Notebook1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "This notebook is part of the Otter-Grader tutorial. For more information about Otter, see our [documentation](https://otter-grader.rtfd.io)."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# ASSIGNMENT CONFIG\n",
    "solutions_pdf: true\n",
    "export_cell:\n",
    "    instructions: \"These are some submission instructions.\"\n",
    "generate: \n",
    "    pdf: true\n",
    "    filtering: true\n",
    "    pagebreaks: true\n",
    "    zips: false"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import otter\n",
    "grader = otter.Notebook()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# BEGIN QUESTION\n",
    "name: q1\n",
    "points: 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 1:** Write a function `square` that returns the square of its argument."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# BEGIN SOLUTION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def square(x):\n",
    "    return x**2 # SOLUTION"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# END SOLUTION"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# BEGIN TESTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "square(1) == 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "square(0) == 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# HIDDEN\n",
    "square(2.5) == 6.25"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# END TESTS"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# END QUESTION"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# BEGIN QUESTION\n",
    "name: q2\n",
    "points: 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 2:** Write an infinite generator of the Fibonacci sequence `fiberator` that is *not* recursive."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# BEGIN SOLUTION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fiberator():\n",
    "    # BEGIN SOLUTION\n",
    "    yield 0\n",
    "    yield 1\n",
    "    x, y = 0, 1\n",
    "    while True:\n",
    "        x, y = y, x + y\n",
    "        yield y\n",
    "    # END SOLUTION"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# END SOLUTION"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# BEGIN TESTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = fiberator()\n",
    "assert next(f) == 0\n",
    "assert next(f) == 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# HIDDEN\n",
    "f = fiberator()\n",
    "assert next(f) == 0\n",
    "assert next(f) == 1\n",
    "assert next(f) == 1\n",
    "assert next(f) == 2\n",
    "assert next(f) == 3\n",
    "assert next(f) == 5\n",
    "assert next(f) == 8\n",
    "assert next(f) == 13\n",
    "assert next(f) == 21"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# END TESTS"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# END QUESTION"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# BEGIN QUESTION\n",
    "name: q3\n",
    "points: 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 3:** Create a DataFrame mirroring the table below and assign this to `data`. Then group by the `flavor` column and find the mean price for each flavor; assign this **series** to `price_by_flavor`.\n",
    "\n",
    "| flavor | scoops | price |\n",
    "|-----|-----|-----|\n",
    "| chocolate | 1 | 2 |\n",
    "| vanilla | 1 | 1.5 |\n",
    "| chocolate | 2 | 3 |\n",
    "| strawberry | 1 | 2 |\n",
    "| strawberry | 3 | 4 |\n",
    "| vanilla | 2 | 2 |\n",
    "| mint | 1 | 4 |\n",
    "| mint | 2 | 5 |\n",
    "| chocolate | 3 | 5 |"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# BEGIN SOLUTION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "flavor\n",
       "chocolate     3.333333\n",
       "mint          4.500000\n",
       "strawberry    3.000000\n",
       "vanilla       1.750000\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# BEGIN SOLUTION NO PROMPT\n",
    "data = pd.DataFrame({\n",
    "    \"flavor\": [\"chocolate\", \"vanilla\", \"chocolate\", \"strawberry\", \"strawberry\", \"vanilla\", \"mint\", \n",
    "               \"mint\", \"chocolate\"],\n",
    "    \"scoops\": [1, 1, 2, 1, 3, 2, 1, 2, 3],\n",
    "    \"price\": [2, 1.5, 3, 2, 4, 2, 4, 5, 5]\n",
    "})\n",
    "price_by_flavor = data.groupby(\"flavor\").mean()[\"price\"]\n",
    "# END SOLUTION\n",
    "\"\"\" # BEGIN PROMPT\n",
    "data = ...\n",
    "price_by_flavor = ...\n",
    "\"\"\" # END PROMPT\n",
    "price_by_flavor"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# END SOLUTION"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# BEGIN TESTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data[\"flavor\"].unique()) == 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "for l in [\"chocolate\", \"vanilla\", \"strawberry\", \"mint\"]:\n",
    "    assert l in data[\"flavor\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert type(price_by_flavor) == pd.Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert len(price_by_flavor) == 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# HIDDEN\n",
    "np.isclose(price_by_flavor[\"chocolate\"], 3.33333333)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# HIDDEN\n",
    "np.isclose(price_by_flavor[\"mint\"], 4.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# HIDDEN\n",
    "np.isclose(price_by_flavor[\"strawberry\"], 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# HIDDEN\n",
    "np.isclose(price_by_flavor[\"vanilla\"], 1.75)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# END TESTS"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# END QUESTION"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# BEGIN QUESTION\n",
    "name: q4\n",
    "manual: true\n",
    "points: 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 4:** Create a barplot of `price_by_flavor`."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# BEGIN SOLUTION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "price_by_flavor.plot.bar(); # SOLUTION"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# END SOLUTION"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# END QUESTION"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# BEGIN QUESTION\n",
    "name: q5\n",
    "manual: true\n",
    "points: 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 5:** What do you notice about the bar plot?"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# BEGIN SOLUTION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**SOLUTION:** mint is the highest...?"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# END SOLUTION"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# END QUESTION"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# BEGIN QUESTION\n",
    "name: q6\n",
    "points: 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 6:** Write a function called sieve that takes in a positive integer n and returns a set of the prime numbers less than or equal to n. Use the Sieve of Eratosthenes to find the prime"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# BEGIN SOLUTION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sieve(n):\n",
    "    \"\"\"\n",
    "    Generate a set of prime numbers less than or equal to a positive integer.\n",
    "    \"\"\"\n",
    "    # BEGIN SOLUTION\n",
    "    is_prime = [True for _ in range(n + 1)]\n",
    "    p = 2\n",
    "    while p ** 2 <= n:\n",
    "        if is_prime[p]:\n",
    "            for i in range(p ** 2, n + 1, p):\n",
    "                is_prime[i] = False\n",
    "        p += 1\n",
    "\n",
    "    is_prime[0]= False\n",
    "    is_prime[1]= False\n",
    "\n",
    "    return set(i for i in range(n + 1) if is_prime[i])\n",
    "    # END SOLUTION"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# END SOLUTION"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# BEGIN TESTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_low_primes(sieve):\n",
    "    assert sieve(1) == set()\n",
    "    assert sieve(2) == {2}\n",
    "    assert sieve(3) == {2, 3}\n",
    "\n",
    "test_low_primes(sieve)  # IGNORE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# HIDDEN\n",
    "def test_higher_primes(sieve):\n",
    "    assert sieve(20) == {2, 3, 5, 7, 11, 13, 17, 19}\n",
    "    assert sieve(100) == {2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97}\n",
    "\n",
    "test_higher_primes(sieve)  # IGNORE"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# END TESTS"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# END QUESTION"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
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     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
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     "varRefreshCmd": "cat(var_dic_list()) "
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   "types_to_exclude": [
    "module",
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