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l'm(Z(m)Z)m*Z*m+Z+m,Z,m-Z-m.Z.m/Z/m0Z0m1Z1m2Z2m3Z3m4Z4m5Z5m6Z6 dd l7m8Z8 dd l9m:Z:m;Z; dd l<m=Z=m>Z>m?Z?m@Z@mAZA ddlBmCZCmDZD ddlEmFZF ddlGmHZH ddlImJZJ ddlKmLZLmMZMmNZNmOZO ddlPmQZQ ddlRmSZSmTZTmUZUmVZVmWZWmXZX erÎddlYmZZZ G dd dZ[G dd dZ\G dd de\Z]d d d d d Z^G d!d" d"ee Z_G d#d$ d$e_Z`G d%d& d&e_Zad-d'd(d)d d"d*d+d,ZbdS ).a
|
Provide classes to perform the groupby aggregate operations.
|
|
These are not exposed to the user and provide implementations of the grouping
|
operations, primarily in cython. These classes (BaseGrouper and BinGrouper)
|
are contained *in* the SeriesGroupBy and DataFrameGroupBy objects.
|
é )Ú annotationsN)Ú TYPE_CHECKINGÚCallableÚGenericÚHashableÚIteratorÚSequenceÚfinal)ÚNaTÚlib)Ú ArrayLikeÚAxisIntÚDtypeObjÚNDFrameTÚShapeÚnpt©ÚAbstractMethodError)Úcache_readonly)Úmaybe_cast_pointwise_resultÚmaybe_downcast_to_dtype)Úensure_float64Ú ensure_int64Úensure_platform_intÚ ensure_uint64Úis_1d_only_ea_dtypeÚ is_bool_dtypeÚis_complex_dtypeÚis_datetime64_any_dtypeÚis_float_dtypeÚis_integer_dtypeÚis_numeric_dtypeÚis_period_dtypeÚ is_sparseÚis_timedelta64_dtypeÚneeds_i8_conversion)ÚCategoricalDtype)ÚisnaÚ
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|
Dispatch logic for functions defined in _libs.groupby
|
|
Parameters
|
----------
|
kind: str
|
Whether the operation is an aggregate or transform.
|
how: str
|
Operation name, e.g. "mean".
|
has_dropped_na: bool
|
True precisely when dropna=True and the grouper contains a null value.
|
ÚrankÚcountÚsizeZidxminZidxmaxÚstrÚboolÚNone)ÚkindÚhowÚhas_dropped_naÚreturnc C s || _ || _|| _d S ©N©rF rG rH )ÚselfrF rG rH © rM úNd:\z\workplace\vscode\pyvenv\venv\Lib\site-packages\pandas/core/groupby/ops.pyÚ__init__x s zWrappedCythonOp.__init__Z group_sumZ
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group_prodZ group_minZ group_maxZ
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|
group_lastZ
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group_ohlc)
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ÚsumÚprodÚminÚmaxÚmeanÚmedianÚvarÚfirstÚlastÚohlcZ group_cumprodZ group_cumsumZ group_cumminZ group_cummaxZ
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is_numericc C s |j }| j| | }tt|}|r&|S |t t¡krv|dkrRtd| d| dd|jkrrtd| d| d|S td|d S )N)rU rZ z2function is not implemented for this dtype: [how->z,dtype->ú]ÚobjectzPThis should not be reached. Please report a bug at github.com/pandas-dev/pandas/) ÚnameÚ_CYTHON_FUNCTIONSÚgetattrÚ
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libgroupbyÚnpr` rc ÚNotImplementedErrorZ__signatures__)ÚclsrF rG r` ra Z dtype_strZftypeÚfrM rM rN Ú_get_cython_function s&
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np.ndarray)ÚvaluesrI c C sp | j }|dkrt|}nT|jjdkrl|dks<| jdkrF| jrFt|}n&|dkrl|jjdkrdt|}nt|}|S )zÖ
|
Cast numeric dtypes to float64 for functions that only support that.
