class 07 Python Data Science toolbox/class2 Using iterators in PythonLand.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "25f59bc3",
   "metadata": {},
   "source": [
    "# **`1-Using iterators in PythonLand`**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "287dd742",
   "metadata": {},
   "source": [
    "# **`1.1- Introduction to iterators`**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ccf76d9",
   "metadata": {},
   "source": [
    "#### Iterating with a for loop\n",
    "- We can iterate over a list using a for loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9462d610",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Qasim\n",
      "Hassan\n",
      "Muneeb\n"
     ]
    }
   ],
   "source": [
    "employees = ['Qasim','Hassan','Muneeb']\n",
    "for employee in employees:\n",
    "    print(employee)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a4e8282",
   "metadata": {},
   "source": [
    "- We can iterate over a string using a for loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d7dd529f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D\n",
      "a\n",
      "t\n",
      "a\n",
      "C\n",
      "a\n",
      "m\n",
      "p\n"
     ]
    }
   ],
   "source": [
    "for letter in 'DataCamp':\n",
    "    print(letter)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de458710",
   "metadata": {},
   "source": [
    "- We can iterate over a range object using a for loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "545f5c21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "for i in range(4):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ae684a6",
   "metadata": {},
   "source": [
    "### Iterators vs. iterables\n",
    "##### Iterable\n",
    "- Examples: lists, strings, dictionaries, file connnections\n",
    "- An object with an associated `iter()` method\n",
    "- Applying `iter()` to an iterable creates an iterator\n",
    "    \n",
    "##### Iterator\n",
    "- Produces next value with `next()`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a302cf35",
   "metadata": {},
   "source": [
    "### Iterating over iterables: `next()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d5157534",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'D'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word = 'Da'\n",
    "it = iter(word)\n",
    "next(it)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0e8bb621",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(it)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a39cf8b2",
   "metadata": {},
   "outputs": [
    {
     "ename": "StopIteration",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mStopIteration\u001b[0m                             Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-8-c6f05272dbc9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mit\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# B/c String finish\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mStopIteration\u001b[0m: "
     ]
    }
   ],
   "source": [
    "next(it) # B/c String finish"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "036f046b",
   "metadata": {},
   "source": [
    "### Iterating at once with *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "729a0ca9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D a t a\n"
     ]
    }
   ],
   "source": [
    "word = 'Data'\n",
    "it = iter(word)\n",
    "print(*it)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4b88b936",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "print(*it) # Will print Nothing B/c No more values to go through!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5118c25c",
   "metadata": {},
   "source": [
    "### Iterating over dictionaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5ddecba3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Qasim S/O Hassan\n",
      "Ammad S/O Mohsin\n"
     ]
    }
   ],
   "source": [
    "pythonistas = {'Qasim': 'Hassan','Ammad': 'Mohsin'}\n",
    "for key, value in pythonistas.items():\n",
    "    print(key+\" S/O \"+ value)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74a0ded1",
   "metadata": {},
   "source": [
    "### Iterating over file connections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a84cf04e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hi, You're learning Python Data Science Toolbox.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "file = open('./datasets/file.txt')\n",
    "it = iter(file)\n",
    "print(next(it))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f2a1d075",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the Second line & you're going really great.\n"
     ]
    }
   ],
   "source": [
    "print(next(it))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ef59353",
   "metadata": {},
   "source": [
