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BUG: coerce pd.wide_to_long suffixes to ints #17628

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Dec 10, 2017
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BUG: coerce pd.wide_to_long suffixes to ints #17628

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3d18d42
BUG: coerce pd.wide_to_long suffixes to numeric
tdpetrou Sep 22, 2017
54c69a7
reverting whatsnew 0.21.0.txt attempt2
tdpetrou Nov 20, 2017
e1c2204
small changes
tdpetrou Dec 6, 2017
b9d3e62
used versionchanged in docstring
tdpetrou Dec 10, 2017
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1 change: 1 addition & 0 deletions doc/source/whatsnew/v0.22.0.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -191,6 +191,7 @@ Other API Changes
- Refactored ``setup.py`` to use ``find_packages`` instead of explicitly listing out all subpackages (:issue:`18535`)
- Rearranged the order of keyword arguments in :func:`read_excel()` to align with :func:`read_csv()` (:pr:`16672`)
- :func:`pandas.merge` now raises a ``ValueError`` when trying to merge on incompatible data types (:issue:`9780`)
- :func:`wide_to_long` previously kept numeric-like suffixes as ``object`` dtype. Now they are cast to numeric if possible (:issue:`17627`)

.. _whatsnew_0220.deprecations:

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69 changes: 61 additions & 8 deletions pandas/core/reshape/melt.py
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Expand Up @@ -13,6 +13,7 @@

import re
from pandas.core.dtypes.missing import notna
from pandas.core.tools.numeric import to_numeric


@Appender(_shared_docs['melt'] %
Expand Down Expand Up @@ -199,6 +200,9 @@ def wide_to_long(df, stubnames, i, j, sep="", suffix=r'\d+'):

.. versionadded:: 0.20.0

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I would add an example here (in the doc-string); I think all the other examples are resulting as strings anyhow (could also modify an example to avoid making this longer). you can check docs in reshape.rst to see if anything needs updating.

.. versionchanged:: 0.22.0
When all suffixes are numeric, they are cast to int64/float64.

Returns
-------
DataFrame
Expand Down Expand Up @@ -278,8 +282,8 @@ def wide_to_long(df, stubnames, i, j, sep="", suffix=r'\d+'):

Going from long back to wide just takes some creative use of `unstack`

>>> w = l.reset_index().set_index(['famid', 'birth', 'age']).unstack()
>>> w.columns = pd.Index(w.columns).str.join('')
>>> 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
Expand Down Expand Up @@ -333,26 +337,76 @@ def wide_to_long(df, stubnames, i, j, sep="", suffix=r'\d+'):
>>> 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
... # 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

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 typicaly case.
in a typical case.
"""
def get_var_names(df, stub, sep, suffix):
regex = "^{stub}{sep}{suffix}".format(
regex = r'^{stub}{sep}{suffix}$'.format(
stub=re.escape(stub), sep=re.escape(sep), suffix=suffix)
return df.filter(regex=regex).columns.tolist()
pattern = re.compile(regex)
return [col for col in df.columns if pattern.match(col)]

def melt_stub(df, stub, i, j, value_vars, sep):
newdf = melt(df, id_vars=i, value_vars=value_vars,
value_name=stub.rstrip(sep), var_name=j)
newdf[j] = Categorical(newdf[j])
newdf[j] = newdf[j].str.replace(re.escape(stub + sep), "")

# GH17627 Cast numerics suffixes to int/float
newdf[j] = to_numeric(newdf[j], errors='ignore')

return newdf.set_index(i + [j])

if any(map(lambda s: s in df.columns.tolist(), stubnames)):
if any([col in stubnames for col in df.columns]):
raise ValueError("stubname can't be identical to a column name")

if not is_list_like(stubnames):
Expand All @@ -368,8 +422,7 @@ def melt_stub(df, stub, i, j, value_vars, sep):
if df[i].duplicated().any():
raise ValueError("the id variables need to uniquely identify each row")

value_vars = list(map(lambda stub:
get_var_names(df, stub, sep, suffix), stubnames))
value_vars = [get_var_names(df, stub, sep, suffix) for stub in stubnames]

value_vars_flattened = [e for sublist in value_vars for e in sublist]
id_vars = list(set(df.columns.tolist()).difference(value_vars_flattened))
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