Python for Data Engineers in 1 HOUR! Full Course + Programming Tutorial

00:54:36
https://www.youtube.com/watch?v=IJm--UbuSaM

Résumé

TLDRThe video tutorial is designed to equip viewers with the necessary Python skills for data engineering. It covers foundational programming concepts, data processing, and the development of ETL (Extract, Transform, Load) pipelines. The tutorial is structured into several sections, starting from the basics of Python and its role in data engineering, essential libraries like pandas and numpy, and advances to practical steps for setting the environment and handling various data formats including CSV, JSON, Excel, and Parquet. It demonstrates data processing with pandas, numerical computing with numpy, and handling datetime data. The tutorial provides insight into building and deploying Python packages, working with APIs, and Object-Oriented Programming (OOP) principles such as encapsulation, inheritance, and polymorphism. It includes data quality testing, scripting unit tests, and maintaining code standards. Viewers will gain practical skills in using Google Collab for Python programming, managing data sets, and applying these skills in real-world scenarios. The approach is developed from extensive research into industry trends and best practices, aimed at enabling both beginners and seasoned developers to create scalable data engineering solutions. The video also emphasizes secure API interactions, handling errors, and code testing for robust data workflows.

A retenir

  • 🔧 Python is essential for data engineering.
  • 📚 Learn core Python programming and ETL pipelines.
  • 📈 Data manipulation with pandas and numpy.
  • 🗂️ Handling CSV, JSON, Excel, and Parquet formats.
  • 🚀 Advanced data processing and visualization techniques.
  • 🔐 Secure API interactions and environment setup.
  • 🔄 Understand Python OOP principles for scalable code.
  • 🛠️ Implement data quality testing and code standards.
  • ☁️ Utilize Google Collab for hands-on Python practice.
  • 🔍 Develop and deploy reusable Python packages.

Chronologie

  • 00:00:00 - 00:05:00

    Introduction to Python for data engineering, overview of using Python in data engineering, covering basics to advanced ETL pipelines.

  • 00:05:00 - 00:10:00

    Setting up Python environment, importing essential libraries like pandas and numpy, and creating sample datasets for practice.

  • 00:10:00 - 00:15:00

    Exploring Python core concepts like variables, operators, functions, and control structures crucial for data processing.

  • 00:15:00 - 00:20:00

    Discussing Python's built-in data structures such as tuples, lists, sets, and dictionaries in data engineering context.

  • 00:20:00 - 00:25:00

    File handling with Python for different file formats: text, CSV, JSON, Excel, and Parquet, each suited for specific tasks.

  • 00:25:00 - 00:30:00

    Introduction to pandas for data manipulation, covering data frames, cleaning, aggregation, and basic visualization techniques.

  • 00:30:00 - 00:35:00

    Working with dates and times in Python, focusing on parsing, calculations, and filtering in time-sensitive data.

  • 00:35:00 - 00:40:00

    API interactions with Python, secure API request handling using environment variables, and building data pipelines with API integrations.

  • 00:40:00 - 00:45:00

    Object-oriented programming principles in Python, focusing on creating classes and objects for reusable data workflows.

  • 00:45:00 - 00:54:36

    Building a complete ETL pipeline combining extraction, transformation, and loading steps, with emphasis on modular and robust design.

Afficher plus

Carte mentale

Mind Map

Questions fréquemment posées

  • What will I learn in this video about data engineering?

    You will learn foundational programming concepts, advanced ETL pipelines, data processing with Python, and important industry tools and practices.

  • Is this tutorial suitable for beginners?

    Yes, it starts with basic concepts and progresses to advanced topics, making it suitable for both beginners and experienced developers.

  • How is Python useful in data engineering?

    Python acts as a versatile tool for scripting automated processes, handling data, building ETL pipelines, and managing large datasets.

  • What are some key libraries discussed in this tutorial?

    The tutorial covers libraries such as pandas for data manipulation and numpy for numerical operations, among others.

  • Does the video provide hands-on practice?

    Yes, you can follow along with the complete notebook linked in the video description for hands-on practice.

  • What are the main sections of the tutorial?

    Main sections include Python basics, core Python skills, data processing, ETL pipeline building, and working with APIs and packages.

  • Is the tutorial research-based?

    Yes, it is based on extensive research into industry trends and best practices in data engineering.

  • Does the video discuss handling different data formats?

    Yes, you’ll learn to handle CSV, JSON, Excel, and Parquet formats, which are crucial for data engineering tasks.

  • Will I learn about APIs in this video?

    Yes, the tutorial covers APIs usage, making requests, handling responses, and integrating them into data pipelines.

  • Is there a focus on Python environment setup?

    Yes, the initial sections discuss setting up the Python environment for data engineering tasks.

