ehCJddlmZddlZddlmZddlZddlmZddl m Z ddl m Z ddl mZddlmcmZddlmZdd lmZdd lmZdd lmZdd lmZerdd lmZddlm Z ddl!m"Z"ddZ#eeddddz d ddZ$dddZ% d ddZ&y)) annotationsN) TYPE_CHECKING)Appender) is_list_like) concat_compat)notna) MultiIndex)concat) tile_compat) _shared_docs) to_numeric)Hashable) AnyArrayLike) DataFramec|Gt|s|gSt|trt|tst |dt|SgS)Nz7 must be a list of tuples when columns are a MultiIndex)r isinstancer list ValueError)arg_varsvariablecolumnss V/var/www/skyplay_api_hub/venv/lib/python3.12/site-packages/pandas/core/reshape/melt.pyensure_list_varsrsTH%:   ,Z$5O*ST > ! meltz pd.melt(df, zDataFrame.melt)callerotherc\||jvrtd|dt|d|j}|du}t|d|j}|s|r||jj|}n |j}||z} |j | } | dk(} | j r/t | | D cgc] \} } | s |  }} } td||r'|jddtj| f}n!|j}n|j}| |jj||_|t|jtrt|jjtt!|jjk(r|jj}nt#t|jjDcgc]}d| }}nM|jj$|jj$ndg}nt'|rtd |d |g}|j(\}}|t|z }i}|D]}|j+|}t|j,t.j,sF|d kDrt1|g|zd ||<Rt3|g|j$|j,||<~t/j4|j6|||<||z|gz}|j(dd kDrjt d|j8DsNt1t#|j(dDcgc]}|jdd|fc}j:||<n|j6j=d||<t?|D]2\}}|jjA|jC|||<4|jE||}|stG|jH||_$|Scc} } wcc}wcc}w)Nz value_name (z3) cannot match an element in the DataFrame columns.id_vars value_varszFThe following id_vars or value_vars are not present in the DataFrame: variable_rz var_name=z must be a scalar.rT) ignore_index)namedtypec3nK|]-}t|tj xr |j/yw)N)rnpr% _supports_2d).0dts r zmelt..zs/&=?Jr288 $$88&s35Fr)%rrrget_level_valuesget_indexer_foranyzipKeyErrorilocalgosuniquecopyrr lennamessetranger$rshapepopr%r(r typetile_valuesdtypesvaluesravel enumerate_get_level_valuesrepeat _constructorr index)framerr var_name value_name col_levelr#value_vars_was_not_nonelevellabelsidxmissinglab not_foundmissing_labelsinum_rowsKnum_cols_adjustedmdatacolid_datamcolumnsresults rrr+sU]]":,'% %  w 5==AG(4!*lEMMJJ*  MM229=EMME:%##F+) ;;=*-fg*>&Y)N""0!13  #JJq%,,s"334EJJLE  66yA  emmZ 05==&&'3s5==3F3F/G+HH ==..5:3u}}?R?R;S5TUisOUU',mm&8&8&D ""*H h IH;&89:::++KHaCL(*,E E))C.'--2 1$#WI0A$APTUc +T']2GLL Vc 2CDE#J E!ZL0H {{1~#&CH<<&##',U[[^'< =!UZZ1  = & j"MM//4jH%I3]]44Q7>>xHc I  x  8F "5;;0AB MG.V@ >s8 PP) P$P)cFi}g}t}ttt|j }|j D]j\}}t||k7r t d|D cgc]} || j} } t| ||<|j||j|}lt|jj|} | D](} tj|| j||| <*|rxtj t||dt"} |D]} | t%|| z} | j's&|j Dcic] \}}|||  }}}|j)|| |zScc} wcc}}w)a Reshape wide-format data to long. Generalized inverse of DataFrame.pivot. Accepts a dictionary, ``groups``, in which each key is a new column name and each value is a list of old column names that will be "melted" under the new column name as part of the reshape. Parameters ---------- data : DataFrame The wide-format DataFrame. groups : dict {new_name : list_of_columns}. dropna : bool, default True Do not include columns whose entries are all NaN. Returns ------- DataFrame Reshaped DataFrame. See Also -------- melt : Unpivot a DataFrame from wide to long format, optionally leaving identifiers set. pivot : Create a spreadsheet-style