why the function networkdays get the error "'Series' object has no attribute 'days'

Question

Below is my sample dataframe, I would like to use.networkdays to calculate the working day in columns in my df, but it got the issue as below, can someone help assist on this?

#import lib
import pandas as pd
from workdays import workday, networkdays
#sample dict to create df 
dict1 = {
    'startdate' : ['2022-01-17','2022-02-28'],
    'enddate' : ['2022-01-17','2022-03-15']
}
#convert to datetime format
df['startdate'] = df['startdate'].astype('datetime64')
df['enddate'] = df['enddate'].astype('datetime64')
#create new column count and apply function
df['count']=df.apply(networkdays(df['startdate'],df['enddate']))

#getting error :
AttributeError                            Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_20080/333906513.py in <module>
----> 1 df['count']=df.apply(networkdays(df['startdate'],df['enddate']))

C:\ProgramData\Anaconda3\lib\site-packages\workdays.py in networkdays(start_date, end_date, holidays)
     10 
     11 def networkdays(start_date, end_date, holidays=[]):
---> 12     delta_days = (end_date - start_date).days + 1
     13     full_weeks, extra_days = divmod(delta_days, 7)
     14     # num_workdays = how many days/week you work * total # of weeks

C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\generic.py in __getattr__(self, name)
   5485         ):
   5486             return self[name]
-> 5487         return object.__getattribute__(self, name)
   5488 
   5489     def __setattr__(self, name: str, value) -> None:

AttributeError: 'Series' object has no attribute 'days'

Answer 1

df['startdate'] is a series object, it contains more than one piece of data.

>> df['enddate'] # <class 'pandas.core.series.Series'>
0   2022-01-17
1   2022-03-15

You should pass a lambda to the df.apply function, if you have to apply a function to all the rows in the DataFrame. The parameter of the lambda is the row of data.

df = pd.DataFrame(dict1)
df['startdate'] = df['startdate'].astype('datetime64')
df['enddate'] = df['enddate'].astype('datetime64')
df['count']=df.apply(lambda x: networkdays(x['startdate'], x['enddate']), axis=1)
print(df)
#    startdate    enddate  count
# 0 2022-01-17 2022-01-17      1
# 1 2022-02-28 2022-03-15     12