class 07 Python Data Science toolbox/class03 Bringing it all together! (Case Study).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "034f4000",
   "metadata": {},
   "source": [
    "## Recap"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a9a485e",
   "metadata": {},
   "source": [
    "##### Using `zip()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8f0e0783",
   "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": 4,
   "id": "b6a70430",
   "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": "09bdfb0e",
   "metadata": {},
   "source": [
    "##### Defining a function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0a29c6ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "def raise_both(num1, num2):\n",
    "    \"\"\" Raise num1 to the value of num2 & vice verse\n",
    "    \"\"\"\n",
    "    new_num1 = num1 ** num2\n",
    "    new_num2 = num2 ** num1\n",
    "    new_tuple = (new_num1, new_num2)\n",
    "    return new_tuple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0a7fab09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8, 9)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raise_both(2,3)"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "5d56d92a",
   "metadata": {},
   "source": [
    "##### list comprehensions\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2983abe",
   "metadata": {},
   "source": [
    "##### Generators for the large data limit\n",
    "- Use a generator to load a file line by line\n",
    "- Works on streaming data!\n",
    "- Read and process the file until all lines are exhausted"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5c23110",
   "metadata": {},
   "source": [
    "##### Build a generator function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "44e681aa",
   "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": 8,
   "id": "d847554b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<generator object num_sequence at 0x0000020A0ADF89E0>\n"
     ]
    }
   ],
   "source": [
    "result = num_sequence(2)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6c301441",
   "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": 10,
   "id": "959cffcc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3]\n"
     ]
    }
   ],
   "source": [
    "print(list(num_sequence(4)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1022e332",
   "metadata": {},
   "source": [
    "##### Reading files in chunks\n",
    "- `read_csv()` function and `chunk_size` argument\n",
    "- Look at specific indicators in specific countries\n",
    "- write a functionto generalize tasks"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7f52c7a",
   "metadata": {},
   "source": [
    "# **`Welcome to the case study!`**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e5ef0c9",
   "metadata": {},
   "source": [
    "#### About dataset: World bank data\n",
    "- Data on world economies for over half a century\n",
    "- Indicators\n",
    "    - Population\n",
    "    - Electricity consumption\n",
    "    - CO2 emissions\n",
    "    - Literacy rates\n",
    "    - Unemployment\n",
    "    - Mortality rates\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f94ea3ac",
   "metadata": {},
   "source": [
    "### Dictionaries for data science"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "50eb71ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_names = ['CountryName',\n",
    "                 'CountryCode',\n",
    "                 'IndicatorName',\n",
    "                 'IndicatorCode',\n",
    "                 'Year','Value']\n",
    "\n",
    "row_vals = ['Arab World',\n",
    "            'ARB',\n",
    "            'Adolescent fertility rate (births per 1,000 women ages 15-19)',\n",
    "            'SP.ADO.TFRT',\n",
    "            '1960',\n",
    "            '133.56090740552298']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "145860f7",
   "metadata": {},
   "source": [
    "### TASK -1 \n",
    "- Create a zip object by calling `zip()` and passing to it `feature_names` and `row_vals`. Assign the result to `zipped_lists`.\n",
    "- Create a dictionary from the `zipped_lists` zip object by calling `dict()` with `zipped_lists`. Assign the resulting dictionary to `rs_dict`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "10c7ecda",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'CountryName': 'Arab World', 'CountryCode': 'ARB', 'IndicatorName': 'Adolescent fertility rate (births per 1,000 women ages 15-19)', 'IndicatorCode': 'SP.ADO.TFRT', 'Year': '1960', 'Value': '133.56090740552298'}\n"
     ]
    }
   ],
   "source": [
    "# Zip lists: zipped_lists\n",
    "zipped_lists = zip(feature_names, row_vals)\n",
    "\n",
    "# Create a dictionary: rs_dict\n",
    "rs_dict = dict(zipped_lists)\n",
    "\n",
    "# Print the dictionary\n",
    "print(rs_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93b56777",
   "metadata": {},
   "source": [
    "### Writing a function to help you\n",
    "- Suppose you needed to repeat the same process done in the previous exercise to many, many rows of data. Rewriting your code again and again could become very tedious, repetitive, and unmaintainable.\n",
    "\n",
    "### TASK -2\n",
    "- Define the function `lists2dict()` with two parameters: first is `list1` and second is `list2`.\n",
    "- Return the resulting dictionary `rs_dict` in `lists2dict()`.\n",
    "- Call the `lists2dict()` function with the arguments `feature_names` and `row_vals`. Assign the result of the function call to `rs_fxn`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "803a2785",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'CountryName': 'Arab World', 'CountryCode': 'ARB', 'IndicatorName': 'Adolescent fertility rate (births per 1,000 women ages 15-19)', 'IndicatorCode': 'SP.ADO.TFRT', 'Year': '1960', 'Value': '133.56090740552298'}\n"
     ]
    }
   ],
   "source": [
    "# Define lists2dict()\n",
    "def lists2dict(list1, list2):\n",
    "    \"\"\"Return a dictionary where list1 provides\n",
    "    the keys and list2 provides the values.\"\"\"\n",
    "\n",
    "    # Zip lists: zipped_lists\n",
    "    zipped_lists = zip(list1, list2)\n",
    "\n",
    "    # Create a dictionary: rs_dict\n",
    "    rs_dict = dict(zipped_lists)\n",
    "\n",
    "    # Return the dictionary\n",
    "    return rs_dict\n",
    "\n",
    "# Call lists2dict: rs_fxn\n",
    "rs_fxn = lists2dict(feature_names, row_vals)\n",
    "\n",
    "# Print rs_fxn\n",
    "print(rs_fxn)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "712a8591",
   "metadata": {},
   "source": [
    "### Using a list comprehension\n",
    "- This time, you're going to use the `lists2dict()` function you defined in the recently to turn a bunch of lists into a list of dictionaries with the help of a list comprehension."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7ff3a7ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "row_lists = [['Arab World',\n",
    "  'ARB',\n",
    "  'Adolescent fertility rate (births per 1,000 women ages 15-19)',\n",
    "  'SP.ADO.TFRT',\n",
    "  '1960',\n",
    "  '133.56090740552298'],\n",
    " ['Arab World',\n",
    "  'ARB',\n",
    "  'Age dependency ratio (% of working-age population)',\n",
    "  'SP.POP.DPND',\n",
    "  '1960',\n",
    "  '87.7976011532547'],\n",
    " ['Arab World',\n",
    "  'ARB',\n",
    "  'Age dependency ratio, old (% of working-age population)',\n",
    "  'SP.POP.DPND.OL',\n",
    "  '1960',\n",
    "  '6.634579191565161'],\n",
    " ['Arab World',\n",
    "  'ARB',\n",
    "  'Age dependency ratio, young (% of working-age population)',\n",
    "  'SP.POP.DPND.YG',\n",
    "  '1960',\n",
    "  '81.02332950839141'],\n",
    " ['Arab World',\n",
    "  'ARB',\n",
    "  'Arms exports (SIPRI trend indicator values)',\n",
    "  'MS.MIL.XPRT.KD',\n",
    "  '1960',\n",
    "  '3000000.0'],\n",
    " ['Arab World',\n",
    "  'ARB',\n",
    "  'Arms imports (SIPRI trend indicator values)',\n",
    "  'MS.MIL.MPRT.KD',\n",
    "  '1960',\n",
    "  '538000000.0'],\n",
    " ['Arab World',\n",
    "  'ARB',\n",
    "  'Birth rate, crude (per 1,000 people)',\n",
    "  'SP.DYN.CBRT.IN',\n",
    "  '1960',\n",
    "  '47.697888095096395'],\n",
    " ['Arab World',\n",
    "  'ARB',\n",
    "  'CO2 emissions (kt)',\n",
    "  'EN.ATM.CO2E.KT',\n",
    "  '1960',\n",
    "  '59563.9892169935'],\n",
    " ['Arab World',\n",
    "  'ARB',\n",
    "  'CO2 emissions (metric tons per capita)',\n",
    "  'EN.ATM.CO2E.PC',\n",
    "  '1960',\n",
    "  '0.6439635478877049'],\n",
    " ['Arab World',\n",
    "  'ARB',\n",
    "  'CO2 emissions from gaseous fuel consumption (% of total)',\n",
    "  'EN.ATM.CO2E.GF.ZS',\n",
    "  '1960',\n",
    "  '5.041291753975099'],\n",
    " ['Arab World',\n",
    "  'ARB',\n",
    "  'CO2 emissions from liquid fuel consumption (% of total)',\n",
    "  'EN.ATM.CO2E.LF.ZS',\n",
    "  '1960',\n",
    "  '84.8514729446567'],\n",
    " ['Arab World',\n",
    "  'ARB',\n",
    "  'CO2 emissions from liquid fuel consumption (kt)',\n",
    "  'EN.ATM.CO2E.LF.KT',\n",
    "  '1960',\n",
    "  '49541.707291032304'],\n",
    " ['Arab World',\n",
    "  'ARB',\n",
    "  'CO2 emissions from solid fuel consumption (% of total)',\n",
    "  'EN.ATM.CO2E.SF.ZS',\n",
    "  '1960',\n",
    "  '4.72698138789597'],\n",
    " ['Arab World',\n",
    "  'ARB',\n",
    "  'Death rate, crude (per 1,000 people)',\n",
    "  'SP.DYN.CDRT.IN',\n",
    "  '1960',\n",
    "  '19.7544519237187'],\n",
    " ['Arab World',\n",
    "  'ARB',\n",
    "  'Fertility rate, total (births per woman)',\n",
    "  'SP.DYN.TFRT.IN',\n",
    "  '1960',\n",
    "  '6.92402738655897'],\n",
    " ['Arab World',\n",
    "  'ARB',\n",
    "  'Fixed telephone subscriptions',\n",
    "  'IT.MLT.MAIN',\n",
    "  '1960',\n",
    "  '406833.0'],\n",
    " ['Arab World',\n",
    "  'ARB',\n",
    "  'Fixed telephone subscriptions (per 100 people)',\n",
    "  'IT.MLT.MAIN.P2',\n",
    "  '1960',\n",
    "  '0.6167005703199'],\n",
    " ['Arab World',\n",
    "  'ARB',\n",
    "  'Hospital beds (per 1,000 people)',\n",
    "  'SH.MED.BEDS.ZS',\n",
    "  '1960',\n",
    "  '1.9296220724398703'],\n",
    " ['Arab World',\n",
    "  'ARB',\n",
    "  'International migrant stock (% of population)',\n",
    "  'SM.POP.TOTL.ZS',\n",
    "  '1960',\n",
    "  '2.9906371279862403'],\n",
    " ['Arab World',\n",
    "  'ARB',\n",
    "  'International migrant stock, total',\n",
    "  'SM.POP.TOTL',\n",
    "  '1960',\n",
    "  '3324685.0']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a2280f1",
   "metadata": {},
   "source": [
    "### TASK -3\n",
    "- Inspect the contents of `row_lists` by printing the first two lists in `row_lists`.\n",
    "- Create a list comprehension that generates a dictionary using `lists2dict()` for each sublist in `row_lists`. The keys are from the `feature_names` list and the values are the row entries in `row_lists`. Use `sublist` as your iterator variable and assign the resulting list of dictionaries to `list_of_dicts`.\n",
    "- Look at the first two dictionaries in `list_of_dicts` by printing them out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0d890da0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Arab World', 'ARB', 'Adolescent fertility rate (births per 1,000 women ages 15-19)', 'SP.ADO.TFRT', '1960', '133.56090740552298']\n",
      "['Arab World', 'ARB', 'Age dependency ratio (% of working-age population)', 'SP.POP.DPND', '1960', '87.7976011532547']\n",
      "\n",
      "Turn list of lists into list of dicts: list_of_dicts...\n",
      "Print the first two dictionaries in list_of_dicts...\n",
      "\n",