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Défilement automatique:
  • 00:00:00
    if you're ready to dive into Data
  • 00:00:01
    engineering or want to elevate your
  • 00:00:03
    python skills for this field youve come
  • 00:00:05
    to the right place python has earned its
  • 00:00:08
    reputation as a Swiss army knife of data
  • 00:00:11
    engineering and today we're going to
  • 00:00:12
    leverage its versatility in this video
  • 00:00:15
    we'll cover everything you need from
  • 00:00:16
    foundational programming Concepts to
  • 00:00:18
    Advanced ETL pipelines whether you're
  • 00:00:21
    beginner or season developer looking for
  • 00:00:23
    refresher by the end you'll have the
  • 00:00:25
    knowledge and confidence to build deploy
  • 00:00:27
    and scale data engineering Solutions
  • 00:00:30
    this tutorial is built from extensive
  • 00:00:32
    research into industry Trends essential
  • 00:00:34
    tools and best practices in data
  • 00:00:36
    engineering I've condensed my ears of
  • 00:00:39
    insights into a practical step-by-step
  • 00:00:41
    guide you will find the time stamps on
  • 00:00:43
    screen so you can skip sections if
  • 00:00:45
    you're already familiar with certain
  • 00:00:46
    topics although I think it would serve
  • 00:00:48
    as a great refresher to go through them
  • 00:00:50
    as well here's the breakdown section one
  • 00:00:53
    and two contain the introduction to
  • 00:00:55
    Python and the environment setup this
  • 00:00:57
    contains what data engineering is
  • 00:01:00
    Python's role essential libraries and
  • 00:01:02
    setting up the environment with sample
  • 00:01:03
    data sets sections 3 to five contain the
  • 00:01:06
    core python skills for data engineering
  • 00:01:08
    which is the basics of Python
  • 00:01:09
    Programming handling essential data
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    structures like lists dictionaries and
  • 00:01:14
    tles and file handling techniques with
  • 00:01:16
    CSV Json Excel and partk formats
  • 00:01:19
    sections 6 through S focus on data
  • 00:01:21
    processing with pandas and numai which
  • 00:01:24
    includes data manipulation cleaning
  • 00:01:26
    aggregation and visualization with
  • 00:01:27
    pandas and numerical computing
  • 00:01:30
    with numai for array operations
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    statistics and indexing section eight is
  • 00:01:34
    all about working with dates and times
  • 00:01:36
    that is parsing formatting and working
  • 00:01:38
    with daytime data in pipelines sections
  • 00:01:41
    9 through 11 cover apis objectoriented
  • 00:01:44
    programming and building ETL pipelines
  • 00:01:47
    in section 12 we cover data quality
  • 00:01:49
    testing and code standard which includes
  • 00:01:51
    data validation techniques unit testing
  • 00:01:53
    and maintaining code standards with
  • 00:01:55
    tools like flake 8 and grade
  • 00:01:57
    expectations section 13 is all about
  • 00:02:00
    building and deploying python packages
  • 00:02:01
    where we create build and deploy python
  • 00:02:03
    packages which will make your code
  • 00:02:05
    reusable and scalable before we start if
  • 00:02:07
    you want more data engineering tips and
  • 00:02:08
    resources don't forget to subscribe and
  • 00:02:10
    turn on notifications I post new videos
  • 00:02:13
    every week I also want to let you know
  • 00:02:15
    that the complete notebook that is used
  • 00:02:17
    in this tutorial is linked in the video
  • 00:02:18
    description you can download it and
  • 00:02:20
    follow along step by step to ensure
  • 00:02:22
    everything works seamlessly on your end
  • 00:02:24
    as well in this section we'll set up the
  • 00:02:26
    python environment and demonstrate how
  • 00:02:29
    to create and manage various data sets
  • 00:02:31
    in various formats by the end of this
  • 00:02:33
    section you will understand the process
  • 00:02:35
    of preparing a working directory
  • 00:02:37
    generating data sets and saving them in
  • 00:02:40
    specified formats for this entire
  • 00:02:43
    tutorial I'll be using Google collab to
  • 00:02:45
    use collab you simply have to create a
  • 00:02:47
    Google collab account using your Google
  • 00:02:50
    account and once created simply click on
  • 00:02:52
    the new notebook button to start a new
  • 00:02:54
    jupyter node let's set up our python
  • 00:02:56
    environment in Python libraries are
  • 00:02:58
    pre-rt code modules that simplify
  • 00:02:59
    complex task for example instead of
  • 00:03:02
    writing code for Matrix operations from
  • 00:03:04
    scratch we can use the numpy library use
  • 00:03:07
    a library we simply imported using the
  • 00:03:10
    import keyword libraries like pandas and
  • 00:03:13
    numpy are particularly useful for data
  • 00:03:16
    analysis and numerical
  • 00:03:18
    computations We Begin by preparing the
  • 00:03:21
    directory for saving data sets this step
  • 00:03:23
    involves checking if a specific folder
  • 00:03:25
    exists in your workspace and creating it
  • 00:03:28
    if it doesn't exist this ensures that
  • 00:03:30
    all your data sets are stored in one
  • 00:03:32
    location keeping the workspace organized
  • 00:03:34
    we then generate multiple data sets each
  • 00:03:37
    designed to mimic real world data
  • 00:03:38
    formats and scenarios Titanic data set
  • 00:03:41
    is a sample data set inspired by the
  • 00:03:43
    Titanic passenger manifest key features
  • 00:03:46
    include passenger details like ID name
  • 00:03:48
    class and survival status we on purpose
  • 00:03:51
    include missing values and duplicate
  • 00:03:53
    entries to reflect real world challenges
  • 00:03:55
    we then create the employee data which
  • 00:03:57
    is in Json format this represents nested
  • 00:04:00
    in hierarchical data often seen in apis
  • 00:04:03
    each employee has attributes such as the
  • 00:04:06
    name department and
  • 00:04:08
    salary it includes a list of projects
  • 00:04:10
    showcasing how Json handles structured
  • 00:04:12
    data sales data is an Excel format it is
  • 00:04:15
    a Time series data set with daily sales
  • 00:04:18
    records it has columns for date sales
  • 00:04:22
    and it has columns for date sales
  • 00:04:24
    figures and product
  • 00:04:26
    names this highlights Excel suitability
  • 00:04:28
    for tabular and business business
  • 00:04:29
    related data next we create user
  • 00:04:32
    purchase data in the P format the P
  • 00:04:35
    format is optimized for analytics
  • 00:04:36
    queries and storage efficiency we
  • 00:04:39
    include Fields like the user ID age
  • 00:04:41
    location and purchase amount product
  • 00:04:44
    data is a straightforward data set
  • 00:04:46
    listing the products key features are
  • 00:04:48
    columns like product name price and
  • 00:04:50
    stock availability weather data is a CSV
  • 00:04:53
    which tracks feather patterns over time
  • 00:04:55
    this has columns for time stamps
  • 00:04:57
    temperature and humidity levels and just
  • 00:04:59
    ending different data formats is crucial
  • 00:05:01
    for data Engineers as each format is
  • 00:05:04
    suited for specific applications
  • 00:05:06
    preparing data sets helps practice
  • 00:05:08
    common tasks like data cleaning and
  • 00:05:10
    aggregation in section three we will
  • 00:05:12
    dive into Python's basic concepts such
  • 00:05:15
    as variables operators and control
  • 00:05:16
    structures which are essential for
  • 00:05:19
    processing these data sets effectively
  • 00:05:21
    welcome to section three in this section
  • 00:05:23
    we'll explore the foundational elements
  • 00:05:25
    of Python Programming that are essential
  • 00:05:26
    for data engineering these Concepts
  • 00:05:29
    include variables data types operators
  • 00:05:32
    control structures functions and string
  • 00:05:35
    man operators by the end of this section
  • 00:05:37
    you'll have a clear understanding of
  • 00:05:39
    Python's building blocks which you'll
  • 00:05:41
    frequently use while handling data now
  • 00:05:43
    let's look at variables and data types
  • 00:05:45
    what are variables variables are
  • 00:05:47
    placeholders for storing data values
  • 00:05:50
    allow us to name data for easy access
  • 00:05:52
    and manipulation later think of
  • 00:05:55
    variables as labeled storage boxes where
  • 00:05:57
    you can place information common data
  • 00:05:59
    type in Python include integer which
  • 00:06:01
    represents whole number such as 10 or 42
  • 00:06:04
    used for counts or IDs float which
  • 00:06:07
    represents a number in decimal points
  • 00:06:09
    for example 3.14 or 7.5 Ed for
  • 00:06:12
    measurement or calculation requiring
  • 00:06:14
    Precision strings represent text for
  • 00:06:16
    example Alice or data engineering these
  • 00:06:19
    are used for names description or
  • 00:06:20
    categorical data Boolean represents true
  • 00:06:24
    or false it is commonly used for
  • 00:06:26
    conditional checks python is a
  • 00:06:28
    dynamically typed language
  • 00:06:30
    that means you don't have to declare
  • 00:06:32
    what type of variable you want
  • 00:06:34
    explicitly python infers the type based
  • 00:06:37
    on the values assigned for example if
  • 00:06:39
    you assign 10 Pyon considers it an
  • 00:06:42
    integer and if you assign 10.5 it's
  • 00:06:45
    automatically treated as a float now
  • 00:06:47
    let's look at operators operators
  • 00:06:50
    perform operations on variables and
  • 00:06:52
    values python supports different types
  • 00:06:55
    of operations for example arithmetic
  • 00:06:57
    operations where we can perform
  • 00:06:59
    mathematical calculations like addition
  • 00:07:02
    subtraction multiplication and
  • 00:07:04
    division for example you can calculate
  • 00:07:07
    total sales or average
  • 00:07:08
