4. EDA with SQL.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\n<h1 align=center><font size = 5>Assignment: SQL Notebook for Peer Assignment</font></h1>\n\nEstimated time needed: **60** minutes.\n\n## Introduction\n\nUsing this Python notebook you will:\n\n1.  Understand the Spacex DataSet\n2.  Load the dataset  into the corresponding table in a Db2 database\n3.  Execute SQL queries to answer assignment questions\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Overview of the DataSet\n\nSpaceX has gained worldwide attention for a series of historic milestones.\n\nIt is the only private company ever to return a spacecraft from low-earth orbit, which it first accomplished in December 2010.\nSpaceX advertises Falcon 9 rocket launches on its website with a cost of 62 million dollars wheras other providers cost upward of 165 million dollars each, much of the savings is because Space X can reuse the first stage.\n\nTherefore if we can determine if the first stage will land, we can determine the cost of a launch.\n\nThis information can be used if an alternate company wants to bid against SpaceX for a rocket launch.\n\nThis dataset includes a record for each payload carried during a SpaceX mission into outer space.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Download the datasets\n\nThis assignment requires you to load the spacex dataset.\n\nIn many cases the dataset to be analyzed is available as a .CSV (comma separated values) file, perhaps on the internet. Click on the link below to download and save the dataset (.CSV file):\n\n<a href=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/labs/module_2/data/Spacex.csv?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2021-01-01\" target=\"_blank\">Spacex DataSet</a>\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Store the dataset in database table\n\n**it is highly recommended to manually load the table using the database console LOAD tool in DB2**.\n\n<img src = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/labs/module_2/images/spacexload.png\">\n\nNow open the Db2 console, open the LOAD tool, Select / Drag the .CSV file for the  dataset, Next create a New Table, and then follow the steps on-screen instructions to load the data. Name the new table as follows:\n\n**SPACEXDATASET**\n\n**Follow these steps while using old DB2 UI which is having Open Console Screen**\n\n**Note:While loading Spacex dataset, ensure that detect datatypes is disabled. Later click on the pencil icon(edit option).**\n\n1.  Change the Date Format by manually typing DD-MM-YYYY and timestamp format as DD-MM-YYYY HH\\:MM:SS.\n\n    Here you should place the cursor at Date field and manually type as DD-MM-YYYY.\n\n2.  Change the PAYLOAD_MASS\\_\\_KG\\_  datatype  to INTEGER.\n\n<img src = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/labs/module_2/images/spacexload2.png\">\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "**Changes to be considered when having DB2 instance with the new UI having Go to UI screen**\n\n*   Refer to this insruction in this <a href=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DB0201EN-SkillsNetwork/labs/Labs_Coursera_V5/labs/Lab%20-%20Sign%20up%20for%20IBM%20Cloud%20-%20Create%20Db2%20service%20instance%20-%20Get%20started%20with%20the%20Db2%20console/instructional-labs.md.html?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2021-01-01\">link</a> for viewing  the new  Go to UI screen.\n\n*   Later click on **Data link(below SQL)**  in the Go to UI screen  and click on **Load Data** tab.\n\n*   Later browse for the downloaded spacex file.\n\n<img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/labs/module_2/images/browsefile.png\" width=\"800\"/>\n\n*   Once done select the schema andload the file.\n\n <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/labs/module_2/images/spacexload3.png\" width=\"800\"/>\n"
        },
        {
            "cell_type": "code",
            "execution_count": 1,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Collecting sqlalchemy==1.3.9\n  Downloading SQLAlchemy-1.3.9.tar.gz (6.0 MB)\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| 6.0 MB 14.7 MB/s eta 0:00:01\n\u001b[?25hBuilding wheels for collected packages: sqlalchemy\n  Building wheel for sqlalchemy (setup.py) ... \u001b[?25ldone\n\u001b[?25h  Created wheel for sqlalchemy: filename=SQLAlchemy-1.3.9-cp39-cp39-linux_x86_64.whl size=1159949 sha256=e93856c33e64821cde757d1aa84aa7f13936bc4537720e8f32b6092518c054f6\n  Stored in directory: /tmp/wsuser/.cache/pip/wheels/5b/43/0d/de1699809f9e6aaa54a97275298fa07075cb19acc557b18955\nSuccessfully built sqlalchemy\nInstalling collected packages: sqlalchemy\n  Attempting uninstall: sqlalchemy\n    Found existing installation: SQLAlchemy 1.4.27\n    Uninstalling SQLAlchemy-1.4.27:\n      Successfully uninstalled SQLAlchemy-1.4.27\nSuccessfully installed sqlalchemy-1.3.9\nRequirement already satisfied: ibm_db_sa in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (0.3.7)\nRequirement already satisfied: ibm-db>=2.0.0 in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from ibm_db_sa) (3.1.0)\nRequirement already satisfied: sqlalchemy>=0.7.3 in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from ibm_db_sa) (1.3.9)\nCollecting ipython-sql\n  