在 Jupyter Notebook 中的 2 个数据集上应用rolling("1D").max() 时，我得到了 2 种不同的行为。
我需要计算每天的滚动最大值。
Sample:
df = pd.DataFrame({'B': [0, 4, 3, 3, 4, 2, 1, 2, 3, 4]},
index = [pd.Timestamp('20130101 09:00:00'),
pd.Timestamp('20130101 09:02:02'),
pd.Timestamp('20130101 09:03:03'),
pd.Timestamp('20130101 09:04:05'),
pd.Timestamp('20130101 09:15:06'),
pd.Timestamp('20130102 09:16:06'),
pd.Timestamp('20130102 09:17:06'),
pd.Timestamp('20130102 09:35:06'),
pd.Timestamp('20130102 09:36:06'),
pd.Timestamp('20130102 09:37:06')])
df.rolling("1D").max() #gives desired output
B
2013-01-01 09:00:00 0.0
2013-01-01 09:02:02 4.0
2013-01-01 09:03:03 4.0
2013-01-01 09:04:05 4.0
2013-01-01 09:15:06 4.0
2013-01-02 09:16:06 2.0 # <- 2 is the highest value for new day
2013-01-02 09:17:06 2.0
2013-01-02 09:35:06 2.0
2013-01-02 09:36:06 3.0
2013-01-02 09:37:06 4.0
当我尝试应用于实际数据时，我得到了
# Sample data
data = '{"High":{"1611221400000":0.99615,"1611222300000":0.9751,"1611223200000":1.035,"1611224100000":0.9894,"1611225000000":1.385,"1611225900000":1.345,"1611226800000":1.235,"1611227700000":1.245,"1611228600000":1.315,"1611229500000":1.295,"1611230400000":1.28,"1611231300000":1.295,"1611232200000":1.415,"1611233100000":1.415,"1611234000000":1.355,"1611234900000":1.385,"1611235800000":1.335,"1611236700000":1.325,"1611237600000":1.365,"1611238500000":1.445,"1611239400000":1.515,"1611240300000":1.475,"1611241200000":1.405,"1611242100000":1.375,"1611243000000":1.255,"1611243900000":1.225,"1611307800000":1.375,"1611308700000":1.415,"1611309600000":1.495}}'
df2 = pd.read_json(data)
df2.rolling("1D").max()
# keeps rolling from previous day
High
Date
2021-01-21 09:30:00 0.99615
2021-01-21 09:45:00 0.99615
2021-01-21 10:00:00 1.03500
2021-01-21 10:15:00 1.03500
2021-01-21 10:30:00 1.38500
2021-01-21 10:45:00 1.38500
2021-01-21 11:00:00 1.38500
2021-01-21 11:15:00 1.38500
2021-01-21 11:30:00 1.38500
2021-01-21 11:45:00 1.38500
2021-01-21 12:00:00 1.38500
2021-01-21 12:15:00 1.38500
2021-01-21 12:30:00 1.41500
2021-01-21 12:45:00 1.41500
2021-01-21 13:00:00 1.41500
2021-01-21 13:15:00 1.41500
2021-01-21 13:30:00 1.41500
2021-01-21 13:45:00 1.41500
2021-01-21 14:00:00 1.41500
2021-01-21 14:15:00 1.44500
2021-01-21 14:30:00 1.51500
2021-01-21 14:45:00 1.51500
2021-01-21 15:00:00 1.51500
2021-01-21 15:15:00 1.51500
2021-01-21 15:30:00 1.51500
2021-01-21 15:45:00 1.51500
2021-01-22 09:30:00 1.51500 # <- value got rolled from previous day
2021-01-22 09:45:00 1.51500
2021-01-22 10:00:00 1.51500
Pandas 版本 = 0.25.1
两个 DF 都有 DatetimeIndex、dtype='datetime64[ns]'、freq=None
知道为什么会发生这种情况吗？
最佳答案
在这两种情况下，滚动窗口都会打开一天的过滤器(等于 24 小时)。
我对你的第一个示例做了一些修改，请查看输出:
df = pd.DataFrame({'B': [0, 4, 3, 3, 4, 2, 1, 2, 3, 4]},
index = [pd.Timestamp('20130101 09:00:00'),
pd.Timestamp('20130101 09:02:02'),
pd.Timestamp('20130101 09:03:03'),
pd.Timestamp('20130101 09:04:05'),
pd.Timestamp('20130101 09:15:06'),
pd.Timestamp('20130102 09:13:06'), # <-- minus 3 minutes
pd.Timestamp('20130102 09:17:06'),
pd.Timestamp('20130102 09:35:06'),
pd.Timestamp('20130102 09:36:06'),
pd.Timestamp('20130102 09:37:06')])
df.rolling("1D").max()
>>>
B
2013-01-01 09:00:00 0.0
2013-01-01 09:02:02 4.0
2013-01-01 09:03:03 4.0
2013-01-01 09:04:05 4.0
2013-01-01 09:15:06 4.0
2013-01-02 09:13:06 4.0 # <-- overlap of days
2013-01-02 09:17:06 2.0
2013-01-02 09:35:06 2.0
2013-01-02 09:36:06 3.0
2013-01-02 09:37:06 4.0
这意味着在这两种情况下，滚动的作用是相同的。
如果您想获得每天的滚动最大值，您可能需要这样做:
df = df.groupby(df.index.day).rolling('1D').max()
和
df2 = df2.groupby(df2.index.day).rolling('1D').max()
这将返回一个带有 MultiIndex 的 DataFrame。
可以在下一步中使用减少 MultiIndex
df.index = df.index.droplevel(0)
和
df2.index = df2.index.droplevel(0)
关于python - Pandas 时间序列数据的滚动最大值，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/65848107/