NamedAgg error: aggregate() missing 1 required positional argument: 'func'

Code Sample, a copy-pastable example if possible

resampled_bookings = bookings.resample('BMS').agg(
    unique_identity_ids=pd.NamedAgg(column="identity_id", aggfunc=pd.Series.nunique),
    booking_count=pd.NamedAgg(column="booking_id", aggfunc=pd.Series.count),
    booking_distance=pd.NamedAgg(column="distance_m", aggfunc=pd.Series.sum)
)

Problem description

This error prevents me from aggregating data and (re)naming columns at the same time. Instead, it is necessary to use the following approach, which isn’t altogether bad:

resampled_bookings = bookings.resample('BMS').agg({
    "booking_id": "count",
    "distance_m": sum,
    "identity_id": pd.Series.nunique
})

resampled_bookings.rename(columns={
    "booking_id": "booking_count",
    "distance_m": "booking_distance_m",
    "identity_id": "unique_identity_ids"
}, inplace=True)

Expected Output

The aggregate function would produce a new DataFrame with the following columns:

• booking_count
• booking_distance_m
• unique_identity_ids

Output of pd.show_versions()

INSTALLED VERSIONS

commit : None python : 3.7.5.final.0 python-bits : 64 OS : Linux OS-release : 5.3.0-19-generic machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8

pandas : 1.0.1 numpy : 1.18.1 pytz : 2019.3 dateutil : 2.8.1 pip : 18.1 setuptools : 40.8.0 Cython : None pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : 2.11.1 IPython : 7.12.0 pandas_datareader: None bs4 : None bottleneck : None fastparquet : None gcsfs : None lxml.etree : None matplotlib : 3.2.0 numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pytables : None pytest : None pyxlsb : None s3fs : None scipy : 1.4.1 sqlalchemy : None tables : None tabulate : None xarray : None xlrd : None xlwt : None xlsxwriter : None numba : None

[MarcoGorelli]

Pretty sure this is the same as the issue I opened here, so I’ll copy over the example I put there:

import pandas as pd

df = pd.DataFrame(
    {"group": ["a"], "col": [1.0]}, index=[pd.to_datetime("2019-11-04 10:32:09.737")]
)
df.groupby("group").resample("1D").agg(open=pd.NamedAgg("col", "first"))

all will close that one

[GhostLee]

you can see the comment in pandas.core.base.SelectionMixin._aggregate, line 320&331 pandas.core.base.SpecificationError: nested renamer is not supported but u can use these code

resampled_bookings = bookings.resample('BMS').agg(
    {
        "identity_id": pd.Series.nunique,
        "booking_id": pd.Series.count,
        "distance_m": pd.Series.sum
    }
)

or use multiple group func

resampled_bookings = bookings.resample('BMS').agg(
    {
        "identity_id": [pd.Series.nunique],
        "booking_id": [pd.Series.count],
        "distance_m": [pd.Series.sum]
    }
)

or give clear indication of func param

resampled_bookings = bookings.resample('BMS').agg(
   func= {
        "identity_id": [pd.Series.nunique],
        "booking_id": [pd.Series.count],
        "distance_m": [pd.Series.sum]
    }
)