      "{'CountryName': 'Arab World', 'CountryCode': 'ARB', 'IndicatorName': 'Adolescent fertility rate (births per 1,000 women ages 15-19)', 'IndicatorCode': 'SP.ADO.TFRT', 'Year': '1960', 'Value': '133.56090740552298'}\n",
      "{'CountryName': 'Arab World', 'CountryCode': 'ARB', 'IndicatorName': 'Age dependency ratio (% of working-age population)', 'IndicatorCode': 'SP.POP.DPND', 'Year': '1960', 'Value': '87.7976011532547'}\n"
     ]
    }
   ],
   "source": [
    "# Print the first two lists in row_lists\n",
    "print(row_lists[0])\n",
    "print(row_lists[1])\n",
    "\n",
    "print('')\n",
    "print('Turn list of lists into list of dicts: list_of_dicts...')\n",
    "\n",
    "# Turn list of lists into list of dicts: list_of_dicts\n",
    "list_of_dicts = [lists2dict(feature_names, sublist) for sublist in row_lists]\n",
    "\n",
    "print('Print the first two dictionaries in list_of_dicts...')\n",
    "print('')\n",
    "\n",
    "# Print the first two dictionaries in list_of_dicts\n",
    "\n",
    "print(list_of_dicts[0])\n",
    "print(list_of_dicts[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0409ba00",
   "metadata": {},
   "source": [
    "### Turning this all into a DataFrame\n",
    "- You will now use all of these to convert the list of dictionaries into a pandas DataFrame.\n",
    "- You will see how convenient it is to generate a DataFrame from dictionaries with the DataFrame() function from the pandas package.\n",
    "\n",
    "\n",
    "### TASK - 4\n",
    "- To use the `DataFrame()` function you need, first import the `pandas` package with the alias `pd`.\n",
    "- Create a DataFrame from the list of dictionaries in `list_of_dicts` by calling `pd.DataFrame()`. Assign the resulting DataFrame to `df`.\n",
    "- Inspect the contents of df printing the `head` of the DataFrame. Head of the DataFrame df can be accessed by calling `df.head()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "80c62f96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>CountryName</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>IndicatorName</th>\n",
       "      <th>IndicatorCode</th>\n",
       "      <th>Year</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Arab World</td>\n",
       "      <td>ARB</td>\n",
       "      <td>Adolescent fertility rate (births per 1,000 wo...</td>\n",
       "      <td>SP.ADO.TFRT</td>\n",
       "      <td>1960</td>\n",
       "      <td>133.56090740552298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Arab World</td>\n",
       "      <td>ARB</td>\n",
       "      <td>Age dependency ratio (% of working-age populat...</td>\n",
       "      <td>SP.POP.DPND</td>\n",
       "      <td>1960</td>\n",
       "      <td>87.7976011532547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arab World</td>\n",
       "      <td>ARB</td>\n",
       "      <td>Age dependency ratio, old (% of working-age po...</td>\n",
       "      <td>SP.POP.DPND.OL</td>\n",
       "      <td>1960</td>\n",
       "      <td>6.634579191565161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arab World</td>\n",
       "      <td>ARB</td>\n",
       "      <td>Age dependency ratio, young (% of working-age ...</td>\n",
       "      <td>SP.POP.DPND.YG</td>\n",
       "      <td>1960</td>\n",
       "      <td>81.02332950839141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Arab World</td>\n",
       "      <td>ARB</td>\n",
       "      <td>Arms exports (SIPRI trend indicator values)</td>\n",
       "      <td>MS.MIL.XPRT.KD</td>\n",
       "      <td>1960</td>\n",
       "      <td>3000000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CountryName CountryCode                                      IndicatorName  \\\n",
       "0  Arab World         ARB  Adolescent fertility rate (births per 1,000 wo...   \n",
       "1  Arab World         ARB  Age dependency ratio (% of working-age populat...   \n",
       "2  Arab World         ARB  Age dependency ratio, old (% of working-age po...   \n",
       "3  Arab World         ARB  Age dependency ratio, young (% of working-age ...   \n",
       "4  Arab World         ARB        Arms exports (SIPRI trend indicator values)   \n",
       "\n",
       "    IndicatorCode  Year               Value  \n",
       "0     SP.ADO.TFRT  1960  133.56090740552298  \n",
       "1     SP.POP.DPND  1960    87.7976011532547  \n",
       "2  SP.POP.DPND.OL  1960   6.634579191565161  \n",
       "3  SP.POP.DPND.YG  1960   81.02332950839141  \n",
       "4  MS.MIL.XPRT.KD  1960           3000000.0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the pandas package\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Turn list of lists into list of dicts: list_of_dicts\n",
    "list_of_dicts = [lists2dict(feature_names, sublist) for sublist in row_lists]\n",
    "\n",
    "# Turn list of dicts into a DataFrame: df\n",
    "df = pd.DataFrame(list_of_dicts)\n",
    "\n",
    "# Print the head of the DataFrame\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e1fd5c6",
   "metadata": {},
   "source": [
    "# **`Using Python generators for streaming data`**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "950866c2",
   "metadata": {},
   "source": [
    "### Processing data in chunks\n",
    "- Sometimes, data sources can be so large in size that storing the entire dataset in memory becomes too `resource-intensive`.\n",
    "- In this exercise, you will process the `first 1000 rows of a file line by line`, to create a dictionary of the counts of how many times each country appears in a column in the dataset.\n",
    "\n",
    "### TASK - 5\n",
    "- Use `open()` to bind the csv file `'world_dev_ind.csv'` as `file` in the context manager.\n",
