Modules allow the organization of Python code into sharable files. A module is a Python file with a .py extension that contains definitions and statements. The module's contents can be imported into other modules using the import statement. Modules can contain functions, variables, classes and other code that can then be accessed using the module name. Modules are searched for using the module search path which includes the current directory and directories specified in PYTHONPATH.
Modules allow Python code to be logically organized into files and reused across programs. A module is a Python file with a .py extension that contains functions, classes, and variables that can be imported and used by other files. The module search path determines where Python looks for modules, and modules can import other modules to reuse their code. Packages are groups of modules that make up reusable components in Python applications.
1) A Python module allows you to organize related code into a logical group and makes the code easier to understand and use.
2) Modules are imported using import statements and can contain functions, classes, and variables that can then be accessed from other code.
3) The import process searches specific directories to locate the module file based on the module name and import path.
CLASS-11 & 12 ICT PPT Functions in Python.pptxseccoordpal
ICT skills are abilities that help you understand and operate a wide range of technology software. This can include helping users with tasks on computers, such as making video calls, searching on the internet or using a mobile device like a tablet or phone.
This document discusses Python functions and modules. It explains that functions are named blocks of code that perform computations, and modules allow code reuse through importing definitions from other files. It provides examples of defining functions, importing modules, and using built-in and user-defined functions. It also covers topics like function parameters, scopes, and default arguments.
In Python, a function is a reusable block of code that performs a specific task. Functions are used to break down large programs into smaller, manageable, and modular parts, improving code readability, reusability, and efficiency.
Key Aspects of Functions in Python
1. Defining a Function: Functions are defined using the def keyword, followed by the function name and parentheses ( ). Inside the parentheses, you can specify parameters if the function requires inputs.
def function_name(parameters):
# Function body
# Code to execute
2. Calling a Function: Once defined, a function can be called by its name, followed by parentheses and any required arguments.
function_name(arguments)
3. Parameters and Arguments:
• Parameters: Variables specified in the function definition that hold the values passed to the function.
• Arguments: Actual values passed to the function when it is called.
4. Return Statement: Functions can use a return statement to send a result back to the caller. If no return statement is used, the function will return None by default.
def add(a, b):
return a + b
result = add(5, 3) # result will be 8
5. Types of Functions:
• Built-in Functions: Python has many built-in functions like print(), len(), and sum(), which are available by default.
• User-Defined Functions: Functions created by the user to perform specific tasks.
6. Anonymous Functions (Lambda Functions): Python also supports lambda functions, which are small, unnamed functions defined with the lambda keyword. They’re useful for short, simple operations.
square = lambda x: x * x
print(square(5)) # Output: 25
7. Function Scope: Variables defined within a function are local to that function and can’t be accessed outside of it. This scope management helps prevent unexpected changes to variables.
Example of a Function in Python
# Define a function to calculate the area of a rectangle
def calculate_area(length, width):
area = length * width
return area
# Call the function with arguments
result = calculate_area(5, 3)
print("Area:", result) # Output: Area: 15
Benefits of Using Functions
• Code Reusability: Functions allow you to write code once and reuse it whenever needed.
• Modularity: Functions help to organize code into blocks, making it easier to maintain and debug.
• Readability: Breaking down a program into functions makes it more readable and easier to understand.
Python functions are a fundamental tool in programming, allowing you to structure your code for efficiency and clarity.
Python uses modules, packages, and libraries to organize code. A module is a .py file containing functions, classes, and variables. Related modules are grouped into packages, which are directories containing an __init__.py file. Libraries are collections of packages that provide specific functionality. The Python standard library includes common modules like math and random. Modules can be imported and used to reuse code in other files or packages.
This document discusses Python packages, modules, and the use of the __init__.py file to define packages. It explains that packages allow for hierarchical structuring of modules using dot notation to avoid name collisions. Creating a package only requires making a directory with an __init__.py file and modules. The __init__.py file can initialize package-level data and automatically import modules. Importing from packages works similarly to modules but uses additional dot notation to separate packages from subpackages.
