!hCBddlmZddlZddlmZddlZddlmZddl m Z ddl m Z ddl mZddlmcmZddlmZdd lmZdd lmZdd lmZdd lmZerdd lmZddlm Z ddl!m"Z"d-dZ#eeddddz d.d/d!Z$d0d1d&Z% d2d3d,Z&dS)4) annotationsN) TYPE_CHECKING)Appender) is_list_like) concat_compat)notna) MultiIndex)concat) tile_compat) _shared_docs) to_numeric)Hashable) AnyArrayLike) DataFramevariablestrreturnlistc|]t|s|gSt|tr't|tst |dt|SgS)Nz7 must be a list of tuples when columns are a MultiIndex)r isinstancer r ValueError)arg_varsrcolumnss Rc:\xampp_lite_8_4\www\timesheet\venv\Lib\site-packages\pandas/core/reshape/melt.pyensure_list_varsrstH%% ":   , , "Z$5O5O "TTT >> ! meltz pd.melt(df, zDataFrame.melt)callerothervalueTframer value_namer ignore_indexboolc|jvrtd|dt|dj}|du}t|dj}|s|r|j|}nj}||z} || } | dk} | r,dt | | D} td| |r$jddtj | fn) n |j|_|tjtrtjjtt!jjkr jj}nrdt#tjjD}nAjj jjnd g}n%t'|rtd |d |g}j\} }|t|z }i}|D]}|}t|jt.jsH|d krt1|g|zd ||<Ut3|g|j|j||<~t/j|j|||<||z|gz}jdd krZt djDszmelt..Gs1&Y)rzFThe following id_vars or value_vars are not present in the DataFrame: cg|]}d|S) variable_r*)r+is rr.zmelt..^sUUUOOOUUUrrz var_name=z must be a scalar.rT)r#)namedtypec3ZK|]&}t|tj o|jV'dSN)rnpr3 _supports_2d)r+dts r zmelt..zsI&&=?Jr28 $ $$8&&&&&&rc2g|]}jdd|fSr6)iloc)r+r1r!s rr.zmelt..~s( = = =!UZ1  = = =rFr)%rrrget_level_valuesget_indexer_foranyzipKeyErrorr<algosuniquecopyrr lennamessetranger2rshapepopr3r7r typetile_valuesdtypesvaluesravel enumerate_get_level_valuesrepeat _constructorr index)r!r&r'var_namer" col_levelr#value_vars_was_not_nonelevellabelsidxmissingmissing_labelsnum_rowsKnum_cols_adjustedmdatacolid_datamcolumnsr1results` rrr+sOU]"" %: % % %   w 5=AAG(4!*lEMJJJ*  M229==EEME:%##F++) ;;== *-fg*>*>N3"033  # !Jqqq%,s"3"334EEJJLLEE  66yAA  emZ 0 0 5=&''3s5=3F/G/G+H+HHH =.UUU3u}?R;S;S5T5TUUU',m&8&D ""*HH h  9H999::::+KHaCLL(*,E E E))C..'-22 E 1$$#WI0A$APTUUUc +T']]2GL VVVc 2CDDE#JJ!ZL0H {1~#&&CH<&&&### = = = =uU[^'<'< = = =   j"M//44jH%%II3]44Q77>>xHHc   x  8 8F C"5;0ABB Mrdatagroupsdictdropnac i}g}t}ttt|}|D]q\}}t||krt dfd|D} t| ||<||| |}rtj |} | D]%} tj| j||| <&|rtjt||dt" |D]} t%|| z  s fd|D}|| |zS)a Reshape wide-format data to long. Generalized inverse of DataFrame.pivot. Accepts a dictionary, ``groups``, in which each key is a new column name and each value is a list of old column names that will be "melted" under the new column name as part of the reshape. Parameters ---------- data : DataFrame The wide-format DataFrame. groups : dict {new_name : list_of_columns}. dropna : bool, default True Do not include columns whose entries are all NaN. Returns ------- DataFrame Reshaped DataFrame. See Also -------- melt : Unpivot a DataFrame from wide to long format, optionally leaving identifiers set. pivot : Create a spreadsheet-style pivot table as a DataFrame. DataFrame.pivot : Pivot without aggregation that can handle non-numeric data. DataFrame.pivot_table : Generalization of pivot that can handle duplicate values for one index/column pair. DataFrame.unstack : Pivot based on the index values instead of a column. wide_to_long : Wide panel to long format. Less flexible but more user-friendly than melt. Examples -------- >>> data = pd.DataFrame({'hr1': [514, 573], 'hr2': [545, 526], ... 'team': ['Red Sox', 'Yankees'], ... 'year1': [2007, 2007], 'year2': [2008, 2008]}) >>> data hr1 hr2 team year1 year2 0 514 545 Red Sox 2007 2008 1 573 526 Yankees 2007 2008 >>> pd.lreshape(data, {'year': ['year1', 'year2'], 'hr': ['hr1', 'hr2']}) team year hr 0 Red Sox 2007 514 1 Yankees 2007 573 2 Red Sox 2008 545 3 Yankees 2008 526 z$All column lists must be same lengthc*g|]}|jSr*)rO)r+rdrhs rr.zlreshape..s 8883T#Y&888rr)r3c(i|]\}}||Sr*r*)r+kvmasks r zlreshape..s#:::DAqQ$:::rr>)rIrGnextiterrQitemsrrappendunionrr differencer7rNrOonesr$rallrV)rhrirkrc pivot_colsall_colsratargetrH to_concatid_colsrdcrqs` @rlreshapersj EJ!eeH Dfmmoo&& ' '((A))  u::??CDD D8888%888 %i00f &!!!>>%((4<**84455G33WT#Y.22c  ;ws5A/00=== $ $A E%(OO #DDxxzz ;::::EKKMM:::E   UGj,@  A AAr\d+dfsepsuffixc d d}dd}t|s|g}nt|}|j|rt dt|s|g}nt|}||rt dg}g} |D]H} ||| ||} | | |||| ||| |It|d } |j | } || }t|d kr(| | | S|| |  ||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 stubrrrcdtj|tj||d}|j|jj|S)N^$)reescaperrmatch)rrrrregexs r get_var_namesz#wide_to_long..get_var_namessK?RYt__?binn?f???z"*...u5566rcft||||||}||jt j||zdd||< t ||||<n#tttf$rYnwxYw| ||gzS)N)r&r'r"rXrT)r) rrstriprreplacerrr TypeErrorr OverflowError set_index)rrr1jr'rnewdfs r melt_stubzwide_to_long..melt_stubs !{{3''    8<'' $*(=(=r'NNa !%(++E!HH:}5    D qA3w'''s$A==BBz,stubname can't be identical to a column namez3the id variables need to uniquely identify each rowr4)axis)onN)rrrrrr)rrrr)rrrisinrAr duplicatedextendrvr rxrGrjoinmerge reset_index)r stubnamesr1rrrrr_meltedvalue_vars_flattenedr value_varmeltedr&news r wide_to_longrs\7777((((&  " "$K OO  zy!!%%''IGHHH ?? 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