pivot table as a DataFrame. DataFrame.pivot : Pivot without aggregation that can handle non-numeric data. DataFrame.pivot_table : Generalization of pivot that can handle duplicate values for one index/column pair. DataFrame.unstack : Pivot based on the index values instead of a column. wide_to_long : Wide panel to long format. Less flexible but more user-friendly than melt. Examples -------- >>> data = pd.DataFrame({'hr1': [514, 573], 'hr2': [545, 526], ... 'team': ['Red Sox', 'Yankees'], ... 'year1': [2007, 2007], 'year2': [2008, 2008]}) >>> data hr1 hr2 team year1 year2 0 514 545 Red Sox 2007 2008 1 573 526 Yankees 2007 2008 >>> pd.lreshape(data, {'year': ['year1', 'year2'], 'hr': ['hr1', 'hr2']}) team year hr 0 Red Sox 2007 514 1 Yankees 2007 573 2 Red Sox 2008 545 3 Yankees 2008 526 z$All column lists must be same lengthr)r%r.)r:r8nextiterrBitemsrr@rappendunionrr differencer(r?onesboolrallrG)datagroupsdropnarY pivot_colsall_colsrWtargetr9rZ to_concatid_colsmaskckvs rlreshapertsj EJ!eH Dfmmo& '(A)  u:?CD D2783T#Y&&8 8%i0f &!>>%()4<<**845G3WWT#Y..2c 3wws5A/0= $A E%(O #D $xxz,1KKM:DAqQ$Z:E:   UGj,@  AA#9;s -F/Fc dd}d d}t|s|g}n t|}|jj|j r t dt|s|g}n t|}||j j r t dg}g} |D]:} ||| ||} | j| |j||| ||| |<t|d} |jj| } || }t|dk(r |j|j| S|j| j|j||gzS) ax Unpivot a DataFrame from wide to long format. Less flexible but more user-friendly than melt. With stubnames ['A', 'B'], this function expects to find one or more group of columns with format A-suffix1, A-suffix2,..., B-suffix1, B-suffix2,... You specify what you want to call this suffix in the resulting long format with `j` (for example `j='year'`) Each row of these wide variables are assumed to be uniquely identified by `i` (can be a single column name or a list of column names) All remaining variables in the data frame are left intact. Parameters ---------- df : DataFrame The wide-format DataFrame. stubnames : str or list-like The stub name(s). The wide format variables are assumed to start with the stub names. i : str or list-like Column(s) to use as id variable(s). j : str The name of the sub-observation variable. What you wish to name your suffix in the long format. sep : str, default "" A character indicating the separation of the variable names in the wide format, to be stripped from the names in the long format. For example, if your column names are A-suffix1, A-suffix2, you can strip the hyphen by specifying `sep='-'`. suffix : str, default '\\d+' A regular expression capturing the wanted suffixes. '\\d+' captures numeric suffixes. Suffixes with no numbers could be specified with the negated character class '\\D+'. You can also further disambiguate suffixes, for example, if your wide variables are of the form A-one, B-two,.., and you have an unrelated column A-rating, you can ignore the last one by specifying `suffix='(!?one|two)'`. When all suffixes are numeric, they are cast to int64/float64. Returns ------- DataFrame A DataFrame that contains each stub name as a variable, with new index (i, j). See Also -------- melt : Unpivot a DataFrame from wide to long format, optionally leaving identifiers set. pivot : Create a spreadsheet-style pivot table as a DataFrame. DataFrame.pivot : Pivot without aggregation that can handle non-numeric data. DataFrame.pivot_table : Generalization of pivot that can handle duplicate values for one index/column pair. DataFrame.unstack : Pivot based on the index values instead of a column. Notes ----- All extra variables are left untouched. This simply uses `pandas.melt` under the hood, but is hard-coded to "do the right thing" in a typical case. Examples -------- >>> np.random.seed(123) >>> df = pd.DataFrame({"A1970" : {0 : "a", 1 : "b", 2 : "c"}, ... "A1980" : {0 : "d", 1 : "e", 2 : "f"}, ... "B1970" : {0 : 2.5, 1 : 1.2, 2 : .7}, ... "B1980" : {0 : 3.2, 1 : 1.3, 2 : .1}, ... "X" : dict(zip(range(3), np.random.randn(3))) ... }) >>> df["id"] = df.index >>> df A1970 A1980 B1970 B1980 X id 0 a d 2.5 3.2 -1.085631 0 1 b e 1.2 1.3 0.997345 1 2 c f 0.7 0.1 0.282978 2 >>> pd.wide_to_long(df, ["A", "B"], i="id", j="year") ... # doctest: +NORMALIZE_WHITESPACE X A B id year 0 1970 -1.085631 a 2.5 1 1970 0.997345 b 1.2 2 1970 0.282978 c 0.7 0 1980 -1.085631 d 3.2 1 1980 0.997345 e 1.3 2 1980 0.282978 f 0.1 With multiple id columns >>> df = pd.DataFrame({ ... 'famid': [1, 1, 1, 2, 2, 2, 3, 3, 3], ... 'birth': [1, 2, 3, 1, 2, 3, 1, 2, 3], ... 'ht1': [2.8, 2.9, 2.2, 2, 1.8, 1.9, 2.2, 2.3, 2.1], ... 'ht2': [3.4, 3.8, 2.9, 3.2, 2.8, 2.4, 3.3, 3.4, 2.9] ... }) >>> df famid birth ht1 ht2 0 1 1 2.8 3.4 1 1 2 2.9 3.8 2 1 3 2.2 2.9 3 2 1 2.0 3.2 4 2 2 1.8 2.8 5 2 3 1.9 2.4 6 3 1 2.2 3.3 7 3 2 2.3 3.4 8 3 3 2.1 2.9 >>> l = pd.wide_to_long(df, stubnames='ht', i=['famid', 'birth'], j='age') >>> l ... # doctest: +NORMALIZE_WHITESPACE ht famid birth age 1 1 1 2.8 2 3.4 2 1 2.9 2 3.8 3 1 2.2 2 2.9 2 1 1 2.0 2 3.2 2 1 1.8 2 2.8 3 1 1.9 2 2.4 3 1 1 2.2 2 3.3 2 1 2.3 2 3.4 3 1 2.1 2 2.9 Going from long back to wide just takes some creative use of `unstack` >>> w = l.unstack() >>> w.columns = w.columns.map('{0[0]}{0[1]}'.format) >>> w.reset_index() famid birth ht1 ht2 0 1 1 2.8 3.4 1 1 2 2.9 3.8 2 1 3 2.2 2.9 3 2 1 2.0 3.2 4 2 2 1.8 2.8 5 2 3 1.9 2.4 6 3 1 2.2 3.3 7 3 2 2.3 3.4 8 3 3 2.1 2.9 Less wieldy column names are also handled >>> np.random.seed(0) >>> df = pd.DataFrame({'A(weekly)-2010': np.random.rand(3), ... 'A(weekly)-2011': np.random.rand(3), ... 'B(weekly)-2010': np.random.rand(3), ... 'B(weekly)-2011': np.random.rand(3), ... 