1.1.4

import pandas
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 =  pandas.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(5))

数据采集完成，已加载到DataFrame中
UserID UserName Age ... PurchaseCategory ReviewScore LoginFrequency
0 1 User_1 62 ... Clothing 3 Monthly
1 2 User_2 65 ... Electronics 4 Weekly
2 3 User_3 18 ... Home & Garden 3 Weekly
3 4 User_4 21 ... Books 2 Weekly
4 5 User_5 21 ... Home & Garden 1 Monthly

[5 rows x 10 columns]

# 2. 数据清洗与预处理
# 处理缺失值（删除）  2分
data = data.dropna()

# 数据类型转换
data['Age'] = data['Age'].astype(int)   # Age数据类型转换为int 2分
data['PurchaseAmount'] = data['PurchaseAmount'].astype(float) # PurchaseAmount数据类型转换为float  2分
data['ReviewScore'] = data['ReviewScore'].astype(int)   # ReviewScore数据类型转换为int 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 一共三种标准化类型： 1. 离插标准化 2. 标准差
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'")

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 2分
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 2分
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 2分
bins = [18, 26, 36, 46, 56, 66, np.inf]
labels = ['18-25', '26-35', '36-45', '46-55', '56-65', '65+']
data['AgeGroup'] = pandas.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)

每个购买类别的用户数:
PurchaseCategory
Clothing 57
Electronics 52
Home & Garden 51
Food 48
Books 43
Name: count, dtype: int64
不同性别的平均购买金额:
Gender
Female -0.042691
Male 0.047355
Name: PurchaseAmount, dtype: float64
不同年龄段的用户数:
AgeGroup
18-25 39
26-35 48
36-45 46
46-55 52
56-65 36
65+ 30
Name: count, dtype: int64