    Matrix operators like addition can
  • 00:07:11
    behave differently based on the data
  • 00:07:13
    type for numbers it performs addition
  • 00:07:15
    while for Strings it joins or
  • 00:07:17
    concatenates them similarly operators
  • 00:07:19
    like Star can work on strings to repeat
  • 00:07:22
    them a given number of
  • 00:07:24
    times this adaptability makes python
  • 00:07:27
    very versatile
  • 00:07:30
    we can also do comparison using
  • 00:07:32
    operators where we compare two values
  • 00:07:34
    and return a Boolean result that is true
  • 00:07:35
    or false for example dou equal to checks
  • 00:07:38
    if the value are equal and greater than
  • 00:07:40
    checks if one value is greater than the
  • 00:07:42
    other for example we could use this for
  • 00:07:45
    filtering rows in a data set based on
  • 00:07:47
    condition for example we could find rows
  • 00:07:49
    where multiple criteria are met just age
  • 00:07:52
    greater than 30 and salary greater than
  • 00:07:54
    50,000 control structures allow us to
  • 00:07:57
    control the flow of our code enabling us
  • 00:07:59
    to make decisions and repeat tasks
  • 00:08:02
    conditional statements such as if else
  • 00:08:05
    if and else execute specific code blocks
  • 00:08:08
    of code based on condition for example
  • 00:08:11
    if a product stock is greater than 100
  • 00:08:13
    label it as high stock you define the
  • 00:08:16
    conditions and python evaluates them in
  • 00:08:18
    order until one is satisfied Loops are
  • 00:08:21
    used to iterate over collections like
  • 00:08:23
    list or dictionaries for example using
  • 00:08:25
    the for Loop we iterate over the product
  • 00:08:27
    names and print one after the other each
  • 00:08:31
    item in the collection is processed one
  • 00:08:33
    at a time while Loops are used to repeat
  • 00:08:36
    a block of code as long as a condition
  • 00:08:39
    is true for example print numbers until
  • 00:08:42
    a counter reaches three the condition is
  • 00:08:44
    checked before each iteration now let's
  • 00:08:46
    talk about functions modules and
  • 00:08:48
    packages what are functions functions
  • 00:08:51
    are reusable blocks of code that perform
  • 00:08:53
    specific tasks they can accept inputs
  • 00:08:56
    that is arguments and return outputs we
  • 00:08:59
    use functions because you want to avoid
  • 00:09:01
    code repetition and make code more
  • 00:09:03
    organized and readable to define a fun
  • 00:09:06
    we Define a function with a name and
  • 00:09:08
    optional parameters inside the function
  • 00:09:11
    you write the logic or operations to be
  • 00:09:13
    performed you call the function whenever
  • 00:09:16
    you need to perform that task modules
  • 00:09:18
    are python files that contain functions
  • 00:09:20
    classes and variables for example you
  • 00:09:23
    could use the math module to calculate
  • 00:09:25
    square roots packages are collections of
  • 00:09:28
    modules group together for example
  • 00:09:30
    pandas and nump are packages used for
  • 00:09:33
    data analysis in numerical Computing
  • 00:09:34
    Panda functions are small Anonymous
  • 00:09:36
    functions defined in a single line
  • 00:09:39
    they're very useful when you need a
  • 00:09:41
    simple function for a short duration
  • 00:09:43
    such as when applying a transformation
  • 00:09:45
    to data unlike a regular function
  • 00:09:48
    defined using def a Lambda function does
  • 00:09:51
    not need a name and it is ideal for
  • 00:09:54
    short throwaway
  • 00:09:55
    Tas let's look at the syntax of the
  • 00:09:58
    Lambda function the Lambda keyword is
  • 00:10:00
    followed by one or more arguments
  • 00:10:03
    separated by commas and then a colon
  • 00:10:07
    after the colon you write a single
  • 00:10:08
    expression that the function will
  • 00:10:10
    compute and return for instance add two
  • 00:10:13
    numbers you could write Lambda X Y X + 5
  • 00:10:18
    let's look at some examples to
  • 00:10:20
    understand how the Lambda function works
  • 00:10:23
    the first example we use the Lambda
  • 00:10:25
    syntax to Define an anonymous function
  • 00:10:28
    that adds to numb numbers the Lambda X Y
  • 00:10:32
    X + Y function takes two arguments X and
  • 00:10:36
    Y adds them together and returns results
  • 00:10:40
    the result of adding three and five is
  • 00:10:43
    printed next we use the map function
  • 00:10:46
    with Lambda function the map function is
  • 00:10:49
    a buil-in python function that applies a
  • 00:10:52
    given function to every item in a itable
  • 00:10:56
    such as a list the Lambda function X
  • 00:10:59
    colon X star star 2 takes one argument X
  • 00:11:04
    and returns its Square when we use a map
  • 00:11:07
    function with Lambda it squares every
  • 00:11:10
    number in the numbers
  • 00:11:12
    list finally we show another example of
  • 00:11:15
    using the map with Lambda to convert a
  • 00:11:18
    list of words to
  • 00:11:20
    uppercase why manipulate strings string
  • 00:11:23
    manipulation is vital in data cleaning
  • 00:11:25
    where data must be standardized we can
  • 00:11:28
    do various operations such as
  • 00:11:29
    concatenation where they combine two or
  • 00:11:31
    more strings for example we could join a
  • 00:11:34
    greeting with a name to create a
  • 00:11:36
    personalized message we could also
  • 00:11:37
    format our data where we embed variables
  • 00:11:39
    into Strings using F strings for example
  • 00:11:42
    including a person's name and Department
  • 00:11:44
    in a sentence dynamically we can also
  • 00:11:45
    use slicing to extract specific parts of
  • 00:11:48
    a string using index ranges for example
  • 00:11:52
    the index zero indicates the first
  • 00:11:54
    element and the last element mentioned
  • 00:11:56
    in the range is not included so when we
  • 00:11:58
    write colon 3 it excludes the last
  • 00:12:00
    element that is the fourth element and
  • 00:12:02
    takes the first through third element
  • 00:12:04
    methods such as lower convert a string
  • 00:12:06
    to lower case and upper converts string
  • 00:12:09
    to upper case repl replaces occurrences
  • 00:12:12
    of a substring with another errors are
  • 00:12:14
    inevitable in our C especially when
  • 00:12:17
    dealing with real world data handling
  • 00:12:19
    errors ensures that our program doesn't
  • 00:12:21
    crash unexpectedly we use a tri block to
  • 00:12:24
    write code that might raise a error we
  • 00:12:27
    use a except block to catch and handle
  • 00:12:29
    specific errors optionally we could use
  • 00:12:32
    else to execute code if no error occurs
  • 00:12:35
    we use the finally block to execute code
  • 00:12:38
    that must run regardless of an
  • 00:12:40
    error for example we read a file that
  • 00:12:43
    doesn't exist the program should display
  • 00:12:46
    an error message but continue running
  • 00:12:48
    without interruption in this section we
  • 00:12:49
    covered python core elements such as
  • 00:12:51
    variables operators control structures
  • 00:12:53
    functions string manipulators and error
  • 00:12:56
    handling these building blocks are the
  • 00:12:58
    foundation for writing script scripts
  • 00:12:59
    that process and analyze data
  • 00:13:01
    efficiently in section four we'll be
  • 00:13:03
    discussing Python's built-in structures
  • 00:13:05
    such as coules lists sets and
  • 00:13:08
    dictionaries these collections are
  • 00:13:10
    essential for organizing and
  • 00:13:12
    manipulating data in Python especially
  • 00:13:15
    for data engineering Tas we'll explain
  • 00:13:17
    how each collection Works their key
  • 00:13:19
    characteristics and practical use cases
  • 00:13:22
    let's get a overview of python
  • 00:13:24
    collections python collections allow us
  • 00:13:26
    to manage data efficiently by grouping
  • 00:13:28
    values together together each type has
  • 00:13:30
    unique properties and is suited for
  • 00:13:32
    specific
  • 00:13:33
    scenarios tles are ordered and immutable
  • 00:13:36
    ideal for data that must remain constant
  • 00:13:39
    lists are ordered and mutable used for
  • 00:13:42
    dynamic sequential data sets are
  • 00:13:45
    unordered and unique great for
  • 00:13:47
    eliminating duplicates and Performing
  • 00:13:50
    fast membership tests dictionaries are
  • 00:13:53
    key value pairs ideal for structured
  • 00:13:55
    data that needs fast lookups by key
  • 00:13:59
    let's look at what tles are tles are
  • 00:14:02
    ordered collection of data that cannot
  • 00:14:04
    be modified after creation that is
  • 00:14:07
    immutable they are commonly used when
  • 00:14:09
    the structure of data is fixed and
  • 00:14:11
    should not change immutability
  • 00:14:13
    guarantees that once you create a tuple
  • 00:14:15
    you cannot add remove or modify elements
  • 00:14:18
    in a tle you can access elements using
  • 00:14:21
    their index just like list tles are
  • 00:14:24
    ideal for sewing constant data like
  • 00:14:26
    coordinates or RGB value for colors in
  • 00:14:29
    our script tles store the coordinates
  • 00:14:31
    for London by accessing elements using
  • 00:14:34
    indices the script retrieves latitude
  • 00:14:37
    and longitudes this demonstrates how
  • 00:14:39
    tles provide a simple way to store and
  • 00:14:42
    reference immutable data lists are
  • 00:14:45
    ordered collection of data that grow
  • 00:14:47
    shrink or can be modified they are one
  • 00:14:50
    of the most versatile data structures in
  • 00:14:52
    Python you can add remove and update
  • 00:14:54
    items and you can access elements using
  • 00:14:56
    indices or extract parts of a list with
  • 00:15:00
    slicing list automatically resize when
  • 00:15:03
    items are added or removed lists are
  • 00:15:06
    perfect for maintaining ordered sequence
  • 00:15:08
    of items such as a list of products or
