i ! pandasc V ||jvrtd|dt|d|j}|du}t|d|j}t|jj |t|kDr td|s|r||jj |}n |j}||z} |j | } | dk(} | j r1t| | d D cgc] \} } | s |  }} } td ||r'|jddtj| f}n%|jd }n|jd }| |jj ||_|t|jtrt|jjtt!|jjk(r|jj}nt#t|jjDcgc]}d | }}n|jj$|jj$ndg}nt'|rt|jtrht)|r t+|}t|t|jkDrCtd|dt|dt|jdtd|d|g}|j,\}}|t|z }i}|D]}|j/|}t|j0t2j0sF|dkDrt5|g|zd||<Rt7|g|j$|j0||<~t3j8|j:|||<||z|gz}|j,ddkDrlt d|j<DsPt5t#|j,dDcgc]}|jdd|fc}dj>||<n|j:jAd||<tC|D]2\}}|jjE|jG|||<4|jI||}|sRt3j8t3jJt||}|jLjO||_&|Scc} } wcc}wcc}w)a Unpivot a DataFrame from wide to long format, optionally leaving identifiers set. This function is useful to reshape a DataFrame into a format where one or more columns are identifier variables (`id_vars`), while all other columns are considered measured variables (`value_vars`), and are "unpivoted" to the row axis, leaving just two non-identifier columns, 'variable' and 'value'. Parameters ---------- frame : DataFrame The DataFrame to unpivot. id_vars : scalar, tuple, list, or ndarray, optional Column(s) to use as identifier variables. value_vars : scalar, tuple, list, or ndarray, optional Column(s) to unpivot. If not specified, uses all columns that are not set as `id_vars`. var_name : scalar, tuple, list, or ndarray, optional Name to use for the 'variable' column. If None it uses ``frame.columns.name`` or 'variable'. Must be a scalar if columns are a MultiIndex. value_name : scalar, default 'value' Name to use for the 'value' column, can't be an existing column label. col_level : scalar, optional If columns are a MultiIndex then use this level to melt. ignore_index : bool, default True If True, original index is ignored. If False, the original index is retained. Index labels will be repeated as necessary. Returns ------- DataFrame Unpivoted DataFrame. See Also -------- DataFrame.melt : Identical method. pivot_table : Create a spreadsheet-style pivot table as a DataFrame. DataFrame.pivot : Return reshaped DataFrame organized by given index / column values. DataFrame.explode : Explode a DataFrame from list-like columns to long format. Notes ----- Reference :ref:`the user guide ` for more examples. Examples -------- >>> df = pd.DataFrame( ... { ... "A": {0: "a", 1: "b", 2: "c"}, ... "B": {0: 1, 1: 3, 2: 5}, ... "C": {0: 2, 1: 4, 2: 6}, ... } ... ) >>> df A B C 0 a 1 2 1 b 3 4 2 c 5 6 >>> pd.melt(df, id_vars=["A"], value_vars=["B"]) A variable value 0 a B 1 1 b B 3 2 c B 5 >>> pd.melt(df, id_vars=["A"], value_vars=["B", "C"]) A variable value 0 a B 1 1 b B 3 2 c B 5 3 a C 2 4 b C 4 5 c C 6 The names of 'variable' and 'value' columns can be customized: >>> pd.melt( ... df, ... id_vars=["A"], ... value_vars=["B"], ... var_name="myVarname", ... value_name="myValname", ... ) A myVarname myValname 0 a B 1 1 b B 3 2 c B 5 Original index values can be kept around: >>> pd.melt(df, id_vars=["A"], value_vars=["B", "C"], ignore_index=False) A variable value 0 a B 1 1 b B 3 2 c B 5 0 a C 2 1 b C 4 2 c C 6 If you have multi-index columns: >>> df.columns = [list("ABC"), list("DEF")] >>> df A B C D E F 0 a 1 2 1 b 3 4 2 c 5 6 >>> pd.melt(df, col_level=0, id_vars=["A"], value_vars=["B"]) A variable value 0 a B 1 1 b B 3 2 c B 5 >>> pd.melt(df, id_vars=[("A", "D")], value_vars=[("B", "E")]) (A, D) variable_0 variable_1 value 0 a B E 1 1 b B E 3 2 c B E 5 z value_name (z3) cannot match an element in the DataFrame