Exploratory Data Analysis with SQL SpaceX.ipynb

{
    "cells": [
        {
            "cell_type": "markdown",
            "id": "22caaa44-c27e-4d29-b5ee-49dca4cc7cf3",
            "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-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork865-2022-01-01\" target=\"_blank\">\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\n<h1 align=center><font size = 5>Assignment: SQL Notebook for Peer Assignment</font></h1>\n\nEstimated time needed: **60** minutes.\n\n## Introduction\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",
            "id": "1ae8e773-a2d5-4721-b534-598518e7c7e7",
            "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\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",
            "id": "8a3770ec-da5d-4ee2-b913-12094cd3dfb0",
            "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\" target=\"_blank\">Spacex DataSet</a>\n\n"
        },
        {
            "cell_type": "markdown",
            "id": "99833862-9852-410a-9211-b655a30013b0",
            "metadata": {},
            "source": "**Navigate to the 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\">link</a> for viewing  the   Go to UI screen. \n\n\n* Later click on **Data link(below SQL)**  in the Go to UI screen  and click on **Load Data** tab.  \n\n\n\n* Later browse for the downloaded spacex file.\n\n\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\n* Once done select the schema andload the file.  \n\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 \n"
        },
        {
            "cell_type": "markdown",
            "id": "f76e14f8-fa6a-445f-b2cc-7a4cf400a353",
            "metadata": {},
            "source": "\nIf you are facing a problem in uploading the dataset (which is a csv file), you can follow the steps below to upload the .sql file instead of the CSV file:\n\n* Download the file <a href=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/Spacex%20.sql\">Spacex.sql</a>\n\n* Later click on **SQL** in the  **Go to UI Screen**.\n\n* Use the **From file** option to browse for the **SQL** file and upload it.\n\n<img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/labs/module_2/images/sqlfile.png\">\n\n* Once you upload the script,you can use the **Run All** option to run all the queries to insert the data.\n\n<img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/labs/module_2/images/runall.png\">\n\n    \n"
        },
        {
            "cell_type": "code",
            "execution_count": 1,
            "id": "80dbd24e-b3c4-456b-a0a0-e6b252d5172f",
            "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[2K     \u001b[90m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[32m6.0/6.0 MB\u001b[0m \u001b[31m79.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m00:01\u001b[0m\n\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25ldone\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-cp310-cp310-linux_x86_64.whl size=1159595 sha256=102615ce095a5e88adaf1680dee3c40dfc6bdafe601ee2c6d6aa367324cf82a9\n  Stored in directory: /tmp/wsuser/.cache/pip/wheels/8b/6d/9c/e1793e5a2d85b89b8c362a4f0e3def54b786bcd14923b9ff00\nSuccessfully built sqlalchemy\nInstalling collected packages: sqlalchemy\n  Attempting uninstall: sqlalchemy\n    Found existing installation: SQLAlchemy 1.4.39\n    Uninstalling SQLAlchemy-1.4.39:\n      Successfully uninstalled SQLAlchemy-1.4.39\nSuccessfully installed sqlalchemy-1.3.9\nRequirement already satisfied: ibm_db_sa in /opt/conda/envs/Python-3.10/lib/python3.10/site-packages (0.3.8)\nRequirement already satisfied: sqlalchemy>=0.7.3 in /opt/conda/envs/Python-3.10/lib/python3.10/site-packages (from ibm_db_sa) (1.3.9)\nRequirement already satisfied: ibm-db>=2.0.0 in /opt/conda/envs/Python-3.10/lib/python3.10/site-packages (from ibm_db_sa) (3.1.3)\nCollecting ipython-sql\n  