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lmZmZ G d d « Z eeg d ¢d¬« G d dee« « Z G d dee« Zy)zSparse accessoré )Ú annotations)Ú TYPE_CHECKINGN)Úimport_optional_dependency)Úfind_common_type)Ú SparseDtype)ÚPandasDelegateÚdelegate_names)Ú SparseArray)Ú DataFrameÚSeriesc ó e Zd ZdZdddZd Zy)Ú BaseAccessorz5Can only use the '.sparse' accessor with Sparse data.Nc ó4 || _ | j |« y ©N)Ú_parentÚ _validate©ÚselfÚdatas úUH:\Change_password\venv_build\Lib\site-packages\pandas/core/arrays/sparse/accessor.pyÚ__init__zBaseAccessor.__init__ s Ø Ø tÕ ó c ó t r )ÚNotImplementedErrorr s r r zBaseAccessor._validate! s Ü!Ð!r r )ÚreturnÚNone)Ú__name__Ú
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__module__Ú __qualname__Ú_validation_msgr r © r r r r s ØMOô ó"r r )ÚnpointsÚdensityÚ
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fill_valueÚ sp_valuesÚproperty)Útypc óL e Zd ZdZd Zd dZd dZed
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d d« Zd d dZ ddZ
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y)ÚSparseAccessorzö
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Accessor for SparseSparse from other sparse matrix data types.
|
|
Examples
|
--------
|
>>> ser = pd.Series([0, 0, 2, 2, 2], dtype="Sparse[int]")
|
>>> ser.sparse.density
|
0.6
|
>>> ser.sparse.sp_values
|
array([2, 2, 2])
|
c ób t |j t « st | j « y r )Ú
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isinstanceÚdtyper ÚAttributeErrorr r s r r zSparseAccessor._validate5 s& Ü$**¤kÔ2Ü ×!5Ñ!5Ó6Ð 6ð 3r c óB t | j j |« S r )Úgetattrr Úarray©r ÚnameÚargsÚkwargss r Ú_delegate_property_getz%SparseAccessor._delegate_property_get9 s Üt||×)Ñ)¨4Ó0Ð0r c ój |dk( r | j |i |¤S |dk( r | j |i |¤S t )NÚfrom_cooÚto_coo)r7 r8 Ú
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ValueErrorr1 s r Ú_delegate_methodz SparseAccessor._delegate_method< sD Ø :Ò Ø 4== $Ð1¨&Ñ1Ð 1Ø XÒ Ø 4;; Ð/¨Ñ/Ð /ä Ð r c óp ddl m} ddlm} |||¬« } ||j |j
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d¬« }|S )ae
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Create a Series with sparse values from a scipy.sparse.coo_matrix.
|
|
Parameters
|
----------
|
A : scipy.sparse.coo_matrix
|
dense_index : bool, default False
|
If False (default), the index consists of only the
|
coords of the non-null entries of the original coo_matrix.
|
If True, the index consists of the full sorted
|
(row, col) coordinates of the coo_matrix.
|
|
Returns
|
-------
|
s : Series
|
A Series with sparse values.
|
|
Examples
|
--------
|
>>> from scipy import sparse
|
|
>>> A = sparse.coo_matrix(
|
... ([3.0, 1.0, 2.0], ([1, 0, 0], [0, 2, 3])), shape=(3, 4)
|
... )
|
>>> A
|
<COOrdinate sparse matrix of dtype 'float64'
|
with 3 stored elements and shape (3, 4)>
|
|
>>> A.todense()
|
matrix([[0., 0., 1., 2.],
|
[3., 0., 0., 0.],
|
[0., 0., 0., 0.]])
|
|
>>> ss = pd.Series.sparse.from_coo(A)
|
>>> ss
|
0 2 1.0
|
3 2.0
|
1 0 3.0
|
dtype: Sparse[float64, nan]
|
r ©r )Úcoo_to_sparse_series)Ú dense_indexF)ÚindexÚcopy)Úpandasr Ú&pandas.core.arrays.sparse.scipy_sparser= r0 r? )ÚclsÚAr> r r= Úresults r r7 zSparseAccessor.from_cooD s2 õT "ÝOá% a°[ÔAÙ ¨F¯L©L¸uÔEà r c óL ddl m} || j |||¬« \ }}}|||fS )aþ
|
Create a scipy.sparse.coo_matrix from a Series with MultiIndex.
|
|
Use row_levels and column_levels to determine the row and column
|
coordinates respectively. row_levels and column_levels are the names
|
(labels) or numbers of the levels. {row_levels, column_levels} must be
|
a partition of the MultiIndex level names (or numbers).
|
|
Parameters
|
----------
|
row_levels : tuple/list
|
column_levels : tuple/list
|
sort_labels : bool, default False
|
Sort the row and column labels before forming the sparse matrix.
|
When `row_levels` and/or `column_levels` refer to a single level,
|
set to `True` for a faster execution.
