办公神器小浣熊：有你我怎能不摸鱼

# Reading the content of the provided SQL file to understand its structure and datafile_path = '/mnt/data/products.sql'
with open(file_path, 'r', encoding='utf-8') as file:    sql_content = file.read()
# Displaying the first 500 characters of the SQL file to get an idea of its structuresql_content[:500]
from sqlite3 import connect
# Connect to an in-memory SQLite databaseconn = connect(':memory:')cursor = conn.cursor()
# Execute the SQL script to create table and insert datacursor.executescript(sql_content)
# Commit the changesconn.commit()
# Fetch the first few rows to verify the datacursor.execute("SELECT * FROM Products LIMIT 5;")rows = cursor.fetchall()
# Display the first few rows to verify the datarows
###################首先，让我们找出所有手机并计算其均值。#################
# Filter out the rows that are mobile phones based on the product name containing "iPhone" or "Galaxy"cursor.execute("""SELECT product_id, product_name, price, stockFROM ProductsWHERE product_name LIKE '%iPhone%' OR product_name LIKE '%Galaxy%'""")
mobile_phones = cursor.fetchall()
# Calculate the average price of all mobile phonesaverage_price = sum(row[2] for row in mobile_phones) / len(mobile_phones)
# Display the mobile phones and their average pricemobile_phones, average_price
我们找到了两款手机：
iPhone 12，价格为 $1000.00Samsung Galaxy S21，价格为 $900.00所有手机的平均价格为 $950.00

###########################接下来，我们将计算所有手机的总价格。######################### Calculate the total price of all mobile phonestotal_price = sum(row[2] * row[3] for row in mobile_phones)
# Display the total price of all mobile phonestotal_price
###########################最后，我们来计算如果按照净利润为总金额的10%进行定价，每个手机应该如何定价。
# Calculate the new pricing for each mobile phone based on the net profit margin of 10%net_profit_margin = 0.10new_prices = [(row[2] * row[3]) / (1 + net_profit_margin) for row in mobile_phones]
# Display the new pricing for each mobile phonenew_prices

按照净利润为总金额的10%进行定价后，每个手机的新定价如下：
iPhone 12：约 $90,909.09Samsung Galaxy S21：约 $163,636.36

import pandas as pd
# 读取Excel文件file_path = '/mnt/data/缺失值处理.xlsx'df = pd.read_excel(file_path)
# 将空值填充为0df.fillna(0, inplace=True)
# 导出修改后的文件output_file_path = '/mnt/data/缺失值处理_空值填充为0.xlsx'df.to_excel(output_file_path, index=False)
output_file_path

import pandas as pd
# 读取CSV文件file_path = '/mnt/data/f1db-races-race-results.csv'df = pd.read_csv(file_path)
# 显示前几行数据以了解其结构df.head()import matplotlib.pyplot as pltimport numpy as np
# 筛选出Lewis Hamilton和Michael Schumacher的数据hamilton_data = df[df['driverId'] == 'lewis-hamilton']schumacher_data = df[df['driverId'] == 'michael-schumacher']
# 计算总胜利次数、总杆位次数和总最快圈速次数hamilton_wins = hamilton_data['positionText'].value_counts().get('1', 0)hamilton_poles = hamilton_data['gridPositionText'].value_counts().get('1', 0)hamilton_fastest_laps = hamilton_data['fastestLap'].sum()
schumacher_wins = schumacher_data['positionText'].value_counts().get('1', 0)schumacher_poles = schumacher_data['gridPositionText'].value_counts().get('1', 0)schumacher_fastest_laps = schumacher_data['fastestLap'].sum()
# 准备雷达图数据labels = ['总胜利次数', '总杆位次数', '总最快圈速次数']hamilton_values = [hamilton_wins, hamilton_poles, hamilton_fastest_laps]schumacher_values = [schumacher_wins, schumacher_poles, schumacher_fastest_laps]
angles = np.linspace(0, 2 * np.pi, len(labels), endpoint=False).tolist()hamilton_values += hamilton_values[:1]schumacher_values += schumacher_values[:1]angles += angles[:1]
# 绘制雷达图fig, ax = plt.subplots(figsize=(8, 8), subplot_kw=dict(polar=True))ax.fill(angles, hamilton_values, color='blue', alpha=0.25, label='Lewis Hamilton')ax.fill(angles, schumacher_values, color='red', alpha=0.25, label='Michael Schumacher')
# 添加标签ax.set_yticklabels([])ax.set_xticks(angles[:-1])ax.set_xticklabels(labels)
# 添加图例和标题ax.legend(loc='upper right', bbox_to_anchor=(0.1, 0.1))plt.title("Lewis Hamilton vs Michael Schumacher")
plt.show()