python pandas数据帧if else没有迭代思想数据帧

Question

我想在df中添加一列.这个新df的值将取决于其他列的值.例如

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

现在我想添加另一个列D,其值取决于A,B,C的值.所以例如,如果迭代通过df,我会这样做:

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 

有没有办法在没有for循环或使用where()或apply()函数的情况下执行此操作.

谢谢

Answer 1

apply 应该适合你:

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

这是一个开始：

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

编辑，你可能应该继续将整个事情转换为浮点数，除非你出于某种原因真的关心整数：

df = df.astype(np.float)

然后你就不必不断地在通话本身中进行转换