We will use skipna=True to ignore the null or NA data. Let us check what happens if it is set to True ( skipna=True )
import numpy as np
import pandas as pd
my_dict={'NAME':['Ravi','Raju','Alex','Ron','King','Jack'],
'ID':[1,2,3,4,5,6],
'MATH':[80,40,70,70,70,30],
'ENGLISH':[80,70,np.nan,50,60,30]}
my_data = pd.DataFrame(data=my_dict)
print(my_data.sum(skipna=False))
Output
NAME RaviRajuAlexRonKingJack
ID 21
MATH 360
ENGLISH NaN
Check the sum of ENGLISH it is returned as NAN, the value will change to 290 if we set skipna=True
numeric_only
Default value is None, we can set it to True ( numeric_only=True ) to include only float, int, boolean columns. We can included all by setting it to False ( numeric_only=False ) . Let us see the outputs .
print(my_data.sum(numeric_only=False))
Output is here
NAME RaviRajuAlexRonKingJack
ID 21
MATH 360
ENGLISH 330.5
Now let us change the option numeric_only to True ( numeric_only=True ).
print(my_data.sum(numeric_only=True))
Output
ID 21.0
MATH 360.0
ENGLISH 330.5
min_count
Default value is 0, it accepts int. It is required number of valid values to perform.