Apply a fucntion with lambda to Pandas - python-3.x

Question

I have 2 time-series and I would like to find the nearest date from each date in time-series1 to time-series2. I found how to do it separately per date, but I would like to apply it to the entire time-series1. They are in two different dataframes called o and p
This is how my data looks like:
Time-series1:
o['date']
>>>0 2020-01-26
1 2020-01-28
2 2020-01-31
3 2020-02-15
4 2020-02-17
...
86 2021-01-10
87 2021-01-20
88 2021-01-27
89 2021-01-30
90 2021-02-14
Name: date, Length: 91, dtype: datetime64[ns]
Time-series2:
p['date']
>>>1 2020-02-17
3 2020-03-02
4 2020-03-03
5 2020-03-04
6 2020-03-05
...
172 2021-01-30
173 2021-02-06
174 2021-02-07
177 2021-02-12
179 2021-02-14
Name: date, Length: 144, dtype: datetime64[ns]
The function that I use:
def nearest(pivot,items):
return min(items, key=lambda x: abs(x - pivot))
Which works on a separate singular date, for example:
nearest(o['date'][6], p['date'])
>>>Timestamp('2020-03-02 00:00:00')
When I try to apply it to the whole pandas Series I get an error:
o['date'].apply(nearest, args=(p['date']))
>>>---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-17-82c86ffd48ff> in <module>()
----> 1 o['date'].apply(nearest, args=(p['date']))
C:\Users\ran\Anaconda3\envs\main\lib\site-packages\pandas\core\series.py in apply(self, func, convert_dtype, args, **kwds)
4180
4181 # handle ufuncs and lambdas
-> 4182 if kwds or args and not isinstance(func, np.ufunc):
4183
4184 def f(x):
C:\Users\ran\Anaconda3\envs\main\lib\site-packages\pandas\core\generic.py in __nonzero__(self)
1325 def __nonzero__(self):
1326 raise ValueError(
-> 1327 f"The truth value of a {type(self).__name__} is ambiguous. "
1328 "Use a.empty, a.bool(), a.item(), a.any() or a.all()."
1329 )
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
I feel that there is something basic I'm missing.
I guess I can do:
[nearest(x, p['date']) for x in o['date']]
But I would like to know how to apply it to a Pandas Series

Answer 1

Use Series.apply with lambda function:
s = o['date'].apply(lambda x: nearest(x, p['date']))
Or with args parameter:
s = o['date'].apply(nearest, args=(p['date'], ))
Numpy alternative with numpy.argmin should be faster:
a = o['date'].to_numpy()
b = p['date'].to_numpy()
pos = np.argmin(np.abs(a- b[:, None]), axis=0)
s = pd.Series(b[pos], index=o.index)