archive.git - Gitblit

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Functions for preparing various inputs passed to the DataFrame or Series
constructors before passing them to a BlockManager.
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    Segregate Series based on type and coerce into matrices.
 
    Needs to handle a lot of exceptional cases.
    NT©Ú extract_numpyézYArrays must be 1-dimensional np.ndarray or ExtensionArray with length matching len(index)z#len(arrays) must match len(columns)Úblock)r;Úrefsrú2'typ' needs to be one of {'block', 'array'}, got 'ú') Ú_extract_indexr%Ú_homogenizerÚlenÚ
isinstanceÚnpÚndarrayrÚndimÚ
ValueErrorr1r()ÚarraysÚcolumnsÚindexr8r9r:r;rAÚxÚarrÚaxess           úUH:\Change_password\venv_build\Lib\site-packages\pandas/core/internals/construction.pyÚ arrays_to_mgrrS`sDñà=Ü" 6Ó*Eä  Ó'Eô# 6¨5°%Ó8ôUÓ#Ø@FÖG¸1- °Ö6ÐGÐGàvFÑ#ðò        Cäs¤R§Z¡Z´Ð$@ÔAØ88q=Üs8s 5zÓ)ä ð6óðð         ô7Ó#GÜ
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    Extract from a masked rec array and create the manager.
    ©r8r:)    rÚgetdatar$rFr%Ú    to_arraysÚreorder_arraysrSÚcopy)
ÚdatarNrMr8rYr:ÚfdatarLÚarr_columnsÚmgrs
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dg«}|S|j«}|r|j«}t|g|jg«}|St!d|d«cc}wcc}w)    zè
    Convert to specific type of Manager. Does not copy if the type is already
    correct. Does not guarantee a copy otherwise. `copy` keyword only controls
    whether conversion from Block->ArrayManager copies the 1D arrays.
    r@érr?)r:rrBrC)rGr.rJrSrLrQr/Ú
from_arrayrNr(ÚrangerFÚiget_valuesrYÚinternal_valuesr)rK)r]r:rYÚnew_mgrÚirLrPrs        rRÚ
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s1Ã?W*Í$"W/ÏW4Ï&W9ÑW>Ñ5XÒ  XÕA X có0|jdt|«k7s|jdt|«k7r`|jddcxk(rt|«krtd«|j}t|«t|«f}td|d|«y)z\
    Check that the shape implied by our axes matches the actual shape of the
    data.
    r?rz)Empty data passed with indices specified.zShape of passed values is z, indices imply N)rrFrK)rrNrMÚpassedÚimplieds     rRrrsð||A#g,Ò&¨&¯,©,°q©/¼SÀ»ZÒ*Gð<<?aÔ,¤# e£*Ò,ÜÐHÓIÐIð-ðÜu:s 7|Ð,ÜÐ5°f°XÐ=MÈgÈYÐWÓXÐXð+Hr_r@)r8r:rYcóè|Lddlm}|||t¬«}|j«}|t    ||«}nt|«}|j «rãt|«sØ|O|jj«d}    |    D],}
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<.ntjd«}ttjt!|«|«} |j#«}|r| g|z}n&t%|«Dcgc]}| j'«}}||j(|<t+|«}t|«}nVt+|j-««}|rt/|«n
t1d«}|Dcgc]}t3j4||«}}|r§|dk(rw|Dcgc]k}t7|t8«r|j'«nHt7|t.«s%t7|t:«r't=|j«r|j'd¬«n|m}}n+|Dcgc] }t?|d    «r|j'«n|"}}tA||||||¬
«Scc}wcc}wcc}wcc}w)z
    Segregate Series based on type and coerce into matrices.
    Needs to handle a lot of exceptional cases.
 
