As my question states, I would like to invoke custom function on run-time to a dataframe. Use of custom function will be to calculate difference between two date (i.e. age), convert year to months, find max-min from two columns etc.
So Far, I succeeded in performing arithmetic operations and few functions like abs(), sqrt() but couldn't get min()-max() working.Things working are,
df.eval('TT = sqrt(Q1)',inplace=True)
df.eval('TT1 = abs(Q1-Q2)',inplace=True)
df.eval('TT2 = (Q1+Q2)*Q3',inplace=True)
Following code works with eval. How can I use the same with dataframe eval ?
def find_max(x,y):
return np.maximum(x,y)
eval('max1')(4,7)
def find_age(date_col1,date_col2):
return 'I know how to calc age but how to call func this with df.eval and assign to new col'
Sample dataframe:
op_d = {'ID': [1, 2,3],'V':['F','G','H'],'AAA':[0,1,1],'D':['2019/12/04','2019/02/01','2019/01/01'],'DD':['2019-12-01','2016-05-31','2015-02-15'],'CurrentRate':[7.5,2,2],'NoteRate':[2,3,3],'BBB':[0,4,4],'Q1':[2,8,10],'Q2':[3,5,7],'Q3':[5,6,8]}
df = pd.DataFrame(data=op_d)
Any help or link to Doc is appreciated.
helpful links I found but not addressing my issues are:
Dynamic Expression Evaluation in pandas using pd.eval()
Using local variables with multiple assignments with pandas eval function
Functions can be called as usual, you need to reference them with the @ synbol:
df
A B
0 1 0
1 0 0
2 0 1
def my_func(x, y): return x + y
df.eval('@my_func(A, B)')
0 1
1 0
2 1
dtype: int64
Of course, the expectation here is that your functions expect series as arguments. Otherwise, wrap your function in a call to np.vectorize, as appropriate.