chipotle2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "df = pd.read_csv('./chipotle.tsv', sep='\\t')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "   order_id  quantity                              item_name  \\\n0         1         1           Chips and Fresh Tomato Salsa   \n1         1         1                                   Izze   \n2         1         1                       Nantucket Nectar   \n3         1         1  Chips and Tomatillo-Green Chili Salsa   \n4         2         2                           Chicken Bowl   \n\n                                  choice_description item_price  \n0                                                NaN     $2.39   \n1                                       [Clementine]     $3.39   \n2                                            [Apple]     $3.39   \n3                                                NaN     $2.39   \n4  [Tomatillo-Red Chili Salsa (Hot), [Black Beans...    $16.98   ",
      "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  </tbody>\n</table>\n</div>"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4622 entries, 0 to 4621\n",
      "Data columns (total 5 columns):\n",
      " #   Column              Non-Null Count  Dtype \n",
      "---  ------              --------------  ----- \n",
      " 0   order_id            4622 non-null   int64 \n",
      " 1   quantity            4622 non-null   int64 \n",
      " 2   item_name           4622 non-null   object\n",
      " 3   choice_description  3376 non-null   object\n",
      " 4   item_price          4622 non-null   object\n",
      "dtypes: int64(2), object(3)\n",
      "memory usage: 180.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [],
   "source": [
    "df.choice_description = df.choice_description.fillna('[]')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "data": {
      "text/plain": "   order_id  quantity                              item_name  \\\n0         1         1           Chips and Fresh Tomato Salsa   \n1         1         1                                   Izze   \n2         1         1                       Nantucket Nectar   \n3         1         1  Chips and Tomatillo-Green Chili Salsa   \n4         2         2                           Chicken Bowl   \n\n                                  choice_description item_price  \n0                                                 []     $2.39   \n1                                       [Clementine]     $3.39   \n2                                            [Apple]     $3.39   \n3                                                 []     $2.39   \n4  [Tomatillo-Red Chili Salsa (Hot), [Black Beans...    $16.98   ",
      "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>[]</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>[]</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  </tbody>\n</table>\n</div>"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "data": {
      "text/plain": "order_id              0\nquantity              0\nitem_name             0\nchoice_description    0\nitem_price            0\ndtype: int64"
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4622 entries, 0 to 4621\n",
      "Data columns (total 5 columns):\n",
      " #   Column              Non-Null Count  Dtype \n",
      "---  ------              --------------  ----- \n",
      " 0   order_id            4622 non-null   int64 \n",
      " 1   quantity            4622 non-null   int64 \n",
      " 2   item_name           4622 non-null   object\n",
      " 3   choice_description  4622 non-null   object\n",
      " 4   item_price          4622 non-null   object\n",
      "dtypes: int64(2), object(3)\n",
      "memory usage: 180.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [
    {
     "data": {
      "text/plain": "          order_id     quantity\ncount  4622.000000  4622.000000\nmean    927.254868     1.075725\nstd     528.890796     0.410186\nmin       1.000000     1.000000\n25%     477.250000     1.000000\n50%     926.000000     1.000000\n75%    1393.000000     1.000000\nmax    1834.000000    15.000000",
      "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    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>4622.000000</td>\n      <td>4622.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>927.254868</td>\n      <td>1.075725</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>528.890796</td>\n      <td>0.410186</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>1.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>477.250000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>926.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>1393.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>1834.000000</td>\n      <td>15.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "df.item_price = df.item_price.apply(lambda x: x[1:]).astype(float)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "outputs": [
    {
     "data": {
      "text/plain": "          order_id     quantity   item_price\ncount  4622.000000  4622.000000  4622.000000\nmean    927.254868     1.075725     7.464336\nstd     528.890796     0.410186     4.245557\nmin       1.000000     1.000000     1.090000\n25%     477.250000     1.000000     3.390000\n50%     926.000000     1.000000     8.750000\n75%    1393.000000     1.000000     9.250000\nmax    1834.000000    15.000000    44.250000",
      "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_price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>4622.000000</td>\n      <td>4622.000000</td>\n      <td>4622.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>927.254868</td>\n      <td>1.075725</td>\n      <td>7.464336</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>528.890796</td>\n      <td>0.410186</td>\n      <td>4.245557</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.090000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>477.250000</td>\n      <td>1.000000</td>\n      <td>3.390000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>926.000000</td>\n      <td>1.000000</td>\n      <td>8.750000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>1393.000000</td>\n      <td>1.000000</td>\n      <td>9.250000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>1834.000000</td>\n      <td>15.000000</td>\n      <td>44.250000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "outputs": [