'X' : np.random.randint(3, size=3)}) >>> df['id'] = df.index >>> df # doctest: +NORMALIZE_WHITESPACE, +ELLIPSIS A(weekly)-2010 A(weekly)-2011 B(weekly)-2010 B(weekly)-2011 X id 0 0.548814 0.544883 0.437587 0.383442 0 0 1 0.715189 0.423655 0.891773 0.791725 1 1 2 0.602763 0.645894 0.963663 0.528895 1 2 >>> pd.wide_to_long(df, ['A(weekly)', 'B(weekly)'], i='id', ... j='year', sep='-') ... # doctest: +NORMALIZE_WHITESPACE X A(weekly) B(weekly) id year 0 2010 0 0.548814 0.437587 1 2010 1 0.715189 0.891773 2 2010 1 0.602763 0.963663 0 2011 0 0.544883 0.383442 1 2011 1 0.423655 0.791725 2 2011 1 0.645894 0.528895 If we have many columns, we could also use a regex to find our stubnames and pass that list on to wide_to_long >>> stubnames = sorted( ... set([match[0] for match in df.columns.str.findall( ... r'[A-B]\(.*\)').values if match != []]) ... ) >>> list(stubnames) ['A(weekly)', 'B(weekly)'] All of the above examples have integers as suffixes. It is possible to have non-integers as suffixes. >>> df = pd.DataFrame({ ... 'famid': [1, 1, 1, 2, 2, 2, 3, 3, 3], ... 'birth': [1, 2, 3, 1, 2, 3, 1, 2, 3], ... 'ht_one': [2.8, 2.9, 2.2, 2, 1.8, 1.9, 2.2, 2.3, 2.1], ... 'ht_two': [3.4, 3.8, 2.9, 3.2, 2.8, 2.4, 3.3, 3.4, 2.9] ... }) >>> df famid birth ht_one ht_two 0 1 1 2.8 3.4 1 1 2 2.9 3.8 2 1 3 2.2 2.9 3 2 1 2.0 3.2 4 2 2 1.8 2.8 5 2 3 1.9 2.4 6 3 1 2.2 3.3 7 3 2 2.3 3.4 8 3 3 2.1 2.9 >>> l = pd.wide_to_long(df, stubnames='ht', i=['famid', 'birth'], j='age', ... sep='_', suffix=r'\w+') >>> l ... # doctest: +NORMALIZE_WHITESPACE ht famid birth age 1 1 one 2.8 two 3.4 2 one 2.9 two 3.8 3 one 2.2 two 2.9 2 1 one 2.0 two 3.2 2 one 1.8 two 2.8 3 one 1.9 two 2.4 3 1 one 2.2 two 3.3 2 one 2.3 two 3.4 3 one 2.1 two 2.9 cdtj|tj||d}|j|jjj |S)N^$)reescaperstrmatch)dfstubsepsuffixregexs r get_var_namesz#wide_to_long..get_var_namessLRYYt_%biin%5fXQ?zz"**....u566rc6t||||j||}||jjt j ||zdd||< t ||||<|j||gzS#tttf$rY+wxYw)N)rr rKrJT)r) rrstripr{replaceryrzr TypeErrorr OverflowError set_index)r}r~rUjr rnewdfs r melt_stubzwide_to_long..melt_stubs !{{3'  8<<'' $*(=r'Na !%(+E!H qA3w'' :}5   sBBBz,stubname can't be identical to a column namez3the id variables need to uniquely identify each rowr&)axis)on)r~r{rr{rr{)r~r{rr{)rrrisinr1r duplicatedextendrbr rdr8rjoinmerge reset_index)r} stubnamesrUrrrrr_meltedvalue_vars_flattenedr~ value_varmeltedrnews r wide_to_longrs[\7(&  "K O  zzy!%%'GHH ? C G !uNOOGB!"dC8 ##I.yT1aC@AB G! $Fjj##$89G W+C 1v{}}Q$$V,,yy++-!y4>>qA3wGGr)rr{returnr)NNNvalueNT)rIrrKrr#rfrr)T)rhrridictrjrfrr)rz\d+)r}rrr{rr{rr)' __future__rrytypingrnumpyr(pandas.util._decoratorsrpandas.core.dtypes.commonrpandas.core.dtypes.concatrpandas.core.dtypes.missingrpandas.core.algorithmscore algorithmsr5pandas.core.indexes.apir pandas.core.reshape.concatr pandas.core.reshape.utilr pandas.core.shared_docsr pandas.core.tools.numericr collections.abcrpandas._typingrpandasrrrrtrrrrs" ,23,&&.-000(+   ,v NEU!V VW  "^ ^  ^^^X^BMBbBHcHcH),cH;>cHcHr