我有一個如下所示的資料集，它的時間列基于毫秒。
pid_col ,timestamp_col ,value_col
31,2019-03-29 07:14:56.999999756,0.0
31,2019-03-29 07:14:57.250000,0.614595
31,2019-03-29 07:14:57.500000,0.678615
31,2019-03-29 07:14:57.750000,0.687578
31,2019-03-29 07:14:58.000000244,0.559804
31,2019-03-29 07:14:58.250000,0.522672
31,2019-03-29 07:14:58.499999512,0.51627
31,2019-03-29 07:14:58.750000,0.51627
31,2019-03-29 07:14:59.000000244,0.517551
31,2019-03-29 07:14:59.250000,0.51627
31,2019-03-29 07:14:59.500000244,0.509868
31,2019-03-29 07:14:59.750000488,0.513709
31,2019-03-29 07:15:00,0.513709
31,2019-03-29 07:15:00.249999512,0.518831
31,2019-03-29 07:15:00.500000,0.531635
我怎么能每 5 秒計算一次值的平均值？我使用過重新采樣，但沒有用。這是我的代碼：
col_list = ["timestamp", "pid","value"]
df = read_csv("data.csv", usecols=col_list)
df['timestamp'] = to_datetime(df['timestamp'], unit='ms')
timestamp_col=df['timestamp'].tolist()
pid_col=df['pid'].tolist()
value_col=df['value'].tolist()
df['timestamp'].resample('5S').mean()
timestamp_col=df['timestamp'].tolist()
感謝您的幫助
uj5u.com熱心網友回復：
timestamp_col在重新采樣之前定義為索引：
>>> df.set_index('timestamp').groupby('pid') \
.resample('5S')['value'].mean().reset_index()
pid timestamp value
0 31 2019-03-29 07:14:55 0.512767
1 31 2019-03-29 07:15:00 0.521392
更新
也許你更喜歡這個版本：
>>> df.groupby(['pid', pd.Grouper(freq='5S', key='timestamp')], as_index=False) \
.agg({'pid': 'first', 'timestamp': 'first', 'value': 'mean'})
pid timestamp value
0 31 2019-03-29 07:14:56.999999756 0.512767
1 31 2019-03-29 07:15:00.000000000 0.521392
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