    "# **`1.2- Playing with iterators`**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41ee4d90",
   "metadata": {},
   "source": [
    "### Using enumerate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cbb24b67",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'enumerate'>\n"
     ]
    }
   ],
   "source": [
    "avengers = ['hawkeye','iron man','thor','quicksilver']\n",
    "e = enumerate(avengers)\n",
    "print(type(e))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0b677301",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 'hawkeye'), (1, 'iron man'), (2, 'thor'), (3, 'quicksilver')]\n"
     ]
    }
   ],
   "source": [
    "e_list = list(e)\n",
    "print(e_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47261bc9",
   "metadata": {},
   "source": [
    "### enumerate() and unpack"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "520c34e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index no:0 have HAWKEYE\n",
      "Index no:1 have IRON MAN\n",
      "Index no:2 have THOR\n",
      "Index no:3 have QUICKSILVER\n"
     ]
    }
   ],
   "source": [
    "avengers = ['hawkeye','iron man','thor','quicksilver']\n",
    "for index, name in enumerate(avengers):\n",
    "    print(\"Index no:\"+str(index)+ \" have \"+name.upper())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "30311adf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index no:10 have HAWKEYE\n",
      "Index no:11 have IRON MAN\n",
      "Index no:12 have THOR\n",
      "Index no:13 have QUICKSILVER\n"
     ]
    }
   ],
   "source": [
    "avengers = ['hawkeye','iron man','thor','quicksilver']\n",
    "for index, name in enumerate(avengers, start=10):\n",
    "    print(\"Index no:\"+str(index)+ \" have \"+name.upper())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f7ecf7c",
   "metadata": {},
   "source": [
    "### Using zip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9f02fd4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'zip'>\n"
     ]
    }
   ],
   "source": [
    "avengers = ['hawkeye','iron man','thor','quicksilver']\n",
    "names = ['barton','stark','odinson','maximoff']\n",
    "z = zip(avengers, names)\n",
    "print(type(z))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8dad2eb8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('hawkeye', 'barton'), ('iron man', 'stark'), ('thor', 'odinson'), ('quicksilver', 'maximoff')]\n"
     ]
    }
   ],
   "source": [
    "print(list(z))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fbf0460",
   "metadata": {},
   "source": [
    "### `zip()` and unpack"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "5bbb403a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HAWKEYE <-- real name --> BARTON \n",
      "IRON MAN <-- real name --> STARK \n",
      "THOR <-- real name --> ODINSON \n",
      "QUICKSILVER <-- real name --> MAXIMOFF \n"
     ]
    }
   ],
   "source": [
    "avengers = ['hawkeye','iron man','thor','quicksilver']\n",
    "names = ['barton','stark','odinson','maximoff']\n",
    "\n",
    "for z1, z2 in zip(avengers, names):\n",
    "    print(f\"{z1.upper()} <-- real name --> {z2.upper()} \")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf2f965e",
   "metadata": {},
   "source": [
    "### Print zip with *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "8f76b5db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('hawkeye', 'barton') ('iron man', 'stark') ('thor', 'odinson') ('quicksilver', 'maximoff')\n"
     ]
    }
   ],
   "source": [
    "avengers = ['hawkeye','iron man','thor','quicksilver']\n",
    "names = ['barton','stark','odinson','maximoff']\n",
    "z = zip(avengers, names)\n",
    "print(*z)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14323bc1",
   "metadata": {},
   "source": [
    "# **`1.3- Using iterators to load large files into memory`**\n",
    "\n",
    "### Loading data in chunks\n",
    "- There can be too much data to hold in memory\n",
    "- Solution: load data in chunks!\n",
    "- Pandas function: `read_csv()`\n",
    "    - Specify the chunk: `chunk_size`\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ca74c00",
   "metadata": {},
   "source": [
    "## Iterating over data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "523f6e91",
   "metadata": {},
   "outputs": [
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       "      <td>714960401759412224</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>{'retweeted': False, 'lang': 'en', 'favorite_c...</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>RT @BIackPplTweets: Young Donald trump meets h...</td>\n",
       "      <td>1459294817758</td>\n",
       "      <td>False</td>\n",
       "      <td>{'utc_offset': None, 'profile_image_url_https'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tue Mar 29 23:40:18 +0000 2016</td>\n",