    Used in DataFrame.__init__
    r)ÚSeries)rNr8rjr}r@T)Údeepr8)r8r:r;)!Úpandas.core.seriesr¦r}ÚisnarDr%rrrÚnonzeror ÚiatrHr8rrrFÚsumrcrYÚlocrÚkeysr"r$ÚcomÚmaybe_iterable_to_listrGrrrÚhasattrrS)rZrNrMr8r:rYr¦rLÚmissingÚmidxsrgrPÚ    nan_dtypeÚvalÚnmissingÚrhsÚ_r®ÚkrOs                    rRÚdict_to_mgrrº§sEð"ÑÝ-á G´6Ô:Ø++-Ø=ô# 6¨7¨(Ñ#3Ó4Eä  Ó'Eð;;=Ô!1°%Ô!8ðÐ ð ×.Ñ.Ó0°Ñ3Øò(AÜ(¨¯©°A©¸ÀUÔKCØ$'FJJqMñ(ô
HH XÓ.    Ü8¼¿¹ÄÀUÃÈYÓWØ";;=ÙØ% (Ñ*Cô05°X«Ö?¨!388:Ð?CÐ?Ø&)

7Ñ#äfÜwÓ'ôDIIKÓ Ù!%%+¬=¸Ó+;Ø?CÖD¸!#×,Ñ,¨T°!©WÕ5ÐDÐDáØ'>ð öðôa¤Ô0ðôq¤%Ô(Ü! !¤YÔ/Ü+¨A¯G©GÔ4ð    VV VÔ&ðñðFñðGMÖMÀ¤'¨!¨WÔ"5affh¸1Ñ<ÐMFÐMä ¨%°uÀ#ÐSWÔXÐXùò?@ùòEùòùòNsÄI ÆI%Æ1A0I*È(%I/cóþt|d«r|t|dj«}t|||¬«\}}t|«}|3t    |dt
«rt |«}ntt|««}|||fS)zA
    Convert a single sequence of arrays to multiple arrays.
    rrj)    rr%Ú_fieldsrWrGrÚ_get_names_from_indexr$rF)rZrMrNr8rLs     rRÚnested_data_to_arraysr¾úszôd1gÔ 7 ?Üt AwÓ/ä  g°UÔ;OFGÜ7Ó#Gà}Üd1gyÔ)Ü)¨$Ó/Eä!¤# d£)Ó,Eà7EÐ!Ð!r_có°t|«dkDxrGt|d«xr7t|ddd«dk(xr"t|t«xr|j
dk(S)z7
    Check if we should use nested_data_to_arrays.
    rrJr?ra)rFrr~rGrrJ)rZs rRÚtreat_as_nestedrÀscô
    D    A ò    FÜaÓ!ò    FäDGV QÓ'¨1Ñ,ò    FôD¤.Ó1ÒD°d·i±iÀ1±nÐEð    r_có>t|«dk(rtjdt¬«St    |t
«r t |«}|dtjfSd}t|d«r4tj|Dcgc]
}||«c}«}t|«St    |dtj«rF|djdk(r4tj|Dcgc]
}||«c}«}t|«S||«}t|«Scc}wcc}w)Nr)rrrj.cópt|«rt|t«r|St|d¬«}t    |«}|S)NTr=)rrGrrr)ÚvÚress  rRÚconvertz"_prep_ndarraylike.<locals>.convert/s4ÜA¤*¨Q´Ô"=ØHä!¨4Ô0Ü$ QÓ'ð
r_) rFrHr|r}rGrcrÚnewaxisrrrIrJr)rrYrPrÅrÃs     rRrr#sôô6{aÒôxx¤fÔ-Ð-Ü    FEÔ    "ÜvÓ&Ø3

