人工智能代码20240928

import pandas as pd import numpy as np import matplotlib.pyplot as plt # 1. 数据采集 # 从本地文件中读取数据 2分 data = pd.read_csv('user_behavior_data.csv') print("数据采集完成，已加载到DataFrame中") # 打印数据的前5条记录 2分 print(data.head()) # 2. 数据清洗与预处理 # 处理缺失值 2分 data = data.dropna() # 数据类型转换 data['Age'] = data['Age'].astype(int) # Age数据类型转换 2分 data['PurchaseAmount'] = data['PurchaseAmount'].astype(float) # PurchaseAmount数据类型转换 2分 data['ReviewScore'] = data['ReviewScore'].astype(int) # ReviewScore数据类型转换 2分 # 处理异常值 2分 data = data[(data['Age'].between(18, 70)) & (data['PurchaseAmount'] > 0) & (data['ReviewScore'].between(1, 5))] # 数据标准化 data['PurchaseAmount'] = (data['PurchaseAmount'] - data['PurchaseAmount'].mean()) / data['PurchaseAmount'].std() # PurchaseAmount数据标准化 2分 data['ReviewScore'] = (data['ReviewScore'] - data['ReviewScore'].mean()) / data['ReviewScore'].std() # ReviewScore数据标准化 2分 # 保存清洗后的数据 1分 data.to_csv('cleaned_user_behavior_data.csv', index=False) print("数据清洗完成，已保存为 'cleaned_user_behavior_data.csv'") # 3. 数据统计 # 统计每个购买类别的用户数 purchase_category_counts = data['PurchaseCategory'].value_counts() print("每个购买类别的用户数:\n", purchase_category_counts) # 统计不同性别的平均购买金额 gender_purchase_amount_mean = data.groupby('Gender')['PurchaseAmount'].mean() print("不同性别的平均购买金额:\n", gender_purchase_amount_mean) # 统计不同年龄段的用户数 bins = [18, 25, 35, 45, 55, 65, 70] labels = ['18-25', '26-35', '36-45', '46-55', '56-65', '65+'] data['AgeGroup'] = pd.cut(data['Age'], bins=bins, labels=labels, right=False) age_group_counts = data['AgeGroup'].value_counts().sort_index() print("不同年龄段的用户数:\n", age_group_counts)