  • 00:15:10
    file paths in our script we create a
  • 00:15:13
    list of product names that is widget a
  • 00:15:15
    widget B and widget C to add an element
  • 00:15:17
    we append a new product that is widget B
  • 00:15:19
    to the list you can also remove elements
  • 00:15:22
    from the array and we remove widget B in
  • 00:15:24
    this example finally the script prints
  • 00:15:26
    the updated list demonstrating how list
  • 00:15:29
    are dynamically and easily modified sets
  • 00:15:31
    are ordered collection of unique items
  • 00:15:34
    they optimized for eliminating
  • 00:15:36
    duplicates and Performing operations
  • 00:15:38
    like Union intersection and difference
  • 00:15:41
    if duplicate values are added in sets
  • 00:15:43
    they're automatically removed elements
  • 00:15:45
    are stored without a specific order so
  • 00:15:47
    indexing is not possible we can perform
  • 00:15:50
    set operations such as Union
  • 00:15:52
    intersection and difference sets are
  • 00:15:54
    great for D duplication or checking
  • 00:15:56
    membership in our code we create a set
  • 00:15:58
    of product IDs the duplicate ID 101 is
  • 00:16:02
    automatically removed to add elements we
  • 00:16:05
    use the do add the final set is then
  • 00:16:07
    displayed illustrating how sets ensure
  • 00:16:10
    uniqueness and are well suited for
  • 00:16:12
    managing IDs or other unique values in
  • 00:16:14
    dictionary is store data as key value
  • 00:16:16
    pairs making them ideal for structured
  • 00:16:19
    and easily accessible data for example
  • 00:16:21
    you can map product IDs to the
  • 00:16:23
    corresponding names or
  • 00:16:25
    prices each key in the dictionary maps
  • 00:16:27
    to a value
  • 00:16:29
    keys and values can be added updated or
  • 00:16:32
    deleted you can retrieve values quickly
  • 00:16:34
    using the corresponding Keys
  • 00:16:36
    dictionaries are perfect for tasks
  • 00:16:38
    requiring labeled data such as storing
  • 00:16:40
    user profiles or product cataloges in
  • 00:16:42
    our script we Define a dictionary to
  • 00:16:44
    represent a product the keys are product
  • 00:16:47
    ID name price and stock the values are
  • 00:16:50
    the corresponding data for each
  • 00:16:52
    attribute this script retrieves the
  • 00:16:54
    product name and price using their keys
  • 00:16:57
    the stock count is up updated to 120 and
  • 00:17:00
    a new key category is added with the
  • 00:17:02
    value gadgets dictionary is then printed
  • 00:17:04
    showing how it allows easy organization
  • 00:17:07
    and access to structured data choosing
  • 00:17:10
    the right data structure can optimize
  • 00:17:12
    our code for both performance and
  • 00:17:14
    readability we would use stles for fixed
  • 00:17:17
    data lists for order and modifiable
  • 00:17:19
    collections sets to ensure data
  • 00:17:21
    uniqueness and dictionaries for key
  • 00:17:23
    value mappings this example will provide
  • 00:17:25
    a practical example of a list of sales
  • 00:17:27
    records each record is represented by a
  • 00:17:30
    dictionary with keys like dates products
  • 00:17:32
    and sales by iterating through the list
  • 00:17:35
    the script calculates the total sales
  • 00:17:37
    for each product this demonstrates how
  • 00:17:39
    list and dictionaries can work together
  • 00:17:41
    effectively these collections form the
  • 00:17:44
    foundation of Python Programming and the
  • 00:17:46
    Mastery of them is essential for
  • 00:17:47
    handling data in any project now let's
  • 00:17:49
    move on to Section Five Section Five is
  • 00:17:51
    all about file handling in Python file
  • 00:17:54
    handling is an essential skill in data
  • 00:17:55
    engineering it enables us to read write
  • 00:17:58
    and manage man data in various formats
  • 00:18:00
    in this section we'll explore how python
  • 00:18:02
    handles text files CSV files Json files
  • 00:18:06
    Excel files and park files by the end
  • 00:18:09
    you'll understand how to work with each
  • 00:18:11
    of these file types efficiently and why
  • 00:18:14
    each format is suited for specific tasks
  • 00:18:17
    text files what are text files text
  • 00:18:20
    files are the simplest file format
  • 00:18:22
    storing plain human readable text
  • 00:18:24
    they're often used for logs
  • 00:18:26
    configuration files or lightwe data
  • 00:18:28
    storage
  • 00:18:29
    text files can be created or overwritten
  • 00:18:32
    by opening a file in write mode to read
  • 00:18:35
    from text files we open a file in the
  • 00:18:37
    read mode that is R mode to retrieve its
  • 00:18:39
    contents line by line or as a string
  • 00:18:42
    also append to text files using the a
  • 00:18:44
    mode text files are ideal for
  • 00:18:47
    lightweight tasks like storing logs or
  • 00:18:49
    configuration details in our script we
  • 00:18:52
    create a text file called sampl text.txt
  • 00:18:54
    and write lines like hello data
  • 00:18:57
    engineering into it then we reopen the
  • 00:18:59
    same file and read its contents
  • 00:19:01
    displaying it this demonstrates how
  • 00:19:04
    python can handle basic file operations
  • 00:19:06
    in the CSV files are widely used for
  • 00:19:09
    storing tabular data each row represents
  • 00:19:12
    a record and columns are separated by
  • 00:19:15
    commas these are human readable and
  • 00:19:17
    compatible with most data tools use
  • 00:19:20
    Python's Panda's library to write data
  • 00:19:22
    to a CSV for structured tabular storage
  • 00:19:26
    pandas also allows you to read CSV files
  • 00:19:29
    and load their contents into a data
  • 00:19:32
    frame CSC files excellent for exchanging
  • 00:19:35
    small to medium siiz data sets between
  • 00:19:38
    applications in our script we create a
  • 00:19:40
    data frame representing products with
  • 00:19:42
    columns like product price and stock
  • 00:19:46
    then we write it to productor data.csv
  • 00:19:48
    while reading the file Back The Script
  • 00:19:51
    displays its content in a tabular format
  • 00:19:54
    showing how CSV files are an efficient
  • 00:19:57
    way to manage tabular data
  • 00:19:59
    now let's move on to Json files Json
  • 00:20:02
    that is Javascript object notation is a
  • 00:20:04
    structured file format for storing
  • 00:20:07
    hierarchical data it's human readable
  • 00:20:10
    lightweight and widely used in apis and
  • 00:20:12
    configuration files to write to Json
  • 00:20:15
    files we use Python's Json modules to
  • 00:20:17
    create a Json file from dictionaries Or
  • 00:20:20
    List Json module can also pass Json
  • 00:20:23
    files into python objects like
  • 00:20:25
    dictionaries Or List vice versa Json
  • 00:20:27
    files are IDE for nested structured data
  • 00:20:30
    the configuration settings or API
  • 00:20:33
    responses in our script we Define
  • 00:20:35
    employees data including nested Fields
  • 00:20:38
    like projects and write them back to
  • 00:20:40
    employees. Json then we read these files
  • 00:20:43
    back and print its content showing how
  • 00:20:46
    Json allows for storing and passing of
  • 00:20:47
    hierarchical
  • 00:20:50
    data Excel files are widely used in
  • 00:20:53
    business for data sharing and Reporting
  • 00:20:55
    they support multiple sheets and allow
  • 00:20:57
    for formatted tabular data you can use
  • 00:21:00
    pandas to write a data frame to an Excel
  • 00:21:02
    file and you can similarly use pandas to
  • 00:21:05
    read Excel files back into Data frame
  • 00:21:07
    for
  • 00:21:08
    analysis Excel files are commonly used
  • 00:21:10
    for sharing data with non-technical
  • 00:21:12
    stakeholders or for small scale
  • 00:21:14
    reporting the script creates a data
  • 00:21:16
    frame with columns like date product
  • 00:21:18
    sales which represent a daily sales
  • 00:21:20
    record and save it as sales dat.
  • 00:21:25
    xlsx it reads the file back and displays
  • 00:21:28
    the cards illustrating Excel suitability
  • 00:21:30
    for data storage and
  • 00:21:32
    exchange par is a binary columnar
  • 00:21:35
    storage which is optimized for large
  • 00:21:37
    data sets it's ideal for analytics
  • 00:21:40
    workflows due to its efficient
  • 00:21:42
    compression and fast saring capabilities
  • 00:21:45
    pandas to write a data frame to a park
  • 00:21:47
    file and vice versa read Park files into
  • 00:21:50
    Data frames in our script we create the
  • 00:21:53
    user purchase data which includes Fields
  • 00:21:55
    like the user ID age and purchase amount
  • 00:21:59
    we then save it to user purchases. Park
  • 00:22:03
    a section text files are best for
  • 00:22:05
    lightweight data like logs CSV files
  • 00:22:07
    ideal for small to medium tabular data
  • 00:22:10
    Jon files are excellent for hierarchical
  • 00:22:12
    or nested data Excel files are widely
  • 00:22:15
    used in business for reporting Park
  • 00:22:17
    files are perfect for large data sets
  • 00:22:19
    due to their efficiency master of these
  • 00:22:21
    file formats ensures that you can handle
  • 00:22:22
    diverse data types whether they're
  • 00:22:25
    lightweight logs or massive analytics
  • 00:22:26
    data sets in section six we'll be
  • 00:22:29
    discussing data processing with
  • 00:22:30
    pandas pandas is one of the most
  • 00:22:33
    powerful libraries in Python for data
  • 00:22:35
    manipulation and Analysis in this
  • 00:22:37
    section we'll cover introduction to
  • 00:22:39
    pandas and data frames data cleaning and
  • 00:22:41
    pre-processing techniques data
  • 00:22:42
    manipulation and aggregation and basic
  • 00:22:45
    visualization for quick insides by the
  • 00:22:47
    end of the section you'll understand how
  • 00:22:49
    to effectively process and analyze data
  • 00:22:51
    using python let's understand what
  • 00:22:53
    pandas data frames are a data frame is a
