{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Comp Lesson 2: Introduction to Python\n",
    "<HR>\n",
    "\n",
    "## Good afternoon\n",
    "\n",
    "Continue our tour of Python:\n",
    "    \n",
    "1. Calculator and overloading\n",
    "2. Basic I/O and exception handling    \n",
    "3. Packages\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The interpreter as a calculator\n",
    "\n",
    "Mathematical operators include +, -, *, /\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "100 - 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "9 * 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "9 ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "21 / 7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "/ always returns a float\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.142857142857143"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "22 / 7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use // to truncate and retain only the integer portion:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "22 // 7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or convert the result to an int()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int(22/7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "101"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = 100\n",
    "A += 1\n",
    "A"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Discuss immutable aspect of int, and what happens to 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['fork', 'spoon', 'knife']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "utensils = [\"fork\", \"spoon\", \"knife\"]\n",
    "utensils"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\\+ is overloaded:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['fork', 'spoon', 'knife', 'chop_sticks']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "utensils += [\"chop_sticks\"]\n",
    "utensils"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this context, the right operand must be 'iterable':"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'int' object is not iterable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[0;32mIn [12]\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0m utensils \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m(\u001b[38;5;241m22\u001b[39m\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m7\u001b[39m)\n",
      "\u001b[0;31mTypeError\u001b[0m: 'int' object is not iterable"
     ]
    }
   ],
   "source": [
    "utensils += int(22/7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make this work, to add 5 to the list, wrap it in a list itself:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['fork', 'spoon', 'knife', 'chop_sticks', 3]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "utensils = [\"fork\", \"spoon\", \"knife\"]\n",
    "utensils += [\"chop_sticks\"]\n",
    "utensils += [int(22/7)]\n",
    "utensils"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You could have also used the `append` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['fork', 'spoon', 'knife', 'chop_sticks']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "utensils = [\"fork\", \"spoon\", \"knife\"]\n",
    "utensils.append(\"chop_sticks\")\n",
    "utensils"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What happens when you add a string directly?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['fork',\n",
       " 'spoon',\n",
       " 'knife',\n",
       " 'c',\n",
       " 'h',\n",
       " 'o',\n",
       " 'p',\n",
       " '_',\n",
       " 's',\n",
       " 't',\n",
       " 'i',\n",
       " 'c',\n",
       " 'k',\n",
       " 's']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "utensils = [\"fork\", \"spoon\", \"knife\"]\n",
    "utensils += \"chop_sticks\"\n",
    "utensils"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What happens when you append a string wrapped as a list?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['fork', 'spoon', 'knife', ['chop_sticks']]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "utensils = [\"fork\", \"spoon\", \"knife\"]\n",
    "utensils.append([\"chop_sticks\"])\n",
    "utensils"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basic I/O and exception handling   \n",
    "\n",
    "Here, we are going to look at very basic way to get input, and how to anticipate and handle errors.\n",
    "\n",
    "Let's try to write a program that will ask the user for words, then prints them out in reverse order\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please enter a word, followed by Return. When you are done, hit return again\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Your word:  apple\n",
      "Your word:  orange\n",
      "Your word:  grape\n",
      "Your word:  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------\n",
      "2 grape\n",
      "1 orange\n",
      "0 apple\n"
     ]
    }
   ],
   "source": [
    "print(\"Please enter a word, followed by Return. When you are done, hit return again\")\n",
    "sentence = []\n",
    "while True:\n",
    "    word = input(\"Your word: \")\n",
    "    if(word):\n",
    "        sentence += [word]\n",
    "    else:\n",
    "        break\n",
    "\n",
    "print(\"-------------------------------\")\n",
    "for i in range( len(sentence)-1, -1, -1):\n",
    "    print(i, sentence[i])\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's modify this slightly to ask for series of integers, which our program will sum:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please enter an integer, followed by Return. When you are done, hit return again\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Your integer:  1\n",
