Dictionaries.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dictionary associates values with keys\n",
    "empty_dict = {}\n",
    "empty_dict2 = dict()\n",
    "grades = {\"Tim\":80 , \"Sam\":90}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tims_grade = grades[\"Tim\"]\n",
    "tims_grade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No grade for kate!\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    kates_grade = grades[\"kate\"]\n",
    "except KeyError:\n",
    "    print \"No grade for kate!\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#existence of a key\n",
    "tim_has_grades = \"Tim\" in grades\n",
    "tim_has_grades"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kate_has_grades = \"Kate\" in grades\n",
    "kate_has_grades"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get method returns a default value (instead of raising an exception) \n",
    "tims_grades = grades.get(\"Tim\",0)\n",
    "tims_grades"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kates_grades = grades.get(\"Kate\",0)\n",
    "kates_grades"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "no_ones_grade = grades.get(\"No One\")\n",
    "print no_ones_grade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades[\"Tim\"]=90\n",
    "grades[\"Kate\"]=80\n",
    "len(grades)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweet = {\n",
    "    \"user\" : \"tanvi\",\n",
    "    \"text\" : \"Data science is awesome\",\n",
    "    \"retweet_count\" : 100,\n",
    "    \"hashtags\" : [\"#data\", \"#science\", \"#awesome\"]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweet_keys = tweet.keys()\n",
    "tweet_values = tweet.values()\n",
    "tweet_items = tweet.items()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['text', 'retweet_count', 'hashtags', 'user'] ['Data science is awesome', 100, ['#data', '#science', '#awesome'], 'tanvi'] [('text', 'Data science is awesome'), ('retweet_count', 100), ('hashtags', ['#data', '#science', '#awesome']), ('user', 'tanvi')]\n"
     ]
    }
   ],
   "source": [
    "print tweet_keys,tweet_values,tweet_items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"user\" in tweet_keys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"user\" in tweet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"tanvi\" in tweet_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['the', 'quick', 'brown', 'fox', 'jumps', 'over', 'the', 'lazy', 'dog.']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "str = 'the quick brown fox jumps over the lazy dog.'\n",
    "words = str.split()\n",
    "words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'brown': 1,\n",
       " 'dog.': 1,\n",
       " 'fox': 1,\n",
       " 'jumps': 1,\n",
       " 'lazy': 1,\n",
       " 'over': 1,\n",
       " 'quick': 1,\n",
       " 'the': 2}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#METHOD 1 for word count\n",
    "word_count = {}\n",
    "\n",
    "for word in words:\n",
    "    if word in word_count:\n",
    "        word_count[word]+=1\n",
    "    else:\n",
    "        word_count[word]=1\n",
    "        \n",
    "word_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'brown': 1,\n",
       " 'dog.': 1,\n",
       " 'fox': 1,\n",
       " 'jumps': 1,\n",
       " 'lazy': 1,\n",
       " 'over': 1,\n",
       " 'quick': 1,\n",
       " 'the': 2}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_count = {}\n",
    "\n",
    "for word in words:\n",
    "    try:\n",
    "        word_count[word]+=1\n",
    "    except KeyError:\n",
    "        word_count[word]=1\n",
    "        \n",
    "word_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'brown': 1,\n",
       " 'dog.': 1,\n",
       " 'fox': 1,\n",
       " 'jumps': 1,\n",
       " 'lazy': 1,\n",
       " 'over': 1,\n",
       " 'quick': 1,\n",
       " 'the': 2}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_count = {}\n",
    "\n",
    "for word in words:\n",
    "    previous_count = word_count.get(word,0)\n",
    "    word_count[word] = previous_count+1\n",
    "        \n",
    "word_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(int,\n",
       "            {'brown': 1,\n",
       "             'dog.': 1,\n",
       "             'fox': 1,\n",
       "             'jumps': 1,\n",
       "             'lazy': 1,\n",
       "             'over': 1,\n",
       "             'quick': 1,\n",
       "             'the': 2})"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#DefaultDict\n",
    "from collections import defaultdict\n",
    "\n",
    "word_count = defaultdict(int)\n",
    "for word in words:\n",
    "    word_count[word]+=1\n",
    "    \n",
    "word_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(list, {2: [1]})"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd_list = defaultdict(list)\n",
    "dd_list[2].append(1)\n",
    "dd_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(dict, {'Joel': {'City': 'Seattle'}})"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd_dict = defaultdict(dict)\n",
    "dd_dict[\"Joel\"][\"City\"] = \"Seattle\"\n",
    "dd_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(<function __main__.<lambda>>, {2: [0, 1]})"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd_pair = defaultdict(lambda: [0, 0]) \n",
    "dd_pair[2][1] = 1  \n",
    "dd_pair"
   ]
  }
 ],
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