  • 00:22:56
    two-dimensional tabular structure in
  • 00:22:59
    pandas it is similar to a spreadsheet or
  • 00:23:01
    database tables it contains labeled rows
  • 00:23:04
    and columns making it easy to access
  • 00:23:07
    manipulate and analyze data data set is
  • 00:23:10
    loaded into a data frame this data set
  • 00:23:13
    contains information about passengers
  • 00:23:15
    including their age class and survival
  • 00:23:18
    status by using the head method the
  • 00:23:21
    first few rows of a data set are
  • 00:23:23
    displayed providing an overview of its
  • 00:23:25
    structure and contents rows represent
  • 00:23:28
    the individual records and columns
  • 00:23:31
    represent attributes of the data such as
  • 00:23:33
    age or fair this sets a foundation for
  • 00:23:36
    exploring and transforming data now
  • 00:23:38
    let's look at data cleaning and
  • 00:23:40
    pre-processing why is cleaning important
  • 00:23:42
    real world data sets often have missing
  • 00:23:45
    values duplicates or inconsistent
  • 00:23:47
    formats cleaning ensures that data is
  • 00:23:50
    accurate and actually ready for analysis
  • 00:23:52
    now let's clean our data the very first
  • 00:23:54
    thing we do is check for missing values
  • 00:23:57
    missing values can skew analysis so
  • 00:23:59
    identifying them is the first step we
  • 00:24:01
    can use the is null. sum function to
  • 00:24:04
    count missing values in each column for
  • 00:24:06
    handling missing values in the H column
  • 00:24:08
    missing values are replaced with the
  • 00:24:10
    median ensuring no empty values remain
  • 00:24:13
    maintaining data Integrity for the fair
  • 00:24:16
    column rows with missing values are
  • 00:24:18
    dropped as they are considered critical
  • 00:24:20
    duplicate rows are removed to ensure
  • 00:24:22
    data consistency using the drop
  • 00:24:24
    duplicates
  • 00:24:25
    method after cleaning the script
  • 00:24:27
    verifies the absence of missing values
  • 00:24:29
    and duplicates ensuring a clean data set
  • 00:24:32
    is processed now let's look at data
  • 00:24:34
    manipulation and
  • 00:24:35
    aggregation data manipulation involves
  • 00:24:38
    modifying data to suit specific analysis
  • 00:24:40
    needs as filtering sorting or
  • 00:24:43
    aggregation we can filter passengers
  • 00:24:45
    with FS greater than 50 demonstrating
  • 00:24:48
    how pandas can quickly subset data data
  • 00:24:51
    set is sorted by the AG column in
  • 00:24:53
    descending order making it easier to
  • 00:24:55
    identify the oldest passengers we can
  • 00:24:57
    also add aggregate the data by Passenger
  • 00:25:00
    class and calculate the average fair and
  • 00:25:03
    age for each class this provides insight
  • 00:25:06
    into how passenger demographics and
  • 00:25:08
    fairs vary across classes these
  • 00:25:11
    operations show how pandas enables
  • 00:25:13
    efficient exploration and summarization
  • 00:25:15
    of data which is crucial for decision
  • 00:25:17
    making and Reporting visualizations help
  • 00:25:20
    uncover patterns Trends and outliers are
  • 00:25:23
    difficult to spot in raw data in our
  • 00:25:25
    code we create two visualizations that
  • 00:25:28
    is a age distribution histogram which
  • 00:25:30
    shows the passengers ages helping
  • 00:25:33
    identify the most common age groups you
  • 00:25:35
    also show the average fairback class
  • 00:25:37
    which is a bar plot which highlights the
  • 00:25:39
    key differences integrate prices across
  • 00:25:42
    classes Anda simplifies data cleaning by
  • 00:25:45
    handling missing values and duplicates
  • 00:25:47
    it enables data manipulation tasks like
  • 00:25:49
    filtering sorting and aggregation and
  • 00:25:51
    also helps us quickly create actionable
  • 00:25:54
    insights using visualizations in this
  • 00:25:57
    section we'll explore
  • 00:25:59
    the library at the heart of numerical
  • 00:26:01
    Computing in Python it provides
  • 00:26:03
    efficient tools for handling arrays and
  • 00:26:05
    Performing mathematical operations
  • 00:26:07
    here's brought we'll cover the basics of
  • 00:26:09
    numi arrays array operations indexing
  • 00:26:12
    and slicing linear operations and
  • 00:26:14
    statistical functions by the end of this
  • 00:26:17
    section you'll understand how to perform
  • 00:26:18
    fast and efficient numerical
  • 00:26:20
    computations using numai let's
  • 00:26:22
    understand the basics of numpy arrays
  • 00:26:25
    numpy arrays are similar to python lists
  • 00:26:27
    but are optimized for numerical
  • 00:26:29
    calculation they store elements of the
  • 00:26:32
    same data type and support a wide range
  • 00:26:34
    of mathematical operations in our script
  • 00:26:37
    we create a one-dimensional array from a
  • 00:26:39
    list this is ideal for representing
  • 00:26:42
    simple sequence of numbers a
  • 00:26:45
    two-dimensional array is Created from a
  • 00:26:47
    list of lists this represents a matrix
  • 00:26:50
    like structure often used in linear
  • 00:26:53
    algebra or image
  • 00:26:55
    data python arrays are faster and and
  • 00:26:58
    more memory efficient than python
  • 00:27:00
    list each array also has attributes that
  • 00:27:03
    is properties like shape size and dat
  • 00:27:06
    type which can be accessed to understand
  • 00:27:08
    its structures array operations allow
  • 00:27:11
    you to manipulate data efficiently with
  • 00:27:13
    numi you can perform operations on
  • 00:27:16
    entire arrays without the need for Loops
  • 00:27:19
    in our script we perform addition and
  • 00:27:21
    multiplication to each element of two
  • 00:27:23
    arrays this is useful in scenarios like
  • 00:27:26
    scaling or combining data sets
  • 00:27:29
    mathematical functions like square root
  • 00:27:31
    are applied to all elements simplifying
  • 00:27:33
    complex transformation instead of
  • 00:27:34
    iterating over elements numpy applies
  • 00:27:37
    the operation to the entire array this
  • 00:27:39
    significantly speeds up calculations
  • 00:27:42
    indexing and slicing lets you access or
  • 00:27:45
    modify specific sections of array making
  • 00:27:47
    it easy to isolate or analyze subsets of
  • 00:27:50
    data slicing helps a select sections of
  • 00:27:53
    array that are selected using ranges is
  • 00:27:55
    the first three elements Boolean
  • 00:27:57
    indexing are conditions that are applied
  • 00:27:59
    to filter elements as selecting values
  • 00:28:01
    greater than 25 in Python arrays lists
  • 00:28:05
    and data frames use zerob based indexing
  • 00:28:08
    meaning the first element is indexed at
  • 00:28:11
    zero you can also slice arrays using
  • 00:28:14
    ranges for example array 0 to three
  • 00:28:18
    retrieves the first three
  • 00:28:20
    elements negative indexing lets you
  • 00:28:23
    access elements from the end while
  • 00:28:25
    methods like iock in pandas allow for
  • 00:28:29
    more advanced indexing we can also
  • 00:28:31
    perform linear algebra operations like
  • 00:28:34
    matrix multiplication and solving
  • 00:28:36
    equations that are essential for
  • 00:28:37
    numerical analysis in our script we show
  • 00:28:40
    example of how matrix multiplication can
  • 00:28:43
    be done in Pyon two matrices are
  • 00:28:46
    multiplied to compute their dot product
  • 00:28:48
    this is widely used in Transformations
  • 00:28:50
    and neuron networks numi provides
  • 00:28:53
    buil-in functions for solving equations
  • 00:28:55
    of systems of linear equations as well
  • 00:28:58
    statistical functions also help us
  • 00:29:00
    summarize data identify Trends and
  • 00:29:03
    understand
  • 00:29:04
    distributions in our script we calculate
  • 00:29:07
    the mean and median which provide
  • 00:29:09
    measures of central tendency standard
  • 00:29:11
    deviation and variance is also
  • 00:29:13
    calculated which indicates the spread of
  • 00:29:15
    data you also do a cumulative sum which
  • 00:29:17
    calculates the running total of
  • 00:29:20
    elements these functions process entire
  • 00:29:23
    arrays efficiently delivering quick
  • 00:29:25
    insights into Data distributions and
  • 00:29:27
    patterns
  • 00:29:29
    numpy arrays are efficient and versatile
  • 00:29:31
    tools for numerical computations array
  • 00:29:34
    operations like slicing and indexing
  • 00:29:36
    simplified data manipulation built-in
  • 00:29:38
    functions support Advanced mathematical
  • 00:29:40
    and statistical tasks in Section 8 we'll
  • 00:29:44
    explore how we can work with date and
  • 00:29:46
    times in this section we'll delve into
  • 00:29:48
    handling date and times in Python which
  • 00:29:51
    is a crucial skill for managing time
  • 00:29:53
    series data scheduling and logging in
  • 00:29:57
    this section we'll explore passing and
  • 00:29:58
    formatting datetime data common datetime
  • 00:30:01
    operations and handling datetime data
  • 00:30:03
    inail pipelines by the end of this
  • 00:30:05
    section you'll understand how to parse
  • 00:30:07
    manipulate and analyze datetime data
  • 00:30:10
    efficiently parsing is converting a
  • 00:30:13
    datetime string into a python datetime
  • 00:30:16
    object which is structured and easily
  • 00:30:18
    manipulated formatting is transferring
  • 00:30:21
    vice versa that is a datetime object
  • 00:30:23
    back into a string often used to display
  • 00:30:26
    it in specific format in our script we
  • 00:30:29
    pass a datetime string into a datetime
  • 00:30:32
    object using predefined format codes we
  • 00:30:35
    use codes like percentage Capital by for
  • 00:30:38
    year percentage small M for month and
  • 00:30:40
    percentage small D for day these Define
  • 00:30:43
    how the strings are
  • 00:30:45
    interpreted we also format the date time
  • 00:30:47
