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B �5�g�, � @ s� d Z ddlZddlmZ ddlmZ ddlmZm Z ddl m Z ddl m Z G dd � d �Ze e d d d d gdd�G dd� dee��ZG dd� dee�ZdS )zSparse accessor� N)�import_optional_dependency)�find_common_type)�PandasDelegate�delegate_names)� SparseArray)� SparseDtypec @ s" e Zd ZdZddd�Zdd� ZdS )� BaseAccessorz5Can only use the '.sparse' accessor with Sparse data.Nc C s || _ | �|� d S )N)�_parent� _validate)�self�data� r �X/home/digitalm-up/venv/lib/python3.7/site-packages/pandas/core/arrays/sparse/accessor.py�__init__ s zBaseAccessor.__init__c C s t �d S )N)�NotImplementedError)r r r r r r s zBaseAccessor._validate)N)�__name__� __module__� __qualname__�_validation_msgr r r r r r r s r Znpoints�densityZ fill_value� sp_values�property)�typc @ sH e Zd ZdZdd� Zdd� Zdd� Zedd d ��Zdd d�Z dd� Z dS )�SparseAccessorzH Accessor for SparseSparse from other sparse matrix data types. c C s t |jt�st| j��d S )N)� isinstance�dtyper �AttributeErrorr )r r r r r r $ s zSparseAccessor._validatec O s t | jj|�S )N)�getattrr �array)r �name�args�kwargsr r r �_delegate_property_get( s z%SparseAccessor._delegate_property_getc O s0 |dkr| j ||�S |dkr(| j||�S t�d S )N�from_coo�to_coo)r# r$ � ValueError)r r r r! r r r �_delegate_method+ s z SparseAccessor._delegate_methodFc C s: ddl m} ddlm} |||d�}||j|jdd�}|S )a� 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 SparseSeries index consists of only the coords of the non-null entries of the original coo_matrix. If True, the SparseSeries 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 <3x4 sparse matrix of type '<class 'numpy.float64'>' with 3 stored elements in COOrdinate format> >>> 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 )�Series)�coo_to_sparse_series)� dense_indexF)�index�copy)�pandasr' �&pandas.core.arrays.sparse.scipy_sparser( r r* )�cls�Ar) r' r( �resultr r r r# 3 s * zSparseAccessor.from_coo�r �� c C s. ddl m} || j|||d�\}}}|||fS )a� 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. 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 <3x4 sparse matrix of type '<class 'numpy.float64'>' with 3 stored elements in COOrdinate format> >>> 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 )�sparse_series_to_coo)� sort_labels)r- r4 r )r Z row_levelsZ column_levelsr5 r4 r/ �rows�columnsr r r r$ e s I zSparseAccessor.to_cooc C s* ddl m} || jj�� | jj| jjd�S )a Convert a Series from sparse values to dense. .. versionadded:: 0.25.0 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' )r* r )r, r' r r �to_denser* r )r r' r r r r8 � s zSparseAccessor.to_denseN)F)r1 r2 F) r r r �__doc__r r" r& � classmethodr# r$ r8 r r r r r s 1 Pr c @ sT e Zd ZdZdd� Zeddd��Zdd� Zd d � Ze e d �d d ��Z e dd� �Z dS )�SparseFrameAccessorzK DataFrame accessor for sparse data. .. versionadded:: 0.25.0 c C s& |j }tdd� |D ��s"t| j��d S )Nc s s | ]}t |t�V qd S )N)r r )�.0�tr r r � <genexpr>� s z0SparseFrameAccessor._validate.<locals>.<genexpr>)�dtypes�allr r )r r r? r r r r � s z SparseFrameAccessor._validateNc C s� ddl m} ddlm} |�� }| �|||�\}}|j\}}|�� |j}|j } |j } t | j d�} g } xTt |�D ]H} t| | | | d �}|||| dd�}t�| | || �}| �|� qpW |j| ||dd�S )a Create a new DataFrame from a scipy sparse matrix. .. versionadded:: 0.25.0 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) >>> pd.DataFrame.sparse.from_spmatrix(mat) 0 1 2 0 1.0 0.0 0.0 1 0.0 1.0 0.0 2 0.0 0.0 1.0 r )�IntIndex)� DataFramer3 F)Zcheck_integrity)r7 r* Zverify_integrity)Zpandas._libs.sparserA r, rB Ztocsc� _prep_index�shapeZ sort_indices�indices�indptrr r r �range�slicer Z _simple_new�appendZ _from_arrays)r. r r* r7 rA rB Zn_rowsZ n_columnsrE rF Z array_datar Zarrays�i�sl�idxZarrr r r � from_spmatrix� s$ z!SparseFrameAccessor.from_spmatrixc C s6 ddl m} dd� | j�� D �}||| jj| jjd�S )a� Convert a DataFrame with sparse values to dense. .. versionadded:: 0.25.0 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 )rB c S s i | ]\}}|j �� |�qS r )r r8 )r< �k�vr r r � <dictcomp>3 s z0SparseFrameAccessor.to_dense.<locals>.<dictcomp>)r* r7 )r, rB r �itemsr* r7 )r rB r r r r r8 s z SparseFrameAccessor.to_densec C s� t d� ddlm} t| jj�� �}t|t�r4|j }g g g }}}xft | j�D ]X\}}| j| }|j j � � j} |�t�|t| ��� |�| � |�|j jj|dd�� qPW t�|�}t�|�}t�|�}||||ff| jjd�S )a� Return the contents of the frame as a sparse SciPy COO matrix. .. versionadded:: 0.25.0 Returns ------- coo_matrix : 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. Zscipyr )� coo_matrixF)r+ )rD )r Z scipy.sparserR r r r? Zto_listr r �subtype� enumerater Zsp_indexZ to_int_indexrE rI �np�repeat�lenr ZastypeZ concatenaterD ) r rR r �colsr6 r �colr �s�rowr r r r$ 6 s zSparseFrameAccessor.to_coo)�returnc C s t �dd� | j�� D ��}|S )zJ Ratio of non-sparse points to total (dense) data points. c S s g | ]\}}|j j�qS r )r r )r< �_�columnr r r � <listcomp>e s z/SparseFrameAccessor.density.<locals>.<listcomp>)rU Zmeanr rQ )r �tmpr r r r ` s zSparseFrameAccessor.densityc C s� ddl m} dd lm m m} | j\}}|d krB|�|�}n||�}|d kr^|�|�}n||�}t|�|kr�t dt|�� d|� ���t|�|kr�t dt|�� d|� ���||fS )Nr )� ensure_indexzColumn length mismatch: z vs. zIndex length mismatch: ) Zpandas.core.indexes.apira Zpandas.core.indexes.base�coreZindexes�baserD Z default_indexrW r% )r r* r7 ra Zibase�N�Kr r r rC h s z SparseFrameAccessor._prep_index)NN)r r r r9 r r: rM r8 r$ r �floatr � staticmethodrC r r r r r; � s 8*r; )r9 �numpyrU Zpandas.compat._optionalr Zpandas.core.dtypes.castr Zpandas.core.accessorr r Z pandas.core.arrays.sparse.arrayr Z pandas.core.arrays.sparse.dtyper r r r; r r r r �<module> s :
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