      "Your integer:  2\n",
      "Your integer:  3\n",
      "Your integer:  4\n",
      "Your integer:  5\n",
      "Your integer:  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "unsupported operand type(s) for +=: 'int' and 'str'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[0;32mIn [20]\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     11\u001b[0m my_sum \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m     12\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m( \u001b[38;5;28mlen\u001b[39m(nums)\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m):\n\u001b[0;32m---> 13\u001b[0m     my_sum \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m nums[i]\n\u001b[1;32m     14\u001b[0m     \u001b[38;5;28mprint\u001b[39m(i,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m: \u001b[39m\u001b[38;5;124m\"\u001b[39m,nums[i])\n\u001b[1;32m     16\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYour total is \u001b[39m\u001b[38;5;124m\"\u001b[39m, my_sum)\n",
      "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +=: 'int' and 'str'"
     ]
    }
   ],
   "source": [
    "print(\"Please enter an integer, followed by Return. When you are done, hit return again\")\n",
    "nums = []\n",
    "while True:\n",
    "    your_int = input(\"Your integer: \")\n",
    "    if(your_int):\n",
    "        nums += [your_int]\n",
    "    else:\n",
    "        break\n",
    "\n",
    "print(\"-------------------------------\")\n",
    "my_sum = 0\n",
    "for i in range( len(nums)-1, -1, -1):\n",
    "    my_sum += nums[i]\n",
    "    print(i,\": \",nums[i])\n",
    "    \n",
    "print(\"Your total is \", my_sum)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please enter an integer, followed by Return. When you are done, hit return again\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Your integer:  1\n",
      "Your integer:  2\n",
      "Your integer:  3\n",
      "Your integer:  4\n",
      "Your integer:  5\n",
      "Your integer:  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------\n",
      "4 :  5\n",
      "3 :  4\n",
      "2 :  3\n",
      "1 :  2\n",
      "0 :  1\n",
      "Your total is  15\n"
     ]
    }
   ],
   "source": [
    "print(\"Please enter an integer, followed by Return. When you are done, hit return again\")\n",
    "nums = []\n",
    "while True:\n",
    "    your_int = input(\"Your integer: \")\n",
    "    if(your_int):\n",
    "        nums += [int(your_int)]\n",
    "    else:\n",
    "        break\n",
    "\n",
    "print(\"-------------------------------\")\n",
    "my_sum = 0\n",
    "for i in range( len(nums)-1, -1, -1):\n",
    "    my_sum += nums[i]\n",
    "    print(i,\": \",nums[i])\n",
    "    \n",
    "print(\"Your total is \", my_sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please enter an integer, followed by Return. When you are done, hit return again\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Your integer:  1\n",
      "Your integer:  2\n",
      "Your integer:  3\n",
      "Your integer:  4\n",
      "Your integer:  computer\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "invalid literal for int() with base 10: 'computer'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Input \u001b[0;32mIn [22]\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m your_int \u001b[38;5;241m=\u001b[39m \u001b[38;5;28minput\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYour integer: \u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m(your_int):\n\u001b[0;32m----> 6\u001b[0m     nums \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m [\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43myour_int\u001b[49m\u001b[43m)\u001b[49m]\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m      8\u001b[0m     \u001b[38;5;28;01mbreak\u001b[39;00m\n",
      "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: 'computer'"
     ]
    }
   ],
   "source": [
    "print(\"Please enter an integer, followed by Return. When you are done, hit return again\")\n",
    "nums = []\n",
    "while True:\n",
    "    your_int = input(\"Your integer: \")\n",
    "    if(your_int):\n",
    "        nums += [int(your_int)]\n",
    "    else:\n",
    "        break\n",
    "\n",
    "print(\"-------------------------------\")\n",
    "my_sum = 0\n",
    "for i in range( len(nums)-1, -1, -1):\n",
    "    my_sum += nums[i]\n",
    "    print(i,\": \",nums[i])\n",
    "    \n",
    "print(\"Your total is \", my_sum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need is a way to reasonably handle situations like this. We can do so with \"exceptions\". Exceptions are objects created at runtime but functions indicating that something is amiss. What is nice about exceptions is that we can engineer code to handle them and behave accordingly. Let's make our code a bit more robust with a *try* clause:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please enter an integer, followed by Return. When you are done, hit return again\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Your integer:  1\n",
      "Your integer:  2\n",
      "Your integer:  3\n",
      "Your integer:  4\n",
      "Your integer:  computer\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "invalid literal for int() with base 10: 'computer'\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Your integer:  5\n",
      "Your integer:  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------\n",
      "4 :  5\n",
      "3 :  4\n",
      "2 :  3\n",
      "1 :  2\n",
      "0 :  1\n",
      "Your total is  15\n"
     ]
    }
   ],
   "source": [
    "print(\"Please enter an integer, followed by Return. When you are done, hit return again\")\n",
    "nums = []\n",