|
|
Parameters
|
----------
|
values : np.ndarray
|
|
Returns
|
-------
|
values : np.ndarray
|
rU )ÚiÚu)rV rT r] )rP rY rQ r[ rZ ro )rG r r` rF rH r r )rL rn rG rM rM rN Ú_get_cython_vals¹ s
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z WrappedCythonOp._get_cython_valsFr )r` ra c C sê | j }|rdS t|trj|dkr4t| d| d|dkrR|jsRtd| d|dkræt| d n|t|rt| d ndt|r¤|dkrætd
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Check if we can do this operation with our cython functions.
|
|
Raises
|
------
|
TypeError
|
This is not a valid operation for this dtype.
|
NotImplementedError
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This may be a valid operation, but does not have a cython implementation.
|
N)rP rQ r[ rZ z type does not support z operations)rR rS r@ zCannot perform z with non-ordered Categorical)r@ z dtype not supportedz!datetime64 type does not support z Period type does not support )rQ rZ z"timedelta64 type does not support )
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ÿz!WrappedCythonOp._get_output_shape)r` rI c C s< | j }|dkrd}n t|r.|j |j }nd}t |¡S )Nr@ Úfloat64rc )rG r! rF Úitemsizerh r` )rL r` rG Ú out_dtyperM rM rN Ú_get_out_dtype s z WrappedCythonOp._get_out_dtypec C s^ | j }|dkr*|t t¡krZt tj¡S n0|dkrZt|sBt|rF|S t|rZt tj¡S |S )a
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Get the desired dtype of a result based on the
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input dtype and how it was computed.
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|
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dtype : np.dtype
|
|
Returns
|
-------
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np.dtype
|
The desired dtype of the result.
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This is an internal Grouper class, which actually holds
|
the generated groups
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|
Parameters
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|
axis : Index
|
groupings : Sequence[Grouping]
|
all the grouping instances to handle in this grouper
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for example for grouper list to groupby, need to pass the list
|
sort : bool, default True
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whether this grouper will give sorted result or not
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|
r4 r± TzSequence[grouper.Grouping]rD rE )r± Ú groupingsÚsortÚdropnarI c C s2 t |tst||| _t|| _|| _|| _d S rJ )rr r4 r r± ÚlistÚ
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Groupby iterator
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|
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Generator yielding sequence of (name, subsetted object)
|
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r° )Z minlengthr )Úindexr` )rÛ rh Zbincountr7 rí )rL rÜ rÝ rw r¤ rM rM rN rB B s
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zBaseGrouper.groupsc C s t | jd jS ©Nr )r4 rÛ Zis_monotonic_increasingrÆ rM rM rN Ú is_monotonicY s zBaseGrouper.is_monotonicc C s t | jd dk ¡ S )zE
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obj : Series
|
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|
|
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BinGrouperaå
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This is an internal Grouper class
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Parameters
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----------
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bins : the split index of binlabels to group the item of axis
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binlabels : the label list
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indexer : np.ndarray[np.intp], optional
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the indexer created by Grouper
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some groupers (TimeGrouper) will sort its axis and its
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group_info is also sorted, so need the indexer to reorder
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Examples
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--------
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bins: [2, 4, 6, 8, 10]
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binlabels: DatetimeIndex(['2005-01-01', '2005-01-03',
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'2005-01-05', '2005-01-07', '2005-01-09'],
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dtype='datetime64[ns]', freq='2D')
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the group_info, which contains the label of each item in grouped
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axis, the index of label in label list, group number, is
|
|
(array([0, 0, 1, 1, 2, 2, 3, 3, 4, 4]), array([0, 1, 2, 3, 4]), 5)
|
|
means that, the grouped axis has 10 items, can be grouped into 5
|
labels, the first and second items belong to the first label, the
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third and forth items belong to the second label, and so on
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