在获得了一系列交货数量最高的天数之后,如何过滤掉这些天的原始数据帧?考虑到这两个因素:
most_liquid_contracts.head(20)
Out[32]:
2007-04-26 706
2007-04-27 706
2007-04-29 706
2007-04-30 706
2007-05-01 706
2007-05-02 706
2007-05-03 706
2007-05-04 706
2007-05-06 706
2007-05-07 706
2007-05-08 706
2007-05-09 706
2007-05-10 706
2007-05-11 706
2007-05-13 706
2007-05-14 706
2007-05-15 706
2007-05-16 706
2007-05-17 706
2007-05-18 706
dtype: int64
df.head(20).to_string
Out[40]:
<bound method DataFrame.to_string of
delivery volume
2007-04-27 11:55:00+01:00 705 1
2007-04-27 13:46:00+01:00 705 1
2007-04-27 14:15:00+01:00 705 1
2007-04-27 14:33:00+01:00 705 1
2007-04-27 14:35:00+01:00 705 1
2007-04-27 17:05:00+01:00 705 16
2007-04-27 17:07:00+01:00 705 1
2007-04-27 17:12:00+01:00 705 1
2007-04-27 17:46:00+01:00 705 1
2007-04-27 18:25:00+01:00 705 2
2007-04-26 23:00:00+01:00 706 10
2007-04-26 23:01:00+01:00 706 12
2007-04-26 23:02:00+01:00 706 1
2007-04-26 23:05:00+01:00 706 21
2007-04-26 23:06:00+01:00 706 10
2007-04-26 23:07:00+01:00 706 19
2007-04-26 23:08:00+01:00 706 1
2007-04-26 23:13:00+01:00 706 10
2007-04-26 23:14:00+01:00 706 62
2007-04-26 23:15:00+01:00 706 3>
我试过:
^{pr2}$或者我需要合并吗?它看起来不那么优雅,我也不确定。。我试过:
# ATTEMPT 1
most_liquid_contracts.index = pd.to_datetime(most_liquid_contracts.index, unit='d')
df['days'] = pd.to_datetime(df.index.date, unit='d')
mlc = most_liquid_contracts.to_frame(name='delivery')
mlc['days'] = mlc.index.date
data = pd.merge(mlc, df, on=['delivery', 'days'], left_index=True)
# ATTEMPT 2
liquid = pd.merge(mlc, df, on='delivery', how='inner', left_index=True)
# this gets me closer (ie. retains granularity), but somehow seems to be an outer join? it includes the union but not the intersection. this should be a subset of df, but instead has about x50 the rows, at around 195B. df originally has 4B
但我似乎无法保留原始“df”中所需的分钟级粒度。基本上,我只需要“df”来表示最具流动性的合同(它来自于最具流动性的合同系列;例如,4月27日只包括“706”标签的合同,4月29日只包括“706”标签的合同)。第二个df则正好相反:所有其他合约的df(即流动性最高的合约)。在
更新:了解更多信息--
棘手的部分是合并具有不同日期时间分辨率索引的两个序列/数据帧。一旦你智能地组合它们,你就可以正常地过滤了。在
现在你可以做一些类似
^{pr2}$这将产生
data
(df
)中的行,其中data.delivery
等于当天most_liquid_contracts
中的值。在我假设我对你的理解是正确的,最流动的合同系列是包含N个整数N的最大交货量的系列。你想过滤df,只包括交货数足够高的天数,以便列在清单上。因此,你不能简单地把所有的东西都去掉。在
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