    "- Complete the for loop so that it iterates `1000` times to perform the loop body and process only the first 1000 rows of data of the `file`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "30c6f6f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Arab World': 5, 'Caribbean small states': 5, 'Central Europe and the Baltics': 5, 'East Asia & Pacific (all income levels)': 5, 'East Asia & Pacific (developing only)': 5, 'Euro area': 5, 'Europe & Central Asia (all income levels)': 5, 'Europe & Central Asia (developing only)': 5, 'European Union': 5, 'Fragile and conflict affected situations': 5, 'Heavily indebted poor countries (HIPC)': 5, 'High income': 5, 'High income: nonOECD': 5, 'High income: OECD': 5, 'Latin America & Caribbean (all income levels)': 5, 'Latin America & Caribbean (developing only)': 5, 'Least developed countries: UN classification': 5, 'Low & middle income': 5, 'Low income': 5, 'Lower middle income': 5, 'Middle East & North Africa (all income levels)': 5, 'Middle East & North Africa (developing only)': 5, 'Middle income': 5, 'North America': 5, 'OECD members': 5, 'Other small states': 5, 'Pacific island small states': 5, 'Small states': 5, 'South Asia': 5, 'Sub-Saharan Africa (all income levels)': 5, 'Sub-Saharan Africa (developing only)': 5, 'Upper middle income': 5, 'World': 4, 'Afghanistan': 4, 'Albania': 4, 'Algeria': 4, 'American Samoa': 4, 'Andorra': 4, 'Angola': 4, 'Antigua and Barbuda': 4, 'Argentina': 4, 'Armenia': 4, 'Aruba': 4, 'Australia': 4, 'Austria': 4, 'Azerbaijan': 4, '\"Bahamas': 4, 'Bahrain': 4, 'Bangladesh': 4, 'Barbados': 4, 'Belarus': 4, 'Belgium': 4, 'Belize': 4, 'Benin': 4, 'Bermuda': 4, 'Bhutan': 4, 'Bolivia': 4, 'Bosnia and Herzegovina': 4, 'Botswana': 4, 'Brazil': 4, 'Brunei Darussalam': 4, 'Bulgaria': 4, 'Burkina Faso': 4, 'Burundi': 4, 'Cabo Verde': 4, 'Cambodia': 4, 'Cameroon': 4, 'Canada': 4, 'Cayman Islands': 4, 'Central African Republic': 4, 'Chad': 4, 'Channel Islands': 4, 'Chile': 4, 'China': 4, 'Colombia': 4, 'Comoros': 4, '\"Congo': 8, 'Costa Rica': 4, \"Cote d'Ivoire\": 4, 'Croatia': 4, 'Cuba': 4, 'Curacao': 4, 'Cyprus': 4, 'Czech Republic': 4, 'Denmark': 4, 'Djibouti': 4, 'Dominica': 4, 'Dominican Republic': 4, 'Ecuador': 4, '\"Egypt': 4, 'El Salvador': 4, 'Equatorial Guinea': 4, 'Eritrea': 4, 'Estonia': 4, 'Ethiopia': 4, 'Faeroe Islands': 4, 'Fiji': 4, 'Finland': 4, 'France': 4, 'French Polynesia': 4, 'Gabon': 4, '\"Gambia': 4, 'Georgia': 4, 'Germany': 4, 'Ghana': 4, 'Greece': 4, 'Greenland': 4, 'Grenada': 4, 'Guam': 4, 'Guatemala': 4, 'Guinea': 4, 'Guinea-Bissau': 4, 'Guyana': 4, 'Haiti': 4, 'Honduras': 4, '\"Hong Kong SAR': 4, 'Hungary': 4, 'Iceland': 4, 'India': 4, 'Indonesia': 4, '\"Iran': 4, 'Iraq': 4, 'Ireland': 4, 'Isle of Man': 4, 'Israel': 4, 'Italy': 4, 'Jamaica': 4, 'Japan': 4, 'Jordan': 4, 'Kazakhstan': 4, 'Kenya': 4, 'Kiribati': 4, '\"Korea': 8, 'Kuwait': 4, 'Kyrgyz Republic': 4, 'Lao PDR': 4, 'Latvia': 4, 'Lebanon': 4, 'Lesotho': 4, 'Liberia': 4, 'Libya': 4, 'Liechtenstein': 4, 'Lithuania': 4, 'Luxembourg': 4, '\"Macao SAR': 4, '\"Macedonia': 4, 'Madagascar': 4, 'Malawi': 4, 'Malaysia': 4, 'Maldives': 4, 'Mali': 4, 'Malta': 4, 'Marshall Islands': 4, 'Mauritania': 4, 'Mauritius': 4, 'Mexico': 4, '\"Micronesia': 4, 'Moldova': 4, 'Monaco': 4, 'Mongolia': 4, 'Montenegro': 4, 'Morocco': 4, 'Mozambique': 4, 'Myanmar': 4, 'Namibia': 4, 'Nepal': 4, 'Netherlands': 4, 'New Caledonia': 4, 'New Zealand': 4, 'Nicaragua': 4, 'Niger': 4, 'Nigeria': 4, 'Northern Mariana Islands': 4, 'Norway': 4, 'Oman': 4, 'Pakistan': 4, 'Palau': 4, 'Panama': 4, 'Papua New Guinea': 4, 'Paraguay': 4, 'Peru': 4, 'Philippines': 4, 'Poland': 4, 'Portugal': 4, 'Puerto Rico': 4, 'Qatar': 4, 'Romania': 4, 'Russian Federation': 4, 'Rwanda': 4, 'Samoa': 4, 'San Marino': 4, 'Sao Tome and Principe': 4, 'Saudi Arabia': 4, 'Senegal': 4, 'Seychelles': 4, 'Sierra Leone': 4, 'Singapore': 4, 'Slovak Republic': 4, 'Slovenia': 4, 'Solomon Islands': 4, 'Somalia': 4, 'South Africa': 4, 'South Sudan': 4, 'Spain': 4, 'Sri Lanka': 4, 'St. Kitts and Nevis': 4, 'St. Lucia': 4, 'St. Vincent and the Grenadines': 4, 'Sudan': 4, 'Suriname': 4, 'Swaziland': 4, 'Sweden': 4, 'Switzerland': 4, 'Syrian Arab Republic': 4, 'Tajikistan': 4, 'Tanzania': 4, 'Thailand': 4, 'Timor-Leste': 4, 'Togo': 4, 'Tonga': 4, 'Trinidad and Tobago': 4, 'Tunisia': 4, 'Turkey': 4, 'Turkmenistan': 4, 'Turks and Caicos Islands': 4, 'Tuvalu': 4, 'Uganda': 4, 'Ukraine': 4, 'United Arab Emirates': 4, 'United Kingdom': 4, 'United States': 4, 'Uruguay': 4, 'Uzbekistan': 4, 'Vanuatu': 4, '\"Venezuela': 4, 'Vietnam': 4, 'Virgin Islands (U.S.)': 4, '\"Yemen': 4, 'Zambia': 4, 'Zimbabwe': 4}\n"
     ]
    }
   ],
   "source": [
    "# Open a connection to the file\n",
    "with open('./datasets/world_ind_pop_data.csv') as file:\n",
    "\n",
    "    # Skip the column names\n",
    "    file.readline()\n",
    "\n",
    "    # Initialize an empty dictionary: counts_dict\n",
    "    counts_dict = {}\n",
    "\n",
    "    # Process only the first 1000 rows\n",
    "    for j in range(1000):\n",
    "\n",
    "        # Split the current line into a list: line\n",
    "        line = file.readline().split(',')\n",
    "\n",
    "        # Get the value for the first column: first_col\n",
    "        first_col = line[0]\n",
    "\n",
    "        # If the column value is in the dict, increment its value\n",
    "        if first_col in counts_dict.keys():\n",
    "            counts_dict[first_col] += 1\n",
    "\n",
    "        # Else, add to the dict and set value to 1\n",
    "        else:\n",
    "            counts_dict[first_col] = 1\n",
    "\n",
    "# Print the resulting dictionary\n",
    "print(counts_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0247aed",
   "metadata": {},
   "source": [
    "### Writing a generator to load data in chunks\n",
    "- Recently, you processed a file line by line for a given number of lines. What if, however, you want to do this for the entire file?\n",
    "\n",
