checkpoint0.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "44bff40d",
   "metadata": {
    "colab_type": "text",
    "id": "0a8IYAJUshu1"
   },
   "source": [
    "# Checkpoint 0 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02215935",
   "metadata": {},
   "source": [
    "These exercises are a mix of Python and Pandas practice. Most should be no more than a few lines of code! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a0f62714",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Jo6wuTgkshu1"
   },
   "outputs": [],
   "source": [
    "# here is a Python list:\n",
    "\n",
    "a = [1, 2, 3, 4, 5, 6]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "779d96b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4, 5, 6]\n"
     ]
    }
   ],
   "source": [
    "# get a list containing the last 3 elements of a\n",
    "# Yes, you can just type out [4, 5, 6] but we really want to see you demonstrate you know how to do that in Python\n",
    "b = a[-3::]\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b6a54def",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100]\n"
     ]
    }
   ],
   "source": [
    "# create a list of numbers from 1 to 100\n",
    "c = list(range(1, 101))\n",
    "print(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "487873ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 46, 48, 50, 52, 54, 56, 58, 60, 62, 64, 66, 68, 70, 72, 74, 76, 78, 80, 82, 84, 86, 88, 90, 92, 94, 96, 98, 100]\n"
     ]
    }
   ],
   "source": [
    "# now get a list with only the even numbers between 1 and 100\n",
    "# you may or may not make use of the list you made in the last cell\n",
    "d = list(range(2, 101, 2))\n",
    "print(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "3d4bb5dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# write a function that takes two numbers as arguments\n",
    "# and returns the first number divided by the second\n",
    "def divide(num1, num2):\n",
    "    return num1 / num2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b93669fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# write a function that takes a string as input\n",
    "# and return that string in all caps\n",
    "def capitalize(string):\n",
    "    return string.upper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "f55df04e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "fizz\n",
      "4\n",
      "buzz\n",
      "fizz\n",
      "7\n",
      "8\n",
      "fizz\n",
      "buzz\n",
      "11\n",
      "fizz\n",
      "13\n",
      "14\n",
      "fizzbuzz\n",
      "16\n",
      "17\n",
      "fizz\n",
      "19\n",
      "buzz\n",
      "fizz\n",
      "22\n",
      "23\n",
      "fizz\n",
      "buzz\n",
      "26\n",
      "fizz\n",
      "28\n",
      "29\n",
      "fizzbuzz\n",
      "31\n",
      "32\n",
      "fizz\n",
      "34\n",
      "buzz\n",
      "fizz\n",
      "37\n",
      "38\n",
      "fizz\n",
      "buzz\n",
      "41\n",
      "fizz\n",
      "43\n",
      "44\n",
      "fizzbuzz\n",
      "46\n",
      "47\n",
      "fizz\n",
      "49\n",
      "buzz\n",
      "fizz\n",
      "52\n",
      "53\n",
      "fizz\n",
      "buzz\n",
      "56\n",
      "fizz\n",
      "58\n",
      "59\n",
      "fizzbuzz\n",
      "61\n",
      "62\n",
      "fizz\n",
      "64\n",
      "buzz\n",
      "fizz\n",
      "67\n",
      "68\n",
      "fizz\n",
      "buzz\n",
      "71\n",
      "fizz\n",
      "73\n",
      "74\n",
      "fizzbuzz\n",
      "76\n",
      "77\n",
      "fizz\n",
      "79\n",
      "buzz\n",
      "fizz\n",
      "82\n",
      "83\n",
      "fizz\n",
      "buzz\n",
      "86\n",
      "fizz\n",
      "88\n",
      "89\n",
      "fizzbuzz\n",
      "91\n",
      "92\n",
      "fizz\n",
      "94\n",
      "buzz\n",
      "fizz\n",
      "97\n",
      "98\n",
      "fizz\n",
      "buzz\n"
     ]
    }
   ],
   "source": [
    "# optional challenge - fizzbuzz\n",
    "# you will need to use both iteration and control flow \n",
    "# go through all numbers from 1 to 100 in order\n",
    "# if the number is a multiple of 3, print fizz\n",
    "# if the number is a multiple of 5, print buzz\n",
    "# if the number is a multiple of 3 and 5, print fizzbuzz and NOTHING ELSE\n",
    "# if the number is neither a multiple of 3 nor a multiple of 5, print the number\n",
    "\n",
    "for num in list(range(1, 101)):\n",
    "    if((num % 3 == 0) & (num % 5 == 0)):\n",
    "        print(\"fizzbuzz\")\n",
    "    elif(num % 3 == 0):\n",
    "        print(\"fizz\")\n",
    "    elif(num % 5 == 0):\n",
    "        print(\"buzz\")\n",
    "    else:\n",
    "        print(num)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "78aace0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a dictionary that reflects the following menu pricing (taken from Ahmo's)\n",
