问题描述
我不确定这是一个错误还是设计使然——也许我遗漏了一些东西，并且 ohlc 聚合器不应该与数据帧一起使用.也许这种行为是设计使然，因为除了索引列和价格列之外的数据框可能会产生奇怪的结果?其他聚合器(mean、stdev 等)使用数据框.无论如何，我正在尝试从这些数据中获取 OHLC，并且转换为时间序列似乎也不起作用.
I am not sure if this is a bug or if it's by design-- perhaps I am missing something and the ohlc aggregator isn't supposed to work with dataframes. Perhaps this behavior is by design because a dataframe with anything other than an index column and a price column could yield strange results? Other aggregators (mean,stdev, etc.) work with a dataframe. In any case, I'm trying to get OHLC from this data, and converting to a timeseries doesn't seem to work either.
这是一个例子:
import pandas as pd
rng = pd.date_range('1/1/2012', periods=1000, freq='S')
ts = pd.Series(randint(0, 500, len(rng)), index=rng)
df = pd.DataFrame(randint(0,500, len(rng)), index=rng)
ts.resample('5Min', how='ohlc') # works great
df.resample('5Min', how='ohlc') # throws a "NotImplementedError"
newts = pd.TimeSeries(df) #am I missing an index command in this line?
# the above line yields this error "TypeError: Only valid with DatetimeIndex or
PeriodIndex"
<小时>
Full NotImplementedError paste:
NotImplementedError Traceback (most recent call last)
/home/jeff/<ipython-input-7-85a274cc0d8c> in <module>()
----> 1 df.resample('5Min', how='ohlc')
/usr/local/lib/python2.7/dist-packages/pandas-0.9.2.dev-py2.7-linux-x86_64.egg/pandas/core/generic.pyc in resample(self, rule, how, axis, fill_method, closed, label, convention, kind, loffset, limit, base)
231 fill_method=fill_method, convention=convention,
232 limit=limit, base=base)
--> 233 return sampler.resample(self)
234
235 def first(self, offset):
/usr/local/lib/python2.7/dist-packages/pandas-0.9.2.dev-py2.7-linux-x86_64.egg/pandas/tseries/resample.pyc in resample(self, obj)
66
67 if isinstance(axis, DatetimeIndex):
---> 68 rs = self._resample_timestamps(obj)
69 elif isinstance(axis, PeriodIndex):
70 offset = to_offset(self.freq)
/usr/local/lib/python2.7/dist-packages/pandas-0.9.2.dev-py2.7-linux-x86_64.egg/pandas/tseries/resample.pyc in _resample_timestamps(self, obj)
189 if len(grouper.binlabels) < len(axlabels) or self.how is not None:
190 grouped = obj.groupby(grouper, axis=self.axis)
--> 191 result = grouped.aggregate(self._agg_method)
192 else:
193 # upsampling shortcut
/usr/local/lib/python2.7/dist-packages/pandas-0.9.2.dev-py2.7-linux-x86_64.egg/pandas/core/groupby.pyc in aggregate(self, arg, *args, **kwargs)
1538 """
1539 if isinstance(arg, basestring):
-> 1540 return getattr(self, arg)(*args, **kwargs)
1541
1542 result = {}
/usr/local/lib/python2.7/dist-packages/pandas-0.9.2.dev-py2.7-linux-x86_64.egg/pandas/core/groupby.pyc in ohlc(self)
384 For multiple groupings, the result index will be a MultiIndex
385 """
--> 386 return self._cython_agg_general('ohlc')
387
388 def nth(self, n):
/usr/local/lib/python2.7/dist-packages/pandas-0.9.2.dev-py2.7-linux-x86_64.egg/pandas/core/groupby.pyc in _cython_agg_general(self, how, numeric_only)
1452
1453 def _cython_agg_general(self, how, numeric_only=True):
-> 1454 new_blocks = self._cython_agg_blocks(how, numeric_only=numeric_only)
1455 return self._wrap_agged_blocks(new_blocks)
1456
/usr/local/lib/python2.7/dist-packages/pandas-0.9.2.dev-py2.7-linux-x86_64.egg/pandas/core/groupby.pyc in _cython_agg_blocks(self, how, numeric_only)
1490 values = com.ensure_float(values)
1491
-> 1492 result, _ = self.grouper.aggregate(values, how, axis=agg_axis)
1493 newb = make_block(result, block.items, block.ref_items)
1494 new_blocks.append(newb)
/usr/local/lib/python2.7/dist-packages/pandas-0.9.2.dev-py2.7-linux-x86_64.egg/pandas/core/groupby.pyc in aggregate(self, values, how, axis)
730 values = values.swapaxes(0, axis)
731 if arity > 1:
--> 732 raise NotImplementedError
733 out_shape = (self.ngroups,) + values.shape[1:]
734
NotImplementedError:
推荐答案
您可以对单个列重新采样(因为每个列都是时间序列):
You can resample over an individual column (since each of these is a timeseries):
In [9]: df[0].resample('5Min', how='ohlc')
Out[9]:
open high low close
2012-01-01 00:00:00 136 136 136 136
2012-01-01 00:05:00 462 499 0 451
2012-01-01 00:10:00 209 499 0 495
2012-01-01 00:15:00 25 499 0 344
2012-01-01 00:20:00 200 498 0 199
In [10]: type(df[0])
Out[10]: pandas.core.series.TimeSeries
我不清楚这应该如何输出更大的 DataFrame(具有多列)，但也许你可以制作一个面板:
It's not clear to me how this should output for a larger DataFrames (with multiple columns), but perhaps you could make a Panel:
In [11]: newts = Panel(dict((col, df[col].resample('5Min', how='ohlc'))
for col in df.columns))
In [12]: newts[0]
Out[12]:
open high low close
2012-01-01 00:00:00 136 136 136 136
2012-01-01 00:05:00 462 499 0 451
2012-01-01 00:10:00 209 499 0 495
2012-01-01 00:15:00 25 499 0 344
2012-01-01 00:20:00 200 498 0 199
注意:也许有一个用于重新采样 DataFrame 的规范输出，并且它尚未实现?
这篇关于OHLC 聚合器不适用于 Pandas 上的数据框?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持跟版网！