Final Exam: Dynamic Data Handling with Python

Python 3.6+
  • 1 Video | 35s
  • Includes Assessment
  • Earns a Badge
Final Exam: Dynamic Data Handling with Python will test your knowledge and application of the topics presented throughout the Dynamic Data Handling with Python track of the Skillsoft Aspire Pythonista to Python Master Journey.

WHAT YOU WILL LEARN

  • add a foreign key constraint between tables
    analyze the common and distinct values in two tables using various petl functions
    automate operations using triggers
    calculate aggregate statistics for a field in a table using the aggregate function
    configure an httpx.Cookies instance to send a collection of properties to a remote server
    create and invoke stored procedures
    create and use a SQL primary key constraint with autoincrement
    create a table and insert rows into it
    create a table in SQLite and read it into petl using SQLAlchemy and SQLite3
    create SQL indexes on tables
    create tables using object relational mapping
    define and submit a POST request containing JSON and binary data
    download a set of files sequentially using HTTPX
    drop a table, recreate it, and insert rows into it
    execute alter operations to add constraints and indices to tables
    explore different ways to use logical operators for querying data
    get data from MS Excel and perform basic operations on the data
    identify the different options available to stream large volumes of data in an HTTP response
    identify when a redirect has taken place upon submitting an HTTP request
    implement an httpx.Cookies instance to send a collection of properties to a remote server
    implement check constraints
    implement insert and delete operations
    implement slicing, dicing, and merging operations on petl data tables
    implement specialized types of joins such as anti joins and cross joins
    implement split operations on data stored within petl tables
    insert and edit columns and rows in petl data tables
    insert and edit rows and columns in petl data tables
    insert data into views
    insert rows into a table
    install petl and create a basic petl data table out of toy data
  • install SQLAlchemy and connect to MySQL
    install SQLAlchemy and connect to MySQL
    install the latest available version of HTTPX on your system
    limit the amount of time your app spends waiting to get served a response to an HTTP request using timeouts
    make use of the rowreduce() function to reduce rows and compute aggregate statistics
    map fields in a petl table to transformation based on functions
    perform insert and delete operations
    perform joins based on overlapping intervals rather than absolute values
    perform SQL like equi-joins on petl data tables
    perform various grouping operations on the data in a table
    perform various import and export operations on CSV, TSV, and TXT files.
    perform various update and replace operations on petl data tables by defining functions to perform transformations
    query data using object relational mapping
    read in data from the serialized pickle and XML file formats
    read JSON data, perform various operations, and export it to a persistent format
    recall the types of exceptions that can be encountered when sending and processing requests with HTTPX
    recognize the messages conveyed in the different status codes sent in an HTTP response
    recognize the types of exceptions that can be encountered when sending and processing requests with HTTPX
    recognize when a redirect has taken place upon submitting an HTTP request
    retrieve information about a remote resource using a HEAD request
    submit data to a remote server using a POST request with HTTPX
    submit HTTP GET requests with one or more parameters using HTTPX
    use an httpx.Cookies instance to send a collection of properties to a remote server
    use slicing, dicing, and merging operations on petl data tables
    use the aggregate function to calculate aggregate statistics for a field in a table
    use the facet petl function to define filters on specific fields in a table
    use the fetchmany() function to retrieve the output of a select query
    use the HTTPX AsynClient to download a set of files asynchronously
    use triggers to automate operations
    work with transaction aborts

IN THIS COURSE

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    Dynamic Data Handling with Python
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