    "# Gyro: $9 \n",
    "# Burger: $9\n",
    "# Greek Salad: $8\n",
    "# Philly Steak: $10\n",
    "\n",
    "menu = {\"Gyro\":9, \"Burger\":9, \"Greek Salad\":8, \"Philly Steak\":10}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "a2a78a4b",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WzCQ5HOJshvA"
   },
   "outputs": [],
   "source": [
    "# load in the \"starbucks.csv\" dataset\n",
    "# refer to how we read the cereal.csv dataset in the tutorial\n",
    "import pandas\n",
    "df = pandas.read_csv(\"starbucks.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "68210b5f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     calories  sugars  protein\n",
      "0           3       0      0.3\n",
      "40          5       0      0.4\n",
      "80        350      58     15.0\n",
      "120       140      20      6.0\n",
      "160       110      24      2.0\n",
      "200       200      41      3.0\n",
      "240       180      35      3.0\n"
     ]
    }
   ],
   "source": [
    "# output the calories, sugars, and protein columns only of every 40th row. \n",
    "print(df.iloc[0::40][[\"calories\", \"sugars\", \"protein\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "ac0f0c12",
   "metadata": {},
   "outputs": [],
   "source": [
    "# select all rows with more than and including 400 calories\n",
    "hi_cal_rows = df[df[\"calories\"] >= 400]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "ee8f8241",
   "metadata": {},
   "outputs": [],
   "source": [
    "# select all rows whose vitamin c content is higher than the iron content\n",
    "vitc_greaterthan_iron_rows = df[df[\"vitamin c\"] > df[\"iron\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "d4de48bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a new column containing the caffeine per calories of each drink\n",
    "df[\"caffeine per calories\"] = df[\"caffeine\"] / df[\"calories\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "3a72465a",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rIoxaSxHshvB"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "193.87190082644628"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# what is the average calorie across all items?\n",
    "df[\"calories\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "7714895a",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ABX7i49FshvD"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how many different categories of beverages are there?\n",
    "df[\"beverage_category\"].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "62392999",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "P9QatZAzshvE"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "beverage_category\n",
       "classic espresso drinks             140.172414\n",
       "coffee                                4.250000\n",
       "frappuccino blended coffee          276.944444\n",
       "frappuccino blended crme            233.076923\n",
       "frappuccino light blended coffee    162.500000\n",
       "shaken iced beverages               114.444444\n",
       "signature espresso drinks           250.000000\n",
       "smoothies                           282.222222\n",
       "tazo tea drinks                     177.307692\n",
       "Name: calories, dtype: float64"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# what is the average # calories for each beverage category?\n",
    "bev_categories = df.groupby(\"beverage_category\")\n",
    "bev_categories[\"calories\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "435e9d80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: title={'center': 'Distribution of Calories'}, ylabel='Frequency'>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the distribution of the number of calories in drinks with a histogram\n",
    "df[\"calories\"].plot.hist(edgecolor=\"black\", title = \"Distribution of Calories\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "ba8948eb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: title={'center': 'Calories vs Total Fat'}, xlabel='calories', ylabel='total fat'>"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot calories against total fat with a scatterplot\n",
    "df.plot.scatter(x=\"calories\", y=\"total fat\", title=\"Calories vs Total Fat\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4fe7fb2a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ebada65",
   "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": "3.8.9"
  },
  "vscode": {
   "interpreter": {
    "hash": "6cf8df3ff69f85f626faf55c10df6fe2cb9d1236b4dc73844ee4dc01369c2c99"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}