Downloading ipython_sql-0.5.0-py3-none-any.whl (20 kB)\nCollecting sqlalchemy>=2.0\n  Downloading SQLAlchemy-2.0.5.post1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.7 MB)\n\u001b[2K     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satisfied: pickleshare in /opt/conda/envs/Python-3.10/lib/python3.10/site-packages (from ipython->ipython-sql) (0.7.5)\nRequirement already satisfied: stack-data in /opt/conda/envs/Python-3.10/lib/python3.10/site-packages (from ipython->ipython-sql) (0.2.0)\nRequirement already satisfied: wcwidth in /opt/conda/envs/Python-3.10/lib/python3.10/site-packages (from prettytable->ipython-sql) (0.2.5)\nRequirement already satisfied: parso<0.9.0,>=0.8.0 in /opt/conda/envs/Python-3.10/lib/python3.10/site-packages (from jedi>=0.16->ipython->ipython-sql) (0.8.3)\nRequirement already satisfied: ptyprocess>=0.5 in /opt/conda/envs/Python-3.10/lib/python3.10/site-packages (from pexpect>4.3->ipython->ipython-sql) (0.7.0)\nRequirement already satisfied: pure-eval in /opt/conda/envs/Python-3.10/lib/python3.10/site-packages (from stack-data->ipython->ipython-sql) (0.2.2)\nRequirement already satisfied: executing in /opt/conda/envs/Python-3.10/lib/python3.10/site-packages (from stack-data->ipython->ipython-sql) (0.8.3)\nRequirement already satisfied: asttokens in /opt/conda/envs/Python-3.10/lib/python3.10/site-packages (from stack-data->ipython->ipython-sql) (2.0.5)\nInstalling collected packages: sqlparse, sqlalchemy, prettytable, ipython-sql\n  Attempting uninstall: sqlalchemy\n    Found existing installation: SQLAlchemy 1.3.9\n    Uninstalling SQLAlchemy-1.3.9:\n      Successfully uninstalled SQLAlchemy-1.3.9\nSuccessfully installed ipython-sql-0.5.0 prettytable-3.6.0 sqlalchemy-2.0.5.post1 sqlparse-0.4.3\n"
                }
            ],
            "source": "!pip install sqlalchemy==1.3.9\n!pip install ibm_db_sa\n!pip install ipython-sql"
        },
        {
            "cell_type": "markdown",
            "id": "4d078618-5f50-48d9-9b65-20da01d2228c",
            "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,
            "id": "dbecf5cd-c2d8-4b82-b84a-244c41e72af4",
            "metadata": {},
            "outputs": [],
            "source": "%load_ext sql"
        },
        {
            "cell_type": "markdown",
            "id": "b6599c7d-66c8-4569-ac9e-9f57523a8cc1",
            "metadata": {},
            "source": "\n\n\n**DB2 magic in case of  UI service credentials.**\n\n\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": 4,
            "id": "3a777815-54d0-492b-addb-78cdc1ee0dd7",
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Traceback (most recent call last):\n  File \"/opt/conda/envs/Python-3.10/lib/python3.10/site-packages/sql/connection.py\", line 45, in __init__\n    engine = sqlalchemy.create_engine(\n  File \"<string>\", line 2, in create_engine\n  File \"/opt/conda/envs/Python-3.10/lib/python3.10/site-packages/sqlalchemy/util/deprecations.py\", line 277, in warned\n    return fn(*args, **kwargs)  # type: ignore[no-any-return]\n  File \"/opt/conda/envs/Python-3.10/lib/python3.10/site-packages/sqlalchemy/engine/create.py\", line 556, in create_engine\n    entrypoint = u._get_entrypoint()\n  File \"/opt/conda/envs/Python-3.10/lib/python3.10/site-packages/sqlalchemy/engine/url.py\", line 754, in _get_entrypoint\n    cls = registry.load(name)\n  File \"/opt/conda/envs/Python-3.10/lib/python3.10/site-packages/sqlalchemy/util/langhelpers.py\", line 352, in load\n    return self.impls[name]()\n  File \"/opt/conda/envs/Python-3.10/lib/python3.10/importlib/metadata/__init__.py\", line 171, in load\n    module = import_module(match.group('module'))\n  File \"/opt/conda/envs/Python-3.10/lib/python3.10/importlib/__init__.py\", line 126, in import_module\n    return _bootstrap._gcd_import(name[level:], package, level)\n  File \"<frozen