code_listings/03.10-Working-With-Strings.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Vectorized String Operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4,  6, 10, 14, 22, 26])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "x = np.array([2, 3, 5, 7, 11, 13])\n",
    "x * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Peter', 'Paul', 'Mary', 'Guido']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = ['peter', 'Paul', 'MARY', 'gUIDO']\n",
    "[s.capitalize() for s in data]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'NoneType' object has no attribute 'capitalize'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-fc1d891ab539>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'peter'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Paul'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'MARY'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'gUIDO'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;34m[\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcapitalize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-3-fc1d891ab539>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'peter'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Paul'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'MARY'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'gUIDO'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;34m[\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcapitalize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'capitalize'"
     ]
    }
   ],
   "source": [
    "data = ['peter', 'Paul', None, 'MARY', 'gUIDO']\n",
    "[s.capitalize() for s in data]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    peter\n",
       "1     Paul\n",
       "2     None\n",
       "3     MARY\n",
       "4    gUIDO\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "names = pd.Series(data)\n",
    "names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Peter\n",
       "1     Paul\n",
       "2     None\n",
       "3     Mary\n",
       "4    Guido\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names.str.capitalize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "monte = pd.Series(['Graham Chapman', 'John Cleese', 'Terry Gilliam',\n",
    "                   'Eric Idle', 'Terry Jones', 'Michael Palin'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    graham chapman\n",
       "1       john cleese\n",
       "2     terry gilliam\n",
       "3         eric idle\n",
       "4       terry jones\n",
       "5     michael palin\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monte.str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    14\n",
       "1    11\n",
       "2    13\n",
       "3     9\n",
       "4    11\n",
       "5    13\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monte.str.len()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2     True\n",
       "3    False\n",
       "4     True\n",
       "5    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monte.str.startswith('T')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    [Graham, Chapman]\n",
       "1       [John, Cleese]\n",
       "2     [Terry, Gilliam]\n",
       "3         [Eric, Idle]\n",
       "4       [Terry, Jones]\n",
       "5     [Michael, Palin]\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monte.str.split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     Graham\n",
       "1       John\n",
       "2      Terry\n",
       "3       Eric\n",
       "4      Terry\n",
       "5    Michael\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monte.str.extract('([A-Za-z]+)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    [Graham Chapman]\n",
       "1                  []\n",
       "2     [Terry Gilliam]\n",
       "3                  []\n",
       "4       [Terry Jones]\n",
       "5     [Michael Palin]\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monte.str.findall(r'^[^AEIOU].*[^aeiou]$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Gra\n",
       "1    Joh\n",
       "2    Ter\n",
       "3    Eri\n",
       "4    Ter\n",
       "5    Mic\n",
       "dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monte.str[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Chapman\n",
       "1     Cleese\n",
       "2    Gilliam\n",
       "3       Idle\n",
       "4      Jones\n",
       "5      Palin\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monte.str.split().str.get(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>info</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B|C|D</td>\n",
       "      <td>Graham Chapman</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B|D</td>\n",
       "      <td>John Cleese</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A|C</td>\n",
       "      <td>Terry Gilliam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B|D</td>\n",
       "      <td>Eric Idle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B|C</td>\n",
       "      <td>Terry Jones</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>B|C|D</td>\n",
       "      <td>Michael Palin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    info            name\n",
       "0  B|C|D  Graham Chapman\n",
       "1    B|D     John Cleese\n",
       "2    A|C   Terry Gilliam\n",
       "3    B|D       Eric Idle\n",
       "4    B|C     Terry Jones\n",
       "5  B|C|D   Michael Palin"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_monte = pd.DataFrame({'name': monte,\n",
    "                           'info': ['B|C|D', 'B|D', 'A|C', 'B|D', 'B|C', 'B|C|D']})\n",
    "full_monte"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A  B  C  D\n",
       "0  0  1  1  1\n",
       "1  0  1  0  1\n",
       "2  1  0  1  0\n",
       "3  0  1  0  1\n",
       "4  0  1  1  0\n",
       "5  0  1  1  1"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_monte['info'].str.get_dummies('|')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# !curl -O http://openrecipes.s3.amazonaws.com/recipeitems-latest.json.gz\n",
