pandas整列时间戳转上海时间_pandas 上海时区时间戳-优快云博客

本文链接：https://blog.youkuaiyun.com/zcg359670476/article/details/108021154

本文探讨了使用Python pandas库进行复杂数据处理的方法，包括DataFrame的创建、条件筛选及时间序列数据的转换，以适应不同区域时区的需求。

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                  0  1  2  3  4  5
8703  1597042824370  2  1  4  1  1
8704  1597042829380  2  1  4  1  1
8705  1597042834397  2  1  4  1  1
8706  1597042839529  2  1  4  1  1
8707  1597042844495  2  1  4  1  1
...             ... .. .. .. .. ..
9698  1597098576457  2  1  4  0  0
9699  1597098581419  2  1  4  0  0
9700  1597098586385  2  1  4  0  0
9701  1597098591377  2  1  4  0  0
9702  1597098596495  2  1  4  0  0

                        0  1  2  3  4  5
8703  2020-08-10 15:00:24  2  1  4  1  1
8704  2020-08-10 15:00:29  2  1  4  1  1
8705  2020-08-10 15:00:34  2  1  4  1  1
8706  2020-08-10 15:00:39  2  1  4  1  1
8707  2020-08-10 15:00:44  2  1  4  1  1
...                   ... .. .. .. .. ..
9698  2020-08-11 06:29:36  2  1  4  0  0
9699  2020-08-11 06:29:41  2  1  4  0  0
9700  2020-08-11 06:29:46  2  1  4  0  0
9701  2020-08-11 06:29:51  2  1  4  0  0
9702  2020-08-11 06:29:56  2  1  4  0  0

    device_df = pd.DataFrame(device_data).fillna(0)
    result = pd.eval(f"device_df.loc[{et_strap}>=device_df[0]>={st_strap}]")
    if len(result) == 0:
        result = device_df[-1000:]
    result_pansas = result[want_index]
    print(result_pansas)
    result_pansas[0] = pd.to_datetime(result_pansas[0].values, utc=True, unit='ms').tz_convert(
        "Asia/Shanghai").to_period("S")





‘W’, ‘D’, ‘T’, ‘S’, ‘L’, ‘U’, or ‘N’

‘days’ or ‘day’

‘hours’, ‘hour’, ‘hr’, or ‘h’

‘minutes’, ‘minute’, ‘min’, or ‘m’

‘seconds’, ‘second’, or ‘sec’

‘milliseconds’, ‘millisecond’, ‘millis’, or ‘milli’

‘microseconds’, ‘microsecond’, ‘micros’, or ‘micro’

‘nanoseconds’, ‘nanosecond’, ‘nanos’, ‘nano’, or ‘ns’