       "      <td>{'hashtags': [], 'user_mentions': [{'screen_na...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>low</td>\n",
       "      <td>NaN</td>\n",
       "      <td>714960402791145472</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>{'retweeted': False, 'lang': 'en', 'favorite_c...</td>\n",
       "      <td>&lt;a href=\"http://twitter.com\" rel=\"nofollow\"&gt;Tw...</td>\n",
       "      <td>RT @trumpresearch: @WaitingInBagdad @thehill T...</td>\n",
       "      <td>1459294818004</td>\n",
       "      <td>False</td>\n",
       "      <td>{'utc_offset': 10800, 'profile_image_url_https...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tue Mar 29 23:40:17 +0000 2016</td>\n",
       "      <td>{'hashtags': [], 'user_mentions': [{'screen_na...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>low</td>\n",
       "      <td>NaN</td>\n",
       "      <td>714960402346598400</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>{'retweeted': False, 'lang': 'en', 'favorite_c...</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/android\" ...</td>\n",
       "      <td>RT @HouseCracka: 29,000+ PEOPLE WATCHING TRUMP...</td>\n",
       "      <td>1459294817898</td>\n",
       "      <td>False</td>\n",
       "      <td>{'utc_offset': None, 'profile_image_url_https'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tue Mar 29 23:40:18 +0000 2016</td>\n",
       "      <td>{'hashtags': [], 'user_mentions': [{'screen_na...</td>\n",
       "      <td>{'media': [{'sizes': {'large': {'w': 384, 'h':...</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>low</td>\n",
       "      <td>NaN</td>\n",
       "      <td>714960402849927168</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>{'retweeted': False, 'text': 'RT for Brendon U...</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>RT @urfavandtrump: RT for Brendon Urie\\nFav fo...</td>\n",
       "      <td>1459294818018</td>\n",
       "      <td>False</td>\n",
       "      <td>{'utc_offset': None, 'profile_image_url_https'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tue Mar 29 23:40:18 +0000 2016</td>\n",
       "      <td>{'hashtags': [{'text': 'Trump', 'indices': [34...</td>\n",
       "      <td>{'media': [{'sizes': {'large': {'w': 573, 'h':...</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>low</td>\n",
       "      <td>NaN</td>\n",
       "      <td>714960402853928960</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>{'retweeted': False, 'text': 'This is how I se...</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>RT @trapgrampa: This is how I see #Trump every...</td>\n",
       "      <td>1459294818019</td>\n",
       "      <td>False</td>\n",
       "      <td>{'utc_offset': None, 'profile_image_url_https'...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   contributors  coordinates                      created_at  \\\n",
       "0           NaN          NaN  Tue Mar 29 23:40:17 +0000 2016   \n",
       "1           NaN          NaN  Tue Mar 29 23:40:17 +0000 2016   \n",
       "2           NaN          NaN  Tue Mar 29 23:40:17 +0000 2016   \n",
       "3           NaN          NaN  Tue Mar 29 23:40:17 +0000 2016   \n",
       "4           NaN          NaN  Tue Mar 29 23:40:17 +0000 2016   \n",
       "5           NaN          NaN  Tue Mar 29 23:40:17 +0000 2016   \n",
       "6           NaN          NaN  Tue Mar 29 23:40:18 +0000 2016   \n",
       "7           NaN          NaN  Tue Mar 29 23:40:17 +0000 2016   \n",
       "8           NaN          NaN  Tue Mar 29 23:40:18 +0000 2016   \n",
       "9           NaN          NaN  Tue Mar 29 23:40:18 +0000 2016   \n",
       "\n",
       "                                            entities  \\\n",
       "0  {'hashtags': [], 'user_mentions': [{'screen_na...   \n",
       "1  {'hashtags': [{'text': 'cruzsexscandal', 'indi...   \n",
       "2  {'hashtags': [], 'user_mentions': [], 'symbols...   \n",
       "3  {'hashtags': [], 'user_mentions': [], 'symbols...   \n",
       "4  {'hashtags': [], 'user_mentions': [{'screen_na...   \n",
       "5  {'hashtags': [], 'user_mentions': [{'screen_na...   \n",
       "6  {'hashtags': [], 'user_mentions': [{'screen_na...   \n",
       "7  {'hashtags': [], 'user_mentions': [{'screen_na...   \n",
       "8  {'hashtags': [], 'user_mentions': [{'screen_na...   \n",
       "9  {'hashtags': [{'text': 'Trump', 'indices': [34...   \n",
       "\n",
       "                                   extended_entities  favorite_count  \\\n",
       "0  {'media': [{'sizes': {'large': {'w': 1024, 'h'...               0   \n",
       "1  {'media': [{'sizes': {'large': {'w': 500, 'h':...               0   \n",
       "2                                                NaN               0   \n",