Downloading ipython_sql-0.4.0-py3-none-any.whl (19 kB)\nCollecting sqlparse\n  Downloading sqlparse-0.4.2-py3-none-any.whl (42 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| 42 kB 3.8 MB/s  eta 0:00:01\n\u001b[?25hCollecting prettytable<1\n  Downloading prettytable-0.7.2.zip (28 kB)\nRequirement already satisfied: six in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from ipython-sql) (1.15.0)\nRequirement already satisfied: ipython>=1.0 in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from ipython-sql) (7.29.0)\nRequirement already satisfied: sqlalchemy>=0.6.7 in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from ipython-sql) (1.3.9)\nRequirement already satisfied: ipython-genutils>=0.1.0 in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from ipython-sql) (0.2.0)\nRequirement already satisfied: jedi>=0.16 in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from ipython>=1.0->ipython-sql) (0.18.0)\nRequirement already satisfied: backcall in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from ipython>=1.0->ipython-sql) (0.2.0)\nRequirement already satisfied: pexpect>4.3 in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from ipython>=1.0->ipython-sql) (4.8.0)\nRequirement already satisfied: setuptools>=18.5 in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from ipython>=1.0->ipython-sql) (58.0.4)\nRequirement already satisfied: matplotlib-inline in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from ipython>=1.0->ipython-sql) (0.1.2)\nRequirement already satisfied: pygments in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from ipython>=1.0->ipython-sql) (2.10.0)\nRequirement already satisfied: prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0 in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from ipython>=1.0->ipython-sql) (3.0.20)\nRequirement already satisfied: pickleshare in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from ipython>=1.0->ipython-sql) (0.7.5)\nRequirement already satisfied: decorator in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from ipython>=1.0->ipython-sql) (5.1.0)\nRequirement already satisfied: traitlets>=4.2 in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from ipython>=1.0->ipython-sql) (5.1.1)\nRequirement already satisfied: parso<0.9.0,>=0.8.0 in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from jedi>=0.16->ipython>=1.0->ipython-sql) (0.8.3)\nRequirement already satisfied: ptyprocess>=0.5 in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from pexpect>4.3->ipython>=1.0->ipython-sql) (0.7.0)\nRequirement already satisfied: wcwidth in /opt/conda/envs/Python-3.9/lib/python3.9/site-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->ipython>=1.0->ipython-sql) (0.2.5)\nBuilding wheels for collected packages: prettytable\n  Building wheel for prettytable (setup.py) ... \u001b[?25ldone\n\u001b[?25h  Created wheel for prettytable: filename=prettytable-0.7.2-py3-none-any.whl size=13714 sha256=5075353574bffcdeb653c7ef014dad831f5b4b87998320884afa32ad8b867d13\n  Stored in directory: /tmp/wsuser/.cache/pip/wheels/75/f7/28/77a076f1fa8cbeda61aca712815d04d7a32435f04a26a2dd7b\nSuccessfully built prettytable\nInstalling collected packages: sqlparse, prettytable, ipython-sql\nSuccessfully installed ipython-sql-0.4.0 prettytable-0.7.2 sqlparse-0.4.2\n"
                }
            ],
            "source": "!pip install sqlalchemy==1.3.9\n!pip install ibm_db_sa\n!pip install ipython-sql"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Connect to the database\n\nLet us first load the SQL extension and establish a connection with the database\n"
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "metadata": {},
            "outputs": [],
            "source": "%load_ext sql"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "**DB2 magic in case of old UI service credentials.**\n\nIn the next cell enter your db2 connection string. Recall you created Service Credentials for your Db2 instance before. From the **uri** field of your Db2 service credentials copy everything after db2:// (except the double quote at the end) and paste it in the cell below after ibm_db_sa://\n\n<img src =\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DB0201EN-SkillsNetwork/labs/FinalModule_edX/images/URI.jpg\">\n\nin the following format\n\n**%sql ibm_db_sa://my-username:my-password\\@my-hostname:my-port/my-db-name**\n\n**DB2 magic in case of new UI service credentials.**\n\n<img src =\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/labs/module_2/images/servicecredentials.png\" width=600>  \n\n*   Use the following format.\n\n*   Add security=SSL at the end\n\n**%sql ibm_db_sa://my-username:my-password\\@my-hostname:my-port/my-db-name?security=SSL**\n"
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": ""
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": ""
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Connection info needed in SQLAlchemy format, example:\n               postgresql://username:password@hostname/dbname\n               or an existing connection: dict_keys([])\n[Errno 2] No such file or directory: '/opt/conda/envs/Python-3.9/lib/python3.9/site-packages/SQLAlchemy-1.4.27.dist-info/METADATA'\nConnection info needed in SQLAlchemy format, example:\n               postgresql://username:password@hostname/dbname\n               or an existing connection: dict_keys([])\n"
