simple_chipotle.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "46f9b86b-a7a8-442d-bbff-b4a24195f086",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import DataFrame, Series\n",
    "import re\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fc5e6d22-c7db-4fcd-8e59-660aa1f03476",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   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",
      "\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"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv('./orders.tsv', sep=\"\\t\")\n",
    "df = DataFrame(data)\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0f436370-e88b-48be-a150-724371f68ce6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0                Chips and Fresh Tomato Salsa\n",
      "1                                        Izze\n",
      "2                            Nantucket Nectar\n",
      "3       Chips and Tomatillo-Green Chili Salsa\n",
      "4                                Chicken Bowl\n",
      "                        ...                  \n",
      "4617                            Steak Burrito\n",
      "4618                            Steak Burrito\n",
      "4619                       Chicken Salad Bowl\n",
      "4620                       Chicken Salad Bowl\n",
      "4621                       Chicken Salad Bowl\n",
      "Name: item_name, Length: 4622, dtype: object\n"
     ]
    }
   ],
   "source": [
    "#Take a quick \"human-in-the-loop\" look at the data for anything interesting...\n",
    "items = df.item_name\n",
    "print(items)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d474f4c6-251d-4226-80f9-31f842ab9a2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 4,
     "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": [
    "# In order for plot to appear within jupyter, use %matplotlib inline \n",
    "%matplotlib inline\n",
    "df.item_name.value_counts()[:10].plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "82570454-ed29-4285-8ae3-24e2e0548806",
   "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": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}