       "3                                                NaN               0   \n",
       "4                                                NaN               0   \n",
       "5                                                NaN               0   \n",
       "6                                                NaN               0   \n",
       "7                                                NaN               0   \n",
       "8  {'media': [{'sizes': {'large': {'w': 384, 'h':...               0   \n",
       "9  {'media': [{'sizes': {'large': {'w': 573, 'h':...               0   \n",
       "\n",
       "   favorited filter_level  geo                  id  ...  quoted_status_id  \\\n",
       "0      False          low  NaN  714960401759387648  ...               NaN   \n",
       "1      False          low  NaN  714960401977319424  ...               NaN   \n",
       "2      False          low  NaN  714960402426236928  ...               NaN   \n",
       "3      False          low  NaN  714960402367561730  ...      7.149239e+17   \n",
       "4      False          low  NaN  714960402149416960  ...               NaN   \n",
       "5      False          low  NaN  714960401759412224  ...               NaN   \n",
       "6      False          low  NaN  714960402791145472  ...               NaN   \n",
       "7      False          low  NaN  714960402346598400  ...               NaN   \n",
       "8      False          low  NaN  714960402849927168  ...               NaN   \n",
       "9      False          low  NaN  714960402853928960  ...               NaN   \n",
       "\n",
       "   quoted_status_id_str  retweet_count  retweeted  \\\n",
       "0                   NaN              0      False   \n",
       "1                   NaN              0      False   \n",
       "2                   NaN              0      False   \n",
       "3          7.149239e+17              0      False   \n",
       "4                   NaN              0      False   \n",
       "5                   NaN              0      False   \n",
       "6                   NaN              0      False   \n",
       "7                   NaN              0      False   \n",
       "8                   NaN              0      False   \n",
       "9                   NaN              0      False   \n",
       "\n",
       "                                    retweeted_status  \\\n",
       "0  {'retweeted': False, 'text': \".@krollbondratin...   \n",
       "1  {'retweeted': False, 'text': '@dmartosko Cruz ...   \n",
       "2                                                NaN   \n",
       "3                                                NaN   \n",
       "4  {'retweeted': False, 'text': 'The anti-America...   \n",
       "5  {'retweeted': False, 'lang': 'en', 'favorite_c...   \n",
       "6  {'retweeted': False, 'lang': 'en', 'favorite_c...   \n",
       "7  {'retweeted': False, 'lang': 'en', 'favorite_c...   \n",
       "8  {'retweeted': False, 'text': 'RT for Brendon U...   \n",
       "9  {'retweeted': False, 'text': 'This is how I se...   \n",
       "\n",
       "                                              source  \\\n",
       "0  <a href=\"http://twitter.com\" rel=\"nofollow\">Tw...   \n",
       "1  <a href=\"http://twitter.com\" rel=\"nofollow\">Tw...   \n",
       "2  <a href=\"http://www.facebook.com/twitter\" rel=...   \n",
       "3  <a href=\"http://twitter.com/download/android\" ...   \n",
       "4  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "5  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "6  <a href=\"http://twitter.com\" rel=\"nofollow\">Tw...   \n",
       "7  <a href=\"http://twitter.com/download/android\" ...   \n",
       "8  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "9  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                text   timestamp_ms  \\\n",
       "0  RT @bpolitics: .@krollbondrating's Christopher...  1459294817758   \n",
       "1  RT @HeidiAlpine: @dmartosko Cruz video found.....  1459294817810   \n",
       "2  Njihuni me Zonjën Trump !!! | Ekskluzive https...  1459294817917   \n",
       "3  Your an idiot she shouldn't have tried to grab...  1459294817903   \n",
       "4  RT @AlanLohner: The anti-American D.C. elites ...  1459294817851   \n",
       "5  RT @BIackPplTweets: Young Donald trump meets h...  1459294817758   \n",
       "6  RT @trumpresearch: @WaitingInBagdad @thehill T...  1459294818004   \n",
       "7  RT @HouseCracka: 29,000+ PEOPLE WATCHING TRUMP...  1459294817898   \n",
       "8  RT @urfavandtrump: RT for Brendon Urie\\nFav fo...  1459294818018   \n",
       "9  RT @trapgrampa: This is how I see #Trump every...  1459294818019   \n",
       "\n",
       "   truncated                                               user  \n",
       "0      False  {'utc_offset': 3600, 'profile_image_url_https'...  \n",
