悦见新知（2）：Python流程控制与函数封装（竞赛篇）

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Today, the editor brings the “悦见新知（2）:Python流程控制与函数封装（竞赛篇）””.

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一. 思维导图

二. 核心知识点（聚焦竞赛应用）

II. Core Knowledge Points (Focusing on Competition Applications)

1. 条件判断：竞赛中的决策逻辑

竞赛应用：算法比赛中根据输入数据选择不同策略；建模比赛中根据模型表现决定后续步骤；爬虫比赛中判断页面元素是否存在。

1.Conditional Judgment: Decision-Making Logic in Competitions

Competition Applications: Selecting different strategies based on input data in algorithm competitions; deciding subsequent steps according to model performance in modeling competitions; determining whether page elements exist in web crawling competitions.

实战示例（算法比赛）：成绩等级判定

score = 85

# if/elif/else 结构进行多分支判断

if score >= 90:

grade = “A”

bonus = 5

elif score >= 80:

grade = “B”

bonus = 3

elif score >= 70:

grade = “C”

bonus = 1

else:

grade = “D”

bonus = 0

print(f”得分：{score}, 等级：{grade}, 奖励积分：{bonus}”)

# 输出：得分：85, 等级：B, 奖励积分:

Practical Example (Algorithm Competition): Grade Evaluation Based on Scores

score = 85

# Using if/elif/else structure for multi-branch judgment

if score >= 90:

grade = “A”

bonus = 5

elif score >= 80:

grade = “B”

bonus = 3

elif score >= 70:

grade = “C”

bonus = 1

else:

grade = “D”

bonus = 0

print(f”Score: {score}, Grade: {grade}, Bonus Points: {bonus}”)

# Output: Score: 85, Grade: B, Bonus Points: 3

高频操作：三元表达式（简洁赋值）

# 竞赛中常用于快速初始化或简单条件赋值

# 语法：value_if_true if condition else value_if_false

High-Frequency Operation: Ternary Expressions (Concise Assignment)

#Commonly used in competitions for quick initialization or simple conditional assignments.

#Syntax: value_if_true if condition else value_if_false

# 示例：判断样本是否为有效样本（得分>60为有效）

is_valid = True if score > 60 else False

# 或者更简洁地：is_valid = score > 60

#Example: Determining if a Sample is Valid (a score > 60 is considered valid)

is_valid = True if score > 60 else False

# Or more concisely: is_valid = score > 60

status = “晋级” if score >= 80 else “待定”

print(f”状态：{status}”) # 输出：状态：晋级

status = “Promoted” if score >= 80 else “Pending”

print(f”Status: {status}”) # Output: Status: Promoted

2. 循环：自动化处理竞赛数据

（1）for循环：遍历数据集合

竞赛应用：遍历数据聚焦的所有样本进行处理；循环调用多次模型进行集成学习；遍历网页链接进行批量爬取。

2.Loops: Automating Data Processing in Competitions

(1) For Loop: Iterating Over Data Collections

Competition Applications: Processing all samples in a dataset; repeatedly calling models for ensemble learning; batch crawling web links.

实战示例（建模比赛）：批量数据预处理

# 原始数据列表（例如，从文件读取的多个样本值）

raw_data_list = [89, 105, -5, 78, 320] # 假设包含异常值（如-5, 320）

cleaned_data_list = []

# 使用for循环遍历并清洗每个数据点

for data in raw_data_list:

# 清洗规则：只保留0到100之间的合理值

if 0 <= data <= 100:

cleaned_data_list.append(data)

print(f”清洗后的数据：{cleaned_data_list}”) # 输出：清洗后的数据：[89, 78]

Practical Example (Modeling Competition): Batch Data Preprocessing

# Original data list (e.g., sample values read from a file)

raw_data_list = [89, 105, -5, 78, 320] # Assume it contains outliers (e.g., -5, 320)

cleaned_data_list = []

# Using a for loop to iterate and clean each data point

for data in raw_data_list:

