Consistent dtype output for element wise operations on empty dataframes

pandas-dev / pandas

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

BSD 3-Clause "New" or "Revised" License

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Code Sample, a copy-pastable example if possible

import pandas as pd
s = pd.Series([], dtype='datetime64[ns, UTC]')
df = pd.concat([s, s], axis=1) # empty dataframe of two columns of datetime dtype
# want the minimum of the two datetime columns element wise
min_dates = df.min(axis=1) # output series is of dtype float64

# it's fine if dataframe is non-empty
import pandas as pd
s = pd.Series(pd.to_datetime([f'2020-01-0{i}' for i in range(1, 10)], utc=True))
df = pd.concat([s, s], axis=1) # non-empty dataframe of two columns of datetime dtype
min_dates = df.min(axis=1) # output series is of dtype datetime64[ns, UTC]

Problem description

When dataframe is empty, doing a .min(axis=1) outputs a series of dtype float64 even though all columns were dtype datetime64. Should this be consitent on dtypes and output min_dates with dtype datetime64[ns, UTC].

This is only an issue when the dataframe is empty. If the dataframe is not empty, the dtype is conserved and min_dates outputs with dtype datetime64[ns, UTC].

Output of `pd.show_versions()`

INSTALLED VERSIONS ------------------ commit : None python : 3.6.3.final.0 python-bits : 64 OS : Linux OS-release : 3.10.0-862.3.3.el7.x86_64 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : None LOCALE : None.None pandas : 1.0.2 numpy : 1.16.5 pytz : 2019.3 dateutil : 2.8.0 pip : 20.0.2 setuptools : 41.4.0 Cython : 0.29.13 pytest : 5.0.1 hypothesis : None sphinx : 2.1.2 blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : 2.8.3 (dt dec pq3 ext lo64) jinja2 : 2.10.1 IPython : 7.7.0 pandas_datareader: None bs4 : 4.8.1 bottleneck : None fastparquet : None gcsfs : 0.3.1 lxml.etree : None matplotlib : 3.1.1 numexpr : 2.6.9 odfpy : None openpyxl : None pandas_gbq : None pyarrow : 0.15.1 pytables : None pytest : 5.0.1 pyxlsb : None s3fs : None scipy : 1.3.1 sqlalchemy : 1.3.8 tables : 3.5.2 tabulate : None xarray : 0.14.0 xlrd : 1.2.0 xlwt : None xlsxwriter : None numba : 0.46.0

import pandas as pd s1 = pd.Series([], dtype='datetime64[ns, UTC]') df1 = pd.concat([s1, s1], axis=1) # empty dataframe of two columns of datetime dtype # want the minimum of the two datetime columns element wise min_dates_1 = df1.min(axis=1) # output series is of dtype float64 print(min_dates_1.dtype) s2 = pd.Series(pd.to_datetime([f'2020-01-0{i}' for i in range(1, 10)], utc=True)) df2 = pd.concat([s2, s2], axis=1) # non-empty dataframe of two columns of datetime dtype min_dates_2 = df2.min(axis=1) # output series is of dtype datetime64[ns, UTC] print(min_dates_2.dtype)

pandas-dev / pandas