Python modules allow programmers to split code into multiple files for easier maintenance. A module is simply a Python file with a .py extension. The import statement is used to include modules. Modules can be organized into packages, which are directories containing an __init__.py file. Popular third party modules like ElementTree, Psyco, EasyGUI, SQLObject, and py.test make Python even more powerful.
This presentation educates you about Python modules, The import Statement, Locating Modules, The PYTHONPATH Variable and Packages in Python.
For more topics stay tuned with Learnbay.
Python modules allow for code reusability and organization. There are three main components of a Python program: libraries/packages, modules, and functions/sub-modules. Modules can be imported using import, from, or from * statements. Namespaces and name resolution determine how names are looked up and associated with objects. Packages are collections of related modules and use an __init__.py file to be recognized as packages by Python.
This document discusses Python libraries and modules. It defines a library as a collection of modules that provide specific functionality. The standard library contains commonly used modules like math and random. Other important libraries mentioned are NumPy, SciPy, and tkinter. A module is a .py file that contains related variables, classes, functions etc. Modules can be imported using import, from, or from * statements. Namespaces and module aliasing are also covered. The document concludes by explaining how to create Python packages and the role of the __init__.py file in making a directory a package.
1. The document discusses Python arrays, modules, and packages. It describes how to create and access elements of an array, as well as common array methods.
2. It explains what modules are and how to create, import, rename, and access attributes of modules. Dir() function and module search path are also covered.
3. Python packages and subpackages are defined. Steps to create packages and import from packages and subpackages are provided along with an example.
The document discusses building Odoo modules. It outlines module structure, the manifest file, and XML usage. Key points include:
- Modules are split into separate directories for models, views, controllers, and more.
- The manifest file declares a module and specifies metadata. It configures installation settings.
- XML data files populate the database using <record> elements and follow naming conventions.
- Views define how records are displayed. Common types are tree, form, and search views composed of fields.
Multi-tenant Data Pipeline OrchestrationRomi Kuntsman
Multi-Tenant Data Pipeline Orchestration — Romi Kuntsman @ DataTLV 2025
In this talk, I unpack what it really means to orchestrate multi-tenant data pipelines at scale — not in theory, but in practice. Whether you're dealing with scientific research, AI/ML workflows, or SaaS infrastructure, you’ve likely encountered the same pitfalls: duplicated logic, growing complexity, and poor observability. This session connects those experiences to principled solutions.
Using a playful but insightful "Chips Factory" case study, I show how common data processing needs spiral into orchestration challenges, and how thoughtful design patterns can make the difference. Topics include:
Modeling data growth and pipeline scalability
Designing parameterized pipelines vs. duplicating logic
Understanding temporal and categorical partitioning
Building flexible storage hierarchies to reflect logical structure
Triggering, monitoring, automating, and backfilling on a per-slice level
Real-world tips from pipelines running in research, industry, and production environments
This framework-agnostic talk draws from my 15+ years in the field, including work with Airflow, Dagster, Prefect, and more, supporting research and production teams at GSK, Amazon, and beyond. The key takeaway? Engineering excellence isn’t about the tool you use — it’s about how well you structure and observe your system at every level.
This document discusses Python functions and modules. It explains that functions are named blocks of code that perform computations, and modules allow code reuse through importing definitions from other files. It provides examples of defining functions, importing modules, and using built-in and user-defined functions. It also covers topics like function parameters, scopes, and default arguments.
In Python, a function is a reusable block of code that performs a specific task. Functions are used to break down large programs into smaller, manageable, and modular parts, improving code readability, reusability, and efficiency.
Key Aspects of Functions in Python
1. Defining a Function: Functions are defined using the def keyword, followed by the function name and parentheses ( ). Inside the parentheses, you can specify parameters if the function requires inputs.
def function_name(parameters):
# Function body
# Code to execute
2. Calling a Function: Once defined, a function can be called by its name, followed by parentheses and any required arguments.
function_name(arguments)
3. Parameters and Arguments:
• Parameters: Variables specified in the function definition that hold the values passed to the function.
• Arguments: Actual values passed to the function when it is called.