importlib._bootstrap>\", line 1050, in _gcd_import\n  File \"<frozen importlib._bootstrap>\", line 1027, in _find_and_load\n  File \"<frozen importlib._bootstrap>\", line 992, in _find_and_load_unlocked\n  File \"<frozen importlib._bootstrap>\", line 241, in _call_with_frames_removed\n  File \"<frozen importlib._bootstrap>\", line 1050, in _gcd_import\n  File \"<frozen importlib._bootstrap>\", line 1027, in _find_and_load\n  File \"<frozen importlib._bootstrap>\", line 1006, in _find_and_load_unlocked\n  File \"<frozen importlib._bootstrap>\", line 688, in _load_unlocked\n  File \"<frozen importlib._bootstrap_external>\", line 883, in exec_module\n  File \"<frozen importlib._bootstrap>\", line 241, in _call_with_frames_removed\n  File \"/opt/conda/envs/Python-3.10/lib/python3.10/site-packages/ibm_db_sa/__init__.py\", line 22, in <module>\n    from . import ibm_db, pyodbc, base\n  File \"/opt/conda/envs/Python-3.10/lib/python3.10/site-packages/ibm_db_sa/ibm_db.py\", line 20, in <module>\n    from .base import DB2ExecutionContext, DB2Dialect\n  File \"/opt/conda/envs/Python-3.10/lib/python3.10/site-packages/ibm_db_sa/base.py\", line 661, in <module>\n    class DB2Dialect(default.DefaultDialect):\n  File \"/opt/conda/envs/Python-3.10/lib/python3.10/site-packages/ibm_db_sa/base.py\", line 674, in DB2Dialect\n    returns_unicode_strings = sa_types.String.RETURNS_CONDITIONAL\nAttributeError: type object 'String' has no attribute 'RETURNS_CONDITIONAL'\n\nConnection info needed in SQLAlchemy format, example:\n               postgresql://username:password@hostname/dbname\n               or an existing connection: dict_keys([])\nTraceback (most recent call last):\n  File \"/opt/conda/envs/Python-3.10/lib/python3.10/site-packages/sql/magic.py\", line 196, in execute\n    conn = sql.connection.Connection.set(\n  File \"/opt/conda/envs/Python-3.10/lib/python3.10/site-packages/sql/connection.py\", line 70, in set\n    cls.current = existing or Connection(descriptor, connect_args, creator)\n  File \"/opt/conda/envs/Python-3.10/lib/python3.10/site-packages/sql/connection.py\", line 45, in __init__\n    engine = sqlalchemy.create_engine(\n  File \"<string>\", line 2, in create_engine\n  File \"/opt/conda/envs/Python-3.10/lib/python3.10/site-packages/sqlalchemy/util/deprecations.py\", line 277, in warned\n    return fn(*args, **kwargs)  # type: ignore[no-any-return]\n  File \"/opt/conda/envs/Python-3.10/lib/python3.10/site-packages/sqlalchemy/engine/create.py\", line 556, in create_engine\n    entrypoint = u._get_entrypoint()\n  File \"/opt/conda/envs/Python-3.10/lib/python3.10/site-packages/sqlalchemy/engine/url.py\", line 754, in _get_entrypoint\n    cls = registry.load(name)\n  File \"/opt/conda/envs/Python-3.10/lib/python3.10/site-packages/sqlalchemy/util/langhelpers.py\", line 352, in load\n    return self.impls[name]()\n  File \"/opt/conda/envs/Python-3.10/lib/python3.10/importlib/metadata/__init__.py\", line 171, in load\n    module = import_module(match.group('module'))\n  File \"/opt/conda/envs/Python-3.10/lib/python3.10/importlib/__init__.py\", line 126, in import_module\n    return _bootstrap._gcd_import(name[level:], package, level)\n  File \"<frozen importlib._bootstrap>\", line 1050, in _gcd_import\n  File \"<frozen importlib._bootstrap>\", line 1027, in _find_and_load\n  File \"<frozen importlib._bootstrap>\", line 992, in _find_and_load_unlocked\n  File \"<frozen importlib._bootstrap>\", line 241, in _call_with_frames_removed\n  File \"<frozen importlib._bootstrap>\", line 1050, in _gcd_import\n  File \"<frozen importlib._bootstrap>\", line 1027, in _find_and_load\n  File \"<frozen importlib._bootstrap>\", line 1006, in _find_and_load_unlocked\n  File \"<frozen importlib._bootstrap>\", line 688, in _load_unlocked\n  File \"<frozen importlib._bootstrap_external>\", line 883, in exec_module\n  File \"<frozen