columns.id_varsN value_varsz)id_vars cannot contain duplicate columns.T)strictzFThe following id_vars or value_vars are not present in the DataFrame: F)deep variable_rz var_name=z has z, items, but the dataframe columns only have z levels.z must be a scalar.r) ignore_index)namedtypec3nK|]-}t|tj xr |j/yw)N)rnpr$ _supports_2d).0dts r zmelt..s/&=?Jr288 $$88&s35Fr)(rrrlenget_indexer_forget_level_valuesanyzipKeyErrorilocalgosuniquecopyrr namessetranger#rrrshapepopr$r'r typetile_valuesdtypesvaluesravel enumerate_get_level_valuesrepeat _constructorarangeindextake)framerrvar_name value_name col_levelr"value_vars_was_not_nonelevellabelsidxmissinglab not_foundmissing_labelsinum_rowsKnum_cols_adjustedmdatacolid_datamcolumnsresulttakers rmeltr`,sNU]]":,'% %  w 5==AG(4!*lEMMJJ 5== ( ( 12S\ADEE*  MM229=EMME:%##F+) ;;=*-fgd*K&YyN""0!13  #JJq%,,s"334EJJEJ*E  & 66yA  emmZ 05==&&'3s5==3F3F/G+HH ==..5:3u}}?R?R;S5TUisOUU',mm&8&8&D ""*H h  emmZ 08$>8}s5==11  xks8}o6;;>u}};M:NhX  {*<=> >:++KHaCL(*,E E))C.'--2 1$#WI0A$APTUc +T']2GLL Vc 2CDE#J E!ZL0H {{1~#&CH<<&##',U[[^'< =!UZZ1  =D & j"MM//4jH%I3]]44Q7>>xHc I  x  8F  #e*-/@A{{''. M[.VR >s5 TT+ T!#T&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-)r9r.nextiterrAitemsrr?rappendunionrr differencer'r>onesboolr allrF)datagroupsdropnarZ pivot_colsall_colsrXtargetr8r[ to_concatid_colsmaskckvs rlreshaperwsx 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|jg||S) aV! 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: 0.7}, ... "B1980": {0: 3.2, 1: 1.3, 2: 0.1}, ... "X": dict(zip(range(3), np.random.randn(3), strict=True)), ... } ... ) >>> 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 >>> long_format = pd.wide_to_long(df, stubnames="ht", i=["famid", "birth"], j="age") >>> long_format ... # 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` >>> wide_format = long_format.unstack() >>> wide_format.columns = wide_format.columns.map("{0[0]}{0[1]}".format) >>> wide_format.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 >>> long_format = pd.wide_to_long( ... df, stubnames="ht", i=["famid", "birth"], j="age", sep="_", suffix=r"\w+" ... ) >>> long_format ... # 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^$)reescaperstrmatch)dfstubsepsuffixregexs r get_var_namesz#wide_to_long..get_var_namesosLRYYt_%biin%5fXQ?zz"**....u566rc6t||||j||}||jjt j ||zdd||< t ||||<|jg||S#tttf$rY+wxYw)N)rrrLrKT)r) r`rstripr~replacer|r}r TypeErrorr OverflowError set_index)rrrVjrrnewdfs r melt_stubzwide_to_long..melt_stubss !{{3'  8<<'' $*(=r'Na !%(+E!H wwAw'' :}5   sBBBz,stubname can't be identical to a column namez3the id variables need to uniquely identify each rowr%)axis)on)rr~rr~rr~)rr~rr~)rrrisinr1r duplicatedextendrer rgr.rjoinmerge reset_index)r stubnamesrVrrrrr_meltedvalue_vars_flattenedr value_varmeltedrnews r wide_to_longrps]~7(&  "K O  zzy!%%'GHH ? C G !uNOOGB!"dC8 ##I.yT1aC@AB G! $Fjj##$89G W+C 1v{}}Q$$V,,yy++-!y4>>wwAwGGr)rr~returnr)NNNvalueNT)rJrrLr r"rirr)T)rkrrldictrmrirr)rz\d+)rrrr~rr~rr)$ __future__rr|typingrnumpyr'pandas.util._decoratorsrpandas.core.dtypes.commonrrpandas.core.dtypes.concatrpandas.core.dtypes.missingr pandas.core.algorithmscore algorithmsr5pandas.core.indexes.apir pandas.core.reshape.concatr pandas.core.tools.numericr collections.abcr pandas._typingrrrrr`rwrrrrs" .4,&&.-0(+   H  "i i  iiiiX HSBSBl HAGsHsH),sH;>sHsHsHr