                }
            ],
            "source": "%sql ibm_db_sa://glq11643:AOVxzllolFpzqfUx@21fecfd8-47b7-4937-840d-d791d0218660.bs2io90l08kqb1od8lcg.databases.appdomain.cloud:31864/bludb?security=SSL"
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Environment variable $DATABASE_URL not set, and no connect string given.\nConnection info needed in SQLAlchemy format, example:\n               postgresql://username:password@hostname/dbname\n               or an existing connection: dict_keys([])\n"
                }
            ],
            "source": "%sql SELECT * FROM SPACEXTBL LIMIT 5"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Tasks\n\nNow write and execute SQL queries to solve the assignment tasks.\n\n### Task 1\n\n##### Display the names of the unique launch sites  in the space mission\n"
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Environment variable $DATABASE_URL not set, and no connect string given.\nConnection info needed in SQLAlchemy format, example:\n               postgresql://username:password@hostname/dbname\n               or an existing connection: dict_keys([])\n"
                }
            ],
            "source": "%sql SELECT UNIQUE(launch_site) FROM SPACEXTBL"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Task 2\n\n##### Display 5 records where launch sites begin with the string 'CCA'\n"
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Environment variable $DATABASE_URL not set, and no connect string given.\nConnection info needed in SQLAlchemy format, example:\n               postgresql://username:password@hostname/dbname\n               or an existing connection: dict_keys([])\n"
                }
            ],
            "source": "%%sql\nSELECT launch_site FROM SPACEXTBL\nWHERE launch_site LIKE 'CCA%'\nLIMIT 5"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Task 3\n\n##### Display the total payload mass carried by boosters launched by NASA (CRS)\n"
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Environment variable $DATABASE_URL not set, and no connect string given.\nConnection info needed in SQLAlchemy format, example:\n               postgresql://username:password@hostname/dbname\n               or an existing connection: dict_keys([])\n"
                }
            ],
            "source": "%%sql\nSELECT sum(payload_mass__kg_)\nFROM SPACEXTBL\nWHERE customer = 'NASA (CRS)'"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Task 4\n\n##### Display average payload mass carried by booster version F9 v1.1\n"
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Environment variable $DATABASE_URL not set, and no connect string given.\nConnection info needed in SQLAlchemy format, example:\n               postgresql://username:password@hostname/dbname\n               or an existing connection: dict_keys([])\n"
                }
            ],
            "source": "%%sql\nSELECT AVG(payload_mass__kg_)\nFROM SPACEXTBL\nWHERE booster_version LIKE 'F9 v1.1%'"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Task 5\n\n##### List the date when the first successful landing outcome in ground pad was acheived.\n\n*Hint:Use min function*\n"
        },
        {
            "cell_type": "code",
            "execution_count": 9,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Environment variable $DATABASE_URL not set, and no connect string given.\nConnection info needed in SQLAlchemy format, example:\n               postgresql://username:password@hostname/dbname\n               or an existing connection: dict_keys([])\n"
                }
            ],
            "source": "%%sql\nSELECT min(DATE)\nFROM SPACEXTBL\nWHERE mission_outcome = 'Success' AND landing__outcome = 'Success (ground pad)'"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Task 6\n\n##### List the names of the boosters which have success in drone ship and have payload mass greater than 4000 but less than 6000\n"
        },
        {
            "cell_type": "code",
            "execution_count": 10,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Environment variable $DATABASE_URL not set, and no connect string given.\nConnection info needed in SQLAlchemy format, example:\n               postgresql://username:password@hostname/dbname\n               or an existing connection: dict_keys([])\n"
                }
            ],
            "source": "%%sql\nSELECT booster_version, payload_mass__kg_\nFROM SPACEXTBL\nWHERE landing__outcome = 'Success (drone ship)' AND payload_mass__kg_ BETWEEN 4000 AND 6000"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Task 7\n\n##### List the total number of successful and failure mission outcomes\n"
        },
        {
            "cell_type": "code",
            "execution_count": 11,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Environment variable $DATABASE_URL not set, and no connect string given.\nConnection info needed in SQLAlchemy format, example:\n               postgresql://username:password@hostname/dbname\n               or an existing connection: dict_keys([])\n"
                }
            ],