    "- In this case, it would be useful to use generators. ***Generators allow users to lazily evaluate data. This concept of `lazy evaluation` is useful when you have to deal with very large datasets because it lets you generate values in an efficient manner by yielding only chunks of data at a time instead of the whole thing at once.***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef9e10bb",
   "metadata": {},
   "source": [
    "### TASK -5\n",
    "- In the function `read_large_file()`, read a line from `file_object` by using the method `readline()`. Assign the result to data.\n",
    "- In the function `read_large_file()`, yield the line read from the file data.\n",
    "- In the context manager, create a generator object `gen_file` by calling your generator function `read_large_file()` and passing `file` to it.\n",
    "- Print the first three lines produced by the generator object `gen_file` using `next()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "dd9c5a89",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CountryName,CountryCode,Year,Total Population,Urban population (% of total)\n",
      "\n",
      "Arab World,ARB,1960,92495902.0,31.285384211605397\n",
      "\n",
      "Caribbean small states,CSS,1960,4190810.0,31.5974898513652\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Define read_large_file()\n",
    "def read_large_file(file_object):\n",
    "    \"\"\"A generator function to read a large file lazily.\"\"\"\n",
    "\n",
    "    # Loop indefinitely until the end of the file\n",
    "    while True:\n",
    "\n",
    "        # Read a line from the file: data\n",
    "        data = file_object.readline()\n",
    "\n",
    "        # Break if this is the end of the file\n",
    "        if not data:\n",
    "            break\n",
    "\n",
    "        # Yield the line of data\n",
    "        yield data\n",
    "        \n",
    "# Open a connection to the file\n",
    "with open('./datasets/world_ind_pop_data.csv') as file:\n",
    "\n",
    "    # Create a generator object for the file: gen_file\n",
    "    gen_file = read_large_file(file)\n",
    "\n",
    "    # Print the first three lines of the file\n",
    "    print(next(gen_file))\n",
    "    print(next(gen_file))\n",
    "    print(next(gen_file))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9995c626",
   "metadata": {},
   "source": [
    "- Great! You've just created a generator function that you can use to help you process large files.\n",
    "- For this exercise, however, you won't process just `1000 rows` of data, you'll process the entire dataset!\n",
    "\n",
    "### TASK -6\n",
    "- Bind the file `'world_dev_ind.csv'` to file in the context manager with `open()`.\n",
    "- Complete the for loop so that it iterates over the generator from the call to `read_large_file()` to process all the rows of the `file`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3c23aff5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'CountryName': 1, 'Arab World': 55, 'Caribbean small states': 55, 'Central Europe and the Baltics': 55, 'East Asia & Pacific (all income levels)': 55, 'East Asia & Pacific (developing only)': 55, 'Euro area': 55, 'Europe & Central Asia (all income levels)': 55, 'Europe & Central Asia (developing only)': 55, 'European Union': 55, 'Fragile and conflict affected situations': 55, 'Heavily indebted poor countries (HIPC)': 55, 'High income': 55, 'High income: nonOECD': 55, 'High income: OECD': 55, 'Latin America & Caribbean (all income levels)': 55, 'Latin America & Caribbean (developing only)': 55, 'Least developed countries: UN classification': 55, 'Low & middle income': 55, 'Low income': 55, 'Lower middle income': 55, 'Middle East & North Africa (all income levels)': 55, 'Middle East & North Africa (developing only)': 55, 'Middle income': 55, 'North America': 55, 'OECD members': 55, 'Other small states': 55, 'Pacific island small states': 55, 'Small states': 55, 'South Asia': 55, 'Sub-Saharan Africa (all income levels)': 55, 'Sub-Saharan Africa (developing only)': 55, 'Upper middle income': 55, 'World': 55, 'Afghanistan': 55, 'Albania': 55, 'Algeria': 55, 'American Samoa': 55, 'Andorra': 55, 'Angola': 55, 'Antigua and Barbuda': 55, 'Argentina': 55, 'Armenia': 55, 'Aruba': 55, 'Australia': 55, 'Austria': 55, 'Azerbaijan': 55, '\"Bahamas': 55, 'Bahrain': 55, 'Bangladesh': 55, 'Barbados': 55, 'Belarus': 55, 'Belgium': 55, 'Belize': 55, 'Benin': 55, 'Bermuda': 55, 'Bhutan': 55, 'Bolivia': 55, 'Bosnia and Herzegovina': 55, 'Botswana': 55, 'Brazil': 55, 'Brunei Darussalam': 55, 'Bulgaria': 55, 'Burkina Faso': 55, 'Burundi': 55, 'Cabo Verde': 55, 'Cambodia': 55, 'Cameroon': 55, 'Canada': 55, 'Cayman Islands': 55, 'Central African Republic': 55, 'Chad': 55, 'Channel Islands': 55, 'Chile': 55, 'China': 55, 'Colombia': 55, 'Comoros': 55, '\"Congo': 110, 'Costa Rica': 55, \"Cote d'Ivoire\": 55, 'Croatia': 55, 'Cuba': 55, 'Curacao': 55, 'Cyprus': 55, 'Czech Republic': 55, 'Denmark': 55, 'Djibouti': 55, 'Dominica': 55, 'Dominican Republic': 55, 'Ecuador': 55, '\"Egypt': 55, 'El Salvador': 55, 'Equatorial Guinea': 55, 'Eritrea': 55, 'Estonia': 55, 'Ethiopia': 55, 'Faeroe Islands': 55, 'Fiji': 55, 'Finland': 55, 'France': 55, 'French Polynesia': 55, 'Gabon': 55, '\"Gambia': 55, 'Georgia': 55, 'Germany': 55, 'Ghana': 55, 'Greece': 55, 'Greenland': 55, 'Grenada': 55, 'Guam': 55, 'Guatemala': 55, 'Guinea': 55, 'Guinea-Bissau': 55, 'Guyana': 55, 'Haiti': 55, 'Honduras': 55, '\"Hong Kong SAR': 55, 'Hungary': 55, 'Iceland': 55, 'India': 55, 'Indonesia': 55, '\"Iran': 55, 'Iraq': 55, 'Ireland': 55, 'Isle of Man': 55, 'Israel': 55, 'Italy': 55, 'Jamaica': 55, 'Japan': 55, 'Jordan': 55, 'Kazakhstan': 55, 'Kenya': 55, 'Kiribati': 55, '\"Korea': 110, 'Kuwait': 52, 'Kyrgyz Republic': 55, 'Lao PDR': 55, 'Latvia': 55, 'Lebanon': 55, 'Lesotho': 55, 'Liberia': 55, 'Libya': 55, 'Liechtenstein': 55, 'Lithuania': 55, 'Luxembourg': 55, '\"Macao