Description
Code Sample, a copy-pastable example if possible
import time
df_time = pd.DataFrame(np.random.randint(low=0, high=20, size=(1000, 4)), pd.date_range(start='2000-01-01', end='2000-01-02', periods=1000), columns=['a', 'b', 'c', 'd'])
time0 = time.time()
df_time.resample('1s').last()
print('resampling the datetime64[ns] data frame took {} seconds'.format(time.time()-time0))
df_time.loc[:,'a'] = df_time['a'].astype('category')
time0 = time.time()
df_time.resample('1s').last()
print('resampling the datetime64[ns] data frame took {} seconds'.format(time.time()-time0))
resampling the df containing integers took 0.007719516754150391 seconds
resampling the df containing categoricals took 38.36969518661499 seconds
Problem description
As code above shows resampling df containing categoricals is super slow for aggregation .last(). Same behaviour occurs for .first(), whereas .mean() is performing fine.
Output of pd.show_versions()
INSTALLED VERSIONS
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-1052-aws
machine : x86_64
processor :
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.0
pip : 19.3.1
setuptools : 41.6.0
Cython : 0.29.14
pytest : 5.2.4
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : 0.9.3
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : 7.9.0
pandas_datareader: None
bs4 : 4.8.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : 0.4.0
scipy : 1.3.2
sqlalchemy : 1.3.11
tables : 3.6.1
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None