            "source": "%%sql\nSELECT mission_outcome, COUNT(mission_outcome) AS total_number\nFROM SPACEXTBL\nGROUP BY mission_outcome"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Task 8\n\n##### List the   names of the booster_versions which have carried the maximum payload mass. Use a subquery\n"
        },
        {
            "cell_type": "code",
            "execution_count": 12,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Environment variable $DATABASE_URL not set, and no connect string given.\nConnection info needed in SQLAlchemy format, example:\n               postgresql://username:password@hostname/dbname\n               or an existing connection: dict_keys([])\n"
                }
            ],
            "source": "%%sql\nSELECT booster_version, payload_mass__kg_\nFROM SPACEXTBL\nWHERE payload_mass__kg_ = (SELECT MAX(payload_mass__kg_) FROM SPACEXTBL)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Task 9\n\n##### List the failed landing_outcomes in drone ship, their booster versions, and launch site names for in year 2015\n"
        },
        {
            "cell_type": "code",
            "execution_count": 13,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Environment variable $DATABASE_URL not set, and no connect string given.\nConnection info needed in SQLAlchemy format, example:\n               postgresql://username:password@hostname/dbname\n               or an existing connection: dict_keys([])\n"
                }
            ],
            "source": "%%sql\nSELECT landing__outcome, booster_version, launch_site, DATE\nFROM SPACEXTBL\nWHERE landing__outcome LIKE 'Failure (drone ship)' AND DATE LIKE '2015%'"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Task 10\n\n##### Rank the count of landing outcomes (such as Failure (drone ship) or Success (ground pad)) between the date 2010-06-04 and 2017-03-20, in descending order\n"
        },
        {
            "cell_type": "code",
            "execution_count": 14,
            "metadata": {
                "scrolled": true
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Environment variable $DATABASE_URL not set, and no connect string given.\nConnection info needed in SQLAlchemy format, example:\n               postgresql://username:password@hostname/dbname\n               or an existing connection: dict_keys([])\n"
                }
            ],
            "source": "%%sql\nSELECT landing__outcome, COUNT(landing__outcome) AS outcome_count\nFROM (SELECT landing__outcome, DATE\nFROM SPACEXTBL\nWHERE DATE BETWEEN '2010-06-04' AND '2017-03-20')\nGROUP BY landing__outcome\nORDER BY outcome_count DESC"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Reference Links\n\n*   <a href =\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DB0201EN-SkillsNetwork/labs/Labs_Coursera_V5/labs/Lab%20-%20String%20Patterns%20-%20Sorting%20-%20Grouping/instructional-labs.md.html?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2021-01-01&origin=www.coursera.org\">Hands-on Lab : String Patterns, Sorting and Grouping</a>\n\n*   <a  href=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DB0201EN-SkillsNetwork/labs/Labs_Coursera_V5/labs/Lab%20-%20Built-in%20functions%20/Hands-on_Lab__Built-in_Functions.md.html?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2021-01-01&origin=www.coursera.org\">Hands-on Lab: Built-in functions</a>\n\n*   <a  href=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DB0201EN-SkillsNetwork/labs/Labs_Coursera_V5/labs/Lab%20-%20Sub-queries%20and%20Nested%20SELECTs%20/instructional-labs.md.html?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2021-01-01&origin=www.coursera.org\">Hands-on Lab : Sub-queries and Nested SELECT Statements</a>\n\n*   <a href=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DB0201EN-SkillsNetwork/labs/Module%205/DB0201EN-Week3-1-3-SQLmagic.ipynb?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2021-01-01\">Hands-on Tutorial: Accessing Databases with SQL magic</a>\n\n*   <a href= \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DB0201EN-SkillsNetwork/labs/Module%205/DB0201EN-Week3-1-4-Analyzing.ipynb?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2021-01-01\">Hands-on Lab: Analyzing a real World Data Set</a>\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Author(s)\n\n<h4> Lakshmi Holla </h4>\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Other Contributors\n\n<h4> Rav Ahuja </h4>\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Change log\n\n| Date       | Version | Changed by    | Change Description        |\n| ---------- | ------- | ------------- | ------------------------- |\n| 2021-10-12 | 0.4     | Lakshmi Holla | Changed markdown          |\n| 2021-08-24 | 0.3     | Lakshmi Holla | Added library update      |\n| 2021-07-09 | 0.2     | Lakshmi Holla | Changes made in magic sql |\n| 2021-05-20 | 0.1     | Lakshmi Holla | Created Initial Version   |\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## <h3 align=\"center\"> \u00a9 IBM Corporation 2021. All rights reserved. <h3/>\n"
        }
    ],
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            "display_name": "Python 3.9",
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            "nbconvert_exporter": "python",
            "pygments_lexer": "ipython3",
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