Tips and practical tips to improve Python computing efficiency

Python operator notation skills and practice: secrets to improve calculation efficiency

Introduction:
In Python programming, for most tasks, the program Efficiency is usually not the most important consideration. However, when dealing with large-scale data sets or computationally intensive tasks, it becomes important to optimize the code to improve computational efficiency. Python provides some powerful operator notation techniques that can help us write more efficient code. This article will introduce some commonly used operator notation techniques and provide specific code examples to help readers understand and apply these techniques.

1. Use bit operation symbols instead of arithmetic operation symbols
In some specific cases, bit operation symbols can be used instead of traditional arithmetic operation symbols to improve calculation efficiency. For example, the multiplication of integers can be replaced by the left shift operator (>). Here are some examples:

1.1 Multiplication operator symbol simplification
The traditional multiplication operator symbol (*) usually performs slower than the bitwise operation symbol. Therefore, in some cases, we can use the left shift operator symbol instead.

Code example:
a = 5 * 2 # Traditional multiplication operator symbol
b = 5 print(a ) # Output result: 10
print(b) # Output result: 10

1.2 Simplification of division operation symbols
Traditional division operation symbols (/) usually execute faster than bit operation symbols slow. Therefore, in some cases, we can use the right shift operator symbol instead.

Code example:
a = 10 / 2 # Traditional division operator symbol
b = 10 >> 1 # Right shift operator symbol instead of division operator symbol
print(a ) # Output result: 5.0
print(b) # Output result: 5

2. Use in-place operation symbols
Python provides some in-place operation symbols that can directly modify the value of variables. No need to reassign. In-place arithmetic operations are often more efficient than traditional arithmetic operations, especially when working with large data sets.

2.1 In-place addition operator symbol
The traditional addition operator symbol ( ) will create a new object, while the in-place addition operator symbol ( = ) will directly modify the original object.

Code example:
a = [1, 2, 3] # List object
a = a [4, 5] # Traditional addition operator symbol
print(a) # Output Result: [1, 2, 3, 4, 5]

b = [1, 2, 3] # List object
b = [4, 5] # In-place addition operator symbol
print(b) #Output result: [1, 2, 3, 4, 5]

2.2 In-place multiplication symbol
The traditional multiplication symbol () will create a new object , and the in-place multiplication operator symbol (=) will be modified directly on the original object.

Code example:
a = [1, 2, 3] # List object
a = a * 3 # Traditional multiplication operator symbol
print(a) # Output result: [ 1, 2, 3, 1, 2, 3, 1, 2, 3]

b = [1, 2, 3] # List object
b *= 3 # In-place multiplication symbol
print(b) #Output result: [1, 2, 3, 1, 2, 3, 1, 2, 3]

3. Use short-circuit logic
Python provides short-circuit logic operation symbols (and and or), you can decide whether to continue the calculation of subsequent expressions based on the result of the previous expression. This short-circuiting logic can improve computational efficiency in some cases.

3.1 Short-circuit logical AND operation symbol
If the value of the previous expression is False, subsequent expressions will not be evaluated.

Code example:
a = 5
b = 10
if a > 0 and b/a > 2: # a > 0 is True, but b/a > ; 2 is False, subsequent expressions will not be calculated

print("条件满足")

else:

print("条件不满足")  # 输出结果：条件不满足

3.2 Short-circuit logical or operation symbol
If the value of the previous expression is True, it will not Then calculate the subsequent expressions.

Code example:
a = 5
b = 10
if a > 0 or b/a > 2: # a > 0 is True, and subsequent calculations will not be performed Expression

print("条件满足")  # 输出结果：条件满足

else:

print("条件不满足")

Conclusion:
This article introduces some common Python operation notation techniques and provides specific code examples. By using bitwise operation symbols instead of arithmetic operation symbols, using in-place operation symbols, and applying short-circuiting logic, we can improve the computational efficiency of Python code. When working with large data sets or computationally intensive tasks, these tips can help us complete the task more efficiently. However, it should be noted that during code optimization, we should weigh the balance between computational efficiency and code readability in order to write code that is easy to understand and maintain.

Reference:

• Python Documentation: https://docs.python.org/3/reference/index.html

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