4. Return Statement: Functions can use a return statement to send a result back to the caller. If no return statement is used, the function will return None by default.
def add(a, b):
return a + b
result = add(5, 3) # result will be 8
5. Types of Functions:
• Built-in Functions: Python has many built-in functions like print(), len(), and sum(), which are available by default.
• User-Defined Functions: Functions created by the user to perform specific tasks.
6. Anonymous Functions (Lambda Functions): Python also supports lambda functions, which are small, unnamed functions defined with the lambda keyword. They’re useful for short, simple operations.
square = lambda x: x * x
print(square(5)) # Output: 25
7. Function Scope: Variables defined within a function are local to that function and can’t be accessed outside of it. This scope management helps prevent unexpected changes to variables.
Example of a Function in Python
# Define a function to calculate the area of a rectangle
def calculate_area(length, width):
area = length * width
return area
# Call the function with arguments
result = calculate_area(5, 3)
print("Area:", result) # Output: Area: 15
Benefits of Using Functions
• Code Reusability: Functions allow you to write code once and reuse it whenever needed.
• Modularity: Functions help to organize code into blocks, making it easier to maintain and debug.
• Readability: Breaking down a program into functions makes it more readable and easier to understand.
Python functions are a fundamental tool in programming, allowing you to structure your code for efficiency and clarity.
Python uses modules, packages, and libraries to organize code. A module is a .py file containing functions, classes, and variables. Related modules are grouped into packages, which are directories containing an __init__.py file. Libraries are collections of packages that provide specific functionality. The Python standard library includes common modules like math and random. Modules can be imported and used to reuse code in other files or packages.
This document discusses Python packages, modules, and the use of the __init__.py file to define packages. It explains that packages allow for hierarchical structuring of modules using dot notation to avoid name collisions. Creating a package only requires making a directory with an __init__.py file and modules. The __init__.py file can initialize package-level data and automatically import modules. Importing from packages works similarly to modules but uses additional dot notation to separate packages from subpackages.
Python modules allow programmers to split code into multiple files for easier maintenance. A module is simply a Python file with a .py extension. The import statement is used to include modules. Modules can be organized into packages, which are directories containing an __init__.py file. Popular third party modules like ElementTree, Psyco, EasyGUI, SQLObject, and py.test make Python even more powerful.
This presentation educates you about Python modules, The import Statement, Locating Modules, The PYTHONPATH Variable and Packages in Python.
For more topics stay tuned with Learnbay.
Python modules allow for code reusability and organization. There are three main components of a Python program: libraries/packages, modules, and functions/sub-modules. Modules can be imported using import, from, or from * statements. Namespaces and name resolution determine how names are looked up and associated with objects. Packages are collections of related modules and use an __init__.py file to be recognized as packages by Python.
This document discusses Python libraries and modules. It defines a library as a collection of modules that provide specific functionality. The standard library contains commonly used modules like math and random. Other important libraries mentioned are NumPy, SciPy, and tkinter. A module is a .py file that contains related variables, classes, functions etc. Modules can be imported using import, from, or from * statements. Namespaces and module aliasing are also covered. The document concludes by explaining how to create Python packages and the role of the __init__.py file in making a directory a package.
1. The document discusses Python arrays, modules, and packages. It describes how to create and access elements of an array, as well as common array methods.
2. It explains what modules are and how to create, import, rename, and access attributes of modules. Dir() function and module search path are also covered.
3. Python packages and subpackages are defined. Steps to create packages and import from packages and subpackages are provided along with an example.
The document discusses building Odoo modules. It outlines module structure, the manifest file, and XML usage. Key points include:
- Modules are split into separate directories for models, views, controllers, and more.
- The manifest file declares a module and specifies metadata. It configures installation settings.
- XML data files populate the database using <record> elements and follow naming conventions.
- Views define how records are displayed. Common types are tree, form, and search views composed of fields.
