U
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lmZmZmZ dd l m Z dd l m Z m Z ddl!m" m#Z$ dd l%m&Z& ddl'm(Z( ddl)m*Z* ddl+m,Z,m-Z-m.Z. ddl/m0Z0 ddl1m2Z2 er$ddl3m4Z4 G dd dZ5eG dd dZ6d(ddd d d d d d d d Z7d d!d"d#Z8d$d%d&d'Z9dS ))z]
|
Provide user facing operators for doing the split part of the
|
split-apply-combine paradigm.
|
é )Ú annotations)Ú TYPE_CHECKINGÚHashableÚIteratorÚfinalN)Úusing_copy_on_write)Ú ArrayLikeÚAxisÚNDFrameTÚnpt)ÚInvalidIndexError)Úcache_readonly)Úfind_stack_level)Úis_categorical_dtypeÚ is_list_likeÚ is_scalar)Ú
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algorithms)Ú CategoricalÚExtensionArray)Ú DataFrame)Úops)Úrecode_for_groupby)ÚCategoricalIndexÚIndexÚ
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MultiIndex)ÚSeries)Ú pprint_thing)ÚNDFramec sþ e Zd ZU dZded< ded< ded< ded< dZd ed
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e e d d!d"d#Z e e d$d% Z e e d&d' Ze e d(d) Ze e d*d+ Ze d,d!d-d.Z ZS )2ÚGroupera
|
A Grouper allows the user to specify a groupby instruction for an 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.
|
|
If `axis` and/or `level` are passed as keywords to both `Grouper` and
|
`groupby`, the values passed to `Grouper` take precedence.
|
|
Parameters
|
----------
|
key : str, 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 : str / 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
|
<https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`_.
|
axis : str, int, defaults to 0
|
Number/name of the axis.
|
sort : bool, default to False
|
Whether to sort the resulting labels.
|
closed : {'left' or 'right'}
|
Closed end of interval. Only when `freq` parameter is passed.
|
label : {'left' or 'right'}
|
Interval boundary to use for labeling.
|
Only when `freq` parameter is passed.
|
convention : {'start', 'end', 'e', 's'}
|
If grouper is PeriodIndex and `freq` parameter is passed.
|
|
origin : Timestamp or str, default 'start_day'
|
The timestamp on which to adjust the grouping. The timezone of origin must
|
match the timezone of the index.
|
If string, must be one of the following:
|
|
- 'epoch': `origin` is 1970-01-01
|
- 'start': `origin` is the first value of the timeseries
|
- 'start_day': `origin` is the first day at midnight of the timeseries
|
|
.. versionadded:: 1.1.0
|
|
- 'end': `origin` is the last value of the timeseries
|
- 'end_day': `origin` is the ceiling midnight of the last day
|
|
.. versionadded:: 1.3.0
|
|
offset : Timedelta or str, default is None
|
An offset timedelta added to the origin.
|
|
.. versionadded:: 1.1.0
|
|
dropna : bool, default True
|
If True, and if group keys contain NA values, NA values together with
|
row/column will be dropped. If False, NA values will also be treated as
|
the key in groups.
|
|
.. versionadded:: 1.2.0
|
|
Returns
|
-------
|
A specification for a groupby instruction
|
|
Examples
|
--------
|
Syntactic sugar for ``df.groupby('A')``
|
|
>>> df = pd.DataFrame(
|
... {
|
... "Animal": ["Falcon", "Parrot", "Falcon", "Falcon", "Parrot"],
|
... "Speed": [100, 5, 200, 300, 15],
|
... }
|
... )
|
>>> df
|
Animal Speed
|
0 Falcon 100
|
1 Parrot 5
|
2 Falcon 200
|
3 Falcon 300
|
4 Parrot 15
|
>>> df.groupby(pd.Grouper(key="Animal")).mean()
|
Speed
|
Animal
|
Falcon 200.0
|
Parrot 10.0
|
|
Specify a resample operation on the column 'Publish date'
|
|
>>> df = pd.DataFrame(
|
... {
|
... "Publish date": [
|
... pd.Timestamp("2000-01-02"),
|
... pd.Timestamp("2000-01-02"),
|
... pd.Timestamp("2000-01-09"),
|
... pd.Timestamp("2000-01-16")
|
... ],
|
... "ID": [0, 1, 2, 3],
|
... "Price": [10, 20, 30, 40]
|
... }
|
... )
|
>>> df
|
Publish date ID Price
|
0 2000-01-02 0 10
|
1 2000-01-02 1 20
|
2 2000-01-09 2 30
|
3 2000-01-16 3 40
|
>>> df.groupby(pd.Grouper(key="Publish date", freq="1W")).mean()
|
ID Price
|
Publish date
|
2000-01-02 0.5 15.0
|
2000-01-09 2.0 30.0
|
2000-01-16 3.0 40.0
|
|
If you want to adjust the start of the bins based on a fixed timestamp:
|
|
>>> start, end = '2000-10-01 23:30:00', '2000-10-02 00:30:00'
|
>>> rng = pd.date_range(start, end, freq='7min')
|
>>> ts = pd.Series(np.arange(len(rng)) * 3, index=rng)
|
>>> ts
|
2000-10-01 23:30:00 0
|
2000-10-01 23:37:00 3
|
2000-10-01 23:44:00 6
|
2000-10-01 23:51:00 9
|
2000-10-01 23:58:00 12
|