SAR': 55, '\"Macedonia': 55, 'Madagascar': 55, 'Malawi': 55, 'Malaysia': 55, 'Maldives': 55, 'Mali': 55, 'Malta': 55, 'Marshall Islands': 55, 'Mauritania': 55, 'Mauritius': 55, 'Mexico': 55, '\"Micronesia': 55, 'Moldova': 55, 'Monaco': 55, 'Mongolia': 55, 'Montenegro': 55, 'Morocco': 55, 'Mozambique': 55, 'Myanmar': 55, 'Namibia': 55, 'Nepal': 55, 'Netherlands': 55, 'New Caledonia': 55, 'New Zealand': 55, 'Nicaragua': 55, 'Niger': 55, 'Nigeria': 55, 'Northern Mariana Islands': 55, 'Norway': 55, 'Oman': 55, 'Pakistan': 55, 'Palau': 55, 'Panama': 55, 'Papua New Guinea': 55, 'Paraguay': 55, 'Peru': 55, 'Philippines': 55, 'Poland': 55, 'Portugal': 55, 'Puerto Rico': 55, 'Qatar': 55, 'Romania': 55, 'Russian Federation': 55, 'Rwanda': 55, 'Samoa': 55, 'San Marino': 55, 'Sao Tome and Principe': 55, 'Saudi Arabia': 55, 'Senegal': 55, 'Seychelles': 55, 'Sierra Leone': 55, 'Singapore': 55, 'Slovak Republic': 55, 'Slovenia': 55, 'Solomon Islands': 55, 'Somalia': 55, 'South Africa': 55, 'South Sudan': 55, 'Spain': 55, 'Sri Lanka': 55, 'St. Kitts and Nevis': 55, 'St. Lucia': 55, 'St. Vincent and the Grenadines': 55, 'Sudan': 55, 'Suriname': 55, 'Swaziland': 55, 'Sweden': 55, 'Switzerland': 55, 'Syrian Arab Republic': 55, 'Tajikistan': 55, 'Tanzania': 55, 'Thailand': 55, 'Timor-Leste': 55, 'Togo': 55, 'Tonga': 55, 'Trinidad and Tobago': 55, 'Tunisia': 55, 'Turkey': 55, 'Turkmenistan': 55, 'Turks and Caicos Islands': 55, 'Tuvalu': 55, 'Uganda': 55, 'Ukraine': 55, 'United Arab Emirates': 55, 'United Kingdom': 55, 'United States': 55, 'Uruguay': 55, 'Uzbekistan': 55, 'Vanuatu': 55, '\"Venezuela': 55, 'Vietnam': 55, 'Virgin Islands (U.S.)': 55, '\"Yemen': 55, 'Zambia': 55, 'Zimbabwe': 55, 'Serbia': 25, 'West Bank and Gaza': 25, 'Sint Maarten (Dutch part)': 17}\n"
     ]
    }
   ],
   "source": [
    "# Initialize an empty dictionary: counts_dict\n",
    "counts_dict = {}\n",
    "\n",
    "# Open a connection to the file\n",
    "with open('./datasets/world_ind_pop_data.csv') as file:\n",
    "\n",
    "    # Iterate over the generator from read_large_file()\n",
    "    for line in read_large_file(file):\n",
    "\n",
    "        row = line.split(',')\n",
    "        first_col = row[0]\n",
    "\n",
    "        if first_col in counts_dict.keys():\n",
    "            counts_dict[first_col] += 1\n",
    "        else:\n",
    "            counts_dict[first_col] = 1\n",
    "\n",
    "# Print            \n",
    "print(counts_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4fe7e7a",
   "metadata": {},
   "source": [
    "# **`Using pandas read_csv iterator for streaming data`**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c41ec62a",
   "metadata": {},
   "source": [
    "### Writing an iterator to load data in chunks\n",
    "- Another way to read data too large to store in memory in chunks is to read the file in as DataFrames of a certain length, say, 100. For example, with the pandas package (`imported as pd`), you can do `pd.read_csv(filename, chunksize=100)`. This creates an iterable reader object, which means that you can use `next()` on it.\n",
    "\n",
    "### TASK -7\n",
    "- Use `pd.read_csv()` to read in `'./datasets/world_ind_pop_data.csv'` in chunks of size `10`. Assign the result to `df_reader`.\n",
    "- Print the first two chunks from `df_reader`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6b1239b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                 CountryName CountryCode  Year  \\\n",
      "0                                 Arab World         ARB  1960   \n",
      "1                     Caribbean small states         CSS  1960   \n",
      "2             Central Europe and the Baltics         CEB  1960   \n",
      "3    East Asia & Pacific (all income levels)         EAS  1960   \n",
      "4      East Asia & Pacific (developing only)         EAP  1960   \n",
      "5                                  Euro area         EMU  1960   \n",
      "6  Europe & Central Asia (all income levels)         ECS  1960   \n",
      "7    Europe & Central Asia (developing only)         ECA  1960   \n",
      "8                             European Union         EUU  1960   \n",
      "9   Fragile and conflict affected situations         FCS  1960   \n",
      "\n",
      "   Total Population  Urban population (% of total)  \n",
      "0      9.249590e+07                      31.285384  \n",
      "1      4.190810e+06                      31.597490  \n",
      "2      9.140158e+07                      44.507921  \n",
      "3      1.042475e+09                      22.471132  \n",
      "4      8.964930e+08                      16.917679  \n",
      "5      2.653965e+08                      62.096947  \n",
      "6      6.674890e+08                      55.378977  \n",
      "7      1.553174e+08                      38.066129  \n",
      "8      4.094985e+08                      61.212898  \n",
      "9      1.203546e+08                      17.891972  \n",
      "                                      CountryName CountryCode  Year  \\\n",
      "10         Heavily indebted poor countries (HIPC)         HPC  1960   \n",
      "11                                    High income         HIC  1960   \n",
      "12                           High income: nonOECD         NOC  1960   \n",
      "13                              High income: OECD         OEC  1960   \n",
      "14  Latin America & Caribbean (all income levels)         LCN  1960   \n",
      "15    Latin America & Caribbean (developing only)         LAC  1960   \n",
      "16   Least developed countries: UN classification         LDC  1960   \n",
      "17                            Low & middle income         LMY  1960   \n",
      "18                                     Low income         LIC  1960   \n",
      "19                            Lower middle income         LMC  1960   \n",