    object and it's converted back into a
  • 00:30:49
    readable string with specific formatting
  • 00:30:51
    codes we can customize the output format
  • 00:30:55
    to suit various reporting needs passing
  • 00:30:57
    ures uniformity and allows calculations
  • 00:31:00
    while formatting makes date time human
  • 00:31:02
    readable manipulating dates and times is
  • 00:31:05
    essential for tasks like scheduling
  • 00:31:07
    calculating durations and filtering data
  • 00:31:10
    with specified ranges we can add or
  • 00:31:12
    subtract time intervals to calculate
  • 00:31:14
    future or past dates for example adding
  • 00:31:17
    five days to today's date helps schedule
  • 00:31:20
    tasks or events we can also extract
  • 00:31:23
    components which is a common task you
  • 00:31:26
    can access specific parts of of the
  • 00:31:28
    datetime object such as the year month
  • 00:31:30
    or day this is useful for grouping data
  • 00:31:34
    by month or analyzing Trends over
  • 00:31:36
    time we can also calculate time
  • 00:31:39
    differences between two daytime objects
  • 00:31:42
    to find
  • 00:31:43
    durations for example we could calculate
  • 00:31:45
    the number of days between two
  • 00:31:48
    events using buil-in functions like time
  • 00:31:50
    Delta these operations are
  • 00:31:52
    straightforward and efficient they
  • 00:31:54
    eliminate manual calculations and ensure
  • 00:31:56
    accuracy in detail workflows datetime
  • 00:31:59
    data often needs to be extracted
  • 00:32:01
    transformed and loaded for a Time based
  • 00:32:04
    analytics or reporting in our script
  • 00:32:07
    while loading data datetime columns May
  • 00:32:10
    initially Be Strings passing them into
  • 00:32:12
    datetime objects ensures consistency and
  • 00:32:15
    enables further
  • 00:32:17
    analysis we can also filter by date
  • 00:32:19
    range where data is filtered to include
  • 00:32:22
    only rows that fall within a specified
  • 00:32:24
    time frame this is useful for extracting
  • 00:32:27
    relevant subst set such as sales data or
  • 00:32:29
    specific month we can also calculate
  • 00:32:32
    time differences between rows to analyze
  • 00:32:35
    gaps or intervals such as time between
  • 00:32:37
    successive purchases these operations
  • 00:32:40
    are critical in processing data sets
  • 00:32:41
    like time series weather data or
  • 00:32:43
    transaction logs ensuring the data is
  • 00:32:46
    clean and accurate for analysis section
  • 00:32:49
    nine is all about working with apis and
  • 00:32:51
    external
  • 00:32:53
    Connections in this section we'll
  • 00:32:55
    explore apis that is application program
  • 00:32:58
    interfaces as a critical tool for data
  • 00:33:01
    Engineers apis allow us to fetch data
  • 00:33:04
    from external sources such as web
  • 00:33:06
    services or Cloud
  • 00:33:08
    Platforms in this section we'll talk
  • 00:33:10
    about setting up and making API requests
  • 00:33:12
    handling API responses and errors saving
  • 00:33:16
    API data for future processing and
  • 00:33:18
    building a practical API data pipeline
  • 00:33:21
    additionally in this script we'll use
  • 00:33:23
    environment variables which allow us to
  • 00:33:26
    securely manage sensitive dat as API
  • 00:33:29
    keys by the end of the section you will
  • 00:33:31
    know how to interact with apis securely
  • 00:33:34
    managed credentials and integrate apis
  • 00:33:36
    into Data pipelines in this example we
  • 00:33:39
    use the weather API which gives us
  • 00:33:41
    weather information set up a weather API
  • 00:33:43
    account you can simply sign up with your
  • 00:33:45
    email after signing up you get a free
  • 00:33:47
    API key which you can see that Al be
  • 00:33:49
    using as well store this API key we
  • 00:33:52
    shouldn't hardcode it in our scripts to
  • 00:33:55
    store credentials we use environment
  • 00:33:57
    variables we create a EnV file to store
  • 00:34:01
    API keys or other credentials securely
  • 00:34:04
    this keep sensitive data out of our
  • 00:34:06
    codebase reducing the risk exposure we
  • 00:34:08
    use the python. EnV library to load
  • 00:34:11
    these variables into our script at run
  • 00:34:14
    time to access the keys or secrets in
  • 00:34:17
    our script we use the os. getet EnV
  • 00:34:20
    function this ensures sensitive data is
  • 00:34:23
    only available when needed we first
  • 00:34:25
    Define API endpoint and parameters the
  • 00:34:29
    endpoint is the URL of the service that
  • 00:34:32
    you are accessing while parameters
  • 00:34:34
    specify what data you want specific
  • 00:34:36
    parameters that a API accepts are
  • 00:34:39
    usually available in the API
  • 00:34:40
    documentation you make a API call using
  • 00:34:43
    the request Library a get request
  • 00:34:45
    fetches data from the
  • 00:34:47
    API the request includes headers and
  • 00:34:50
    parameters ensuring authentication and
  • 00:34:53
    specifying query
  • 00:34:56
    details the API returns data usually in
  • 00:34:58
    Json format Json is then passed into
  • 00:35:01
    python dictionaries making it easy to
  • 00:35:04
    work with in the future apis can fail
  • 00:35:06
    due to invalid API Keys network issues
  • 00:35:10
    incorrect parameters or server errors
  • 00:35:12
    hence it's very important to handle
  • 00:35:14
    these errors in our script we check for
  • 00:35:17
    the status Response Code HTTP status
  • 00:35:21
    response codes indicate success such as
  • 00:35:23
    200 being okay or error such as 44 being
  • 00:35:27
    not found in 500 being internal server
  • 00:35:30
    errors we can use try accept blocks to
  • 00:35:33
    prevent our script from crashing due to
  • 00:35:34
    unexpected issues for example a timeout
  • 00:35:37
    error is handled gracefully by retrying
  • 00:35:39
    or logging the
  • 00:35:41
    issue we can raise errors for bad
  • 00:35:44
    responses using Ray for status we can
  • 00:35:48
    ensure that any error code triggers an
  • 00:35:51
    exception allowing us to handle it
  • 00:35:52
    effectively we can also set a timeout
  • 00:35:55
    for API calls to prevent our script from
  • 00:35:57
    waiting indefinitely if the API does not
  • 00:35:59
    respond the data received from apis is
  • 00:36:02
    often used for further analysis so
  • 00:36:05
    saving it in structured format is
  • 00:36:07
    essential the API May return large data
  • 00:36:10
    set but you only select the few
  • 00:36:11
    attributes that you need for example you
  • 00:36:13
    could select the temperature humidity
  • 00:36:15
    and condition from the Val data you
  • 00:36:17
    would next create a data frame where we
  • 00:36:18
    organize extracted Fields into a p data
  • 00:36:20
    frame this makes it easy to analyze or
  • 00:36:23
    save in the future at the end we also
  • 00:36:25
    save it to a CSV for persistent storage
  • 00:36:28
    and future use an API pipeline
  • 00:36:30
    integrates data retrieval transformation
  • 00:36:32
    and storage into a seamless workflow for
  • 00:36:35
    example fetching weather data for
  • 00:36:37
    multiple cities processing it and saving
  • 00:36:39
    it to a central
  • 00:36:41
    file in our script the first step is the
  • 00:36:44
    extract step where we fetch data from
  • 00:36:46
    the weather API by specifying City and
  • 00:36:48
    key we handle errors during the
  • 00:36:50
    extraction process to ensure
  • 00:36:53
    reliability the transform step processes
  • 00:36:55
    the raw API responses by selecting
  • 00:36:57
    fields and standardizing them you also
  • 00:37:00
    clean and format the data to match our
  • 00:37:03
    analysis and storage
  • 00:37:05
    requirements the load step saves the
  • 00:37:07
    transform data to a CSV appending new
  • 00:37:10
    records to avoid overriding existing
  • 00:37:12
    data now this pipeline can be scheduled
  • 00:37:14
    to run daily or hourly ensuring updated
  • 00:37:17
    data is always available for an analysis
  • 00:37:20
    this demonstrate an endtoend integration
  • 00:37:22
    of apis into our data Engineering
  • 00:37:25
    workflows in this section we dive into
  • 00:37:27
    the principles of object-oriented
  • 00:37:29
    programming which is a foundational
  • 00:37:31
    concept for Python objectoriented
  • 00:37:33
    Programming allows you to structure your
  • 00:37:35
    code into reusable maintainable and
  • 00:37:38
    modular
  • 00:37:39
    components this section includes talking
  • 00:37:41
    about classes and objects by the end of
  • 00:37:44
    the section you'll understand how
  • 00:37:45
    objectoriented programming enables data
  • 00:37:47
    Engineers to build scalable and reusable
  • 00:37:50
    workflows let's understand classes and
  • 00:37:53
    objects a class is a blueprint for
  • 00:37:56
    creating objects it defines the
  • 00:37:58
    attributes that is the data and the
  • 00:38:00
    method that is the functions that belong
  • 00:38:02
    to an object an object is an instance of
  • 00:38:05
    a class representing a specific entity
  • 00:38:07
    with its own
  • 00:38:10
    data in our script we define a class
  • 00:38:13
    called passenger which has attributes
  • 00:38:14
    like passenger ID name age passenger
  • 00:38:18
    class and survive these attributes
  • 00:38:21
    represented details of a Titanic
  • 00:38:23
    passenger we then create an object using
  • 00:38:25
    the init method which initially Iz es it
  • 00:38:28
    attribute with specific values for
  • 00:38:30
    example we create passenger one with
  • 00:38:32
    more specific details about the name and
  • 00:38:35
    age methods Define behaviors for the
  • 00:38:38
    class in this case display info method
  • 00:38:41
    returns a formatted string with the
  • 00:38:43
    passenger
  • 00:38:44
    details classes allow you to organize
  • 00:38:47
    related data and behaviors in one
  • 00:38:49
