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
          
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
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
          
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
          
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]