      "\n",
      "    Total Population  Urban population (% of total)  \n",
      "10      1.624912e+08                      12.236046  \n",
      "11      9.075975e+08                      62.680332  \n",
      "12      1.866767e+08                      56.107863  \n",
      "13      7.209208e+08                      64.285435  \n",
      "14      2.205642e+08                      49.284688  \n",
      "15      1.776822e+08                      44.863308  \n",
      "16      2.410728e+08                       9.616261  \n",
      "17      2.127373e+09                      21.272894  \n",
      "18      1.571884e+08                      11.498396  \n",
      "19      9.429116e+08                      19.810513  \n"
     ]
    }
   ],
   "source": [
    "# Import the pandas package\n",
    "import pandas as pd\n",
    "\n",
    "# Initialize reader object: df_reader\n",
    "df_reader = pd.read_csv('./datasets/world_ind_pop_data.csv',chunksize=10)\n",
    "\n",
    "# Print two chunks\n",
    "print(next(df_reader))\n",
    "print(next(df_reader))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d27f1220",
   "metadata": {},
   "source": [
    "- In this exercise, you will read in a file using a bigger DataFrame chunk size and then process the data from the first chunk.\n",
    "- To process the data, you will create another DataFrame composed of only the rows from a specific country. You will then zip together two of the columns from the new DataFrame, `'Total Population'` and `'Urban population (% of total)'`.Finally, you will create a list of tuples from the zip object, where each tuple is composed of a value from each of the two columns mentioned.\n",
    "\n",
    "### TASK-9\n",
    "- Use `pd.read_csv()` to read in the file in `'ind_pop_data.csv'` in chunks of size `1000`. Assign the result to `urb_pop_reader`.\n",
    "- Get the first DataFrame chunk from the iterable `urb_pop_reader` and assign this to `df_urb_pop`.\n",
    "- Select only the rows of `df_urb_pop` that have a `'CountryCode'` of `'CEB'`. To do this, compare whether `df_urb_pop['CountryCode']` is equal to `'CEB'` within the square brackets in `df_urb_pop[____]`.\n",
    "- Using `zip()`, zip together the `'Total Population'` and `'Urban population (% of total)'` columns of `df_pop_ceb`. Assign the resulting zip object to `pops`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9d0cf798",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               CountryName CountryCode  Year  \\\n",
      "0                               Arab World         ARB  1960   \n",
      "1                   Caribbean small states         CSS  1960   \n",
      "2           Central Europe and the Baltics         CEB  1960   \n",
      "3  East Asia & Pacific (all income levels)         EAS  1960   \n",
      "4    East Asia & Pacific (developing only)         EAP  1960   \n",
      "\n",
      "   Total Population  Urban population (% of total)  \n",
      "0      9.249590e+07                      31.285384  \n",
      "1      4.190810e+06                      31.597490  \n",
      "2      9.140158e+07                      44.507921  \n",
      "3      1.042475e+09                      22.471132  \n",
      "4      8.964930e+08                      16.917679  \n",
      "[(91401583.0, 44.5079211390026), (92237118.0, 45.206665319194), (93014890.0, 45.866564696018), (93845749.0, 46.5340927663649), (94722599.0, 47.2087429803526)]\n"
     ]
    }
   ],
   "source": [
    "# Initialize reader object: urb_pop_reader\n",
    "urb_pop_reader = pd.read_csv('./datasets/world_ind_pop_data.csv', chunksize=1000)\n",
    "\n",
    "# Get the first DataFrame chunk: df_urb_pop\n",
    "df_urb_pop = next(urb_pop_reader)\n",
    "\n",
    "# Check out the head of the DataFrame\n",
    "print(df_urb_pop.head())\n",
    "\n",
    "# Check out specific country: df_pop_ceb\n",
    "df_pop_ceb = df_urb_pop[df_urb_pop['CountryCode']=='CEB']\n",
    "\n",
    "# Zip DataFrame columns of interest: pops\n",
    "pops = zip(df_pop_ceb['Total Population'], df_pop_ceb['Urban population (% of total)'])\n",
    "\n",
    "# Turn zip object into list: pops_list\n",
    "pops_list = list(pops)\n",
    "\n",
    "# Print pops_list\n",
    "print(pops_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a027ce84",
   "metadata": {},
   "source": [
    "- You're getting used to reading and processing data in chunks by now. Let's push your skills a little further by adding a column to a DataFrame.\n",
    "\n",
    "### TASK -10\n",
    "- Write a list comprehension to generate a list of values from `pops_list` for the new column `'Total Urban Population'`. The output expression should be the product of the first and second element in each tuple in `pops_list`. Because the 2nd element is a percentage, you also need to either multiply the result by 0.01 or divide it by 100. In addition, note that the column `'Total Urban Population'` should only be able to take on integer values. To ensure this, make sure you cast the output expression to an integer with `int()`.\n",
    "- Create a scatter plot where the x-axis are values from the `'Year'` column and the y-axis are values from the `'Total Urban Population'` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fad43e26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Code from previous exercise\n",
    "urb_pop_reader = pd.read_csv('./datasets/world_ind_pop_data.csv', chunksize=1000)\n",
    "df_urb_pop = next(urb_pop_reader)\n",
    "df_pop_ceb = df_urb_pop[df_urb_pop['CountryCode'] == 'CEB']\n",
    "pops = zip(df_pop_ceb['Total Population'], \n",
    "           df_pop_ceb['Urban population (% of total)'])\n",
    "pops_list = list(pops)\n",
    "\n",
    "# Use list comprehension to create new DataFrame column 'Total Urban Population'\n",
    "df_pop_ceb['Total Urban Population'] = [int(pop[0]*pop[1]*0.01 )for pop  in pops_list]\n",