    structure objects make it easy to create
  • 00:38:52
    multiple instances with similar
  • 00:38:54
    functionality but unique data
  • 00:38:55
    objectoriented programming lies on four
  • 00:38:57
    core principles encapsulation
  • 00:39:00
    inheritance polymorphism and abstraction
  • 00:39:03
    let's break them down now let's break
  • 00:39:06
    down the oop concepts with analogies for
  • 00:39:09
    encapsulation think of a capsule that
  • 00:39:11
    protects its content similarly
  • 00:39:14
    encapsulation hides the object's
  • 00:39:17
    internal State and only exposes
  • 00:39:19
    necessary Parts inheritance is like
  • 00:39:21
    inheriting traits from parents a class
  • 00:39:24
    can inherit features from another class
  • 00:39:28
    polymorphism allows different objects to
  • 00:39:30
    respond to different methods in their
  • 00:39:32
    own way like a universal adapter
  • 00:39:35
    abstraction is similar to using a coffee
  • 00:39:37
    machine you interact with the buttons
  • 00:39:40
    that is the interface without worrying
  • 00:39:41
    about the interns encapsulation
  • 00:39:44
    restricts direct access to some
  • 00:39:45
    attributes making data more secure and
  • 00:39:48
    methods more controlled in our script we
  • 00:39:51
    create private attributes like the
  • 00:39:53
    passenger ID to ensure they're not
  • 00:39:55
    modified directly getto methods like get
  • 00:39:59
    passenger ID are used to access private
  • 00:40:02
    attributes
  • 00:40:04
    safely this protect sensitive data and
  • 00:40:06
    ensures controlled
  • 00:40:08
    access inheritance allows a class that
  • 00:40:11
    is a child to inherit attributes and
  • 00:40:14
    methods from another class that is the
  • 00:40:16
    parent in our script we define a person
  • 00:40:19
    class with common attributes like name
  • 00:40:21
    and age the passenger class then
  • 00:40:24
    inherits from a person adding specific
  • 00:40:26
    attrib rutes like passenger class and
  • 00:40:28
    survive this promotes code reusability
  • 00:40:31
    and reduces redundancy by defining
  • 00:40:33
    shared functionality in parent classes
  • 00:40:36
    polymorphism allows different classes to
  • 00:40:39
    share a method name but provide unique
  • 00:40:43
    implementations in our script different
  • 00:40:46
    classes that is the passenger and crew
  • 00:40:49
    member implement the info method in
  • 00:40:51
    their own
  • 00:40:53
    way a loop iterates over a list of
  • 00:40:56
    objects calling the info and each object
  • 00:40:59
    responds with its own version of the
  • 00:41:01
    method this enables flexibility by
  • 00:41:04
    allowing methods to adapt based on the
  • 00:41:06
    object's
  • 00:41:09
    class abstraction hides the
  • 00:41:11
    implementation details and exposes only
  • 00:41:13
    the essential functionalities in our
  • 00:41:16
    script we abstract the base class data
  • 00:41:18
    loader which defines a method load data
  • 00:41:21
    without
  • 00:41:22
    implementation concrete classes like CSV
  • 00:41:25
    loader and Json loader provide specific
  • 00:41:28
    implementations this simplifies the
  • 00:41:30
    interaction with complex systems by
  • 00:41:32
    focusing on what the class does rather
  • 00:41:35
    than how long it does it first
  • 00:41:37
    understand why objectoriented
  • 00:41:38
    programming is needed in data
  • 00:41:39
    engineering o principles help create
  • 00:41:42
    modular reusable and scalable code which
  • 00:41:45
    is essential for building datail
  • 00:41:46
    pipelines managing data workflows and
  • 00:41:49
    handling large
  • 00:41:50
    systems in our script we create three
  • 00:41:53
    classes that is extract transform and
  • 00:41:56
    load each class represents a step in the
  • 00:41:59
    ETL process encapsulating its
  • 00:42:02
    Logic the extract class simulates data
  • 00:42:05
    retrieval returning a dictionary of raw
  • 00:42:08
    data the separation of this step allows
  • 00:42:11
    for flexibility in fetching data from
  • 00:42:12
    different sources the transform class
  • 00:42:15
    processes raw data as converting names
  • 00:42:18
    to uppercase or standardizing formats
  • 00:42:20
    modularity ensures the transform logic
  • 00:42:23
    and modified
  • 00:42:24
    independently the load class handles the
  • 00:42:27
    saving of transformed data to storage
  • 00:42:29
    such as databases or files separating
  • 00:42:31
    this logic allows a flexibility to load
  • 00:42:33
    data into different systems without
  • 00:42:35
    affecting the extraction or
  • 00:42:36
    transformation step we can combine these
  • 00:42:39
    steps and create a workflow which uses
  • 00:42:41
    the extract transform and load classes
  • 00:42:44
    sequentially to process data showcasing
  • 00:42:46
    how object oriented programming can
  • 00:42:48
    simplify complex workflows object
  • 00:42:50
    oriented programming provides a
  • 00:42:51
    structured approach to code organization
  • 00:42:55
    improving reusability and scalability
  • 00:42:58
    principles like encapsulation and
  • 00:43:00
    inheritance ensure secure and efficient
  • 00:43:02
    workflows polymorphism and abstraction
  • 00:43:04
    simplify complex logic making the code
  • 00:43:07
    flexible and easy to extend in section
  • 00:43:10
    11 we'll be combining the extract
  • 00:43:12
    transform and loading Concepts to build
  • 00:43:14
    a complete ETL
  • 00:43:16
    pipeline by the end you'll understand
  • 00:43:19
    how each of these steps integrates into
  • 00:43:21
    a seamless workflow we'll be talking
  • 00:43:24
    about the ETL workflow and doing a
  • 00:43:26
    practical implement ation of each step
  • 00:43:28
    now let's look at the extract data step
  • 00:43:31
    the extract step retrieves raw data from
  • 00:43:33
    various sources such as databases files
  • 00:43:36
    or apis this step is critical because it
  • 00:43:39
    brings in the data that the rest of the
  • 00:43:41
    pipeline will work on in our in our
  • 00:43:44
    script the function expects the file
  • 00:43:47
    path as input this allows flexibility in
  • 00:43:49
    extracting data from various sources the
  • 00:43:52
    function also ensures that if a file is
  • 00:43:55
    missing or corrupted pipeline doesn't
  • 00:43:57
    crash and instead logs the error and
  • 00:44:00
    continues once the file is successfully
  • 00:44:02
    loaded the extracted data is returned as
  • 00:44:05
    a pandas data frame the transform step
  • 00:44:08
    involves cleaning modifying and
  • 00:44:10
    preparing data for analysis this ensures
  • 00:44:14
    that the raw data becomes structured and
  • 00:44:16
    consistent in our script we handle
  • 00:44:18
    missing values as the missing age is
  • 00:44:21
    replaced with the average age ensuring
  • 00:44:23
    the data set remains usable the missing
  • 00:44:25
    fair is replaced with the medium fair to
  • 00:44:27
    handle outliers
  • 00:44:29
    effectively we also remove duplicates
  • 00:44:33
    and we drop them to prevent redundant
  • 00:44:34
    data from screwing analysis you also
  • 00:44:37
    standardize formats like text columns
  • 00:44:40
    like name and sex are reformatted for
  • 00:44:43
    consistency names are capitalized and
  • 00:44:45
    genders are converted to lower
  • 00:44:47
    case you can also have derived columns a
  • 00:44:50
    new column that is age group is
  • 00:44:52
    introduced categorizing passengers based
  • 00:44:54
    on their age this step enable aables
  • 00:44:57
    group analysis such as understanding
  • 00:44:59
    survival rates by age group the load
  • 00:45:02
    step saves the transform data to a
  • 00:45:05
    Target destination such as a database or
  • 00:45:07
    a file this final step makes data ready
  • 00:45:09
    for use in our code we have created a
  • 00:45:12
    function which accepts the destination
  • 00:45:14
    file path this allows flexibility in
  • 00:45:17
    where you want to write the
  • 00:45:19
    data the transform data is saved as a
  • 00:45:22
    CSV file ensuring its accessibility and
  • 00:45:25
    portability the function also ensures
  • 00:45:27
    that issues like permission errors or
  • 00:45:30
    dis space problems are logged rather
  • 00:45:32
    than causing the pipeline to fail
  • 00:45:34
    silently now let's bring this all
  • 00:45:36
    together in the form of ETL pipeline the
  • 00:45:38
    pipeline integrates the extract
  • 00:45:40
    transform and load functions into a
  • 00:45:42
    single workflow the extracted data is
  • 00:45:45
    passed through the transformation step
  • 00:45:47
    and the clean data is then saved the
  • 00:45:49
    modular design ensures the pipeline can
  • 00:45:52
    handle different data sets with minimal
  • 00:45:54
    changes errors at any stage are logged
  • 00:45:57
    ensuring the pipeline is robust and easy
  • 00:46:00
    to debug when running the when running
  • 00:46:03
    the pipeline with the Titanic data set
  • 00:46:05
    we extract the data from the raw CSV
  • 00:46:07
    file clean and prepare the data and
  • 00:46:10
    handle missing values duplicates and
  • 00:46:12
    formatting consistencies at the end we
  • 00:46:15
    save the clean data into a new CSV file
  • 00:46:17
    ready for
  • 00:46:19
    analysis eil pipeline streamline the
  • 00:46:22
    process of preparing data for analysis
  • 00:46:25
    modular steps for extraction
  • 00:46:27
    transformation and loading ensure
  • 00:46:29
    flexibility and reusability handling
  • 00:46:32
    errors at each stage improve the
  • 00:46:34
    pipeline
  • 00:46:35
    robustness in section 12 let's look at
  • 00:46:37
    data quality testing and code
  • 00:46:40
    standards this section emphasizes
  • 00:46:43
    ensuring high quality data to validation
  • 00:46:46
    implementing rigorous testing for
  • 00:46:48
    pipeline functions and adhering to
  • 00:46:50
    coding standards by the end of this
  • 00:46:52
