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.
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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 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.
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.
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 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.
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.
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This heavy equipment service manual provides detailed repair procedures, troubleshooting guides, and maintenance instructions for technicians and operators. It includes high-quality diagrams, technical specifications, and step-by-step instructions to ensure accurate repairs and diagnostics. Designed for professionals and DIY users alike, the manual covers everything from engine overhauls to hydraulic systems, wiring, and calibration settings. Whether you're working in the field or the shop, this manual is an essential tool to maximize performance, reduce downtime, and maintain your equipment’s reliability. John Deere LX255 Service Manual.pdf
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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 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.
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.
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 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.
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.
From New York highways to California coastlines, Vyncs is trusted by families in all 50 states.
Track smarter. Drive safer.
No monthly fees.
Global coverage.
This heavy equipment service manual provides detailed repair procedures, troubleshooting guides, and maintenance instructions for technicians and operators. It includes high-quality diagrams, technical specifications, and step-by-step instructions to ensure accurate repairs and diagnostics. Designed for professionals and DIY users alike, the manual covers everything from engine overhauls to hydraulic systems, wiring, and calibration settings. Whether you're working in the field or the shop, this manual is an essential tool to maximize performance, reduce downtime, and maintain your equipment’s reliability. John Deere LX255 Service Manual.pdf
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Using a factory-approved service manual ensures accurate disassembly and reassembly, correct torque specs, and proper hydraulic settings. This is crucial for the HM 1000 Marathon, which features an automatic lubrication system and enhanced wear protection.
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2. What are Modules?
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()”
3. More on Modules
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()
4. Executing Modules
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
5. The Module Search Path
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
6. “Compiled” Python Files
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
7. Some Tips
-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
8. Standard Modules
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’)
9. The dir() Function
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
10. Packages
“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
11. Importing * From a Package
* 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 *
12. Intra-package References
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
13. Packages in Multiple
Directories
_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