BUG: DataFrame.apply returns inconsistent index depending on applied function's return type · Issue #35683 · pandas-dev/pandas (original) (raw)

Code Sample

import pandas as pd

def func(col, _type): return _type()

df = pd.DataFrame([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]], columns=['a', 'b'])

for _type in [int, bool, float, str, list, tuple, dict, set]: type_lambda = lambda col: func(col, _type) print(f'Returning values of type {_type}:', df.apply(type_lambda).index)

With pandas 1.1.0, this outputs:

Returning values of type <class 'int'>: Index(['a', 'b'], dtype='object')
Returning values of type <class 'bool'>: Index(['a', 'b'], dtype='object')
Returning values of type <class 'float'>: Index(['a', 'b'], dtype='object')
Returning values of type <class 'str'>: Index(['a', 'b'], dtype='object')
Returning values of type <class 'list'>: RangeIndex(start=0, stop=0, step=1)
Returning values of type <class 'tuple'>: RangeIndex(start=0, stop=0, step=1)
Returning values of type <class 'dict'>: Int64Index([0, 1], dtype='int64')
Traceback (most recent call last):
  File "test.py", line 12, in <module>
    print(f'Returning values of type {_type}:', df.apply(type_lambda).index)
  File "/home/louis/venvs/test/lib/python3.7/site-packages/pandas/core/frame.py", line 7541, in apply
    return op.get_result()
  File "/home/louis/venvs/test/lib/python3.7/site-packages/pandas/core/apply.py", line 180, in get_result
    return self.apply_standard()
  File "/home/louis/venvs/test/lib/python3.7/site-packages/pandas/core/apply.py", line 258, in apply_standard
    return self.wrap_results(results, res_index)
  File "/home/louis/venvs/test/lib/python3.7/site-packages/pandas/core/apply.py", line 299, in wrap_results
    return self.wrap_results_for_axis(results, res_index)
  File "/home/louis/venvs/test/lib/python3.7/site-packages/pandas/core/apply.py", line 352, in wrap_results_for_axis
    result = self.obj._constructor(data=results)
  File "/home/louis/venvs/test/lib/python3.7/site-packages/pandas/core/frame.py", line 467, in __init__
    mgr = init_dict(data, index, columns, dtype=dtype)
  File "/home/louis/venvs/test/lib/python3.7/site-packages/pandas/core/internals/construction.py", line 283, in init_dict
    return arrays_to_mgr(arrays, data_names, index, columns, dtype=dtype)
  File "/home/louis/venvs/test/lib/python3.7/site-packages/pandas/core/internals/construction.py", line 83, in arrays_to_mgr
    arrays = _homogenize(arrays, index, dtype)
  File "/home/louis/venvs/test/lib/python3.7/site-packages/pandas/core/internals/construction.py", line 352, in _homogenize
    val, index, dtype=dtype, copy=False, raise_cast_failure=False
  File "/home/louis/venvs/test/lib/python3.7/site-packages/pandas/core/construction.py", line 452, in sanitize_array
    raise TypeError("Set type is unordered")
TypeError: Set type is unordered

Problem description

As shown above, when different data types are turned by the applied function, the index of the result is changed. From what I have seen, this is neither documented behaviour, nor is it consistent with itself. Additionally, in previous versions of pandas, apply worked differently, as shown below.

Expected Output

This is the output when running with pandas 1.0.5 and pandas 1.0.4, and is what I would expect:

Returning values of type <class 'int'>: Index(['a', 'b'], dtype='object')
Returning values of type <class 'bool'>: Index(['a', 'b'], dtype='object')
Returning values of type <class 'float'>: Index(['a', 'b'], dtype='object')
Returning values of type <class 'str'>: Index(['a', 'b'], dtype='object')
Returning values of type <class 'list'>: Index(['a', 'b'], dtype='object')
Returning values of type <class 'tuple'>: Index(['a', 'b'], dtype='object')
Returning values of type <class 'dict'>: Index(['a', 'b'], dtype='object')
Returning values of type <class 'set'>: Index(['a', 'b'], dtype='object')

Output of pd.show_versions()

Environment with pandas 1.1.0:

INSTALLED VERSIONS

commit : d9fff27
python : 3.7.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-42-generic
Version : #46~18.04.1-Ubuntu SMP Fri Jul 10 07:21:24 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_NZ.UTF-8
LOCALE : en_NZ.UTF-8

pandas : 1.1.0
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.1
setuptools : 39.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.0
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : 0.4.2
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None

Environment with pandas 1.0.5:

INSTALLED VERSIONS

commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-42-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_NZ.UTF-8
LOCALE : en_NZ.UTF-8

pandas : 1.0.5
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.1
setuptools : 39.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : 0.4.2
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None