Вивчайте Practical Applications of Generators: Real-World Use Cases | Mastering Iterators and Generators in Python

Generators can be used as lightweight context managers to manage resources efficiently, such as database connections, file operations, or locking mechanisms. With the contextlib module, generators can handle resource allocation and cleanup seamlessly.


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from contextlib import contextmanager

@contextmanager
def database_connection():
    print("Opening database connection")
    connection = "Database Connection"  # Simulated connection
    try:
        yield connection
    finally:
        print("Closing database connection")

# Using the generator as a context manager
with database_connection() as conn:
    print(f"Using {conn}")

Processing Large Data Efficiently

Generators are ideal for building data pipelines that process large datasets lazily. Each stage of the pipeline can be implemented as a generator, enabling efficient, memory-friendly processing.


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import re

# Stage 1: Read lines lazily
def read_lines(text):
    for line in text.split("\n"):
        yield line

# Stage 2: Filter non-empty lines
def filter_lines(lines):
    for line in lines:
        if line.strip():
            yield line

# Stage 3: Extract words lazily
def extract_words(lines):
    for line in lines:
        for word in re.findall(r'\w+', line):
            yield word

# Stage 4: Transform words to lowercase
def lowercase_words(words):
    for word in words:
        yield word.lower()

# Input text
text = """Generators are powerful tools
They allow efficient data processing
This pipeline demonstrates their usage"""

# Build the pipeline
lines = read_lines(text)
filtered = filter_lines(lines)
words = extract_words(filtered)
lowercased = lowercase_words(words)

# Process the data
print("Processed words:")
for word in lowercased:
    print(word)

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Секція 6. Розділ 5

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              1234567891011121314
            
from contextlib import contextmanager

@contextmanager
def database_connection():
    print("Opening database connection")
    connection = "Database Connection"  # Simulated connection
    try:
        yield connection
    finally:
        print("Closing database connection")

# Using the generator as a context manager
with database_connection() as conn:
    print(f"Using {conn}")

Processing Large Data Efficiently

Generators are ideal for building data pipelines that process large datasets lazily. Each stage of the pipeline can be implemented as a generator, enabling efficient, memory-friendly processing.


              12345678910111213141516171819202122232425262728293031323334353637383940
            
import re

# Stage 1: Read lines lazily
def read_lines(text):
    for line in text.split("\n"):
        yield line

# Stage 2: Filter non-empty lines
def filter_lines(lines):
    for line in lines:
        if line.strip():
            yield line

# Stage 3: Extract words lazily
def extract_words(lines):
    for line in lines:
        for word in re.findall(r'\w+', line):
            yield word

# Stage 4: Transform words to lowercase
def lowercase_words(words):
    for word in words:
        yield word.lower()

# Input text
text = """Generators are powerful tools
They allow efficient data processing
This pipeline demonstrates their usage"""

# Build the pipeline
lines = read_lines(text)
filtered = filter_lines(lines)
words = extract_words(filtered)
lowercased = lowercase_words(words)

# Process the data
print("Processed words:")
for word in lowercased:
    print(word)

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Дякуємо за ваш відгук!

Секція 6. Розділ 5

Practical Applications of Generators: Real-World Use Cases

Processing Large Data Efficiently

1. What happens when a generator function runs out of values to `yield`?

2. What will the following code output?

3. What does the following code do?

Awesome!

Practical Applications of Generators: Real-World Use Cases

Processing Large Data Efficiently

1. What happens when a generator function runs out of values to `yield`?

2. What will the following code output?

3. What does the following code do?

Practical Applications of Generators: Real-World Use Cases

Processing Large Data Efficiently

1. What happens when a generator function runs out of values to yield?

2. What will the following code output?

3. What does the following code do?

Awesome!

Practical Applications of Generators: Real-World Use Cases

Processing Large Data Efficiently

1. What happens when a generator function runs out of values to yield?

2. What will the following code output?

3. What does the following code do?

1. What happens when a generator function runs out of values to `yield`?

1. What happens when a generator function runs out of values to `yield`?