ETL_Engineer_Peer_Review_Assignment.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align:center\">\n",
    "    <a href=\"https://skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkPY0221ENSkillsNetwork899-2023-01-01\">\n",
    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\"  />\n",
    "    </a>\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Peer Review Assignment - Data Engineer - ETL\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estimated time needed: **20** minutes\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objectives\n",
    "\n",
    "In this final part you will:\n",
    "\n",
    "- Run the ETL process\n",
    "- Extract bank and market cap data from the JSON file `bank_market_cap.json`\n",
    "- Transform the market cap currency using the exchange rate data\n",
    "- Load the transformed data into a seperate CSV\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this lab, we are going to be using Python and several Python libraries. Some of these libraries might be installed in your lab environment or in SN Labs. Others may need to be installed by you. The cells below will install these libraries when executed.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#!mamba install pandas==1.3.3 -y\n",
    "#!mamba install requests==2.26.0 -y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports\n",
    "\n",
    "Import any additional libraries you may need here.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import glob\n",
    "import pandas as pd\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the exchange rate fluctuates, we will download the same dataset to make marking simpler. This will be in the same format as the dataset you used in the last section  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2023-07-15 06:53:00--  https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0221EN-SkillsNetwork/labs/module%206/Lab%20-%20Extract%20Transform%20Load/data/bank_market_cap_1.json\n",
      "Resolving cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)... 169.63.118.104\n",
      "Connecting to cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)|169.63.118.104|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 2815 (2.7K) [application/json]\n",
      "Saving to: ‘bank_market_cap_1.json.1’\n",
      "\n",
      "bank_market_cap_1.j 100%[===================>]   2.75K  --.-KB/s    in 0s      \n",
      "\n",
      "2023-07-15 06:53:00 (27.4 MB/s) - ‘bank_market_cap_1.json.1’ saved [2815/2815]\n",
      "\n",
      "--2023-07-15 06:53:01--  https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0221EN-SkillsNetwork/labs/module%206/Lab%20-%20Extract%20Transform%20Load/data/bank_market_cap_2.json\n",
      "Resolving cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)... 169.63.118.104\n",
      "Connecting to cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)|169.63.118.104|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 1429 (1.4K) [application/json]\n",
      "Saving to: ‘bank_market_cap_2.json.1’\n",
      "\n",
      "bank_market_cap_2.j 100%[===================>]   1.40K  --.-KB/s    in 0s      \n",
      "\n",
      "2023-07-15 06:53:01 (12.0 MB/s) - ‘bank_market_cap_2.json.1’ saved [1429/1429]\n",
      "\n",
      "--2023-07-15 06:53:02--  https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0221EN-SkillsNetwork/labs/module%206/Final%20Assignment/exchange_rates.csv\n",
      "Resolving cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)... 169.63.118.104\n",
      "Connecting to cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)|169.63.118.104|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 590 [text/csv]\n",
      "Saving to: ‘exchange_rates.csv.1’\n",
      "\n",
      "exchange_rates.csv. 100%[===================>]     590  --.-KB/s    in 0s      \n",
      "\n",
      "2023-07-15 06:53:02 (4.61 MB/s) - ‘exchange_rates.csv.1’ saved [590/590]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0221EN-SkillsNetwork/labs/module%206/Lab%20-%20Extract%20Transform%20Load/data/bank_market_cap_1.json\n",
    "!wget https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0221EN-SkillsNetwork/labs/module%206/Lab%20-%20Extract%20Transform%20Load/data/bank_market_cap_2.json\n",
    "!wget https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0221EN-SkillsNetwork/labs/module%206/Final%20Assignment/exchange_rates.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### JSON Extract Function\n",
    "\n",
    "This function will extract JSON files.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def extract_from_json(file_to_process):\n",
    "    dataframe = pd.read_json(file_to_process)\n",
    "    return dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract Function\n",
    "\n",
    "Define the extract function that finds JSON file `bank_market_cap_1.json` and calls the function created above to extract data from them. Store the data in a `pandas` dataframe. Use the following list for the columns.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "columns=['Name','Market Cap (US$ Billion)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def extract():\n",
    "    # Write your code here\n",
    "    obj = extract_from_json('bank_market_cap_1.json')\n",
    "    return obj"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Question 1</b> Load the file <code>exchange_rates.csv</code> as a dataframe and find the exchange rate for British pounds with the symbol <code>GBP</code>, store it in the variable  <code>exchange_rate</code>, you will be asked for the number. Hint: set the parameter  <code>index_col</code> to 0.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7323984208000001\n"
     ]
    }
   ],
   "source": [
    "# Write your code here\n",
    "ex_rt_df = pd.read_csv('exchange_rates.csv', index_col=0)\n",
    "exchange_rate = ex_rt_df.loc['GBP', 'Rates']\n",
    "print(exchange_rate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transform\n",
    "\n",
    "Using <code>exchange_rate</code> and the `exchange_rates.csv` file find the exchange rate of USD to GBP. Write a transform function that\n",
    "\n",
    "1. Changes the `Market Cap (US$ Billion)` column from USD to GBP\n",
    "2. Rounds the Market Cap (US$ Billion)` column to 3 decimal places\n",
    "3. Rename `Market Cap (US$ Billion)` to `Market Cap (GBP$ Billion)`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def transform(data, rate):\n",
    "    # Write your code here\n",
    "    data['Market Cap (US$ Billion)'] = data['Market Cap (US$ Billion)'].apply(\n",
    "        lambda x: x * rate\n",
    "    )\n",
