Dask Dataframe: Defining meta for date diff in groubpy - pandas

Question

I'm trying to find inter-purchase times (i.e., days between orders) for customers. Although my code is working correctly without defining meta, I would like to get it working properly and no longer see the warning asking me to provide meta.
Also, I would appreciate any suggestions on how to use map or map_partitions instead of apply.
So far I've tried:
meta={'days_since_last_order': 'datetime64[ns]'}
meta={'days_since_last_order': 'f8'}
meta={'ORDER_DATE_DT':'datetime64[ns]','days_since_last_order': 'datetime64[ns]'}
meta={'ORDER_DATE_DT':'f8','days_since_last_order': 'f8'}
meta=('days_since_last_order', 'f8')
meta=('days_since_last_order', 'datetime64[ns]')
Here is my code:
import numpy as np
import pandas as pd
import datetime as dt
import dask.dataframe as dd
from dask.distributed import wait, Client
client = Client(processes=True)
start = pd.to_datetime('2015-01-01')
end = pd.to_datetime('2018-01-01')
d = (end - start).days + 1
np.random.seed(0)
df = pd.DataFrame()
df['CUSTOMER_ID'] = np.random.randint(1, 4, 10)
df['ORDER_DATE_DT'] = start + pd.to_timedelta(np.random.randint(1, d, 10), unit='d')
print(df.sort_values(['CUSTOMER_ID','ORDER_DATE_DT']))
print(df)
ddf = dd.from_pandas(df, npartitions=2)
# setting ORDER_DATE_DT as index to sort by date
ddf = ddf.set_index('ORDER_DATE_DT')
ddf = client.persist(ddf)
wait(ddf)
ddf = ddf.reset_index()
grp = ddf.groupby('CUSTOMER_ID')[['ORDER_DATE_DT']].apply(
lambda df: df.assign(days_since_last_order=df.ORDER_DATE_DT.diff(1))
# meta=????
)
# for some reason, I'm unable to print grp unless I reset_index()
grp = grp.reset_index()
print(grp.compute())
Here is the printout of df.sort_values(['CUSTOMER_ID','ORDER_DATE_DT'])
Here is the printout of grp.compute()