Essential Python Built-in Functions for Efficient Coding
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Chapter 1 Understanding Python's Built-in Functions
Python is designed to enhance developer efficiency by providing a variety of shorthand syntax and productivity-oriented features. Its user-friendly syntax makes Python code more comprehensible than conventional pseudo-code. While many programming languages require importing standard library modules for function access—like using stdio.h to call printf() in C—Python simplifies this by offering numerous pre-imported global functions. For instance, developers can directly use the built-in input() function to gather user input without needing to import anything.
Every Python programmer is familiar with built-in functions such as range(), print(), and len(). Let's delve into some additional built-in functions that can save time and help developers write cleaner, more efficient Python code.
Section 1.1 Number System Conversions with bin(), oct(), and hex()
Python supports the use of binary, octal, and hexadecimal literals, which can be easily converted from decimal numbers using built-in functions. Here’s how you can perform these conversions:
print(bin(16)) # 0b10000
print(oct(8)) # 0o10
print(hex(255)) # 0xff
To remove the prefixes from the output, string slicing can be utilized:
print(bin(16)[2:]) # 10000
You can also convert back to decimal using the int() function:
print(int('0b10000', 2)) # 16
print(int('0o10', 8)) # 8
print(int('0xff', 16)) # 255
Section 1.2 Using zip() for Concurrent Iteration
In many programming scenarios, we commonly use loops to iterate through a single iterable. However, when the need arises to process multiple lists at the same time, the zip() function becomes invaluable. It enables simultaneous iteration over multiple iterable objects, returning elements as tuples. Here’s an example:
for number, letter in zip(range(5), 'abcde'):
print(number, letter)
The output will be:
0 a
1 b
2 c
3 d
4 e
The zip() function can also accept multiple iterable objects. If their lengths differ, it operates without error, although Python 3.10 introduced a strict mode that raises an error in this case:
for number, letter in zip(range(5), 'abcdef', strict=True):
print(number, letter) # Raises ValueError
Chapter 2 Advanced Iteration Techniques
The first video showcases all 71 built-in Python functions, providing a comprehensive overview of how to utilize them effectively.
Section 2.1 Utilizing enumerate() for Indexed Iteration
While Python's loop syntax is clear and straightforward, it lacks built-in support for accessing the indices of items directly. The enumerate() function addresses this by returning index-value pairs, as illustrated below:
numbers = [100, 200, 400, 500, 140]
for i, n in enumerate(numbers):
print(i, n)
You can also specify a starting index:
products = ['Pen', 'Book', 'Ruler', 'A4 paper']
for i, n in enumerate(products, start=1):
print(i, n)
Section 2.2 Alternatives to List Comprehension: map() and filter()
Python provides a powerful feature known as list comprehension for creating new lists from existing ones. However, the built-in map() and filter() functions also serve similar purposes, enabling you to manipulate data effectively. For example, you can convert a sequence of strings to integers like this:
nums = map(int, input().split())
print(list(nums))
To filter elements, the filter() function is used:
nums = [2, 4, 11, 3]
even_nums = filter(lambda x: x % 2 == 0, nums)
print(list(even_nums)) # [2, 4]
While many developers prefer list comprehensions for their elegance, there are times when using map() and filter() can result in cleaner and more Pythonic code.
The second video discusses useful Python built-in functions targeted at both beginners and intermediates.
Section 2.3 Debugging with breakpoint()
Similar to the JavaScript debugger keyword, Python’s breakpoint() function allows developers to set breakpoints in their code. This function triggers the set_trace() method from the pdb module, launching the interactive debugger:
nums = [2, 4, 11, 3]
for i in nums:
breakpoint()
print(i)
This will pause execution at the print statement, allowing you to inspect variables and use debugging commands.
Section 2.4 Leveraging any() and all() for Conditional Checks
Python is widely used for data science, thanks to its robust libraries and data handling capabilities. The built-in any() and all() functions provide boolean values based on the truthiness of elements in an iterable. For example:
items = [10, 0, 0, 0, 0]
print(any(items)) # True
This outputs True because there is at least one truthy value. Conversely, the all() function checks if all elements are truthy:
lines = ['Hello', 'Python', '...', '.']
print(all(lines)) # True
You can even use these functions to validate lists of numbers:
numbers = [2, 10, 20, 12]
print(all(x % 2 == 0 for x in numbers)) # True
print(all(map(lambda x: x % 2 == 0, numbers))) # True
These built-in functions allow you to perform checks without manually iterating through elements.
To explore all available Python built-in functions, refer to the official documentation.
Thank you for reading!