    {
     "data": {
      "text/plain": "array(['Chips and Fresh Tomato Salsa', 'Izze', 'Nantucket Nectar',\n       'Chips and Tomatillo-Green Chili Salsa', 'Chicken Bowl',\n       'Side of Chips', 'Steak Burrito', 'Steak Soft Tacos',\n       'Chips and Guacamole', 'Chicken Crispy Tacos',\n       'Chicken Soft Tacos', 'Chicken Burrito', 'Canned Soda',\n       'Barbacoa Burrito', 'Carnitas Burrito', 'Carnitas Bowl',\n       'Bottled Water', 'Chips and Tomatillo Green Chili Salsa',\n       'Barbacoa Bowl', 'Chips', 'Chicken Salad Bowl', 'Steak Bowl',\n       'Barbacoa Soft Tacos', 'Veggie Burrito', 'Veggie Bowl',\n       'Steak Crispy Tacos', 'Chips and Tomatillo Red Chili Salsa',\n       'Barbacoa Crispy Tacos', 'Veggie Salad Bowl',\n       'Chips and Roasted Chili-Corn Salsa',\n       'Chips and Roasted Chili Corn Salsa', 'Carnitas Soft Tacos',\n       'Chicken Salad', 'Canned Soft Drink', 'Steak Salad Bowl',\n       '6 Pack Soft Drink', 'Chips and Tomatillo-Red Chili Salsa', 'Bowl',\n       'Burrito', 'Crispy Tacos', 'Carnitas Crispy Tacos', 'Steak Salad',\n       'Chips and Mild Fresh Tomato Salsa', 'Veggie Soft Tacos',\n       'Carnitas Salad Bowl', 'Barbacoa Salad Bowl', 'Salad',\n       'Veggie Crispy Tacos', 'Veggie Salad', 'Carnitas Salad'],\n      dtype=object)"
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.item_name.unique()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "outputs": [
    {
     "data": {
      "text/plain": "Chicken Bowl                             726\nChicken Burrito                          553\nChips and Guacamole                      479\nSteak Burrito                            368\nCanned Soft Drink                        301\nSteak Bowl                               211\nChips                                    211\nBottled Water                            162\nChicken Soft Tacos                       115\nChips and Fresh Tomato Salsa             110\nChicken Salad Bowl                       110\nCanned Soda                              104\nSide of Chips                            101\nVeggie Burrito                            95\nBarbacoa Burrito                          91\nVeggie Bowl                               85\nCarnitas Bowl                             68\nBarbacoa Bowl                             66\nCarnitas Burrito                          59\nSteak Soft Tacos                          55\n6 Pack Soft Drink                         54\nChips and Tomatillo Red Chili Salsa       48\nChicken Crispy Tacos                      47\nChips and Tomatillo Green Chili Salsa     43\nCarnitas Soft Tacos                       40\nSteak Crispy Tacos                        35\nChips and Tomatillo-Green Chili Salsa     31\nSteak Salad Bowl                          29\nNantucket Nectar                          27\nBarbacoa Soft Tacos                       25\nChips and Roasted Chili Corn Salsa        22\nIzze                                      20\nChips and Tomatillo-Red Chili Salsa       20\nVeggie Salad Bowl                         18\nChips and Roasted Chili-Corn Salsa        18\nBarbacoa Crispy Tacos                     11\nBarbacoa Salad Bowl                       10\nChicken Salad                              9\nCarnitas Crispy Tacos                      7\nVeggie Soft Tacos                          7\nBurrito                                    6\nVeggie Salad                               6\nCarnitas Salad Bowl                        6\nSteak Salad                                4\nSalad                                      2\nCrispy Tacos                               2\nBowl                                       2\nCarnitas Salad                             1\nVeggie Crispy Tacos                        1\nChips and Mild Fresh Tomato Salsa          1\nName: item_name, dtype: int64"
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.item_name.value_counts()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "outputs": [
    {
     "data": {
      "text/plain": "[(1, 'a'), (2, 'b'), (3, 'c'), (4, 'd'), (5, 'e'), (6, 'f')]"
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[*enumerate(\"abcdef\", 1)]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "outputs": [
    {
     "data": {
      "text/plain": "[(0, ('Chicken Bowl', 726)),\n (1, ('Chicken Burrito', 553)),\n (2, ('Chips and Guacamole', 479)),\n (3, ('Steak Burrito', 368)),\n (4, ('Canned Soft Drink', 301)),\n (5, ('Steak Bowl', 211)),\n (6, ('Chips', 211)),\n (7, ('Bottled Water', 162)),\n (8, ('Chicken Soft Tacos', 115)),\n (9, ('Chips and Fresh Tomato Salsa', 110))]"
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(enumerate(df.item_name.value_counts()[:10].iteritems()))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2. 726\t- Chicken Bowl\n",
      "3. 553\t- Chicken Burrito\n",
      "4. 479\t- Chips and Guacamole\n",
      "5. 368\t- Steak Burrito\n",
      "6. 301\t- Canned Soft Drink\n",
      "7. 211\t- Steak Bowl\n",
      "8. 211\t- Chips\n",
      "9. 162\t- Bottled Water\n",
      "10. 115\t- Chicken Soft Tacos\n",
      "11. 110\t- Chips and Fresh Tomato Salsa\n"
     ]
    }
   ],
   "source": [
    "for idx, (item, count) in enumerate(df.item_name.value_counts()[:10].iteritems(), 2):\n",
    "    print(f\"{idx}. {count}\\t- {item}\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
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   "display_name": "Python 3",
   "language": "python",
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