       "1      False  {'utc_offset': None, 'profile_image_url_https'...  \n",
       "2      False  {'utc_offset': 7200, 'profile_image_url_https'...  \n",
       "3      False  {'utc_offset': None, 'profile_image_url_https'...  \n",
       "4      False  {'utc_offset': -18000, 'profile_image_url_http...  \n",
       "5      False  {'utc_offset': None, 'profile_image_url_https'...  \n",
       "6      False  {'utc_offset': 10800, 'profile_image_url_https...  \n",
       "7      False  {'utc_offset': None, 'profile_image_url_https'...  \n",
       "8      False  {'utc_offset': None, 'profile_image_url_https'...  \n",
       "9      False  {'utc_offset': None, 'profile_image_url_https'...  \n",
       "\n",
       "[10 rows x 31 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importing pandas\n",
    "import pandas as pd\n",
    "\n",
    "# loading dataset\n",
    "tweets_df = pd.read_csv('./datasets/tweets.csv', chunksize=10)\n",
    "\n",
    "next(tweets_df)\n",
    "# tweets_df.head()"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "7d03504b",
   "metadata": {},
   "source": [
    "#### Processing large amounts of Twitter data\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "539ee704",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'en': 97, 'et': 1, 'und': 2}\n"
     ]
    }
   ],
   "source": [
    "# Initialize an empty dictionary: counts_dict\n",
    "counts_dict = {}\n",
    "\n",
    "# Iterate over the file chunk by chunk\n",
    "for chunk in pd.read_csv('./datasets/tweets.csv', chunksize=10):\n",
    "\n",
    "    # Iterate over the column in DataFrame\n",
    "    for entry in chunk['lang']:\n",
    "        if entry in counts_dict.keys():\n",
    "            counts_dict[entry] += 1\n",
    "        else:\n",
    "            counts_dict[entry] = 1\n",
    "\n",
    "# Print the populated dictionary\n",
    "print(counts_dict)\n"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "856ce1c9",
   "metadata": {},
   "source": [
    "#### Extracting information for large amounts of Twitter data\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "62c352b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'en': 97, 'et': 1, 'und': 2}\n"
     ]
    }
   ],
   "source": [
    "# Define count_entries()\n",
    "def count_entries(csv_file, c_size, colname):\n",
    "    \"\"\"Return a dictionary with counts of\n",
    "    occurrences as value for each key.\"\"\"\n",
    "    \n",
    "    # Initialize an empty dictionary: counts_dict\n",
    "    counts_dict = {}\n",
    "\n",
    "    # Iterate over the file chunk by chunk\n",
    "    for chunk in pd.read_csv(csv_file, chunksize=c_size):\n",
    "\n",
    "        # Iterate over the column in DataFrame\n",
    "        for entry in chunk[colname]:\n",
    "            if entry in counts_dict.keys():\n",
    "                counts_dict[entry] += 1\n",
    "            else:\n",
    "                counts_dict[entry] = 1\n",
    "\n",
    "    # Return counts_dict\n",
    "    return counts_dict\n",
    "\n",
    "# Call count_entries(): result_counts\n",
    "result_counts = count_entries('./datasets/tweets.csv', 10, 'lang')\n",
    "\n",
    "# Print result_counts\n",
    "print(result_counts)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4449ed7a",
   "metadata": {},
   "source": [
    "## Congratulations!\n",
    "\n",
    "#### What’s next?\n",
    "- List comprehensions and generators\n",
    "- List comprehensions:\n",
    "- Create lists from other lists, DataFrame columns, etc.\n",
    "    - Single line of code.\n",
    "    - More efficient than using a loop."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43a72e04",
   "metadata": {},
   "source": [
    "# **`2- List comprehensions and generators`**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71f5753d",
   "metadata": {},
   "source": [
    "# **`2.1 List comprehensions`** "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54a25afc",
   "metadata": {},
   "source": [
    "#### Populate a list with a for loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3e88909e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[13, 9, 22, 4, 17]\n"
     ]
    }
   ],
   "source": [
    "nums = [12, 8, 21, 3, 16]\n",
    "new_nums = []\n",
    "for num in nums:\n",
    "    new_nums.append(num+1)\n",
    "print(new_nums)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85500940",
   "metadata": {},
   "source": [
    "#### A list comprehension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "554f3318",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[13, 9, 22, 4, 17]\n"
     ]
    }
   ],
   "source": [
    "nums = [12, 8, 21, 3, 16]\n",
    "new_nums = [num+1 for num in nums]\n",
    "print(new_nums)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d100107",
   "metadata": {},
   "source": [