# Cleaning rule: Only retain reasonable values between 0 and 100

if 0 <= data <= 100:

cleaned_data_list.append(data)

print(f”Cleaned Data: {cleaned_data_list}”) # Output: Cleaned Data: [89, 78]

高频操作：列表推导式（竞赛代码优化利器）

# 上述循环用列表推导式一行完成，更高效、更Pythonic

cleaned_data_list = [data for data in raw_data_list if 0 <= data <= 100]

print(f”（列表推导式）清洗后的数据：{cleaned_data_list}”)

High-Frequency Operation: List Comprehensions (A Powerful Tool for Optimizing Competition Code)

#Complete the above loop in one line using a list comprehension, which is more efficient and Pythonic.

cleaned_data_list = [data for data in raw_data_list if 0 <= data <= 100]

print(f”(List Comprehension) Cleaned Data: {cleaned_data_list}”)

# 另一个例子：快速生成平方数序列（用于测试数据）

squares = [x**2 for x in range(1, 6)] # [1, 4, 9, 16, 25]

print(f”平方数序列：{squares}”)

# Another example: Quickly generate a sequence of square numbers (for test data)

squares = [x**2 for x in range(1, 6)] # [1, 4, 9, 16, 25]

print(f”Sequence of Square Numbers: {squares}”)

（2）while循环：满足条件时持续运行

竞赛应用：爬虫比赛中的持续翻页直到没有下一页；算法比赛中的迭代优化直到收敛；模拟比赛中的多轮次对决。

(2) While Loop: Continuing to Run While Conditions Are Met

Competition Applications: Continuously turning pages in web crawling competitions until there are no more pages; iterative optimization in algorithm competitions until convergence; multi-round duels in simulation competitions.

实战示例（爬虫比赛）：自动翻页直到末尾

current_page = 1

max_pages = 5 # 假设已知最大页数，实践中可能通过检测”下一页”按钮是否存在来判断

Practical Example (Web Crawling Competition): Automatically Turning Pages Until the End

current_page = 1

max_pages = 5 # Assume the maximum number of pages is known; in practice, it may be determined by detecting the presence of a “Next Page” button

# 模拟爬取过程

while current_page <= max_pages:

print(f”正在爬取第 {current_page} 页…”)

# 这里应放置实际的爬取代码，如 requests.get(…)

# … (爬取逻辑)

print(f”第 {current_page} 页数据爬取完毕！”)

current_page += 1 # 更新页码，至关重大，避免无限循环！

print(“所有页面爬取完成！”)

#Simulate the crawling process

while current_page <= max_pages:

print(f”Crawling page {current_page}…”)

# Place the actual crawling code here, such as requests.get(…)

# … (crawling logic)

print(f”Data from page {current_page} has been crawled successfully!”)

current_page += 1 # Update the page number; crucial to avoid infinite loops!

print(“All pages have been crawled successfully!”)

3. 函数封装：构建可复用的竞赛代码块

竞赛应用：将常用的数据预处理、特征工程、模型评估步骤封装成函数，避免代码重复，提高可读性和协作效率。

3.Function Encapsulation: Building Reusable Code Blocks for Competitions

Competition Applications: Encapsulating commonly used data preprocessing, feature engineering, and model evaluation steps into functions to avoid code duplication, improve readability, and enhance collaboration efficiency.

（1）基础函数：定义、参数与返回值

(1) Basic Functions: Definition, Parameters, and Return Values

实战示例（通用竞赛工具函数）

# 定义一个计算模型评估指标的函数

def calculate_metrics(true_values, predicted_values):

“””

计算模型的准确率与平均绝对误差。

参数:

true_values (list): 真实值列表

predicted_values (list): 预测值列表

返回:

dict: 包含准确率和平均绝对误差的字典

“””

if len(true_values) != len(predicted_values):

return {“error”: “输入列表长度必须一致。”}

# 计算准确率 (以回归问题中在必定误差范围内视为正确为例)

correct_predictions = 0

total_predictions = len(true_values)

absolute_errors = []

for true, pred in zip(true_values, predicted_values):

error = abs(true – pred)

absolute_errors.append(error)

if error <= 2.0: # 假设误差小于等于2.0算作预测正确

correct_predictions += 1

accuracy = correct_predictions / total_predictions

mae = sum(absolute_errors) / len(absolute_errors)

