1. Collecting job data using API's.ipynb

{
    "cells": [
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "<center>\n    <img src=\"https://gitlab.com/ibm/skills-network/courses/placeholder101/-/raw/master/labs/module%201/images/IDSNlogo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\"  />\n</center>\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "# **Collecting Job Data Using APIs**\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Estimated time needed: **45 to 60** minutes\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Objectives\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "After completing this lab, you will be able to:\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "-   Collect job data from GitHub Jobs API\n-   Store the collected data into an excel spreadsheet. \n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Warm-Up Exercise\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Before you attempt the actual lab, here is a fully solved warmup exercise that will help you to learn how to access an API.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Using an API, let us find out who currently are on the International Space Station (ISS).<br> The API at [http://api.open-notify.org/astros.json](http://api.open-notify.org/astros.json?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&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&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&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&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&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&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&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) gives us the information of astronauts currently on ISS in json format.<br>\nYou can read more about this API at [http://open-notify.org/Open-Notify-API/People-In-Space/](http://open-notify.org/Open-Notify-API/People-In-Space?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&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&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&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&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&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&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&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"
        },
        {
            "cell_type": "code",
            "execution_count": 1,
            "metadata": {},
            "outputs": [],
            "source": "import requests # you need this module to make an API call"
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "metadata": {},
            "outputs": [],
            "source": "api_url = \"http://api.open-notify.org/astros.json\" # this url gives use the astronaut data"
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "metadata": {},
            "outputs": [],
            "source": "response = requests.get(api_url) # Call the API using the get method and store the\n                                # output of the API call in a variable called response."
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "metadata": {},
            "outputs": [],
            "source": "if response.ok:             # if all is well() no errors, no network timeouts)\n    data = response.json()  # store the result in json format in a variable called data\n                            # the variable data is of type dictionary."
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "{'people': [{'craft': 'ISS', 'name': 'Mike Hopkins'}, {'craft': 'ISS', 'name': 'Victor Glover'}, {'craft': 'ISS', 'name': 'Shannon Walker'}, {'craft': 'ISS', 'name': 'Soichi Noguchi'}, {'craft': 'ISS', 'name': 'Mark Vande Hei'}, {'craft': 'ISS', 'name': 'Oleg Novitskiy'}, {'craft': 'ISS', 'name': 'Pyotr Dubrov'}], 'number': 7, 'message': 'success'}\n"
                }
            ],
            "source": "print(data)   # print the data just to check the output or for debugging"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Print the number of astronauts currently on ISS.\n"
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "7\n"
                }
            ],
            "source": "print(data.get('number'))"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Print the names of the astronauts currently on ISS.\n"
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "There are 7 astronauts on ISS \nAnd their names are :\nMike Hopkins\nVictor Glover\nShannon Walker\nSoichi Noguchi\nMark Vande Hei\nOleg Novitskiy\nPyotr Dubrov\n"
                }
            ],
            "source": "astronauts = data.get('people')\nla = len(astronauts)\nprint(f\"There are {la} astronauts on ISS \")\nprint(\"And their names are :\")\nfor astr in astronauts:\n    print(astr.get('name'))"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Hope the warmup was helpful. Good luck with your next lab!\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Lab: Collect Jobs Data using GitHub Jobs API\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Before you start doing this lab, get familier with the GitHub Jobs API.<br>\nThe documentation for the GitHub Jobs API can be found at <https://jobs.github.com/api><br>\n\n<li>Understand what urls to use.<br>\n<li>Understand what parameters have to be passed.<br>\n<li>Understand the format of the output data.</li>\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Objective: Determine the number of jobs currently open for various technologies\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Collect the number of job postings for the following languages using the API:\n\n-   C\n-   C#\n-   C++\n-   Java\n-   JavaScript\n-   Python\n-   Scala\n-   Oracle\n-   SQL Server\n-   MySQL Server\n-   PostgreSQL\n-   MongoDB\n"
