2.1.5

import pandas as pd # 加载数据集 data = 💡 # 查看表结构基本信息 print(💡) # 显示每一列的空缺值数量 print(💡) # 删除含有缺失值的行 data_cleaned = 💡 # 转换 'Your age' 列的数据类型为整数类型，并处理异常值 data_cleaned.loc[:, 'Your age'] = 💡(💡, errors='coerce') data_cleaned = data_cleaned.dropna(subset=['Your age']) data_cleaned = data_cleaned[data_cleaned['Your age'] >= 0] data_cleaned.loc[:, 'Your age'] = data_cleaned['Your age'].💡 print(data_cleaned['Your age'].dtype) # 检查和删除重复值 duplicates_removed = data_cleaned.duplicated().sum() data_cleaned = 💡 print(f"Removed {duplicates_removed} duplicate rows") from sklearn.preprocessing import LabelEncoder # 归一化 'How do you describe your current level of fitness ?' 列 label_encoder = LabelEncoder() data_cleaned[💡] = 💡 print(data_cleaned['How do you describe your current level of fitness ?'].unique()) from sklearn.preprocessing import LabelEncoder import matplotlib.pyplot as plt # 去掉列名中的空格 data.columns = data.columns.str.strip() # 显示数据集的列名 print(data.columns) # 删除包含缺失值的行 data_cleaned = data.dropna(subset=['How often do you exercise?']) # 统计不同健身频率的分布情况 exercise_frequency_counts = data_cleaned['How often do you exercise?'].value_counts() # 绘制饼图 plt.figure(figsize=(10, 6)) 💡(autopct='%1.1f%%', startangle=90, colors=plt.cm.Paired.colors) plt.title('Distribution of Exercise Frequency') plt.ylabel('') plt.show() import pandas as pd from sklearn.model_selection import train_test_split import matplotlib.pyplot as plt # 填充缺失值 data_filled = data.apply(lambda x: x.fillna(x.mode()[0])) # 划分数据（测试集占比20%） train_data, test_data = 💡(💡, random_state=42) # 保存处理后的数据 cleaned_file_path = '💡' 💡(💡, index=False)