    "    data['Market Cap (US$ Billion)'] = data['Market Cap (US$ Billion)'].apply(\n",
    "        lambda x: round(x, 3)\n",
    "    )\n",
    "    data.rename(columns={'Market Cap (US$ Billion)':\n",
    "                         'Market Cap (GBP$ Billion)'}, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load\n",
    "\n",
    "Create a function that takes a dataframe and load it to a csv named `bank_market_cap_gbp.csv`. Make sure to set `index` to `False`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def load(df):\n",
    "    # Write your code here\n",
    "    df.to_csv('bank_market_cap_gbp.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logging Function\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write the logging function <code>log</code> to log your data:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def log(msg):\n",
    "    # Write your code here\n",
    "    with open('log.txt', 'a') as logfile:\n",
    "        logfile.write(msg)\n",
    "    print(msg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running the ETL Process\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Log the process accordingly using the following <code>\"ETL Job Started\"</code> and <code>\"Extract phase Started\"</code>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ETL Job Started\n",
      "Extract phase Started\n"
     ]
    }
   ],
   "source": [
    "# Write your code here\n",
    "log(\"ETL Job Started\")\n",
    "log(\"Extract phase Started\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extract\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<code>Question 2</code> Use the function <code>extract</code>, and print the first 5 rows, take a screen shot:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "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>Name</th>\n",
       "      <th>Market Cap (US$ Billion)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>JPMorgan Chase</td>\n",
       "      <td>390.934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Industrial and Commercial Bank of China</td>\n",
       "      <td>345.214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bank of America</td>\n",
       "      <td>325.331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Wells Fargo</td>\n",
       "      <td>308.013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>China Construction Bank</td>\n",
       "      <td>257.399</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      Name  Market Cap (US$ Billion)\n",
       "0                           JPMorgan Chase                   390.934\n",
       "1  Industrial and Commercial Bank of China                   345.214\n",
       "2                          Bank of America                   325.331\n",
       "3                              Wells Fargo                   308.013\n",
       "4                  China Construction Bank                   257.399"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Call the function here\n",
    "qs2 = extract()\n",
    "# Print the rows here\n",
    "qs2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Log the data as <code>\"Extract phase Ended\"</code>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extract phase Ended\n",
      "                                       Name  Market Cap (US$ Billion)\n",
      "0                            JPMorgan Chase                   390.934\n",
      "1   Industrial and Commercial Bank of China                   345.214\n",
      "2                           Bank of America                   325.331\n",
      "3                               Wells Fargo                   308.013\n",
      "4                   China Construction Bank                   257.399\n",
      "5                         HSBC Holdings PLC                   219.270\n",
      "6                Agricultural Bank of China                   203.244\n",
      "7                            Citigroup Inc.                   203.165\n",
      "8                             Bank of China                   181.469\n",
      "9                      China Merchants Bank                   122.616\n",
      "10                     Royal Bank of Canada                   122.564\n",
      "11                          Banco Santander                   115.696\n",
      "12                        Commonwealth Bank                   112.227\n",
      "13           Mitsubishi UFJ Financial Group                   111.660\n",
      "14                    Toronto-Dominion Bank                   108.449\n",
      "15                              BNP Paribas                   103.258\n",
      "16                            Goldman Sachs                   100.705\n",
      "17                       Sberbank of Russia                    99.840\n",
      "18                           Morgan Stanley                    99.651\n",
      "19                             U.S. Bancorp                    94.558\n",
      "20                                HDFC Bank                    88.435\n",
      "21                            Itaú Unibanco                    86.203\n",
      "22                                  Westpac                    84.560\n",
      "23                               Scotiabank                    78.947\n",
      "24                                ING Group                    78.920\n",
      "25                                      UBS                    75.482\n",
      "26                           Charles Schwab                    74.255\n",
      "27                   PNC Financial Services                    72.246\n",
      "28                     Lloyds Banking Group                    71.253\n",
      "29          Sumitomo Mitsui Financial Group                    67.328\n",
      "30                   Bank of Communications                    67.078\n",
      "31  Australia and New Zealand Banking Group                    66.448\n",
      "32                           Banco Bradesco                    64.816\n",
      "33                  National Australia Bank                    63.794\n",
      "34                          Intesa Sanpaolo                    62.032\n",
      "35          Banco Bilbao Vizcaya Argentaria                    60.678\n",
      "36                          Japan Post Bank                    60.525\n",
      "37              The Bank of New York Mellon                    59.823\n",
      "38         Shanghai Pudong Development Bank                    58.796\n",
      "39                  Industrial Bank (China)                    57.388\n",
      "40                Bank of China (Hong Kong)                    55.449\n",
