Question

让我们考虑以下数据框：

data={'close': 1.16155,
  'datetime': Timestamp('2017-11-01 22:29:40'),
  'high': 1.16155,
  'low': 1.16155,
  'open': 1.16155,
  'symbol': 'European Monetary Union Euro - United States dollar',
  'volume': -1.0},
 {'close': 1.00325,
  'datetime': Timestamp('2017-11-01 22:29:40'),
  'high': 1.00325,
  'low': 1.00325,
  'open': 1.00325,
  'symbol': 'United States dollar - Swiss franc',
  'volume': -1.0},
 {'close': 1.324475,
  'datetime': Timestamp('2017-11-01 22:29:40'),
  'high': 1.324475,
  'low': 1.324475,
  'open': 1.324475,
  'symbol': 'British pound - United States dollar',
  'volume': -1.0},
 {'close': 1.324475,
  'datetime': Timestamp('2017-11-01 22:29:45'),
  'high': 1.324475,
  'low': 1.324475,
  'open': 1.324475,
  'symbol': 'British pound - United States dollar',
  'volume': -1.0},
 {'close': 1.16155,
  'datetime': Timestamp('2017-11-01 22:29:45'),
  'high': 1.16155,
  'low': 1.16155,
  'open': 1.16155,
  'symbol': 'European Monetary Union Euro - United States dollar',
  'volume': -1.0}]
df=pd.DataFrame(data)

我想使用groupby按symbol和datetime分组，而不将索引设置为symbol或datetime。

理想情况下，结果应该是这样的：df.groupby(["symbol",pd.TimeGrouper("30T","datetime")]).count()。

我知道可以通过执行来完成 df.set_index("datetime).groupby(["symbol",pd.TimeGrouper("30T")]).count()

但是，我希望不将索引设置为datetime或symbol。

THX！

Answer 1

这就是你想要的吗？

In [198]: df.groupby(["symbol",pd.TimeGrouper("30T", key="datetime")]).count()
Out[198]:
                                                                        close  high  low  open  volume
symbol                                             datetime
British pound - United States dollar               2017-11-01 22:00:00      2     2    2     2       2
European Monetary Union Euro - United States do... 2017-11-01 22:00:00      2     2    2     2       2
United States dollar - Swiss franc                 2017-11-01 22:00:00      1     1    1     1       1

或使用Grouper：

In [203]: df.groupby(["symbol",pd.Grouper(freq="30T", key="datetime")]).count()
Out[203]:
                                                                        close  high  low  open  volume
symbol                                             datetime
British pound - United States dollar               2017-11-01 22:00:00      2     2    2     2       2
European Monetary Union Euro - United States do... 2017-11-01 22:00:00      2     2    2     2       2
United States dollar - Swiss franc                 2017-11-01 22:00:00      1     1    1     1       1

TimeGrouper的PS DocString可能会更加详细：

In [204]: pd.TimeGrouper?
Init signature: pd.TimeGrouper(*args, **kwargs)
Docstring:
Custom groupby class for time-interval grouping

Parameters
----------
freq : pandas date offset or offset alias for identifying bin edges
closed : closed end of interval; left or right
label : interval boundary to use for labeling; left or right
nperiods : optional, integer
convention : {'start', 'end', 'e', 's'}
    If axis is PeriodIndex

pd.Grouper看起来更好：

In [205]: pd.Grouper?
Init signature: pd.Grouper(*args, **kwargs)
Docstring:
A Grouper allows the user to specify a groupby instruction for a target
object

This specification will select a column via the key parameter, or if the
level and/or axis parameters are given, a level of the index of the target
object.

These are local specifications and will override 'global' settings,
that is the parameters axis and level which are passed to the groupby
itself.

Parameters
----------
key : string, defaults to None
    groupby key, which selects the grouping column of the target
level : name/number, defaults to None
    the level for the target index
freq : string / frequency object, defaults to None
    This will groupby the specified frequency if the target selection
    (via key or level) is a datetime-like object. For full specification
    of available frequencies, please see `here
    <http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases>`_.
axis : number/name of the axis, defaults to 0
sort : boolean, default to False
    whether to sort the resulting labels

additional kwargs to control time-like groupers (when freq is passed)

closed : closed end of interval; left or right
label : interval boundary to use for labeling; left or right
convention : {'start', 'end', 'e', 's'}
    If grouper is PeriodIndex

Returns
-------
A specification for a groupby instruction

Examples
--------

Syntactic sugar for ``df.groupby('A')``

>>> df.groupby(Grouper(key='A'))

Specify a resample operation on the column 'date'

>>> df.groupby(Grouper(key='date', freq='60s'))

Specify a resample operation on the level 'date' on the columns axis
with a frequency of 60s

Pandas将timegrouper应用于列

1 个答案: