Attenzione, la stringa qua sotto non é la stringa vuota: è invece una stringa che contiene uno spazio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' '"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\" \""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Le stringhe possono essere assegnate a variabili, esattamente come i valori numerici."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ciao'"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = \"ciao\"\n",
    "r = \"Gianluca\"\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Operazioni su stringhe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Concatenazione"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Due stringhe si possono concatenare tramite l'operazione `+`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ciaogianluca'"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"ciao\" + \"gianluca\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ciaoGianluca'"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s + r"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se si vogliono inserire spazi, vanno indicati esplicitamente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ciao Gianluca'"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s + \" \" + r"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ripetizione"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "È possibile concatenare copie ripetute della stessa stringa moltiplicandola per una costante."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ciaociaociao'"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"ciao\" * 3  # copia 3 volte la stringa \"ciao\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'--------------------------------------------------'"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"-\" * 50"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Funziona anche se il numero sta a sinistra."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'--------------------------------------------------'"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "50 * \"-\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ovviamente non posso moltiplicare due stringhe tra di loro, non ha senso."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "can't multiply sequence by non-int of type 'str'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_1508067/622280143.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m\"ciao\"\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m\"gianluca\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: can't multiply sequence by non-int of type 'str'"
     ]
    }
   ],
   "source": [
    "\"ciao\" * \"gianluca\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Estrazione di caratteri"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data una stringa, è possibile estrarre un singolo carattere. Ogni carattere infatti ha una posizione, che parte da 0. Ricordiamo che la stringa `r`, definita sopra, ha valore `Gianluca`. Quindi il primo carattere, la `G`, è in posizione 0, mentre la `n` è in posizione 3. Per estrarre da una stringa `r` il carattere in posizione `i`, la notazione è `r[i]`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'G'"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'n'"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Si possono mettere anche posizioni negative. In tal caso, `-1` è l'ultima posizione della stringa, `-2` la penultima e così via."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a'"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'c'"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r[-2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Si noti che, a differenza di altri linguaggi come Java o C, il Python non ha un tipo specifico per il singolo carattere."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se tento di estrarre un carattere da una posizione inesistente, ottengo un errore."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "string index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_1508067/439612334.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m: string index out of range"
     ]
    }
   ],
   "source": [
    "r[10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Infine, la stringa a sinistra delle parentesi quadre potrebbe anche non essere una variabile, ma ottenuta in qualunque modo, attraverso operazioni anche complsse."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'G'"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prima si somma \"Ciao\" e \"Gianluca\", ottenendo \"CiaoGianluca\", poi si estrae il carattere in posizione 4 (ovvero il quinto) ovvero \"i\"\n",
    "(\"Ciao\" + \"Gianluca\")[4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vale in generale il principio che **ovunque può andare un valore (numero, intero, stringa, o quant'altro) può andare anche una espressione complicata a piacere**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lunghezza di una stringa"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Per *lughezza* di una stringa si intende il numero di caratteri che la compongono. Per calcolare la lunghezza di una stringa si usa la funzione `len`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(\"Gianluca\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(s)   # ricordiamo che s è la stringa 'ciao'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notare che l'ultima carattere di una stringa `r` ha  posizione `len(r)-1`. Il `-1` è dovuto al fatto che le posizioni delle stringhe partono da zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'o'"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[len(s)-1] # alternatina ad s[-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Estrazione di sottostringhe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Invece di estrarre un singolo carattere da una stringa, se ne può estrarre un intero pezzo, chiamato *sottostringa*. Per estrarre una sottostringa dalla string `s`, la notazione è `s[i:j]`. Il risultato è la sottostringa formata prelevando da `s` i caratteri dalla posizione i (inclusa) alla posizione j (**esclusa**).\n",
    "\n",
    "È molto importante ricordare che la posizione dell'ultimo elemento dell'estrazione è esclusa!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ianl'"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r[1:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se vogliamo estrarre tutti i caratteri di `r` dala seconda posizione (posizione 1) in poi, possiamo scrivere r[1:8], dove 8 è la lunghezza di `r`. Sebbene la posizione 8 non esista, l'estrazione va a buon fine perché l'estremo destro è escluso dall'estrazione."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ianluca'"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r[1:8]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Un realtà, c'è una sintassi speciale per prendere tutti i caratteri da un certa posizione in poi: basta non scrivere niente dopo i due punti."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'anluca'"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r[2:]  # Estrae dalla 2° posizione in poi della stringa r"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analogamente, se vogliamo estrarre dalla prima posizione della stringa `r` fino alla posizione `i` (esclusa), invece di scrivere `r[0:i]` possiamo solo scrivere `r[:i]`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Gi'"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r[:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Esercizio R2.13 (variante)**\n",
    "\n",
    "Scrivete un programma Python che legge una parola e ne visualizza il primo carattere, l'ultimo e il carattere centrale. Se, ad esempio, il dato in ingresso è `Harry`, il programma deve visualizzare `H r y`. Se la lunghezza della parola è pari, il programma deve visualizzare il primo dei due caratteri centrali.\n",
    "\n",
    "**Soluzione**\n",
    "\n",
    "Per la soluzione vedere `programma_231003_2_estrazione_caratteri.py`. L'unica cosa che può risultare difficile è capire in che posizione si trova il carattere centrale, e trovare fuori con una formula che funzioni sia nel caso la stringa in ingresso abbia lunghezza pari, sia nel caso abbia lunghezza dispari. Con un po' di esperimenti, si può capire che la formula corretta è la parte intera di $\\frac{\\text{lunghezza stringa} - 1}{2}$.\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  }
 ],
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