2000-10-02 00:05:00 15
|
2000-10-02 00:12:00 18
|
2000-10-02 00:19:00 21
|
2000-10-02 00:26:00 24
|
Freq: 7T, dtype: int64
|
|
>>> ts.groupby(pd.Grouper(freq='17min')).sum()
|
2000-10-01 23:14:00 0
|
2000-10-01 23:31:00 9
|
2000-10-01 23:48:00 21
|
2000-10-02 00:05:00 54
|
2000-10-02 00:22:00 24
|
Freq: 17T, dtype: int64
|
|
>>> ts.groupby(pd.Grouper(freq='17min', origin='epoch')).sum()
|
2000-10-01 23:18:00 0
|
2000-10-01 23:35:00 18
|
2000-10-01 23:52:00 27
|
2000-10-02 00:09:00 39
|
2000-10-02 00:26:00 24
|
Freq: 17T, dtype: int64
|
|
>>> ts.groupby(pd.Grouper(freq='17min', origin='2000-01-01')).sum()
|
2000-10-01 23:24:00 3
|
2000-10-01 23:41:00 15
|
2000-10-01 23:58:00 45
|
2000-10-02 00:15:00 45
|
Freq: 17T, dtype: int64
|
|
If you want to adjust the start of the bins with an `offset` Timedelta, the two
|
following lines are equivalent:
|
|
>>> ts.groupby(pd.Grouper(freq='17min', origin='start')).sum()
|
2000-10-01 23:30:00 9
|
2000-10-01 23:47:00 21
|
2000-10-02 00:04:00 54
|
2000-10-02 00:21:00 24
|
Freq: 17T, dtype: int64
|
|
>>> ts.groupby(pd.Grouper(freq='17min', offset='23h30min')).sum()
|
2000-10-01 23:30:00 9
|
2000-10-01 23:47:00 21
|
2000-10-02 00:04:00 54
|
2000-10-02 00:21:00 24
|
Freq: 17T, dtype: int64
|
|
To replace the use of the deprecated `base` argument, you can now use `offset`,
|
in this example it is equivalent to have `base=2`:
|
|
>>> ts.groupby(pd.Grouper(freq='17min', offset='2min')).sum()
|
2000-10-01 23:16:00 0
|
2000-10-01 23:33:00 9
|
2000-10-01 23:50:00 36
|
2000-10-02 00:07:00 39
|
2000-10-02 00:24:00 24
|
Freq: 17T, dtype: int64
|
ÚboolÚsortÚdropnaú Index | NoneÚ
|
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|
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|
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|
validate : bool, default True
|
if True, validate the grouper
|
|
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|
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|
a tuple of grouper, obj (possibly sorted)
|
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given an object and the specifications, setup the internal grouper
|
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|
|
Parameters
|
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obj : Series or DataFrame
|
sort : bool, default False
|
whether the resulting grouper should be sorted
|
gpr_index : Index or None, default None
|
|
Returns
|
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|
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|
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|
np.ndarray[np.intp] | None
|
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|
Holds the grouping information for a single key
|
|
Parameters
|
----------
|
index : Index
|
grouper :
|
obj : DataFrame or Series
|
name : Label
|
level :
|
observed : bool, default False
|
If we are a Categorical, use the observed values
|
in_axis : if the Grouping is a column in self.obj and hence among
|
Groupby.exclusions list
|
dropna : bool, default True
|
Whether to drop NA groups.
|
uniques : Array-like, optional
|
When specified, will be used for unique values. Enables including empty groups
|
in the result for a BinGrouper. Must not contain duplicates.
|
|
Attributes
|
-------
|
indices : dict
|
Mapping of {group -> index_list}
|
codes : ndarray
|
Group codes
|
group_index : Index or None
|
unique groups
|
groups : dict
|
Mapping of {group -> label_list}
|
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|
|
t| fS ) aã
|
Create and return a BaseGrouper, which is an internal
|
mapping of how to create the grouper indexers.
|
This may be composed of multiple Grouping objects, indicating
|
multiple groupers
|
|
Groupers are ultimately index mappings. They can originate as:
|
index mappings, keys to columns, functions, or Groupers
|
|
Groupers enable local references to axis,level,sort, while
|
the passed in axis, level, and sort are 'global'.
|
|
This routine tries to figure out what the passing in references
|
are and then creates a Grouping for each one, combined into
|
a BaseGrouper.
|
|
If observed & we have a categorical grouper, only show the observed
|
values.
|
|
If validate, then check for key/level overlaps.
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