10 Python Practices I Let Go After Learning from Experts
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Chapter 1 The Journey of Improvement
As a passionate Python developer, my quest for better coding practices has led me to refine my skills significantly. Through insights gained from seasoned Python experts, I have been able to shed several ineffective habits. In this article, I'll outline ten Python practices I’ve left behind, providing code examples and clarifications for each.
Section 1.1 Transitioning to List Comprehensions
Previously, I relied on for loops to generate new lists like this:
result = []
for num in range(10):
result.append(num * 2)
However, I learned from experts that list comprehensions are the way to go:
result = [num * 2 for num in range(10)]
This approach is not only more succinct but also enhances performance and aligns with Pythonic principles.
Section 1.2 The Importance of Consistent Indentation
In my early coding days, I often mixed tabs and spaces for indentation. Experts highlighted the need for uniformity:
def some_function():
if condition:
print("Indented with spaces") # Good
if condition:
print("Indented with tabs") # Bad
Now, I consistently use four spaces for each level of indentation.
Subsection 1.2.1 The Power of enumerate()
I previously looped through lists using indices:
my_list = [1, 2, 3, 4]
for i in range(len(my_list)):
print(my_list[i])
Experts introduced me to the more efficient enumerate function:
my_list = [1, 2, 3, 4]
for i, item in enumerate(my_list):
print(item)
This method is both cleaner and more Pythonic.
Section 1.3 Efficient File Handling
In the past, I often neglected to close files after opening them:
file = open("example.txt", "r")
data = file.read()
# Oops! Did not close the file.
Experts advised using the with statement:
with open("example.txt", "r") as file:
data = file.read()
This guarantees that the file closes properly, even if errors arise.
Chapter 2 Embracing Modern Python Features
The first video titled "Avoid These BAD Practices in Python OOP" provides essential insights on common pitfalls in Python object-oriented programming and how to avoid them.
The second video, "Embarking on a Relaxed and Friendly Python Coding Journey | Real Python Podcast #203," offers a light-hearted perspective on learning Python and best practices in coding.
Section 2.1 F-Strings for String Formatting
I used to concatenate strings using the + operator:
name = "John"
greeting = "Hello, " + name + "!"
Experts revealed the efficiency of f-strings:
name = "John"
greeting = f"Hello, {name}!"
F-strings enhance readability and performance.
Section 2.2 Using elif for Clarity
I initially employed multiple if statements for various conditions:
if condition1:
# Do something
if condition2:
# Do something else
Experts recommended the use of elif:
if condition1:
# Do something
elif condition2:
# Do something else
This approach improves code clarity and efficiency.
Section 2.3 Leveraging Built-in Functions
I often created custom functions for simple tasks:
def double(x):
return x * 2
Experts pointed out Python's built-in functions like map, filter, and reduce:
my_list = [1, 2, 3, 4]
doubled_list = list(map(lambda x: x * 2, my_list))
These built-in functions are more effective and expressive.
Section 2.4 The Value of Documentation
Experts emphasized the significance of clear documentation:
def add(x, y):
return x + y # No docstring or comments
Now, I ensure my functions include meaningful docstrings and comments to enhance understanding.
Section 2.5 The Necessity of Virtual Environments
I previously installed packages globally, which led to potential conflicts. Experts introduced me to virtual environments:
python -m venv myenv
source myenv/bin/activate
pip install package_name
Virtual environments help manage dependencies in an organized manner.
Section 2.6 Reducing Global Variables
I frequently utilized global variables:
count = 0
def increment():
global count
count += 1
Experts advised minimizing their use, promoting function parameters and return values for better structure.
In summary, the guidance from Python experts has transformed my coding practices, resulting in cleaner, more efficient, and more Pythonic code. What are your thoughts on my insights today?
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