Mastering Python: Advanced Features for Data Analysis

Section 1: List Comprehension

List comprehension is a compact way of creating a new list from an existing list by applying an operation to each element of the original list. This feature is useful for data cleaning and manipulation in data analysis.

For example, suppose you have a list of numbers and you want to create a new list that contains only the even numbers. You can use list comprehension to achieve this as follows:

new_list = [x for x in original_list if x % 2 == 0]

Section 2: Generators

Generators are another feature of Python that allows you to create iterators that can be used to generate values on the fly. Generators are useful for processing large data sets that do not fit into memory.

To create a generator, you can use the yield keyword instead of return in a function. Here is an example of a generator function that generates Fibonacci numbers:

def fibonacci_generator(): a, b = 0, 1 while True: yield a a, b = b, a + b

Section 3: Decorators

Decorators are a powerful feature of Python that allows you to add functionality to existing functions or methods. Decorators are useful for data analysis when you want to modify the behavior of a function without modifying its code.

To create a decorator, you can define a function that takes another function as its argument and returns a modified version of that function. Here is an example of a decorator that logs the execution time of a function:

import time def log_execution_time(func): def wrapper(*args, **kwargs): start = time.time() result = func(*args, **kwargs) end = time.time() print(f'Function {func.__name__} took {end - start} seconds to execute') return result return wrapper

Section 4: Context Managers

Context managers are a feature of Python that allows you to ensure that a block of code is executed in a safe and predictable manner. Context managers are useful for data analysis when you want to ensure that resources are properly released after use.

To create a context manager, you can define a class with __enter__ and __exit__ methods. Here is an example of a context manager that opens and reads a file:

class FileReader: def __init__(self, filename): self.filename = filename def __enter__(self): self.file = open(self.filename, 'r') return self def __exit__(self, exc_type, exc_val, exc_tb): self.file.close()

Section 5: Data Classes

Data classes are a feature of Python that allows you to define classes that primarily exist to store data. Data classes are useful for data analysis when you want to create classes that represent data objects.

To create a data class, you can use the @dataclass decorator. Here is an example of a data class that represents a person:

from dataclasses import dataclass @dataclass class Person: name: str age: int