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

[brylie]

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

[dsaxton]

Can you try to provide a small reproducible example with the error message you're seeing (others don't have access to your data)?

[maximrus23]

I got the same error with resampling and aggregating. However, if you switch order with aggregation and then resampling it works but the result is datetime object which is not aggregated within dates.

def new_dataset(rule,num_points = 9):
    '''
    Generate a new timeseries dataset using intervals in rule string
    and number of points num_points
    
    Returns a series object
    '''
    ind = pd.date_range('1/1/2000', periods=num_points, freq=rule)
    return pd.Series(list(map(lambda x: x*2,range(num_points))), index=ind)

minutes = new_dataset('min')

minutes.resample('2min',axis=0).agg(column1 = ('mean', 'mean'), column2 = ('sum', 'sum'))

The above results in

TypeError: aggregate() missing 1 required positional argument: 'func'

However, with

minutes.resample('2min',axis=0).agg( ('mean', 'mean'), ('sum', 'sum'))

Output: 
	mean	mean
2000-01-01 00:00:00	1	1
2000-01-01 00:02:00	5	5
2000-01-01 00:04:00	9	9
2000-01-01 00:06:00	13	13
2000-01-01 00:08:00	16	16

Call is made twice to only first aggregation which is supported even if call .mean() directly.

Credits: https://towardsdatascience.com/using-the-pandas-resample-function-a231144194c4

[simonjayhawkins]

Can you try to provide a small reproducible example with the error message you're seeing (others don't have access to your data)?

@brylie ideally the code sample could double as the test, so a minimal reproducible example would be beneficial, see https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports

[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]
    }
)