|
|
Returns
|
-------
|
y : scipy.sparse.coo_matrix
|
rows : list (row labels)
|
columns : list (column labels)
|
|
Examples
|
--------
|
>>> s = pd.Series([3.0, np.nan, 1.0, 3.0, np.nan, np.nan])
|
>>> s.index = pd.MultiIndex.from_tuples(
|
... [
|
... (1, 2, "a", 0),
|
... (1, 2, "a", 1),
|
... (1, 1, "b", 0),
|
... (1, 1, "b", 1),
|
... (2, 1, "b", 0),
|
... (2, 1, "b", 1)
|
... ],
|
... names=["A", "B", "C", "D"],
|
... )
|
>>> s
|
A B C D
|
1 2 a 0 3.0
|
1 NaN
|
1 b 0 1.0
|
1 3.0
|
2 1 b 0 NaN
|
1 NaN
|
dtype: float64
|
|
>>> ss = s.astype("Sparse")
|
>>> ss
|
A B C D
|
1 2 a 0 3.0
|
1 NaN
|
1 b 0 1.0
|
1 3.0
|
2 1 b 0 NaN
|
1 NaN
|
dtype: Sparse[float64, nan]
|
|
>>> A, rows, columns = ss.sparse.to_coo(
|
... row_levels=["A", "B"], column_levels=["C", "D"], sort_labels=True
|
... )
|
>>> A
|
<COOrdinate sparse matrix of dtype 'float64'
|
with 3 stored elements and shape (3, 4)>
|
>>> A.todense()
|
matrix([[0., 0., 1., 3.],
|
[3., 0., 0., 0.],
|
[0., 0., 0., 0.]])
|
|
>>> rows
|
[(1, 1), (1, 2), (2, 1)]
|
>>> columns
|
[('a', 0), ('a', 1), ('b', 0), ('b', 1)]
|
r )Úsparse_series_to_coo)Ú sort_labels)rB rG r )r Ú
|
row_levelsÚ column_levelsrH rG rD ÚrowsÚcolumnss r r8 zSparseAccessor.to_coov s6 õV Pá/Ø LL* mÀô
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Ñ4ð $Ð Ð r c óº ddl m} || j j j « | j j
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| j j d¬« S )aà
|
Convert a Series from sparse values to dense.
|
|
Returns
|
-------
|
Series:
|
A Series with the same values, stored as a dense array.
|
|
Examples
|
--------
|
>>> series = pd.Series(pd.arrays.SparseArray([0, 1, 0]))
|
>>> series
|
0 0
|
1 1
|
2 0
|
dtype: Sparse[int64, 0]
|
|
>>> series.sparse.to_dense()
|
0 0
|
1 1
|
2 0
|
dtype: int64
|
r r< F)r? r2 r@ )rA r r r0 Úto_denser? r2 )r r s r rN zSparseAccessor.to_denseÈ sG õ0 "áØ LL× Ñ × 'Ñ 'Ó )Ø,,×$Ñ$Ø ×"Ñ"Øô
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r N)r2 Ústr)F)r> Úboolr r ))r )é F)rH rP )r r ) r r r Ú__doc__r r5 r: Ú classmethodr7 r8 rN r! r r r) r) % s7 ñ
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|
DataFrame accessor for sparse data.
|
|
Examples
|
--------
|
>>> df = pd.DataFrame({"a": [1, 2, 0, 0],
|
... "b": [3, 0, 0, 4]}, dtype="Sparse[int]")
|
>>> df.sparse.density
|
0.5
|
c ój |j }t d |D « « st | j « y )Nc 3 ó< K | ] }t |t « y wr )r+ r )Ú.0Úts r ú <genexpr>z0SparseFrameAccessor._validate.<locals>.<genexpr>ø s è ø Ò>°!:a¤×-Ñ>ùs )ÚdtypesÚallr- r )r r r[ s r r z SparseFrameAccessor._validateö s/ ØÜÑ>°vÔ>Ô>Ü ×!5Ñ!5Ó6Ð 6ð ?r Nc ó ddl m} ddlm} |j « }| j |||« \ }}|j \ }}|j « |j }|j } |j }
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j d« } g } t |« D ]P } t | | | | dz « } |||| d¬« }t j |
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Create a new DataFrame from a scipy sparse matrix.
|
|
Parameters
|
----------
|
data : scipy.sparse.spmatrix
|
Must be convertible to csc format.
|
index, columns : Index, optional
|
Row and column labels to use for the resulting DataFrame.
|
Defaults to a RangeIndex.
|
|
Returns
|
-------
|
DataFrame
|
Each column of the DataFrame is stored as a
|
:class:`arrays.SparseArray`.