?Ñ#Ð#òôF1IÔÜ¨vÖ6¨!7 1:Ò6Ó7ôfÓÐô 
F1IrzzÔ    *¨v°a©y¯~©~ÀÒ/Bä¨vÖ6¨!7 1:Ò6Ó7ôfÓÐñäfÓÐùò7ùò7sÁ>DÃ!Dcó¶|jdk(r"|j|jddf«}|S|jdk7rtd|j«|S)zB
    Reshape 1D values, raise on anything else other than 2D.
    r?rrazMust pass 2-d input. shape=)rJrrrK)rs rRrrIs\ð{{aÒØ §¡¨a¡°!Ð 4Ó5ðMð
Ò    ÜÐ6°v·|±|°nÐEÓFÐFØMr_có¾d}g}g}|D]N}t|ttf«rn||j|d¬«}t|t«r!|j|ur|j|d¬«}|j |j«|j}n¶t|t«rp||jd«}t|ttf«rt|«}nt|«}tj||jtj ¬«}t#|||d¬«}t%j&||«|j d«|j |«Q||fS)NFrnÚO)Údefault©r8rY)rGrr"ÚastyperNr{ÚappendrrÚdictr!r#rr    Ú fast_multigetrHrr r¯Úrequire_length_match)rZrNr8ÚoindexÚhomogenizedrArµs       rRrErETs1ðFØKàDàó ÜcI¤uÐ-Ô.ØÐ Øjj ¨UjÓ3Ü#yÔ)¨c¯i©i¸uÑ.Dðkk %¨ekÓ4ØKKÔ(Ø++Cä#tÔ$ð>Ø"\\¨#Ó.Fäe¤m´^Ð%DÔEä% cÓ*Côs)CÜ×'Ñ'¨¨V¯^©^ÄRÇVÁVÔLä   e°5¸uÔECÜ×$Ñ$ S¨%Ô0ØKKÔà×Ñ3Öð; ð>ÐÐr_cóht|«dk(rtd«Sg}g}d}d}d}|D]Ø}t|t«rd}|j    |j
«1t|t«r+d}|j    t|j«««lt|«r-t|dd«dk(rd}|j    t|««¤t|tj«s¿|jdkDsÏtd«|s |std«|rt|«}n|r t|d¬«}|rztt!|««}t|«dkDrtd    «|rtd
«|r0|dt«k7r-d|ddt|«}    t|    «t|d«}t#«S) zR
    Try to infer an Index from the passed data, raise ValueError on failure.
    rFTrJr?z,Per-column arrays must each be 1-dimensionalz2If using all scalar values, you must pass an index©Úsortz%All arrays must be of the same lengthz<Mixing dicts with non-Series may lead to ambiguous ordering.z array length z does not match index length )rFr$rGrrÍrNrÎrr®rr~rHrIrJrKr'Úsetr%)
rZÚraw_lengthsÚindexesÚhave_raw_arraysÚhave_seriesÚ
have_dictsrµrNÚlengthsÚmsgs
          rRrDrD~s¥ô
4yA~ÜQÓÐàKØ,.GàOØKØJàòMÜc9Ô%ØKØNN399Õ%Ü TÔ "ØJØNN4 §¡£
Ó+Õ,Ü #Ô ¤7¨3°¸Ó#:¸aÒ#?Ø"OØ×Ñs 3xÕ(Ü RZZÕ (¨S¯X©X¸«\ÜÐKÓLÐLðMñ;ÜÐMÓNÐNáÜgÓ&Ù    Üg¨EÔ2áÜs;Ó'Ó(Üw<!ÒÜÐDÓEÐEáÜØNóð ñØqzS ZÒ'à# G¨A¡J <ð0Ü! %j\ð+ðô! oÐ%ä! '¨!¡*Ó-EäÓÐr_có0||j|«sg}|j|«}t|«D][\}}|dk(r;tj|t
¬«}|j tj«n||}|j|«]|}|}||fS)zB
    Pre-emptively (cheaply) reindex arrays with new columns.
    rkrj)    ÚequalsÚget_indexerrrHr|r}ÚfillrrÍ)    rLr\rMÚlengthÚ
new_arraysÚindexerrgr¹rPs             rRrXrX¹sðÐØ~~kÔ*à*,JØ!×-Ñ-¨gÓ6GÜ! 'Ó*ò '1Ø7ä(( 6´Ô8CØHHRVVÕ$à  )CØ×!Ñ! #Õ&ð 'ð FØ!Kà;ÐÐr_cótd|D««}|stt|««Stt    t|«««}d}t|«D]'\}}t |dd«}||||<d|||<|dz })t|«S)Nc3ó:K|]}t|dd«duyw)rzN)r~)rtÚss  rRrvz(_get_names_from_index.<locals>.<genexpr>ÕsèøÒKÀ  6¨4Ó0¸Ô<ÑKùsrrzzUnnamed r?)rr$rFrrcrr~r")rZÚ has_some_namerNÚcountrgrçrs       rRr½r½ÔsÜÑKÀdÔKÓKMÙÜS YÓ'Ð'ä ¤¤s¨4£yÓ!1Ó2EØ EÜ$ò1ÜAvtÓ$Ø=ØE!Hà! % Ð)E!HØQJEð ô<Ðr_cót|t|«}nt|«}|t|«}||fSt|«}||fSrr)r$r%)ÚNÚKrNrMs    rRrræsNð }ÜaÓ äUÓ#àÜ Ó"ð'>ÐôwÓ'Ø'>Ðr_có8ddlm}tt||««S)a·
    Converts a list of dataclass instances to a list of dictionaries.
 
