Python在互联网公司中进行实际应用

import requests

# 发送HTTP请求获取用户信息
def get_user_info():
    url = 'https://api.example.com/user'
    params = {'id': 12345}
    headers = {'Authorization': 'Bearer your_access_token'}

    response = requests.get(url, params=params, headers=headers)
    data = response.json()

    return data

# 处理用户信息
def process_user_info(user_info):
    user_id = user_info['id']
    user_name = user_info['name']
    user_email = user_info['email']

    # 进行一些业务逻辑处理
    # ...

    # 返回处理后的用户信息
    return {
        'id': user_id,
        'name': user_name,
        'email': user_email,
    }

# 发送HTTP请求更新用户信息
def update_user_info(user_info):
    url = 'https://api.example.com/user'
    headers = {'Authorization': 'Bearer your_access_token'}
    data = {
        'id': user_info['id'],
        'name': user_info['name'],
        'email': user_info['email'],
    }

    response = requests.put(url, headers=headers, json=data)
    if response.status_code == 200:
        print('用户信息更新成功')
    else:
        print('用户信息更新失败')

# 主函数
def main():
    # 获取用户信息
    user_info = get_user_info()

    # 处理用户信息
    processed_user_info = process_user_info(user_info)

    # 更新用户信息
    update_user_info(processed_user_info)

if __name__ == '__main__':
    main()

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# 读取时间数据
data = pd.read_csv('time_data.csv')

# 转换时间列为日期格式
data['time'] = pd.to_datetime(data['time'])

# 提取时间信息
data['year'] = data['time'].dt.year
data['month'] = data['time'].dt.month
data['day'] = data['time'].dt.day
data['weekday'] = data['time'].dt.weekday

# 统计每月数据量
monthly_data = data.groupby(['year', 'month']).size().reset_index(name='count')

# 绘制每月数据量折线图
plt.figure(figsize=(10, 6))
sns.lineplot(x='month', y='count', hue='year', data=monthly_data)
plt.xlabel('Month')
plt.ylabel('Data Count')
plt.title('Monthly Data Count')
plt.legend(title='Year')
plt.show()

# 统计每周数据量
weekly_data = data.groupby(['year', 'weekday']).size().reset_index(name='count')

# 绘制每周数据量柱状图
plt.figure(figsize=(10, 6))
sns.barplot(x='weekday', y='count', hue='year', data=weekly_data)
plt.xlabel('Weekday')
plt.ylabel('Data Count')
plt.title('Weekly Data Count')
plt.legend(title='Year')
plt.show()