    "while True:\n",
    "    your_int = input(\"Your integer: \")\n",
    "    if(your_int):\n",
    "        try:\n",
    "            nums += [int(your_int)]\n",
    "        except ValueError as err:\n",
    "            print(err)\n",
    "            continue\n",
    "    else:\n",
    "        break\n",
    "\n",
    "print(\"-------------------------------\")\n",
    "my_sum = 0\n",
    "for i in range( len(nums)-1, -1, -1):\n",
    "    my_sum += nums[i]\n",
    "    print(i,\": \",nums[i])\n",
    "    \n",
    "print(\"Your total is \", my_sum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "mention the err variable, and how python looks for matching except statements, what happens when no matching except clause is found"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Packages \n",
    "\n",
    "One of the strengths of Python is the set of packages that have functionality beyond the built in core functions and data types. You can install these with anaconda or pip. To access these packages, you need to use the import statement. For example, we covered some basic arithmetic operations earlier. You can get more beefy math with the [math module](https://docs.python.org/3/library/math.html):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "120 120\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "a = 5 * 4 * 3 * 2 * 1\n",
    "b = math.factorial(5)\n",
    "print(a, b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To see the functions and data in a package, use the `dir()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['__doc__',\n",
       " '__file__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__package__',\n",
       " '__spec__',\n",
       " 'acos',\n",
       " 'acosh',\n",
       " 'asin',\n",
       " 'asinh',\n",
       " 'atan',\n",
       " 'atan2',\n",
       " 'atanh',\n",
       " 'ceil',\n",
       " 'comb',\n",
       " 'copysign',\n",
       " 'cos',\n",
       " 'cosh',\n",
       " 'degrees',\n",
       " 'dist',\n",
       " 'e',\n",
       " 'erf',\n",
       " 'erfc',\n",
       " 'exp',\n",
       " 'expm1',\n",
       " 'fabs',\n",
       " 'factorial',\n",
       " 'floor',\n",
       " 'fmod',\n",
       " 'frexp',\n",
       " 'fsum',\n",
       " 'gamma',\n",
       " 'gcd',\n",
       " 'hypot',\n",
       " 'inf',\n",
       " 'isclose',\n",
       " 'isfinite',\n",
       " 'isinf',\n",
       " 'isnan',\n",
       " 'isqrt',\n",
       " 'lcm',\n",
       " 'ldexp',\n",
       " 'lgamma',\n",
       " 'log',\n",
       " 'log10',\n",
       " 'log1p',\n",
       " 'log2',\n",
       " 'modf',\n",
       " 'nan',\n",
       " 'nextafter',\n",
       " 'perm',\n",
       " 'pi',\n",
       " 'pow',\n",
       " 'prod',\n",
       " 'radians',\n",
       " 'remainder',\n",
       " 'sin',\n",
       " 'sinh',\n",
       " 'sqrt',\n",
       " 'tan',\n",
       " 'tanh',\n",
       " 'tau',\n",
       " 'trunc',\n",
       " 'ulp']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(math)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get help on a function, use the `help()` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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)",
      "Input \u001b[0;32mIn [26]\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0m help(\u001b[43mlog\u001b[49m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'log' is not defined"
     ]
    }
   ],
   "source": [
    "help(log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on built-in function log in module math:\n",
      "\n",
      "log(...)\n",
      "    log(x, [base=math.e])\n",
      "    Return the logarithm of x to the given base.\n",
      "    \n",
      "    If the base not specified, returns the natural logarithm (base e) of x.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(math.log)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A very useful and efficient math module is [Numpy](https://numpy.org/doc/stable/index.html). Let's import it, but take advantage of `as` to make a shorthand for package contexts:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's also import a commonly used plotting package [Matplotlib](https://matplotlib.org/). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The examples below come straight from the [NumPy Intro](https://numpy.org/doc/stable/user/absolute_beginners.html), which I recommend. \n",
    "\n",
    "Arrays are a fundamental data type in NumPy. Here is how you can create them, convert them to [Pandas](https://pandas.pydata.org/) DataFrames, write them out, and load them:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          0         1         2         3\n",
      "0 -2.582892  0.430148 -1.240820  1.595726\n",
      "1  0.990278  1.171510  0.941257 -0.146925\n",
      "2  0.769893  0.812997 -0.950684  0.117696\n",
      "3  0.204840  0.347845  1.969792  0.519928\n"
     ]
    }
   ],
   "source": [
    "a = np.array([[-2.58289208,  0.43014843, -1.24082018, 1.59572603],\n",
    "              [ 0.99027828, 1.17150989,  0.94125714, -0.14692469],\n",
    "              [ 0.76989341,  0.81299683, -0.95068423, 0.11769564],\n",
    "              [ 0.20484034,  0.34784527,  1.96979195, 0.51992837]])\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame(a)\n",
    "\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('pd.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>Unnamed: 0</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-2.582892</td>\n",
       "      <td>0.430148</td>\n",
       "      <td>-1.240820</td>\n",
       "      <td>1.595726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.990278</td>\n",
       "      <td>1.171510</td>\n",
       "      <td>0.941257</td>\n",
