MhCbSSKJr SSKrSSKJr SSKrSSKJr SSK J r SSK J r SSK Jr SSKJs Jr SSKJr SS KJr SS KJr SS KJr SS KJr \(aSS KJr SSKJ r SSK!J"r" SSjr#\"\SSSS.-5SSSjj5r$SSSjjr%SSSjjr&g)) annotationsN) TYPE_CHECKING)Appender) is_list_like) concat_compat)notna) MultiIndex)concat) tile_compat) _shared_docs) to_numeric)Hashable) AnyArrayLike) DataFramecUbV[U5(dU/$[U[5(a#[U[5(d[ US35e[U5$/$)Nz7 must be a list of tuples when columns are a MultiIndex)r isinstancer list ValueError)arg_varsvariablecolumnss J/var/www/html/env/lib/python3.13/site-packages/pandas/core/reshape/melt.pyensure_list_varsrs]H%%:   , ,Z$5O5O*ST > ! meltz pd.melt(df, zDataFrame.melt)callerothercX@R;a[SUS35e[USUR5nUSLn[USUR5nU(dU(aUbURRU5nO URnX-n UR U 5n U S:Hn U R 5(a6[ X5V V s/sHupU (dMU PM nn n [SU35eU(a(URSS2[R"U 54nO!UR5nOUR5nUb URRU5UlUc[UR[5(a[URR5[[!URR55:XaURRnO[#[URR55Vs/sHnSU3PM nnOTURR$bURR$OS/nO#['U5(a[S U<S 35eU/nUR(unnU[U5- n0nUHnUR+U5n[UR,[.R,5(dCUS :a[1U/U-S S 9UU'MV[3U5"/UR$UR,S9UU'M[.R4"UR6U5UU'M X-U/-nUR(SS :aq[ SUR855(dP[1[#UR(S5Vs/sHoRSS2U4PM sn5R:UU'OUR6R=S5UU'[?U5H3unnURRAU5RCU5UU'M5 UREUUS9nU(dURHU5Ul$U$s sn n fs snfs snf)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)namedtypec3# UH7n[U[R5(+=(a URv M9 g7f)N)rnpr% _supports_2d).0dts r melt..zs-&CORJr288 $$88*>)*> ""0!13  #JJq%,,s"334EJJLE  66yA  emmZ 0 05==&&'3s5==3F3F/G+HH ==..5:3u}}?R?R;S5TU5TisO5TU',mm&8&8&D ""*H h  IH;&89:::++KHaCL(*,E))C.'--22 1$#WI0A$APTUc "']2GLL Vc 2CDE#J!ZL0H {{1~#&CH<<&###',U[[^'< ='>xHc &  x  8F "5;;0AB MG.V@ >s QQQ#Q(cX0n/n[5n[[[UR 5555nUR 5Hlupx[U5U:wa [ S5eUV s/sHoU RPM n n [U 5X7'URU5 URU5nMn [URRU55n U H(n [R"X RU5X9'M* U(a|[R "[X4S5["S9n UHn U [%X=5-n M U R'5(d'UR 5VVs0sH upXU _M nnnUR)X;U-S9$s sn fs snnf)a  Reshape wide-format data to long. Generalized inverse of DataFrame.pivot. Accepts a dictionary, ``groups``, in which each key is a new column name and each value is a list of old column names that will be "melted" under the new column name as part of the reshape. Parameters ---------- data : DataFrame The wide-format DataFrame. groups : dict {new_name : list_of_columns}. dropna : bool, default True Do not include columns whose entries are all NaN. Returns ------- DataFrame Reshaped DataFrame. See Also -------- melt : Unpivot a DataFrame from wide to long format, optionally leaving identifiers set. pivot : Create a spreadsheet-style pivot table as a DataFrame. DataFrame.pivot : Pivot without aggregation that can handle non-numeric data. DataFrame.pivot_table : Generalization of pivot that can handle duplicate values for one index/column pair. DataFrame.unstack : Pivot based on the index values instead of a column. wide_to_long : Wide panel to long format. Less flexible but more user-friendly than melt. Examples -------- >>> data = pd.DataFrame({'hr1': [514, 573], 'hr2': [545, 526], ... 'team': ['Red Sox', 'Yankees'], ... 'year1': [2007, 2007], 'year2': [2008, 2008]}) >>> data hr1 hr2 team year1 year2 0 514 545 Red Sox 2007 2008 1 573 526 Yankees 2007 2008 >>> pd.lreshape(data, {'year': ['year1', 'year2'], 'hr': ['hr1', 'hr2']}) team year hr 0 Red Sox 2007 514 1 Yankees 2007 573 2 Red Sox 2008 545 3 Yankees 2008 526 z$All column lists must be same lengthr)r%r/)r;r9nextiterrCitemsrrArappendunionrr differencer(r@onesboolrallrH)datagroupsdropnarZ pivot_colsall_colsrXtargetr:r[ to_concatid_colsmaskckvs rlreshaperusgj EJ!eH Dfmmo& '(A  u:?CD D278%3#Y&&% 8%i0 &!>>%((4<<**845GWWTY..2 wws5A/0=A E%(O #Dxxzz,1KKM:MDAQ$ZME:   Uj,@  AA#9;s -F!;F&c SSjnS Sjn[U5(dU/nO [U5nURRU5R 5(a [ S5e[U5(dU/nO [U5nXR 5R 5(a [ S5e/n/n UH6n U"X XE5n U RU 5 URU"X X#X55 M8 [USS9n URRU 5n X n[U5S:Xa URU5RU 5$URU R5US9RX#/-5$) a( 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 cS[R"U5[R"U5US3nURURRR U5$)N^$)reescaperstrmatch)dfstubsepsuffixregexs r get_var_names#wide_to_long..get_var_namessLRYYt_%biin%5fXQ?zz"**....u566rc"[UUUURU5US9nXcRR[R "X-5SSS9Xc'[ Xc5Xc'URX#/-5$![[[4a N+f=f)N)rr rLrKT)r) rrstripr|replacerzr{r TypeErrorr OverflowError set_index)r~rrVjr rnewdfs r melt_stubwide_to_long..melt_stubs !{{3'  8<<'' $*(=r'N !%(+EH q3w'' :}5   sA66B Bz,stubname can't be identical to a column namez3the id variables need to uniquely identify each rowr&)axis)on)rr|rr|rr|)rr|rr|)rrrisinr2r duplicatedextendrcr rer9rjoinmerge reset_index)r~ stubnamesrVrrrrr_meltedvalue_vars_flattenedr value_varmeltedrnews r wide_to_longrsO\7(&  " "K O  zzy!%%''GHH ?? C G uNOOG!"C8 ##I.y1@A G! $Fjj##$89G +C 1v{}}Q$$V,,yy++-!y4>>q3wGGr)rr|returnr)NNNvalueNT)rJrrLrr#rgrr)T)rirrjdictrkrgrr)rz\d+)r~rrr|rr|rr)' __future__rrztypingrnumpyr(pandas.util._decoratorsrpandas.core.dtypes.commonrpandas.core.dtypes.concatrpandas.core.dtypes.missingrpandas.core.algorithmscore algorithmsr6pandas.core.indexes.apir pandas.core.reshape.concatr pandas.core.reshape.utilr pandas.core.shared_docsr pandas.core.tools.numericr collections.abcrpandas._typingrpandasrrrrurrrrs" ,23,&&.-000(+   ,v NEU!V VW  "^ ^  ^^^X^BMBbBHcHcH),cH;>cHcHr