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "metadata": {},
            "outputs": [],
            "source": "#Import required libraries\nimport requests"
        },
        {
            "cell_type": "code",
            "execution_count": 9,
            "metadata": {},
            "outputs": [],
            "source": "baseurl = \"https://jobs.github.com/positions.json\""
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Write a function to get the number of jobs for the given technology.<br>\n_Note:_ The API gives a maximum of 50 jobs per page.<br>\nIf you get 50 jobs per page, it means there could be some more job listings available.<br>\nSo if you get 50 jobs per page you should make another API call for next page to check for more jobs.<br>\nIf you get less than 50 jobs per page, you can take it as the final count.<br>\n"
        },
        {
            "cell_type": "code",
            "execution_count": 10,
            "metadata": {},
            "outputs": [],
            "source": "def get_number_of_jobs(technology):\n    number_of_jobs = 0\n    #your code goes here\n    if technology  == 'C#':\n        technology = 'C%23'\n    elif technology == 'C++':\n        technology = 'C%2B%2B'\n    \n    pg = 1\n    counte = 50\n    while (counte == 50):\n        url = baseurl+\"?description=\"+technology+\"&page=\"+str(pg)\n        response2 = requests.get(url)\n        data = response2.json()\n        counte = len(data)\n    \n        for job in data:\n            number_of_jobs +=1\n        pg+=1\n    \n    if technology == 'C%23':\n        technology = 'C#'\n    elif technology == 'C%2B%2B':\n        technology = 'C++'\n    return technology,number_of_jobs"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Call the function for Python and check if it is working.\n"
        },
        {
            "cell_type": "code",
            "execution_count": 11,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "('SQL Server', 29)\n"
                }
            ],
            "source": "print(get_number_of_jobs('SQL Server'))"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Store the results in an excel file\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Call the API for all the given technologies above and write the results in an excel spreadsheet.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "If you do not know how create excel file using python, double click here for **hints**.\n\n<!--\n\nfrom openpyxl import Workbook        # import Workbook class from module openpyxl\nwb=Workbook()                        # create a workbook object\nws=wb.active                         # use the active worksheet\nws.append(['Country','Continent'])   # add a row with two columns 'Country' and 'Continent'\nws.append(['Eygpt','Africa'])        # add a row with two columns 'Egypt' and 'Africa'\nws.append(['India','Asia'])          # add another row\nws.append(['France','Europe'])       # add another row\nwb.save(\"countries.xlsx\")            # save the workbook into a file called countries.xlsx\n\n\n-->\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Create a python list of all technologies for which you need to find the number of jobs postings.\n"
        },
        {
            "cell_type": "code",
            "execution_count": 12,
            "metadata": {},
            "outputs": [],
            "source": "#your code goes here\nlang_list = ['C','C#','C++','Java', 'JavaScript','Python','Scala','Oracle','SQL Server','MySQL Server','PostgreSQL','MongoDB']"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Import libraries required to create excel spreadsheet\n"
        },
        {
            "cell_type": "code",
            "execution_count": 13,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "/opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages/secretstorage/dhcrypto.py:16: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead\n  from cryptography.utils import int_from_bytes\n/opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages/secretstorage/util.py:25: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead\n  from cryptography.utils import int_from_bytes\nCollecting openpyxl\n  Downloading openpyxl-3.0.7-py2.py3-none-any.whl (243 kB)\n\u001b[K     |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 243 kB 13.7 MB/s eta 0:00:01\n\u001b[?25hCollecting et-xmlfile\n  Downloading et_xmlfile-1.0.1.tar.gz (8.4 kB)\nBuilding wheels for collected packages: et-xmlfile\n  Building wheel for et-xmlfile (setup.py) ... \u001b[?25ldone\n\u001b[?25h  Created wheel for et-xmlfile: filename=et_xmlfile-1.0.1-py3-none-any.whl size=8915 sha256=f54b91bb0c78b03a3d55d61a5eaf70ea14546cd3d34df2c2be81c4a1752718fe\n  Stored in directory: /tmp/wsuser/.cache/pip/wheels/e2/bd/55/048b4fd505716c4c298f42ee02dffd9496bb6d212b266c7f31\nSuccessfully built et-xmlfile\nInstalling collected packages: et-xmlfile, openpyxl\nSuccessfully installed et-xmlfile-1.0.1 openpyxl-3.0.7\n"
                }
            ],
            "source": "# your code goes here\n!pip install openpyxl\nimport openpyxl"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Create a workbook and select the active worksheet\n"
