groupby AssertionError with datetime column name · Issue #35876 · pandas-dev/pandas (original) (raw)

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Code Sample, a copy-pastable example

Your code here

import pandas as pd import datetime

column_names = ['dimension_1', 'dt', 'value'] data = [ ('D1', datetime.date(2020, 1, 1), 2.1), ('D1', datetime.date(2020, 1, 2), 4.1), ('D1', datetime.date(2020, 1, 3), 1.7), ] df_stack = pd.DataFrame(data=data, columns=column_names) df_stack['dt'] = pd.to_datetime(df_stack['dt']) df_stack.set_index(['dimension_1', 'dt'], inplace=True)

df_pivot = df_stack.unstack(level=['dt']).transpose().droplevel(None).transpose()

agg_map = {c: 'sum' for c in df_pivot.columns} df_pivot.groupby(level=['dimension_1']).agg(agg_map)

Problem description

When executing groupby (on index) for dataframe whose column-names are of type datetime ('datetime64[ns]'), the groupby fails with AssertionError (result.name == res_name).

AssertionError Traceback (most recent call last) in 1 # do groupby on index 2 agg_map = {c: 'sum' for c in df_pivot.columns} ----> 3 df_pivot.groupby(level=['dimension_1']).agg(agg_map)

c:\users\hli\appdata\local\programs\python\python38-32\lib\site-packages\pandas\core\groupby\generic.py in aggregate(self, func, engine, engine_kwargs, *args, **kwargs)
947 )
948
--> 949 result, how = self._aggregate(func, *args, **kwargs)
950 if how is None:
951 return result

c:\users\hli\appdata\local\programs\python\python38-32\lib\site-packages\pandas\core\base.py in _aggregate(self, arg, *args, **kwargs)
349 keys = list(arg.keys())
350 if isinstance(obj, ABCDataFrame) and len(
--> 351 obj.columns.intersection(keys)
352 ) != len(keys):
353 cols = sorted(set(keys) - set(obj.columns.intersection(keys)))

c:\users\hli\appdata\local\programs\python\python38-32\lib\site-packages\pandas\core\indexes\datetimelike.py in intersection(self, other, sort)
701 # TODO: no tests rely on this; needed?
702 result = result._with_freq("infer")
--> 703 assert result.name == res_name
704 return result
705

Expected Output

The expected result in this example should be the dataframe itself, since it only has one row. This can be seen by changing the column data type to string:

df_pivot.columns = df_pivot.columns.astype(str) agg_map = {c: 'sum' for c in df_pivot.columns} df_pivot.groupby(level=['dimension_1']).agg(agg_map)

Output of pd.show_versions()

INSTALLED VERSIONS

commit : f2ca0a2
python : 3.8.5.final.0
python-bits : 32
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : English_United States.1252

pandas : 1.1.1
numpy : 1.19.0
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.1
setuptools : 47.1.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.17.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : None
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.0
sqlalchemy : 1.3.18
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None