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中级

实现传感器数据按时间窗口聚合

未完成
中级参考 代码结构已给出，请填写 ____ 处 
def solve():
    from pyodide.http import open_url
    from io import StringIO
    import pandas as pd
    readings_csv = open_url(____).read()
    readings = pd.read_csv(StringIO(____))
    WINDOW_SEC = ____
    def aggregate_window(____):
        window_end = window_start + window_sec
        in_window = readings[(____)&(____)&
                             (____)&(____)]
        if in_window.empty: return None
        return {'____':device_id,'____':metric,'____':window_start,'____':window_end,
                '____':round(in_window['____'].min(),____),'____':round(in_window['____'].max(),____),
                '____':round(in_window['____'].mean(),____),'____':len(____)}
    agg_csv = open_url(____).read()
    agg = pd.read_csv(StringIO(____))
    print(____)
    for _, row in agg.head(____).iterrows():
        print("____" + row['____'] + "____" + row['____'] + "____" + str(____) + "____" + str(____) + "____" + str(____) + "____" + str(____))

示例

输入
solve()
期望输出
1-hour aggregations (from pre-computed):
  RPI-001/cpu_temp: min=27.83 max=43.19 avg=33.99 n=4
  RPI-001/cpu_temp: min=34.72 max=48.37 avg=39.19 n=4
  RPI-001/cpu_temp: min=31.19 max=41.93 avg=37.65 n=4
  RPI-001/cpu_temp: min=32.54 max=47.75 avg=39.39 n=4
  RPI-001/cpu_temp: min=27.93 max=35.65 avg=30.44 n=4
  RPI-001/cpu_temp: min=21.5 max=26.09 avg=23.93 n=4
  RPI-002/cpu_temp: min=25.81 max=48.68 avg=37.03 n=4
  RPI-002/cpu_temp: min=22.89 max=45.26 avg=29.27 n=4
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