    "# !gunzip recipeitems-latest.json.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ValueError: Trailing data\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    recipes = pd.read_json('recipeitems-latest.json')\n",
    "except ValueError as e:\n",
    "    print(\"ValueError:\", e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 12)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('recipeitems-latest.json') as f:\n",
    "    line = f.readline()\n",
    "pd.read_json(line).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# read the entire file into a python array\n",
    "with open('recipeitems-latest.json', 'r') as f:\n",
    "    # Extract each line\n",
    "    data = (line.strip() for line in f)\n",
    "    # Reformat so each line is the element of a list\n",
    "    data_json = \"[{0}]\".format(','.join(data))\n",
    "# read the result as a JSON\n",
    "recipes = pd.read_json(data_json)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(173278, 17)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recipes.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "_id                                {'$oid': '5160756b96cc62079cc2db15'}\n",
       "cookTime                                                          PT30M\n",
       "creator                                                             NaN\n",
       "dateModified                                                        NaN\n",
       "datePublished                                                2013-03-11\n",
       "description           Late Saturday afternoon, after Marlboro Man ha...\n",
       "image                 http://static.thepioneerwoman.com/cooking/file...\n",
       "ingredients           Biscuits\\n3 cups All-purpose Flour\\n2 Tablespo...\n",
       "name                                    Drop Biscuits and Sausage Gravy\n",
       "prepTime                                                          PT10M\n",
       "recipeCategory                                                      NaN\n",
       "recipeInstructions                                                  NaN\n",
       "recipeYield                                                          12\n",
       "source                                                  thepioneerwoman\n",
       "totalTime                                                           NaN\n",
       "ts                                             {'$date': 1365276011104}\n",
       "url                   http://thepioneerwoman.com/cooking/2013/03/dro...\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recipes.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    173278.000000\n",
       "mean        244.617926\n",
       "std         146.705285\n",
       "min           0.000000\n",
       "25%         147.000000\n",
       "50%         221.000000\n",
       "75%         314.000000\n",
       "max        9067.000000\n",
       "Name: ingredients, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recipes.ingredients.str.len().describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Carrot Pineapple Spice &amp; Brownie Layer Cake with Whipped Cream &amp; Cream Cheese Frosting and Marzipan Carrots'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recipes.name[np.argmax(recipes.ingredients.str.len())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3524"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recipes.description.str.contains('[Bb]reakfast').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10526"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recipes.ingredients.str.contains('[Cc]innamon').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recipes.ingredients.str.contains('[Cc]inamon').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "spice_list = ['salt', 'pepper', 'oregano', 'sage', 'parsley',\n",
    "              'rosemary', 'tarragon', 'thyme', 'paprika', 'cumin']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cumin</th>\n",
       "      <th>oregano</th>\n",
       "      <th>paprika</th>\n",
       "      <th>parsley</th>\n",
       "      <th>pepper</th>\n",
       "      <th>rosemary</th>\n",
       "      <th>sage</th>\n",
       "      <th>salt</th>\n",
       "      <th>tarragon</th>\n",
       "      <th>thyme</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cumin oregano paprika parsley pepper rosemary   sage   salt tarragon  thyme\n",
       "0  False   False   False   False  False    False   True  False    False  False\n",
       "1  False   False   False   False  False    False  False  False    False  False\n",
       "2   True   False   False   False   True    False  False   True    False  False\n",
       "3  False   False   False   False  False    False  False  False    False  False\n",
       "4  False   False   False   False  False    False  False  False    False  False"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re\n",
    "spice_df = pd.DataFrame(dict((spice, recipes.ingredients.str.contains(spice, re.IGNORECASE))\n",
    "                             for spice in spice_list))\n",
    "spice_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selection = spice_df.query('parsley & paprika & tarragon')\n",
    "len(selection)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2069      All cremat with a Little Gem, dandelion and wa...\n",
       "74964                         Lobster with Thermidor butter\n",
       "93768      Burton's Southern Fried Chicken with White Gravy\n",
       "113926                     Mijo's Slow Cooker Shredded Beef\n",
       "137686                     Asparagus Soup with Poached Eggs\n",
       "140530                                 Fried Oyster Po’boys\n",
       "158475                Lamb shank tagine with herb tabbouleh\n",
       "158486                 Southern fried chicken in buttermilk\n",
       "163175            Fried Chicken Sliders with Pickles + Slaw\n",
       "165243                        Bar Tartine Cauliflower Salad\n",
       "Name: name, dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recipes.name[selection.index]"
   ]
  }
 ],
 "metadata": {
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