    "\n",
    "# Plot urban population data\n",
    "df_pop_ceb.plot(kind='scatter', x='Year', y='Total Urban Population')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "569767d8",
   "metadata": {},
   "source": [
    "- In the previous exercises, you've only processed the data from the first DataFrame chunk. This time, you will aggregate the results over all the DataFrame chunks in the dataset.\n",
    "\n",
    "### TASK -11\n",
    "- Initialize an empty DataFrame data using `pd.DataFrame()`.\n",
    "- In the for loop, iterate over `urb_pop_reader` to be able to process all the DataFrame chunks in the dataset.\n",
    "- Using the method `append()` of the DataFrame data, append `df_pop_ceb` to data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7c5f3f51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize reader object: urb_pop_reader\n",
    "urb_pop_reader = pd.read_csv('./datasets/world_ind_pop_data.csv', chunksize=1000)\n",
    "\n",
    "# Initialize empty DataFrame: data\n",
    "data = pd.DataFrame()\n",
    "\n",
    "# Iterate over each DataFrame chunk\n",
    "for df_urb_pop in urb_pop_reader:\n",
    "\n",
    "    # Check out specific country: df_pop_ceb\n",
    "    df_pop_ceb = df_urb_pop[df_urb_pop['CountryCode'] == 'CEB']\n",
    "\n",
    "    # Zip DataFrame columns of interest: pops\n",
    "    pops = zip(df_pop_ceb['Total Population'],\n",
    "                df_pop_ceb['Urban population (% of total)'])\n",
    "\n",
    "    # Turn zip object into list: pops_list\n",
    "    pops_list = list(pops)\n",
    "\n",
    "    # Use list comprehension to create new DataFrame column 'Total Urban Population'\n",
    "    df_pop_ceb['Total Urban Population'] = [int(tup[0] * tup[1] * 0.01) for tup in pops_list]\n",
    "    \n",
    "    # Append DataFrame chunk to data: data\n",
    "    data = data.append(df_pop_ceb)\n",
    "\n",
    "# Plot urban population data\n",
    "data.plot(kind='scatter', x='Year', y='Total Urban Population')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ed0b474",
   "metadata": {},
   "source": [
    "- This is the last leg. You've learned a lot about processing a large dataset in chunks. In this last exercise, you will put all the code for processing the data into a single function so that you can reuse the code without having to rewrite the same things all over again\n",
    "\n",
    "- You're going to define the function `plot_pop()` which takes two arguments: the filename of the file to be processed, and the country code of the rows you want to process in the dataset.\n",
    "\n",
    "- **Because all of the previous code you've written in the previous exercises will be housed in `plot_pop()`, calling the function already does the following:**\n",
    "    - ***Loading of the file chunk by chunk,***\n",
    "    - ***Creating the new column of urban population values, and***\n",
    "    - ***Plotting the urban population data.***\n",
    "- That's a lot of work, but the function now makes it convenient to repeat the same process for whatever file and country code you want to process and visualize!\n",
    "\n",
    "\n",
    "### TASK -12\n",
    "- Define the function `plot_pop()` that has two arguments: first is filename for the file to process and second is `country_code` for the country to be processed in the dataset.\n",
    "- Call `plot_pop()` to process the data for country code `'CEB'` in the file `'ind_pop_data.csv'`.\n",
    "- Call `plot_pop()` to process the data for country code `'ARB'` in the file `'ind_pop_data.csv'`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0f189e06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define plot_pop()\n",
    "def plot_pop(filename, country_code):\n",
    "\n",
    "    # Initialize reader object: urb_pop_reader\n",
    "    urb_pop_reader = pd.read_csv(filename, chunksize=1000)\n",
    "\n",
    "    # Initialize empty DataFrame: data\n",
    "    data = pd.DataFrame()\n",
    "    \n",
    "    # Iterate over each DataFrame chunk\n",
    "    for df_urb_pop in urb_pop_reader:\n",
    "        # Check out specific country: df_pop_ceb\n",
    "        df_pop_ceb = df_urb_pop[df_urb_pop['CountryCode'] == country_code]\n",
    "\n",
    "        # Zip DataFrame columns of interest: pops\n",
    "        pops = zip(df_pop_ceb['Total Population'],\n",
    "                    df_pop_ceb['Urban population (% of total)'])\n",
    "\n",
    "        # Turn zip object into list: pops_list\n",
    "        pops_list = list(pops)\n",
    "\n",
    "        # Use list comprehension to create new DataFrame column 'Total Urban Population'\n",
    "        df_pop_ceb['Total Urban Population'] = [int(tup[0] * tup[1] * 0.01) for tup in pops_list]\n",
    "    \n",
    "        # Append DataFrame chunk to data: data\n",
    "        data = data.append(df_pop_ceb)\n",
    "\n",
    "    # Plot urban population data\n",
    "    data.plot(kind='scatter', x='Year', y='Total Urban Population')\n",
    "    plt.show()\n",
    "\n",
    "# Set the filename: fn\n",
    "fn = 'ind_pop_data.csv'\n",
    "\n",
    "# Call plot_pop for country code 'CEB'\n",
    "plot_pop('./datasets/world_ind_pop_data.csv', 'CEB')\n",
    "\n",
    "# Call plot_pop for country code 'ARB'\n",
    "plot_pop('./datasets/world_ind_pop_data.csv', 'ARB')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7234f53",
   "metadata": {},
   "source": [
    "# Final thoughts\n",
    "\n",
    "### You’ve applied your skills in:\n",
    "- User-defined functions.\n",
    "- Iterators\n",
    "- List Comprehensions\n",
    "- Generators"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aef7a8f6",
   "metadata": {},
   "source": [
    "### Thank you :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3f16f3a",
   "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
}