|
|
Examples
|
--------
|
>>> import scipy.sparse
|
>>> mat = scipy.sparse.eye(3, dtype=float)
|
>>> pd.DataFrame.sparse.from_spmatrix(mat)
|
0 1 2
|
0 1.0 0 0
|
1 0 1.0 0
|
2 0 0 1.0
|
r )ÚIntIndex©r rQ F)Úcheck_integrity)rL r? Úverify_integrity)Úpandas._libs.sparser^ rA r ÚtocscÚ _prep_indexÚshapeÚ sort_indicesÚindicesÚindptrr r r, ÚrangeÚslicer
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Ú _simple_newÚappendÚ _from_arrays)rC r r? rL r^ r Ún_rowsÚ n_columnsrg rh Ú
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array_datar, ÚarraysÚiÚslÚidxÚarrs r Ú from_spmatrixz!SparseFrameAccessor.from_spmatrixû sû õ: 1å$àzz|Ø ¨¨u°gÓ>wØ JJÑ ð
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ÜJ× ,Ñ ,¨aÓ0ØÜyÓ!ò AÜvay &¨¨Q©¡-Ó0BÙ6 7¨2¡;ÀÔFCÜ ×)Ñ)¨*°R©.¸#¸uÓECØ MM#Õ ð ð
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ddl m} | j j « D ci c] \ }}||j j « " }}} ||| j j | j j ¬« S c c}}w )ay
|
Convert a DataFrame with sparse values to dense.
|
|
Returns
|
-------
|
DataFrame
|
A DataFrame with the same values stored as dense arrays.
|
|
Examples
|
--------
|
>>> df = pd.DataFrame({"A": pd.arrays.SparseArray([0, 1, 0])})
|
>>> df.sparse.to_dense()
|
A
|
0 0
|
1 1
|
2 0
|
r r_ )r? rL )rA r r Úitemsr0 rN r? rL )r r ÚkÚvr s r rN z SparseFrameAccessor.to_dense2 sf õ$ %à26·,±,×2DÑ2DÓ2F×G©$¨!¨Q177×#Ñ#Ó%Ñ%ÐGÑGÙ T§\¡\×%7Ñ%7ÀÇ Á ×AUÑAUÔVÐVùó Hs ¤%A?c ó. t d« ddlm} t | j j
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Return the contents of the frame as a sparse SciPy COO matrix.
|
|
Returns
|
-------
|
scipy.sparse.spmatrix
|
If the caller is heterogeneous and contains booleans or objects,
|
the result will be of dtype=object. See Notes.
|
|
Notes
|
-----
|
The dtype will be the lowest-common-denominator type (implicit
|
upcasting); that is to say if the dtypes (even of numeric types)
|
are mixed, the one that accommodates all will be chosen.
|
|
e.g. If the dtypes are float16 and float32, dtype will be upcast to
|
float32. By numpy.find_common_type convention, mixing int64 and
|
and uint64 will result in a float64 dtype.
|
|
Examples
|
--------
|
>>> df = pd.DataFrame({"A": pd.arrays.SparseArray([0, 1, 0, 1])})
|
>>> df.sparse.to_coo()
|
<COOrdinate sparse matrix of dtype 'int64'
|
with 2 stored elements and shape (4, 1)>
|
Úscipyr )Ú
|
coo_matrixz2fill value must be 0 when converting to COO matrixF)r@ )re )r Ú scipy.sparser} r r r[ Úto_listr+ r ÚsubtypeÚ enumeraterx r0 r$ r9 Úsp_indexrg rl ÚnpÚrepeatÚlenr% ÚastypeÚ concatenatere ) r r} r, ÚcolsrK r ÚcolÚ_ÚserÚsp_arrÚrows r r8 zSparseFrameAccessor.to_cooI s> ô6 # 7Ô+Ý+ä § ¡ ×!4Ñ!4×!<Ñ!<Ó!>Ó?Ü e[Ô )ØMMEà r 2DdÜ & t§|¡|×'9Ñ'9Ó';Ó <ò DMC!SØYYFØ× Ñ AÒ%Ü Ð!UÓVÐVà//×)Ñ)CØ KK #¤s¨3£xÓ0Ô 1Ø KKÔ Ø KK×(Ñ(×/Ñ/°¸EÐ/ÓBÕ Cð Dô ~~dÓ#Ü~~dÓ#Ü~~dÓ#Ù4 $¨ Ð.°d·l±l×6HÑ6HÔIÐIr c óº t j | j j « D cg c] \ }}|j j
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c}}« }|S c c}}w )zá
|
Ratio of non-sparse points to total (dense) data points.
|
|
Examples
|
--------
|
>>> df = pd.DataFrame({"A": pd.arrays.SparseArray([0, 1, 0, 1])})
|
>>> df.sparse.density
|
0.5
|
)r Úmeanr rx r0 r# )r r ÚcolumnÚtmps r r# zSparseFrameAccessor.density{ sC ô gg¸T¿\¹\×=OÑ=OÓ=Q×R± °°6v||×+Ó+ÓRÓSØ
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