    Parameters
    ----------
    data : List[Type[dataclass]]
 
    Returns
    --------
    list_dict : List[dict]
 
    Examples
    --------
    >>> from dataclasses import dataclass
    >>> @dataclass
    ... class Point:
    ...     x: int
    ...     y: int
 
    >>> dataclasses_to_dicts([Point(1, 2), Point(2, 3)])
    [{'x': 1, 'y': 2}, {'x': 2, 'y': 3}]
 
    r)Úasdict)ÚdataclassesrîrÚmap)rZrîs  rRÚdataclasses_to_dictsrñøsõ0#äFDÓ!Ó"Ð"r_cóÈt|«s¯t|tj«r|jj
rt |jj
«}|Dcgc]}||    }}t|«dk(r/t|«D]!\}}|jdk(s|dddf||<#||fSgt g«fSt|tj«rT|jj
>tt|jj
««}|Dcgc]}||    }}||fSt|dttf«rt|«}nst|dtj«rt||«\}}nFt|dt «rt#||«\}}n#|Dcgc] }t|«}}t|«}t%|||«\}    }|    |fScc}wcc}wcc}w)a    
    Return list of arrays, columns.
 
    Returns
    -------
    list[ArrayLike]
        These will become columns in a DataFrame.
    Index
        This will become frame.columns.
 
    Notes
    -----
    Ensures that len(result_arrays) == len(result_index).
    Nrra)rFrGrHrIr8Únamesr%rrJr"rÚtupleÚ_list_to_arraysrÚMappingÚ_list_of_dict_to_arraysrÚ_list_of_series_to_arraysÚ_finalize_columns_and_data)
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          rRrWrWs´ô$t9ÜdBJJÔ'Øzz×ÑÐ+ä& t§z¡z×'7Ñ'7Ó8Ø18Ö9¨$t*Ð9Ð9ät9 >ô#,¨FÓ"3ò23Ø88 q=Ø(+ªA¨q¨D©    F 1Ið2ðwÐ&Ø< Ó#Ð#Ð#ä    D"**Ô    %¨$¯*©*×*:Ñ*:Ð*FäTZZ×-Ñ-Ó.Ó/Ø#*Ö+a$q'Ð+Ð+ØwÐä$q'D¤%=Ô)ÜdÓ#Ü    DGS[[Ô    )Ü.¨t°WÓ=WÜ    DGYÔ    'Ü0°°wÓ?Wð#'Ö'QaÐ'Ð'ÜdÓ#ä1°#°wÀÓFÑGWØGÐÐùò=:ùò,ùò(sÁGÄGÆ"Gcót|dt«rtj|«}|Stj|«}|S)Nr)rGrôr    Úto_object_array_tuplesÚto_object_array)rZrús  rRrõrõQs@ô$q'5Ô!Ü×,Ñ,¨TÓ2ðNô×%Ñ% dÓ+ØNr_cóÞ|3|Dcgc]}t|ttf«s|}}t|d¬«}i}g}|D]}t    |dd«}|tt |««}t|«|vr|t|«}n|j|«x}|t|«<t|d¬«}    |jtj|    |««tj|«}
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|fScc}w)NFrÔrNTr=)rGrrr&r~r$rFÚidràrrÍrÚtake_ndrHÚvstack)rZrMrOÚ    pass_dataÚ indexer_cacheÚaligned_valuesrçrNrärrús           rRrørø\sòðà $ÖQ1¬
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    Convert list of dicts to numpy arrays
 