importlib._bootstrap>\", line 241, in _call_with_frames_removed\n  File \"/opt/conda/envs/Python-3.10/lib/python3.10/site-packages/ibm_db_sa/__init__.py\", line 22, in <module>\n    from . import ibm_db, pyodbc, base\n  File \"/opt/conda/envs/Python-3.10/lib/python3.10/site-packages/ibm_db_sa/ibm_db.py\", line 20, in <module>\n    from .base import DB2ExecutionContext, DB2Dialect\n  File \"/opt/conda/envs/Python-3.10/lib/python3.10/site-packages/ibm_db_sa/base.py\", line 661, in <module>\n    class DB2Dialect(default.DefaultDialect):\n  File \"/opt/conda/envs/Python-3.10/lib/python3.10/site-packages/ibm_db_sa/base.py\", line 674, in DB2Dialect\n    returns_unicode_strings = sa_types.String.RETURNS_CONDITIONAL\nAttributeError: type object 'String' has no attribute 'RETURNS_CONDITIONAL'\n\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://"
        },
        {
            "cell_type": "markdown",
            "id": "1e99228b-31e0-45ab-af44-5d0a98e2edd5",
            "metadata": {},
            "source": "## Tasks\n\nNow write and execute SQL queries to solve the assignment tasks.\n\n### Task 1\n\n\n\n\n##### Display the names of the unique launch sites  in the space mission\n"
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "36cb2cda-f45b-432a-9f96-333a7d577c75",
            "metadata": {},
            "outputs": [],
            "source": "%sql SELECT DISTINCT Launch_Site FROM SPACEXTBL"
        },
        {
            "cell_type": "markdown",
            "id": "0bade2d8-a5cf-4325-8419-8bcd03f4efd1",
            "metadata": {},
            "source": "\n### Task 2\n\n\n#####  Display 5 records where launch sites begin with the string 'CCA' \n"
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "5f4740b9-7e45-495a-8aec-108d52124302",
            "metadata": {},
            "outputs": [],
            "source": "%sql SELECT * FROM SPACEXTBL WHERE Launch_Site like 'CCA%'LIMIT 5"
        },
        {
            "cell_type": "markdown",
            "id": "5f3f3a95-f842-453f-9aec-770976af841d",
            "metadata": {},
            "source": "### Task 3\n\n\n\n\n##### Display the total payload mass carried by boosters launched by NASA (CRS)\n"
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "701d7054-5983-4dad-93a1-8738139c7980",
            "metadata": {},
            "outputs": [],
            "source": "%sql SELECT CUSTOMER, SUM(PAYLOAD_MASS__KG_) FROM SPACEXTBL GROUP BY CUSTOMER HAVING CUSTOMER = 'NASA (CRS)'"
        },
        {
            "cell_type": "markdown",
            "id": "0e14f297-4165-4d28-b82b-c4e502bc7ef3",
            "metadata": {},
            "source": "### Task 4\n\n\n\n\n##### Display average payload mass carried by booster version F9 v1.1\n"
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "5c9846c3-e5dc-4e72-b507-aa2592a054fa",
            "metadata": {},
            "outputs": [],
            "source": "%sql SELECT Booster_Version, AVG(PAYLOAD_MASS__KG_) FROM SPACEXTBL GROUP BY Booster_Version HAVING Booster_Version like \"F9 v1.1\""
        },
        {
            "cell_type": "markdown",
            "id": "a45072a9-7730-4b69-97ed-d0268811c564",
            "metadata": {},
            "source": "### Task 5\n\n##### List the date when the first successful landing outcome in ground pad was acheived.\n\n\n_Hint:Use min function_ \n"
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": "%sql SELECT Date, \"Landing _Outcome\" FROM SPACEXTBL where \"Landing _Outcome\" like \"Success%\" and Date = (Select MIN(Date) from SPACEXTBL WHERE \"Landing _Outcome\" like \"Success%\" )"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "The correct first success is on 22-12-2015. Dates are not correctly handled in this notebook"
        },
        {
            "cell_type": "markdown",
            "id": "f4a27811-c954-4f53-a1e3-8590c09b761e",
