Python pandas if else data frame without iteration of thought data frame

Question

I want to add a column in df. The values ​​of this new df will depend on the values ​​of the other columns. eg,

dc = {'A':[0,9,4,5],'B':[6,0,10,12],'C':[1,3,15,18]}
df = pd.DataFrame(dc)
   A   B   C
0  0   6   1
1  9   0   3
2  4  10  15
3  5  12  18

Now I want to add another column D, the values ​​of which will depend on the values ​​of A, B, C. So, for example, if iteration through df I would just do:

for row in df.iterrows():
    if(row['A'] != 0 and row[B] !=0):
         row['D'] = (float(row['A'])/float(row['B']))*row['C']
    elif(row['C'] ==0 and row['A'] != 0 and row[B] ==0):
         row['D'] == 250.0
    else:
         row['D'] == 20.0 

Is there a way to do this without a for loop or using the where () or apply () functions.

thank

Answer 1

apply should work well for you:

In [20]: def func(row):
            if (row == 0).all():
                return 250.0
            elif (row[['A', 'B']] != 0).all():
                return (float(row['A']) / row['B'] ) * row['C']
            else:
                return 20
       ....:     


In [21]: df['D'] = df.apply(func, axis=1)

In [22]: df
Out[22]: 
   A   B   C     D
0  0   6   1  20.0
1  9   0   3  20.0
2  4  10  15   6.0
3  5  12  18   7.5

[4 rows x 4 columns]

Answer 2

here is the beginning:

df['D'] = np.nan
df['D'].loc[df[(df.A != 0) & (df.B != 0)].index] = df.A / df.B.astype(np.float) * df.C

, , , - :

df = df.astype(np.float)

Answer 3

.where , .apply, , , , /elses, .where. , np.where , pandas 'own .where.

import pandas as pd
import numpy as np
df['D'] = np.where((df.A!=0) & (df.B!=0), ((df.A/df.B)*df.C),
          np.where((df.C==0) & (df.A!=0) & (df.B==0), 250,
          20))

   A   B   C     D
0  0   6   1  20.0
1  9   0   3  20.0
2  4  10  15   6.0
3  5  12  18   7.5

df, , . 10000 df randn 2000 , .apply : 3 5850 . , , .