Multi-tenant Data Pipeline OrchestrationRomi Kuntsman
Multi-Tenant Data Pipeline Orchestration — Romi Kuntsman @ DataTLV 2025
In this talk, I unpack what it really means to orchestrate multi-tenant data pipelines at scale — not in theory, but in practice. Whether you're dealing with scientific research, AI/ML workflows, or SaaS infrastructure, you’ve likely encountered the same pitfalls: duplicated logic, growing complexity, and poor observability. This session connects those experiences to principled solutions.
Using a playful but insightful "Chips Factory" case study, I show how common data processing needs spiral into orchestration challenges, and how thoughtful design patterns can make the difference. Topics include:
Modeling data growth and pipeline scalability
Designing parameterized pipelines vs. duplicating logic
Understanding temporal and categorical partitioning
Building flexible storage hierarchies to reflect logical structure
Triggering, monitoring, automating, and backfilling on a per-slice level
Real-world tips from pipelines running in research, industry, and production environments
This framework-agnostic talk draws from my 15+ years in the field, including work with Airflow, Dagster, Prefect, and more, supporting research and production teams at GSK, Amazon, and beyond. The key takeaway? Engineering excellence isn’t about the tool you use — it’s about how well you structure and observe your system at every level.
Dr. Robert Krug - Expert In Artificial IntelligenceDr. Robert Krug
Dr. Robert Krug is a New York-based expert in artificial intelligence, with a Ph.D. in Computer Science from Columbia University. He serves as Chief Data Scientist at DataInnovate Solutions, where his work focuses on applying machine learning models to improve business performance and strengthen cybersecurity measures. With over 15 years of experience, Robert has a track record of delivering impactful results. Away from his professional endeavors, Robert enjoys the strategic thinking of chess and urban photography.
The third speaker at Process Mining Camp 2018 was Dinesh Das from Microsoft. Dinesh Das is the Data Science manager in Microsoft’s Core Services Engineering and Operations organization.
Machine learning and cognitive solutions give opportunities to reimagine digital processes every day. This goes beyond translating the process mining insights into improvements and into controlling the processes in real-time and being able to act on this with advanced analytics on future scenarios.
Dinesh sees process mining as a silver bullet to achieve this and he shared his learnings and experiences based on the proof of concept on the global trade process. This process from order to delivery is a collaboration between Microsoft and the distribution partners in the supply chain. Data of each transaction was captured and process mining was applied to understand the process and capture the business rules (for example setting the benchmark for the service level agreement). These business rules can then be operationalized as continuous measure fulfillment and create triggers to act using machine learning and AI.
Using the process mining insight, the main variants are translated into Visio process maps for monitoring. The tracking of the performance of this process happens in real-time to see when cases become too late. The next step is to predict in what situations cases are too late and to find alternative routes.
As an example, Dinesh showed how machine learning could be used in this scenario. A TradeChatBot was developed based on machine learning to answer questions about the process. Dinesh showed a demo of the bot that was able to answer questions about the process by chat interactions. For example: “Which cases need to be handled today or require special care as they are expected to be too late?”. In addition to the insights from the monitoring business rules, the bot was also able to answer questions about the expected sequences of particular cases. In order for the bot to answer these questions, the result of the process mining analysis was used as a basis for machine learning.
The history of a.s.r. begins 1720 in “Stad Rotterdam”, which as the oldest insurance company on the European continent was specialized in insuring ocean-going vessels — not a surprising choice in a port city like Rotterdam. Today, a.s.r. is a major Dutch insurance group based in Utrecht.
Nelleke Smits is part of the Analytics lab in the Digital Innovation team. Because a.s.r. is a decentralized organization, she worked together with different business units for her process mining projects in the Medical Report, Complaints, and Life Product Expiration areas. During these projects, she realized that different organizational approaches are needed for different situations.
For example, in some situations, a report with recommendations can be created by the process mining analyst after an intake and a few interactions with the business unit. In other situations, interactive process mining workshops are necessary to align all the stakeholders. And there are also situations, where the process mining analysis can be carried out by analysts in the business unit themselves in a continuous manner. Nelleke shares her criteria to determine when which approach is most suitable.