    "#### For loop & List Comprehension comparsion\n",
    "- `for loop`\n",
    "    - Not efficient\n",
    "    - Take time & long\n",
    "    \n",
    "- `list comprehension`\n",
    "    - Efficient\n",
    "    - Fast & time saving\n",
    "    \n",
    "- There are four characteristics of List comprehension\n",
    "    - One line function\n",
    "    - Without name function\n",
    "    - Not used before \n",
    "    - Not used after"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f6b3a84",
   "metadata": {},
   "source": [
    "#### List comprehension with `range()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dfc0749d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n"
     ]
    }
   ],
   "source": [
    "result = [ num for num in range(10)]\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "549d19ad",
   "metadata": {},
   "source": [
    "#### List comprehensions\n",
    "- Collapse for loops for building lists into a single line\n",
    "- Components\n",
    "    - Iterable\n",
    "    - Iterator variable (represent members of iterable)\n",
    "    - Output expression\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65f4ea75",
   "metadata": {},
   "source": [
    "#### Nested loops Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "75bb42b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 6), (0, 7), (1, 6), (1, 7)]\n"
     ]
    }
   ],
   "source": [
    "paired_nums = []\n",
    "for num1 in range(0,2):\n",
    "    for num2 in range(6,8):\n",
    "        paired_nums.append((num1, num2))\n",
    "print(paired_nums)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed086afd",
   "metadata": {},
   "source": [
    "#####  QUESTION: How to do this with a `list comprehension`?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5ecdd904",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 6), (0, 7), (1, 6), (1, 7)]\n"
     ]
    }
   ],
   "source": [
    "paired_nums =[(num1, num2) for num1 in range(0,2) for num2 in range(6,8)]\n",
    "print(paired_nums)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68110162",
   "metadata": {},
   "source": [
    "##### Tradeoff: readability (only way is keep practising)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef8635a7",
   "metadata": {},
   "source": [
    "# **`2.2 Advanced comprehensions`**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "046818d8",
   "metadata": {},
   "source": [
    "#### Conditionals in comprehensions\n",
    "\n",
    "##### Conditionals on the iterable for even numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ea99f339",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 2, 4, 6, 8, 10]\n"
     ]
    }
   ],
   "source": [
    "even_nums = [num for num in range(11) if num%2==0]\n",
    "print(even_nums)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e6e1cce",
   "metadata": {},
   "source": [
    "Python documentation on the `%` operator: The `% (modulo)`\n",
    "operator yields the remainder from the division of the "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8421c789",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5%2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "daa9b333",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "6%2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0ee6fb8",
   "metadata": {},
   "source": [
    "#### complex Conditionals in comprehensions\n",
    "- Conditionals on the `output expression`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5521aa8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 0, 4, 0, 16, 0, 36, 0, 64, 0, 100]\n"
     ]
    }
   ],
   "source": [
    "even_nums_power2 = [num**2 if num%2==0 else 0 for num in range(11)]\n",
    "print(even_nums_power2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b19e5ec",
   "metadata": {},
   "source": [
    "#### Dict comprehensions\n",
    "- create dictionaries\n",
    "- Use curly braces `{}` instead of brackets `[]`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9aae8082",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: 0, 1: -1, 2: -2, 3: -3, 4: -4, 5: -5, 6: -6, 7: -7}\n"
     ]
    }
   ],
   "source": [
    "pos_neg = {num:-num for num in range(8)}\n",
    "print(pos_neg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "385810f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'dict'>\n"
     ]
    }
   ],
   "source": [
    "print(type(pos_neg))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "485bdd8f",
   "metadata": {},
   "source": [
    "# **`2.3 Introduction to generator expressions`**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "116ef4ee",
   "metadata": {},
   "source": [
    "#### Generator expressions\n",