    tutorial you will understand data
  • 00:46:54
    validation techniques which ensure data
  • 00:46:56
    sets meet expected criteria testing data
  • 00:46:59
    pipelines using unit test to validate
  • 00:47:01
    pipeline functionality Advanced quality
  • 00:47:03
    checks using tools like great
  • 00:47:05
    expectations and static code analysis
  • 00:47:08
    which ensures clean and maintainable
  • 00:47:09
    code with tools like flick it these
  • 00:47:12
    practices essential for building robust
  • 00:47:14
    and maintainable data engineering
  • 00:47:16
    workflows now let's look at data
  • 00:47:18
    validation techniques what is data
  • 00:47:20
    validation data validation ensures that
  • 00:47:23
    data setes meet predefined quality
  • 00:47:25
    criteria such as completeness
  • 00:47:27
    consistency and accuracy this step is
  • 00:47:30
    crucial for preventing errors from
  • 00:47:33
    propagating throughout your data
  • 00:47:34
    pipeline in our script we check for
  • 00:47:37
    missing values the data set is standed
  • 00:47:40
    for empty or Nan values in each column
  • 00:47:43
    we identify and address these gaps to
  • 00:47:46
    ensure analysis aren't skewed by
  • 00:47:48
    incomplete data also validate column
  • 00:47:50
    data types columns are checked to
  • 00:47:52
    confirm they contain the expected data
  • 00:47:54
    type that is numerical or categorical
  • 00:47:57
    this step ensures that the operations
  • 00:47:59
    like aggregations or computations won't
  • 00:48:02
    throw
  • 00:48:03
    errors you also check unique values
  • 00:48:06
    specific columns such as passenger ID
  • 00:48:08
    are validated for uniqueness to avoid
  • 00:48:11
    duplicates for categorical data like sex
  • 00:48:14
    the script checks whether all entries
  • 00:48:16
    belong to allowed categories validating
  • 00:48:19
    data early in the pipeline ensures that
  • 00:48:21
    errors are caught and corrected before
  • 00:48:23
    they affect Downstream processes testing
  • 00:48:26
    data pip lines for unit test unit test
  • 00:48:29
    is a python library for testing
  • 00:48:31
    individual components of the code it
  • 00:48:33
    helps ensure that each function in a
  • 00:48:36
    code behaves as expected in our script
  • 00:48:39
    set up the test data we create a sample
  • 00:48:41
    data set with known issues that is with
  • 00:48:43
    missing values and duplicate data test
  • 00:48:47
    verify that the missing data are handled
  • 00:48:49
    correctly duplicate rows are confirmed
  • 00:48:51
    to be removed during the Transformations
  • 00:48:54
    we also validate new columns the
  • 00:48:55
    presence and act accuracy of derived
  • 00:48:57
    columns such as age group are tested
  • 00:49:00
    text columns are checked for
  • 00:49:02
    standardized formatting that is the
  • 00:49:04
    names
  • 00:49:05
    capitalized assertions are used to
  • 00:49:07
    confirm that the missing values no
  • 00:49:09
    longer exist all duplicate values are
  • 00:49:12
    removed the transform data aderes to
  • 00:49:14
    expected
  • 00:49:16
    formats testing ensures that your
  • 00:49:18
    pipeline functions correctly even as
  • 00:49:21
    data sets or requirements evolve this is
  • 00:49:24
    crucial for maintaining reliability in
  • 00:49:26
    produ production environments we can
  • 00:49:27
    also perform Advanced Data quality
  • 00:49:29
    checks with Great Expectations Great
  • 00:49:32
    Expectations is a powerful tool for
  • 00:49:34
    defining and automating data quality
  • 00:49:36
    checks it provides an intuitive way to
  • 00:49:38
    set expectations for data sets and
  • 00:49:40
    validate them against those rules in our
  • 00:49:43
    script we load the Titanic data set into
  • 00:49:45
    Great Expectations context for
  • 00:49:47
    validation expectations are created to
  • 00:49:49
    ensure that the passenger ID values are
  • 00:49:51
    non n and unique and that the age and
  • 00:49:53
    fair columns belong in valid ranges the
  • 00:49:57
    data set is validated against these
  • 00:49:59
    expectations and the results are loged
  • 00:50:01
    to identify any violations such as the
  • 00:50:03
    missing values or invalid categories
  • 00:50:06
    automating quality checks ensures data
  • 00:50:08
    sets are always compliant with code
  • 00:50:10
    standards even as new data is introduced
  • 00:50:13
    static code analysis with flate static
  • 00:50:16
    code analysis evaluates your code for
  • 00:50:19
    error potential issues and adherence to
  • 00:50:21
    style guides without actually executing
  • 00:50:24
    it tools like flake 8 help identify
  • 00:50:27
    violations of Python's pep8 style guide
  • 00:50:31
    Cod is scanned for issues like improper
  • 00:50:34
    indentation overly long lines or unused
  • 00:50:38
    inputs errors such as undefined
  • 00:50:40
    variables or incorrect functions calls
  • 00:50:42
    are also flagged before run
  • 00:50:44
    time also suggestions are made for
  • 00:50:47
    refactoring code making it maintainable
  • 00:50:50
    and easier for
  • 00:50:51
    collaboration in our code we create a
  • 00:50:54
    mock file which has a lot of errors
  • 00:50:57
    after running the fcked command we able
  • 00:50:58
    to see that these errors are shown after
  • 00:51:00
    we make these Corrections we see that
  • 00:51:02
    errors are no longer visible validation
  • 00:51:04
    ensures your data sets are accurate
  • 00:51:06
    complete and consistent testing with
  • 00:51:08
    unit test confirms pipeline functions
  • 00:51:11
    perform as expected Advanced tools like
  • 00:51:14
    great expectation automate query checks
  • 00:51:17
    making them repeatable and
  • 00:51:20
    scalable static code with flake8 ensures
  • 00:51:23
    clean and maintainable code these practi
  • 00:51:26
    es enhance reliability and reduce errors
  • 00:51:29
    in production pipelines in section 13
  • 00:51:31
    we'll be exploring how we can structure
  • 00:51:34
    maintain and deploy python packages
  • 00:51:37
    packaging your code makes it reusable
  • 00:51:40
    and sharable whether within your
  • 00:51:41
    organization or in a broader python
  • 00:51:45
    Community by the end of this section you
  • 00:51:47
    understand how to structure a python
  • 00:51:48
    package how to define a setup file for
  • 00:51:51
    package metadata how to build and test a
  • 00:51:53
    package locally and how to prepare it
  • 00:51:55
    for distribution
  • 00:51:57
    a python package is a directory
  • 00:52:00
    containing python modules that is files
  • 00:52:02
    with. py extension along with the init
  • 00:52:05
    file to indicate that a package your
  • 00:52:07
    package is usually structured as follows
  • 00:52:09
    data quality analytics Vector contains
  • 00:52:12
    your main package code in it indicates
  • 00:52:15
    that this directory is a package eta. py
  • 00:52:19
    includes all ETL related functions such
  • 00:52:22
    as transforming and loading data quality
  • 00:52:24
    checks contains functions for validating
  • 00:52:26
    data quality test holds unit test to
  • 00:52:29
    ensure your package functions are as
  • 00:52:32
    expected the readme.md provides
  • 00:52:35
    documentation about the package setup.py
  • 00:52:38
    defines the package metadata and
  • 00:52:41
    dependencies a clear structure makes
  • 00:52:43
    your package maintainable and user
  • 00:52:45
    friendly it allows developers to easily
  • 00:52:47
    contribute to your code base now Define
  • 00:52:50
    the setup.py file which is the heart of
  • 00:52:52
    your python package it contains metadata
  • 00:52:55
    about your package P AG that is the name
  • 00:52:57
    version author and specifies
  • 00:52:59
    dependencies required to work the
  • 00:53:02
    package metadata includes the package
  • 00:53:03
    name version description author and
  • 00:53:06
    contact information dependencies list
  • 00:53:09
    libraries such as pandas that are needed
  • 00:53:12
    python version specifies the minimum
  • 00:53:14
    python version that the package
  • 00:53:16
    supports a well defined setup.py ensures
  • 00:53:20
    that users can install your package
  • 00:53:21
    independencies efficiently building a
  • 00:53:24
    package creates a distributable version
  • 00:53:26
    of of your package these files can be
  • 00:53:29
    shared with others and uploaded to code
  • 00:53:32
    repositories we use building tools that
  • 00:53:35
    is the build module to generate a
  • 00:53:37
    package distribution file a generated
  • 00:53:39
    file is in a source archive format or a
  • 00:53:41
    wheel format that is created the wheel
  • 00:53:44
    file format is optimized for easy
  • 00:53:46
    installation in our script we simply
  • 00:53:48
    change the package directory and we use
  • 00:53:50
    the python M build to generate these
  • 00:53:53
    distribution files we also test our
  • 00:53:55
    package locally before we actually
  • 00:53:57
    distribute it it's very important to do
  • 00:53:59
    local tests such as installing locally
  • 00:54:02
    and trying to use the functions thank
  • 00:54:05
    you for joining me on this journey over
  • 00:54:07
    the coming weeks I'll be adding more
  • 00:54:09
    videos on SQL P spark and databases to
  • 00:54:12
    deepen your data engineering skills be
  • 00:54:15
    sure to also check out my data
  • 00:54:17
    engineering career playlist for insights
  • 00:54:19
    on job Trends skills needed and career
  • 00:54:22
    tips don't forget to subscribe for
  • 00:54:25
    advanced tutorials projects and career
  • 00:54:27
    insights the continued practice and
  • 00:54:30
    curiosity you well on your path to
  • 00:54:32
    becoming a skilled data engineer until
  • 00:54:34
    next time good day
Tags
  • Data Engineering
  • Python
  • ETL Pipelines
  • pandas
  • numpy
  • Data Processing
  • Object-Oriented Programming
  • APIs
  • Data Formats
  • Coding Standards