Optimize code efficiency and delve into Python operator precedence order

Explore Python operator priority order and improve code efficiency

Introduction:
When writing Python code, it is very important to understand the priority and order of operators important. Proper use of operator precedence can reduce redundancy and errors in code while improving code execution efficiency. This article will introduce commonly used operators and their precedence order in Python, and provide specific code examples to help readers better understand and apply operator precedence.

1. Overview of Python operator priority order
Python operators are grouped and executed according to priority. High-priority operators will be calculated before low-priority operators. The following are the commonly used operators in Python in order from high to low precedence:

1. Brackets: ()
2. Power operation: **
3. Positive and negative Number: x, -x
4. Multiplication, division, remainder and integer division: * / % //
5. Addition and subtraction: -
6. Comparison operators: > =
7. Logical operator: and or not
8. Assignment operator: = = -= = /= %= //= *=

It should be noted that if there are multiple operators with the same precedence in an expression, the calculation will be performed from left to right.

2. Specific examples of operator priority
The following uses some specific code examples to demonstrate the priority and order of Python operators.

1. The parentheses have the highest priority, and the expression inside them is evaluated first:

a = (1 2) * 3
print(a) # Output Result: 9

2. The exponentiation operation has the second priority and its calculation is performed before other operators:

b = 2 * 3 2
print(b) # Output result: 16

3. Multiplication, division, remainder taking and integer division have the same priority and will be calculated from left to right:

c = 10 / 2 * 3
print(c) # Output result: 15

d = 10 % 3
print(d) # Output result: 1

e = 10 // 3
print(e) # Output result: 3

4. Addition and subtraction also have the same priority and will be calculated from left to right. :

f = 5 4 - 3
print(f) # Output result: 6

5. The comparison operator has a lower priority and will be used in all numbers. The judgment is made after the operation is completed:

g = 5 > 3 * 2
print(g) # Output result: False

h = 10 >= 9 1
print(h) # Output result: True

6. Logical operators have the lowest priority and will be judged after all numerical operations and comparison operations are completed:

i = 5 > 3 and 7 print(i) # Output result: True

j = not 10 >= 9
print(j) # Output result: False

3. Practical suggestions for improving code efficiency
Understanding the priority and order of operators can help us write more efficient code and improve the execution efficiency of the code. Here are some practical suggestions:

1. Use parentheses to clarify the order of operations: In complex expressions, use parentheses to clearly specify the order of operations.

For example, to put the order of operations of multiplication before addition, you can use parentheses to specify it explicitly:

a = (2 3) * (4 5)

2. Reduce unnecessary calculations: How to prove that an expression can stop calculation early, which will save computing resources.

For example, if an expression in an and logical operation is False, then the entire expression will return False, and subsequent expressions do not need to be evaluated.

b = False and func() # If func() is a complex function, the calculation can be ended early

3. Optimize arithmetic operations: for expressions containing multiple arithmetic operators The formula can be simplified based on priority.

For example, in the expression "2 a b - 3 c", you can use parentheses to clarify the priority of multiplication and simplify the operation:

result = ( 2 a) b - (3 c)

Conclusion:
Mastering the priority order of Python operators can help programmers write more efficient and accurate code. This article describes commonly used operator precedence orders and provides specific code examples. At the same time, some practical suggestions for improving code efficiency are given, hoping to be helpful to readers in the actual programming process.

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