            "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": null,
            "id": "ffd9b828-f248-4857-98cb-0ba01dd3d4c3",
            "metadata": {},
            "outputs": [],
            "source": "%sql select Booster_Version,\"Landing _Outcome\",PAYLOAD_MASS__KG_ from SPACEXTBL where (\"Landing _Outcome\" like \"Success%drone ship%\") and (PAYLOAD_MASS__KG_ between 4000 and 6000)"
        },
        {
            "cell_type": "markdown",
            "id": "64a3b1b5-efb7-4156-9918-28655779e419",
            "metadata": {},
            "source": "### Task 7\n\n\n\n\n##### List the total number of successful and failure mission outcomes\n"
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "6f541ead-8838-4db7-8f01-01577b69a76c",
            "metadata": {},
            "outputs": [],
            "source": "%sql select distinct Mission_Outcome, count(Mission_Outcome) from SPACEXTBL group by Mission_Outcome"
        },
        {
            "cell_type": "markdown",
            "id": "3ae7619c-a8c7-409d-b85d-26ce81236e36",
            "metadata": {},
            "source": "### Task 8\n\n\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": null,
            "id": "6d3ca7c3-923c-4609-804e-5a1450e19247",
            "metadata": {},
            "outputs": [],
            "source": "%sql SELECT Booster_Version,PAYLOAD_MASS__KG_ FROM SPACEXTBL where PAYLOAD_MASS__KG_ = (Select Max(PAYLOAD_MASS__KG_) from SPACEXTBL)"
        },
        {
            "cell_type": "markdown",
            "id": "ed179e86-f977-4328-aad5-7be049fe8000",
            "metadata": {},
            "source": "### Task 9\n\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": null,
            "id": "cedcbed9-4e5a-49fb-a18b-1c0d65696894",
            "metadata": {},
            "outputs": [],
            "source": "%sql Select substr(Date, 4, 2) as Month, \"Landing _Outcome\", Booster_Version, Launch_Site FROM SPACEXTBL where substr(Date,7,4)='2015'"
        },
        {
            "cell_type": "markdown",
            "id": "5f78519d-8725-411f-b42e-9d420d1c8b22",
            "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": null,
            "id": "12d7b73d-26de-48c5-befd-0f24b12e1b91",
            "metadata": {},
            "outputs": [],
            "source": "%sql Select Date,\"Landing _Outcome\", RANK() OVER(ORDER By Date) as good_landing from SPACEXTBL where \"Landing _Outcome\" like \"Success%\" and (Date between '04-06-2010' and '20-03-2017') order by Date Desc"
        },
        {
            "cell_type": "markdown",
            "id": "e8d71a31-f2cc-49ba-99cd-1856c388b093",
            "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?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?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?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\">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\">Hands-on Lab: Analyzing a real World Data Set</a>\n\n\n"
        },
        {
            "cell_type": "markdown",
            "id": "fa1521fd-7c86-4eff-8f64-b51e5131fbf8",
            "metadata": {},
            "source": "## Author(s)\n\n<h4> Lakshmi Holla </h4>\n"
        },
        {
            "cell_type": "markdown",
            "id": "572ec773-b1e0-413e-9397-c67c5894fc74",
            "metadata": {},
            "source": "## Other Contributors\n\n<h4> Rav Ahuja </h4>\n"
        },
        {
            "cell_type": "markdown",
            "id": "7f4005d1-e800-4f7b-b002-9a578c750c15",
            "metadata": {},
            "source": "## Change log\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",
            "id": "6a2bdd2d-d066-4ad5-9313-5e53830cd9f8",
            "metadata": {},
            "source": "## <h3 align=\"center\"> \u00a9 IBM Corporation 2021. All rights reserved. <h3/>\n"
        }
    ],
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