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pandas.wide_to_long

pandas.wide_to_long(df, stubnames, i, j, sep='', suffix='\\d+')[source]

Wide panel 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=’-‘

New in version 0.20.0.

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)’

New in version 0.20.0.

Changed in version 0.23.0: 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)

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")
... 
                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
   birth  famid  ht1  ht2
0      1      1  2.8  3.4
1      2      1  2.9  3.8
2      3      1  2.2  2.9
3      1      2  2.0  3.2
4      2      2  1.8  2.8
5      3      2  1.9  2.4
6      1      3  2.2  3.3
7      2      3  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
... 
                  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(quarterly)-2010': np.random.rand(3),
...                    'A(quarterly)-2011': np.random.rand(3),
...                    'B(quarterly)-2010': np.random.rand(3),
...                    'B(quarterly)-2011': np.random.rand(3),
...                    'X' : np.random.randint(3, size=3)})
>>> df['id'] = df.index
>>> df 
   A(quarterly)-2010  A(quarterly)-2011  B(quarterly)-2010  ...
0           0.548814           0.544883           0.437587  ...
1           0.715189           0.423655           0.891773  ...
2           0.602763           0.645894           0.963663  ...
   X  id
0  0   0
1  1   1
2  1   2
>>> pd.wide_to_long(df, ['A(quarterly)', 'B(quarterly)'], i='id',
...                 j='year', sep='-')
... 
         X  A(quarterly)  B(quarterly)
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(quarterly)', 'B(quarterly)']

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
   birth  famid  ht_one  ht_two
0      1      1     2.8     3.4
1      2      1     2.9     3.8
2      3      1     2.2     2.9
3      1      2     2.0     3.2
4      2      2     1.8     2.8
5      3      2     1.9     2.4
6      1      3     2.2     3.3
7      2      3     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='\w')
>>> l
... 
                  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
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