        },
        {
            "cell_type": "code",
            "execution_count": 14,
            "metadata": {},
            "outputs": [],
            "source": "# your code goes here\nfrom openpyxl import Workbook        # import Workbook class from module openpyxl\nwb=Workbook()                        # create a workbook object\nws=wb.active                         # use the active worksheet"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Find the number of jobs postings for each of the technology in the above list.\nWrite the technology name and the number of jobs postings into the excel spreadsheet.\n"
        },
        {
            "cell_type": "code",
            "execution_count": 15,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "C 273\nC# 32\nC++ 21\nJava 120\nJavaScript 92\nPython 73\nScala 69\nOracle 10\nSQL Server 29\nMySQL Server 8\nPostgreSQL 17\nMongoDB 14\n"
                }
            ],
            "source": "#your code goes here\nws.append(['Number,No of Jobs'])\nfor lang in lang_list:\n    outp = get_number_of_jobs(lang)\n    print(outp[0],outp[1])\n    ws.append([outp[0],outp[1]])"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Save into an excel spreadsheet named 'github-job-postings.xlsx'.\n"
        },
        {
            "cell_type": "code",
            "execution_count": 16,
            "metadata": {},
            "outputs": [],
            "source": "wb.save('github-job-posting.xlsx')"
        },
        {
            "cell_type": "code",
            "execution_count": 38,
            "metadata": {},
            "outputs": [],
            "source": "#your code goes here\n\n# check \nimport pandas as pd\ndf = pd.read_excel('github-job-posting.xlsx')\ndf.columns = ['language','No_times']\n"
        },
        {
            "cell_type": "code",
            "execution_count": 39,
            "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>language</th>\n      <th>No_times</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>C</td>\n      <td>273</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>C#</td>\n      <td>32</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>C++</td>\n      <td>21</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Java</td>\n      <td>120</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>JavaScript</td>\n      <td>92</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Python</td>\n      <td>73</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Scala</td>\n      <td>69</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Oracle</td>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>SQL Server</td>\n      <td>29</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>MySQL Server</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>PostgreSQL</td>\n      <td>17</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>MongoDB</td>\n      <td>14</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "        language  No_times\n0              C       273\n1             C#        32\n2            C++        21\n3           Java       120\n4     JavaScript        92\n5         Python        73\n6          Scala        69\n7         Oracle        10\n8     SQL Server        29\n9   MySQL Server         8\n10    PostgreSQL        17\n11       MongoDB        14"
                    },
                    "execution_count": 39,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "df.head(12)"
        },
        {
            "cell_type": "code",
            "execution_count": 63,
            "metadata": {},
            "outputs": [],
            "source": "# df.set_index('language')\ndf = df.sort_values('No_times',ascending = True)"
        },
        {
            "cell_type": "code",
            "execution_count": 64,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "<matplotlib.legend.Legend at 0x7f7923dcd110>"
                    },
                    "execution_count": 64,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": "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\n",
                        "text/plain": "<Figure size 1584x576 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "import matplotlib.pyplot as plt\nvalues = [ 'C','C#','C++','Java','JavaScript','Python','Scala','Oracle','SQL Server','MySQL Server','PostgreSQL','MongoDB']\n\ndf.plot(kind = 'bar',figsize = (22,8),)\nplt.xlabel('Languages')\nplt.xticks(df.index,values)\nplt.legend('')"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Authors\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Ramesh Sannareddy\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| 2020-10-17        | 0.1     | Ramesh Sannareddy | Created initial version of the lab |\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": " Copyright \u00a9 2020 IBM Corporation. This notebook and its source code are released under the terms of the [MIT License](https://cognitiveclass.ai/mit-license?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&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&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&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&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&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 3.7",
            "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": "3.7.10"
        }
    },
    "nbformat": 4,
    "nbformat_minor": 4
}