      "41                         Bank of Montreal                    53.181\n",
      "42                          Crédit Agricole                    52.141\n",
      "43                                 DBS Bank                    51.475\n",
      "44                                   Nordea                    51.185\n",
      "45                              Capital One                    51.107\n",
      "46                            NatWest Group                    50.490\n",
      "47                   Mizuho Financial Group                    50.018\n",
      "48                            Credit Suisse                    48.462\n",
      "49             Postal Savings Bank of China                    47.948\n",
      "50                      China Minsheng Bank                    47.921\n",
      "51                                UniCredit                    47.777\n",
      "52                         China CITIC Bank                    47.451\n",
      "53                           Hang Seng Bank                    46.171\n",
      "54                         Société Générale                    46.051\n",
      "55                                 Barclays                    45.850\n",
      "56       Canadian Imperial Bank of Commerce                    43.544\n",
      "57                        Bank Central Asia                    43.344\n",
      "58                         Truist Financial                    41.923\n",
      "59             Oversea-Chinese Banking Corp                    41.362\n",
      "60                      State Bank of India                    40.562\n",
      "61                             State Street                    39.769\n",
      "62                            Deutsche Bank                    38.649\n",
      "63                                 KBC Bank                    38.394\n",
      "64                              Danske Bank                    38.223\n",
      "65                             Ping An Bank                    37.993\n",
      "66                       Standard Chartered                    37.319\n",
      "67                     United Overseas Bank                    35.128\n",
      "68                                QNB Group                    33.560\n",
      "69                              Bank Rakyat                    33.081\n"
     ]
    }
   ],
   "source": [
    "# Write your code here\n",
    "log(\"Extract phase Ended\")\n",
    "log(qs2.to_string())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transform\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Log the following  <code>\"Transform phase Started\"</code>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transform phase Started\n"
     ]
    }
   ],
   "source": [
    "# Write your code here\n",
    "log(\"Transform phase Started\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<code>Question 3</code> Use the function <code>transform</code> and print the first 5 rows of the output, take a screen shot:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "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>Name</th>\n",
       "      <th>Market Cap (GBP$ Billion)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>JPMorgan Chase</td>\n",
       "      <td>286.319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Industrial and Commercial Bank of China</td>\n",
       "      <td>252.834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bank of America</td>\n",
       "      <td>238.272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Wells Fargo</td>\n",
       "      <td>225.588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>China Construction Bank</td>\n",
       "      <td>188.519</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      Name  Market Cap (GBP$ Billion)\n",
       "0                           JPMorgan Chase                    286.319\n",
       "1  Industrial and Commercial Bank of China                    252.834\n",
       "2                          Bank of America                    238.272\n",
       "3                              Wells Fargo                    225.588\n",
       "4                  China Construction Bank                    188.519"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Call the function here\n",
    "transform(qs2, exchange_rate)\n",
    "# Print the first 5 rows here\n",
    "qs2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Log your data <code>\"Transform phase Ended\"</code>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transform phase Ended\n"
     ]
    }
   ],
   "source": [
    "# Write your code here\n",
    "log(\"Transform phase Ended\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Log the following `\"Load phase Started\"`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Load phase Started\n"
     ]
    }
   ],
   "source": [
    "# Write your code here\n",
    "log(\"Load phase Started\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Call the load function\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Write your code here\n",
    "load(qs2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Log the following `\"Load phase Ended\"`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Load phase Ended\n"
     ]
    }
   ],
   "source": [
    "# Write your code here\n",
    "log(\"Load phase Ended\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Authors\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ramesh Sannareddy, Joseph Santrcangelo and Azim Hirjani\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Other Contributors\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rav Ahuja\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Change Log\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Date (YYYY-MM-DD) | Version | Changed By        | Change Description                 |\n",
    "| ----------------- | ------- | ----------------- | ---------------------------------- |\n",
    "| 2020-11-25        | 0.1     | Ramesh Sannareddy | Created initial version of the lab |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Copyright © 2020 IBM Corporation. This notebook and its source code are released under the terms of the [MIT License](https://cognitiveclass.ai/mit-license?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkPY0221ENSkillsNetwork899-2023-01-01&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBM-DA0321EN-SkillsNetwork-21426264&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ).\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python",
   "language": "python",
   "name": "conda-env-python-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}