How to apply a function on every row on a dataframe?

I am new to Python and I am not sure how to solve the following problem.

I have a function:

def EOQ(D,p,ck,ch):
    Q = math.sqrt((2*D*ck)/(ch*p))
    return Q

Say I have the dataframe

df = pd.DataFrame({"D": [10,20,30], "p": [20, 30, 10]})

    D   p
0   10  20
1   20  30
2   30  10

ch=0.2
ck=5

And ch and ck are float types. Now I want to apply the formula to every row on the dataframe and return it as an extra row 'Q'. An example (that does not work) would be:

df['Q']= map(lambda p, D: EOQ(D,p,ck,ch),df['p'], df['D']) 

(returns only 'map' types)

I will need this type of processing more in my project and I hope to find something that works.

Solution 1:

The following should work:

def EOQ(D,p,ck,ch):
    Q = math.sqrt((2*D*ck)/(ch*p))
    return Q
ch=0.2
ck=5
df['Q'] = df.apply(lambda row: EOQ(row['D'], row['p'], ck, ch), axis=1)
df

If all you're doing is calculating the square root of some result then use the np.sqrt method this is vectorised and will be significantly faster:

In [80]:
df['Q'] = np.sqrt((2*df['D']*ck)/(ch*df['p']))

df
Out[80]:
    D   p          Q
0  10  20   5.000000
1  20  30   5.773503
2  30  10  12.247449

Timings

For a 30k row df:

In [92]:

import math
ch=0.2
ck=5
def EOQ(D,p,ck,ch):
    Q = math.sqrt((2*D*ck)/(ch*p))
    return Q

%timeit np.sqrt((2*df['D']*ck)/(ch*df['p']))
%timeit df.apply(lambda row: EOQ(row['D'], row['p'], ck, ch), axis=1)
1000 loops, best of 3: 622 µs per loop
1 loops, best of 3: 1.19 s per loop

You can see that the np method is ~1900 X faster