How to apply a function with several dataframe columns as arguments?-CodePudding

I'm trying to compute a new column in a pandas dataframe, based upon others columns, and a function I created. Instead of using a for loop, I prefer to apply the function with entires dataframe columns.

My code is like this :

    df['po'] = vect.func1(df['gra'],
                           Se, 
                           df['p_a'], 
                           df['t'], 
                           Tc)

where df['gra'], df['p_a'], and df['t'] are my dataframe columns (parameters), and Se and Tc are others (real) parameters. df['po'] is my new column.

func1 is a function described in my vect package. This function is :

def func1(g, surf_e, Pa, t, Tco):

    if (t <= Tco):
        pos = (g-(Pa*surf_e*g))
    else: 
        pos = 0.0
    return(pos)

When implemented this way, I obtain an error message, which concern the line : if (t <= Tco):

The error is : ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

I read the pandas documentation, but didn't find the solution. Can anybody explain me what is the problem ?

I tried to use apply :

for example :

df['po'] = df['gra'].apply(vect.func1)

but I don't know how to use apply with multiples columns as parameters.

Thank you by advance.

CodePudding user response：

Use np.where with the required condition, value when the condition is True and the default value.

df['po'] = np.where(
    df['t'] <= Tc,                               # Condition
    df['gra'] - (df['P_a'] * Se * df['gra']),    # Value if True
    0                                            # Value if False
)

EDIT:

Don't forget to import numpy as np

Also, you get an error because you are comparing a series to a series and hence obtain a series of boolean values and not an atomic boolean value which if condition needs.