# 返回一个字典，包含多个指标

return {“accuracy”: accuracy, “mean_absolute_error”: mae}

# — 调用函数 —

# 模拟一组真实值和预测值

y_true = [10, 20, 30, 40]

y_pred = [12, 18, 33, 38] # 注意第二个样本误差为2，刚好在阈值内

results = calculate_metrics(y_true, y_pred)

print(f”模型评估结果：{results}”)

# 输出：模型评估结果：{'accuracy': 1.0, 'mean_absolute_error': 2.25}

# 准确率100%，由于所有样本预测误差都在2.0以内

Practical Example (General-Purpose Competition Utility Function)

# Define a function to calculate model evaluation metrics

def calculate_metrics(true_values, predicted_values):

“””

Calculate the accuracy and mean absolute error of a model.

Parameters:

true_values (list): List of true values

predicted_values (list): List of predicted values

Returns:

dict: Dictionary containing accuracy and mean absolute error

“””

if len(true_values) != len(predicted_values):

return {“error”: “Input lists must have the same length.”}

# Calculate accuracy (assuming predictions within a certain error margin are considered correct for regression problems)

correct_predictions = 0

total_predictions = len(true_values)

absolute_errors = []

for true, pred in zip(true_values, predicted_values):

error = abs(true – pred)

absolute_errors.append(error)

if error <= 2.0: # Assume predictions with an error <= 2.0 are considered correct

correct_predictions += 1

accuracy = correct_predictions / total_predictions

mae = sum(absolute_errors) / len(absolute_errors)

# Return a dictionary containing multiple metrics

return {“accuracy”: accuracy, “mean_absolute_error”: mae}

# — Function Call —

# Simulate a set of true and predicted values

y_true = [10, 20, 30, 40]

y_pred = [12, 18, 33, 38] # Note: The error for the second sample is 2, which is within the threshold

results = calculate_metrics(y_true, y_pred)

print(f”Model evaluation results: {results}”)

# Output: Model evaluation results: {'accuracy': 1.0, 'mean_absolute_error': 2.25}

# Accuracy is 100% because all sample prediction errors are within 2.0

（2）高级参数技巧（提升函数灵活性）

(2) Advanced Parameter Techniques (Enhancing Function Flexibility

# 默认参数：为参数提供默认值，调用时可省略

def load_data(file_path, encoding='utf-8', header=True):

“””模拟加载数据，带有默认参数”””

print(f”从 {file_path} 加载数据，编码：{encoding}, 包含表头：{header}”)

# … 实际的数据加载逻辑

return f”Data from {file_path}”

# 调用时可以选择性提供参数

data1 = load_data(“data.csv”) # 使用默认的utf-8编码和True表头

data2 = load_data(“data_gbk.csv”, encoding='gbk') # 指定编码

data3 = load_data(“data_no_header.csv”, header=False) # 指定无表头

# 可变参数 *args：接受任意数量的位置参数

def calculate_sum(*numbers):

“””计算任意个数字的和”””

total = 0

for num in numbers:

total += num

return total

# 在竞赛中可用于计算不定长指标集合的总和或平均值

team_scores = calculate_sum(85, 92, 78, 96)

print(f”团队总得分：{team_scores}”) # 输出：团队总得分：351

# Default parameters: Provide default values for parameters, which can be omitted during calls

def load_data(file_path, encoding='utf-8', header=True):

“””Simulate loading data with default parameters”””

print(f”Loading data from {file_path}, encoding: {encoding}, includes header: {header}”)

# … Actual data loading logic

return f”Data from {file_path}”

# Parameters can be selectively provided during calls

data1 = load_data(“data.csv”) # Use default utf-8 encoding and True for header

data2 = load_data(“data_gbk.csv”, encoding='gbk') # Specify encoding

data3 = load_data(“data_no_header.csv”, header=False) # Specify no header

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参考资料：

· Python 官方文档

· 各类编程竞赛真题与最佳实践

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