我有一个CSV文件:
_id,ltp,volume,time
5f4dde2e9f742701e3d9a15c,214.55,29077675,2020-09-01T11:07:50.000Z
5f4dde2f9f742701e3d9a15d,214.55,29077690,2020-09-01T11:07:50.000Z
5f4dde2f9f742701e3d9a15e,214.65,29077690,2020-09-01T11:07:51.000Z
5f4dde309f742701e3d9a15f,214.65,29077900,2020-09-01T11:07:51.000Z
5f4dde309f742701e3d9a160,214.6,29077900,2020-09-01T11:07:52.000Z
5f4dde319f742701e3d9a161,214.7,29078191,2020-09-01T11:07:53.000Z
5f4dde329f742701e3d9a162,214.6,29078769,2020-09-01T11:07:54.000Z
5f4dde339f742701e3d9a163,214.65,29078832,2020-09-01T11:07:55.000Z
我需要根据给定时间间隔的数据来计算OHLC
。 open
是间隔中的第一个元素,high
是最大,low
是最小,close
是最后一个。
这是通过以下类似的代码实现的:
data = df.resample('1T').agg({'ltp': ['first', 'max', 'min', 'last'], 'volume': 'sum'})
问题1:我无法用上面的代码将open,high,low,close列分开,它位于'ltp'列中。为了访问open
,我需要写data['ltp']['first']
。 (但这是一个小问题,可以忽略)
问题2:主要问题是,在计算volume
时,我目前有sum
,但实际上我想要实现的是,例如volume
处的10:01:00
是{{ 1}},而在100
是10:02:00
,那么该时间段的总交易量是200
,我该如何实现?
答案 0 :(得分:1)
对于第一个问题,您只需重命名列或删除一个级别。对于您的第二个问题,首先进行最后计算,然后计算出差异:
df = pd.DataFrame([["5f4dde2e9f742701e3d9a15c",214.55,29077675,"2020-09-01T11:07:50.000Z"],
["5f4dde2f9f742701e3d9a15d",214.55,29077690,"2020-09-01T11:07:50.000Z"],
["5f4dde2f9f742701e3d9a15e",214.65,29077690,"2020-09-01T11:07:51.000Z"],
["5f4dde309f742701e3d9a15f",214.65,29077900,"2020-09-01T11:07:51.000Z"],
["5f4dde309f742701e3d9a160",214.6,29077900,"2020-09-01T11:07:52.000Z"],
["5f4dde319f742701e3d9a161",214.7,29078191,"2020-09-01T11:07:53.000Z"],
["5f4dde329f742701e3d9a162",214.6,29078769,"2020-09-01T11:07:54.000Z"],
["5f4dde339f742701e3d9a163",214.65,29078832,"2020-09-01T11:07:55.000Z"]], columns = ["_id","ltp","volume","time"])
df["time"] = pd.to_datetime(df["time"])
df = df.set_index("time")
data = df.resample('1S').agg({'ltp': ['first', 'max', 'min', 'last'], 'volume': ['first','last']})
data.columns = ["_".join(x) for x in data.columns.ravel()]
data["volumne_metric"] = data["volume_last"]-data["volume_first"]
输出:
ltp_first ltp_max ltp_min ltp_last volume_first volume_last volumne_metric
time
2020-09-01 11:07:50+00:00 214.55 214.55 214.55 214.55 29077675 29077690 15
2020-09-01 11:07:51+00:00 214.65 214.65 214.65 214.65 29077690 29077900 210
2020-09-01 11:07:52+00:00 214.60 214.60 214.60 214.60 29077900 29077900 0
2020-09-01 11:07:53+00:00 214.70 214.70 214.70 214.70 29078191 29078191 0
2020-09-01 11:07:54+00:00 214.60 214.60 214.60 214.60 29078769 29078769 0
2020-09-01 11:07:55+00:00 214.65 214.65 214.65 214.65 29078832 29078832 0