    "- Recall list comprehension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6c99df1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 2, 4, 6, 8, 10, 12, 14]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[2*num for num in range(8)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f1458e8",
   "metadata": {},
   "source": [
    "- use `()` instead of `[]`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2919879a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<generator object <genexpr> at 0x000001149EB3D510>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(2*num for num in range(8))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39412c19",
   "metadata": {},
   "source": [
    "#### List comprehensions vs. generators\n",
    "- List comprehension - `returns a list`\n",
    "- Generators - `returns a generator object`\n",
    "- Both can be iterated over\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a0c2314",
   "metadata": {},
   "source": [
    "####  printing values from generators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7da8b10b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n"
     ]
    }
   ],
   "source": [
    "result = (num for num in range(8))\n",
    "for num in result:\n",
    "    print(num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7b4278fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 2, 4, 6, 8, 10, 12, 14]\n"
     ]
    }
   ],
   "source": [
    "print(list((2*num for num in range(8))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "31320c1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "result = (num for num in range(6)) # Now will use next()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5687776b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "print(next(result)) # Lazy evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a9a864a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "print(next(result)) # Lazy evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a08ebfb",
   "metadata": {},
   "source": [
    "#### Generator vs. list comprehension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e3183b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Don't run becuase list coprehension doesn't support\n",
    "# [num for num in range(10**100000)] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ad156d66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<generator object <genexpr> at 0x000001149EB3DF90>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generator can easily this the same that list comprehension not\n",
    "(num for num in range(10**100000))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e735f447",
   "metadata": {},
   "source": [
    "#### Conditionals in generator expressions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "673d8cc7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<generator object <genexpr> at 0x000001149EB3D890>\n"
     ]
    }
   ],
   "source": [
    "even_nums = (num for num in range(10) if num%2==0)\n",
    "print(even_nums)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a2da7069",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 2, 4, 6, 8]\n"
     ]
    }
   ],
   "source": [
    "print(list(even_nums))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6af245e3",
   "metadata": {},
   "source": [
    "#### Generator functions\n",
    "- Produces generator objects when called\n",
    "- Define like a regular function - `def`\n",
    "- Yields a sequence of values instead of returning a single value\n",
    "- Generates a value with `yield` keyword\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dce6be5",
   "metadata": {},
   "source": [
    "##### Build a generator function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "361823c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def num_sequence(n):\n",
    "    \"\"\"Generate values from 0 to n.\"\"\"\n",
    "    i=0\n",
    "    while i<n:\n",
    "        yield i\n",
    "        i+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "e26711c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<generator object num_sequence at 0x000001149ECD4580>\n"
     ]
    }
   ],
   "source": [
    "result = num_sequence(2)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "9acb35f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "for item in result:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1275386d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3]\n"
     ]
    }
   ],
   "source": [
    "print(list(num_sequence(4)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3db9c233",
   "metadata": {},
   "source": [
    "# **`2.4 Wrapping up comprehensions and generators.`**"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "7081115c",
   "metadata": {},
   "source": [
    "#### Re-cap: list comprehensions\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce774197",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}