Pandas 数据帧不包括从 UNIX 转换时的时间

Pandas dataframe not including time of day when converting from UNIX

我正在从一个 API 中检索数据,该数据的时间戳以 UNIX 毫秒为单位,我正在尝试将此数据保存到 CSV 文件中。数据以日为间隔,但如前所述以 UNIX 毫秒时间表示。

我正在使用 pandas 函数将毫秒转换为日期时间,但仍未保存时间部分的数据。我的代码如下:

ticker = 'tBTCUSD'
r = requests.get(url, params = params)
data = pd.DataFrame(r.json())
data.set_index([0], inplace = True)
data.index = pd.to_datetime(data.index, unit = 'ms' )
data.to_csv('bitfinex_{}_usd_{}.csv'.format(ticker[1:-3].lower(), '1D'), mode='a', header=False)

它将数据保存为 2020-08-21 而不是 2020-08-21 00:00:00。当我每小时或每 15 分钟轮询 API 时,这仍然包括时间,但在每天的时间间隔内,它不包括在内。我想知道是否缺少将时间从 UNIX 毫秒转换为 %Y-%m-%d %H:%M:%S %Z 格式的步骤?

您始终可以明确指定格式:

data.index = pd.to_datetime(data.index, unit='ms').strftime('%Y-%m-%d %H:%M:%S UTC')
print(data)

                                    1             2            3             4             5
0                                                                                           
2020-09-10 00:00:00 UTC  10241.000000  10333.862868  10516.00000  10233.087967   3427.178984
2020-09-09 00:00:00 UTC  10150.000000  10240.000000  10359.00000  10010.000000   2406.147398
2020-09-08 00:00:00 UTC  10400.000000  10148.000000  10464.00000   9882.400000   6761.138356
2020-09-07 00:00:00 UTC  10275.967600  10397.000000  10430.00000   9913.800000   6301.951492
2020-09-06 00:00:00 UTC  10197.000000  10276.000000  10365.07422  10031.000000   2755.663001
...                               ...           ...          ...           ...           ...
2020-05-18 00:00:00 UTC   9668.200000   9714.825163   9944.00000   9450.000000   9201.536549
2020-05-17 00:00:00 UTC   9386.000000   9668.200000   9883.50000   9329.700000   9663.262087
2020-05-16 00:00:00 UTC   9307.600000   9387.952090   9580.00000   9222.000000   4157.691762
2020-05-15 00:00:00 UTC   9791.000000   9311.200000   9848.90000   9130.200000  11340.269781
2020-05-14 00:00:00 UTC   9311.967387   9790.954158   9938.70000   9266.200000  12867.687617