AI ------------------------------ W1L2.pptxAyeshaJalil6
This lecture provides a foundational understanding of Artificial Intelligence (AI), exploring its history, core concepts, and real-world applications. Students will learn about intelligent agents, machine learning, neural networks, natural language processing, and robotics. The lecture also covers ethical concerns and the future impact of AI on various industries. Designed for beginners, it uses simple language, engaging examples, and interactive discussions to make AI concepts accessible and exciting.
By the end of this lecture, students will have a clear understanding of what AI is, how it works, and where it's headed.
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1. Modules are files containing Python definitions and statements (ex. name.py)
A module’s definitions can be imported into other modules by using “import name”
The module’s name is available as a global variable value
To access a module’s functions, type “name.function()”
2. Modules can contain executable statements along with function definitions
Each module has its own private symbol table used as the global symbol table by all functions in the module
Modules can import other modules
Each module is imported once per interpreter session
reload(name)
Can import names from a module into the importing module’s symbol table
from mod import m1, m2 (or *)
m1()
3. python name.py <arguments>
Runs code as if it was imported
Setting _name_ == “_main_” the file can be used as a script and an importable module
4. The interpreter searches for a file named name.py
Current directory given by variable sys.path
List of directories specified by PYTHONPATH
Default path (in UNIX - .:/usr/local/lib/python)
Script being run should not have the same name as a standard module or an error will occur when the module is imported
5. If files mod.pyc and mod.py are in the same directory, there is a byte-compiled version of the module mod
The modification time of the version of mod.py used to create mod.pyc is stored in mod.pyc
Normally, the user does not need to do anything to create the .pyc file
A compiled .py file is written to the .pyc
No error for failed attempt, .pyc is recognized as invalid
Contents of the .pyc can be shared by different machines
6. -O flag generates optimized code and stores it in .pyo files
Only removes assert statements
.pyc files are ignored and .py files are compiled to optimized bytecode
Passing two –OO flags
Can result in malfunctioning programs
_doc_ strings are removed
Same speed when read from .pyc, .pyo, or .py files, .pyo and .pyc files are loaded faster
Startup time of a script can be reduced by moving its code to a module and importing the module
Can have a .pyc or .pyo file without having a .py file for the same module
Module compileall creates .pyc or .pyo files for all modules in a directory
7. Python comes with a library of standard modules described in the Python Library Reference
Some are built into interpreter
>>> import sys
>>> sys.s1
‘>>> ‘
>>> sys.s1 = ‘c> ‘
c> print ‘Hello’
Hello
c>
sys.path determines the interpreters’s search path for modules, with the default path taken from PYTHONPATH
Can be modified with append() (ex. Sys.path.append(‘SOMEPATH’)
8. Used to find the names a module defines and returns a sorted list of strings
>>> import mod
>>> dir(mod)
[‘_name_’, ‘m1’, ‘m2’]
Without arguments, it lists the names currently defined (variables, modules, functions, etc)
Does not list names of built-in functions and variables
Use _bulltin_to view all built-in functions and variables
9. “dotted module names” (ex. a.b)
Submodule b in package a
Saves authors of multi-module packages from worrying about each other’s module names
Python searches through sys.path directories for the package subdirectory
Users of the package can import individual modules from the package
Ways to import submodules
import sound.effects.echo
from sound.effects import echo
Submodules must be referenced by full name
An ImportError exception is raised when the package cannot be found
10. * does not import all submodules from a package
Ensures that the package has been imported, only importing the names of the submodules defined in the package
import sound.effects.echo
import sound.effects.surround
from sound.effects import *
11. Submodules can refer to each other
Surround might use echo module
import echo also loads surround module
import statement first looks in the containing package before looking in the standard module search path
Absolute imports refer to submodules of sibling packages
sound.filters.vocoder uses echo module
from sound.effects import echo
Can write explicit relative imports
from . import echo
from .. import formats
from ..filters import equalizer
12. _path_ is a list containing the name of the directory holding the package’s _init_.py
Changing this variable can affect futute searches for modules and subpackages in the package
Can be used to extend the set of modules in a package
Not often needed