    if `columns` is not passed, column names are inferred from the records
    - for OrderedDict and dicts, the column names match
      the key insertion-order from the first record to the last.
    - For other kinds of dict-likes, the keys are lexically sorted.
 
    Parameters
    ----------
    data : iterable
        collection of records (OrderedDict, dict)
    columns: iterables or None
 
    Returns
    -------
    content : np.ndarray[object, ndim=2]
    columns : Index
    c3óNK|]}t|j««ywrr)rr®)rtrOs  rRrvz*_list_of_dict_to_arrays.<locals>.<genexpr>sèøÒ, !tAFFH~Ñ,ùs#%c3ó<K|]}t|t«ywrr)rGrÎ)rtÚds  rRrvz*_list_of_dict_to_arrays.<locals>.<genexpr>sèøÒ9¨qz !¤T×*Ñ9ùórÔ)rr    Úfast_unique_multiple_list_genr%rrÎÚdicts_to_arrayr)rZrMÚgenrÕÚpre_colsrrús       rRr÷r÷{sð.Ù, tÔ,ÜÑ9°DÔ9Ó9Ð9Ü×4Ñ4°S¸tÔDÜxÓ(ð8<Ö<°!aDA¤d¨1£gÑ-Ð<DÐ<ä× Ñ  ¤t¨G£}Ó5GØGÐÐùò=sÁ%Bcóøt|j«}    t||«}t|«r-|djtjk(r t||¬«}||fS#t$r}t    |«|d}~wwxYw)zG
    Ensure we have valid columns, cast object dtypes if possible.
    Nrrj)
rrÚ_validate_or_indexify_columnsÚAssertionErrorrKrFr8rHÚobject_Úconvert_object_array)rúrMr8ÚcontentsÚerrs     rRrùrù suôGIIHð'Ü/°¸'ÓBô
8} !×*Ñ*¬b¯j©jÒ8Ü'¨¸Ô>àWÐÐøôò'äo 3Ð&ûð'úsAÁ    A9Á(A4Á4A9cóè|tt|««}|St|t«xrt    d|D««}|s:t|«t|«k7r#tt|«dt|«d«|rrt|Dchc] }t|«c}«dkDrt d«|r@t|d«t|«k7r&t t|d«dt|«d«|Scc}w)a´
    If columns is None, make numbers as column names; Otherwise, validate that
    columns have valid length.
 
    Parameters
    ----------
    content : list of np.ndarrays
    columns : Index or None
 
    Returns
    -------
    Index
        If columns is None, assign positional column index value as columns.
 
    Raises
    ------
    1. AssertionError when content is not composed of list of lists, and if
        length of columns is not equal to length of content.
    2. ValueError when content is list of lists, but length of each sub-list
        is not equal
    3. ValueError when content is list of lists, but length of sub-list is
        not equal to length of content
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    Internal function to convert object array.
 
    Parameters
    ----------
    content: List[np.ndarray]
    dtype: np.dtype or ExtensionDtype
    dtype_backend: Controls if nullable/pyarrow dtypes are returned.
    coerce_float: Cast floats that are integers to int.
 
    Returns
    -------
    List[ArrayLike]
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