电商平台数据库设计(1.电商数据分析——Python基础)

Plain Text
pip install jupyter
jupyter notebook

Python
shop_name = "时尚女装旗舰店" # 字符串（文本）
order_amount = 299.00 # 浮点数（小数）
order_status = "已支付" # 字符串
is_high_value = True # 布尔值（True/False）

Python
print(shop_name)
print(order_amount)

类型

说明

示例

字符串(str)

文本

"ORD001", "已支付"

整数(int)

整数

299, 1000

浮点数(float)

小数

299.00, 45.50

布尔(bool)

真/假

True, False

列表(list)

多个值的集合

["连衣裙","上衣","裤子"]

字典(dict)

键值对

{"店铺":"女装","GMV":50000}

Python
order = {
"order_id": "ORD001",
"amount": 299.00,
"status": "已支付"
}
print(order["amount"]) # 输出299.0

Python
print(type(shop_name)) #
print(type(order_amount)) #

Python
status = "已支付"
if status == "已支付":
print("计入GMV")
elif status == "已完成":
print("也计入GMV")
else:
print("不计入GMV")

Python
amount = 599.00
if amount >= 500:
label = "高价值订单"
elif amount >= 200:
label = "中价值订单"
else:
label = "普通订单"
print(label) # 输出：高价值订单

Python
amounts = [199, 399, 599, 89]
tax_rate = 1.13
for amt in amounts:
after_tax = amt * tax_rate
print(f"原价：{amt}，税后：{after_tax:.2f}")

Python
orders = [
{"id": "ORD001", "amount": 299},
{"id": "ORD002", "amount": 499},
{"id": "ORD003", "amount": 99}
]
for order in orders:
if order["amount"] > 200:
print(f"{order['id']} 是大额订单")

Python
stock = 10
while stock > 0:
print(f"剩余库存：{stock}")
stock -= 1
print("已售罄")

Python
def calculate_discount(amount, rate=0.9):
"""计算折扣后金额，默认9折"""
return amount * rate
# 调用函数
final_price = calculate_discount(299)
print(final_price) # 269.1
# 传入自定义折扣
final_price = calculate_discount(299, 0.85)
print(final_price) # 254.15

Python
def calc_with_tax(amount, tax_rate=0.13):
tax = amount * tax_rate
after_tax = amount + tax
return after_tax, tax
result, tax = calc_with_tax(500)
print(f"税后：{result}，税费：{tax}")

Python
def tag_order(amount, threshold=500):
if amount >= threshold:
return "高价值"
else:
return "普通"
print(tag_order(600)) # 高价值
print(tag_order(300)) # 普通
print(tag_order(300, 400)) # 高价值（阈值改为400）

Python
import math
print(math.sqrt(16)) # 4.0

Python
from math import sqrt, pi
print(sqrt(25)) # 5.0
print(pi) # 3.14159...

Python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

Plain Text
pip install pandas numpy matplotlib

Plain Text
pip install pandas -i https://pypi.tuna.tsinghua.edu.cn/simple

order_id

amount

status

ORD001

299

已支付

ORD002

599

已支付

ORD003

89

已取消

ORD004

1299

已支付

ORD005

45

已支付

Python
# 1. 读取CSV文件
import csv
orders = []
with open('orders.csv', 'r', encoding='utf-8') as f:
reader = csv.DictReader(f)
for row in reader:
# 将金额转为浮点数
row['amount'] = float(row['amount'])
orders.append(row)
# 2. 统计
total_orders = len(orders)
total_amount = sum(order['amount'] for order in orders)
avg_amount = total_amount / total_orders if total_orders > 0 else 0
print(f"总订单数：{total_orders}")
print(f"总金额：{total_amount}")
print(f"平均金额：{avg_amount:.2f}")
# 3. 筛选高价值订单（金额>500）
high_value = [order for order in orders if order['amount'] > 500]
# 4. 保存到新CSV
with open('high_value_orders.csv', 'w', newline='', encoding='utf-8') as f:
fieldnames = ['order_id', 'amount', 'status']
writer = csv.DictWriter(f, fieldnames=fieldnames)
writer.writeheader()
writer.writerows(high_value)
print(f"高价值订单数：{len(high_value)}，已保存到high_value_orders.csv")

Plain Text
总订单数：5
总金额：2331.0
平均金额：466.20
高价值订单数：2，已保存到high_value_orders.csv

场景

错误操作

正确做法

安装Python

忘记勾选“Add Python to PATH”

安装时勾选，或手动添加环境变量

变量命名

用if、for等关键字作为变量名

使用有意义的英文，如order_amount

数据类型

金额用字符串直接求和

先转换为float或int

循环

忘记更新循环变量，导致无限循环

确保循环内有退出条件

文件路径

脚本和CSV不在同一目录

使用绝对路径或os.chdir()切换目录