       "      <td>-0.146925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.769893</td>\n",
       "      <td>0.812997</td>\n",
       "      <td>-0.950684</td>\n",
       "      <td>0.117696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.204840</td>\n",
       "      <td>0.347845</td>\n",
       "      <td>1.969792</td>\n",
       "      <td>0.519928</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0         0         1         2         3\n",
       "0           0 -2.582892  0.430148 -1.240820  1.595726\n",
       "1           1  0.990278  1.171510  0.941257 -0.146925\n",
       "2           2  0.769893  0.812997 -0.950684  0.117696\n",
       "3           3  0.204840  0.347845  1.969792  0.519928"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('pd.csv')\n",
    "data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's plot some data with Matplotlib.\n",
    "\n",
    "This line ensure the matplotlib figures are sent to this notebook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x122865a30>]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.plot(a[:,0], a[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x122959fd0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.scatter(a[:,0], a[:,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We ended at this point in the live lecture. Now let's go further."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have some fun with these packages\n",
    " and install a useful data set, the famous iris data\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import quilt.data.uciml.iris as ir\n",
    "\n",
    "iris = ir.tables.iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width           class\n",
       "0             5.1          3.5           1.4          0.2     Iris-setosa\n",
       "1             4.9          3.0           1.4          0.2     Iris-setosa\n",
       "2             4.7          3.2           1.3          0.2     Iris-setosa\n",
       "3             4.6          3.1           1.5          0.2     Iris-setosa\n",
       "4             5.0          3.6           1.4          0.2     Iris-setosa\n",
       "..            ...          ...           ...          ...             ...\n",
       "145           6.7          3.0           5.2          2.3  Iris-virginica\n",
       "146           6.3          2.5           5.0          1.9  Iris-virginica\n",
       "147           6.5          3.0           5.2          2.0  Iris-virginica\n",
       "148           6.2          3.4           5.4          2.3  Iris-virginica\n",
       "149           5.9          3.0           5.1          1.8  Iris-virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1232b3550>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.scatter(iris['petal_length'], iris['sepal_length'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1233023d0>]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "#fig, ax = plt.subplots(figsize=(5, 5), layout='constrained')\n",
    "#ax.scatter('petal_length', 'sepal_length',data = iris)\n",
    "type(plt)\n",
    "fig, ax = plt.subplots() \n",
    "ax.plot([1, 2, 3, 4], [1, 4, 2, 3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()  # an empty figure with no Axes\n",
    "fig, ax = plt.subplots()  # a figure with a single Axes\n",
    "fig, axs = plt.subplots(2, 2)  # a figure with a 2x2 grid of Axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x123518c10>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1,2)\n",
    "axs[0].scatter(iris['petal_length'], iris['sepal_length'])\n",
    "axs[1].scatter(iris['petal_width'], iris['sepal_width'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "iris.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.5.1\n"
     ]
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "print(mpl.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "iris.plot.box()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Iris-setosa</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-versicolor</th>\n",
       "      <td>5.9</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.35</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-virginica</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.55</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 sepal_length  sepal_width  petal_length  petal_width\n",
       "class                                                                \n",
       "Iris-setosa               5.0          3.4          1.50          0.2\n",
       "Iris-versicolor           5.9          2.8          4.35          1.3\n",
       "Iris-virginica            6.5          3.0          5.55          2.0"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.groupby(\"class\").median()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class\n",
       "Iris-setosa        AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "Iris-versicolor    AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "Iris-virginica     AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "dtype: object"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "iris.groupby(\"class\").plot.box()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The watermark extension is already loaded. To reload it, use:\n",
      "  %reload_ext watermark\n",
      "Python implementation: CPython\n",
      "Python version       : 3.9.10\n",
      "IPython version      : 8.0.1\n",
      "\n",
      "numpy     : 1.21.5\n",
      "pandas    : 1.4.0\n",
      "matplotlib: 3.5.1\n",
      "quilt     : 2.9.15\n",
      "jupyterlab: 3.2.8\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -v -p numpy,pandas,matplotlib,quilt,jupyterlab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}