16-Chipotle.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing Chipotle's Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import Counter\n",
    "\n",
    "# set this so the graphs open internally\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 2. Import the dataset from this address.\n",
    "#### Step 3. Assign it to a variable called chipo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv'\n",
    "\n",
    "chipo = pd.read_csv(url, sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 4. See the first 10 entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>order_id</th>\n",
       "      <th>quantity</th>\n",
       "      <th>item_name</th>\n",
       "      <th>choice_description</th>\n",
       "      <th>item_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Chips and Fresh Tomato Salsa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Izze</td>\n",
       "      <td>[Clementine]</td>\n",
       "      <td>$3.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Nantucket Nectar</td>\n",
       "      <td>[Apple]</td>\n",
       "      <td>$3.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Chips and Tomatillo-Green Chili Salsa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[Tomatillo-Red Chili Salsa (Hot), [Black Beans...</td>\n",
       "      <td>$16.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...</td>\n",
       "      <td>$10.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Side of Chips</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$1.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Burrito</td>\n",
       "      <td>[Tomatillo Red Chili Salsa, [Fajita Vegetables...</td>\n",
       "      <td>$11.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Soft Tacos</td>\n",
       "      <td>[Tomatillo Green Chili Salsa, [Pinto Beans, Ch...</td>\n",
       "      <td>$9.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Burrito</td>\n",
       "      <td>[Fresh Tomato Salsa, [Rice, Black Beans, Pinto...</td>\n",
       "      <td>$9.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  quantity                              item_name  \\\n",
       "0         1         1           Chips and Fresh Tomato Salsa   \n",
       "1         1         1                                   Izze   \n",
       "2         1         1                       Nantucket Nectar   \n",
       "3         1         1  Chips and Tomatillo-Green Chili Salsa   \n",
       "4         2         2                           Chicken Bowl   \n",
       "5         3         1                           Chicken Bowl   \n",
       "6         3         1                          Side of Chips   \n",
       "7         4         1                          Steak Burrito   \n",
       "8         4         1                       Steak Soft Tacos   \n",
       "9         5         1                          Steak Burrito   \n",
       "\n",
       "                                  choice_description item_price  \n",
       "0                                                NaN     $2.39   \n",
       "1                                       [Clementine]     $3.39   \n",
       "2                                            [Apple]     $3.39   \n",
       "3                                                NaN     $2.39   \n",
       "4  [Tomatillo-Red Chili Salsa (Hot), [Black Beans...    $16.98   \n",
       "5  [Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...    $10.98   \n",
       "6                                                NaN     $1.69   \n",
       "7  [Tomatillo Red Chili Salsa, [Fajita Vegetables...    $11.75   \n",
       "8  [Tomatillo Green Chili Salsa, [Pinto Beans, Ch...     $9.25   \n",
       "9  [Fresh Tomato Salsa, [Rice, Black Beans, Pinto...     $9.25   "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chipo.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 5. Create a histogram of the top 5 items bought"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_5 = chipo['item_name'].value_counts().nlargest(5)\n",
    "top_5 = top_5.to_frame()\n",
    "\n",
    "# create the plot\n",
    "top_5.plot(kind='bar')\n",
    "\n",
    "# Set the title and labels\n",
    "plt.xlabel('Items')\n",
    "plt.ylabel('Number of Times Ordered')\n",
    "plt.title('Most ordered Chipotle\\'s Items')\n",
    "\n",
    "# show the plot\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 6. Create a scatterplot with the number of items orderered per order price\n",
    "Hint: Price should be in the X-axis and Items ordered in the Y-axis¶\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>quantity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order_id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1830</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1831</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1832</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1833</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1834</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1834 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          quantity\n",
       "order_id          \n",
       "1                4\n",
       "2                2\n",
       "3                2\n",
       "4                2\n",
       "5                2\n",
       "...            ...\n",
       "1830             2\n",
       "1831             3\n",
       "1832             2\n",
       "1833             2\n",
       "1834             3\n",
       "\n",
       "[1834 rows x 1 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chipo.groupby('order_id').sum()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}