python大 pandas 数据帧如果没有迭代思想数据帧 [英] python pandas data frame if else without iterating thought data frame

问题描述

我想添加一个列到df。这个新df的值将取决于其他列的值。例如

I want to add a column to a df. The values of this new df will be dependent upon 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

现在我要添加另一个列D，其值将取决于A，B，C的值。
所以例如，如果是迭代通过df我将做：

Now I want to add another column D whose values will depend on values of A,B,C. So for example if was iterating through the 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 

有没有for循环或使用where（）或apply（）函数的方法。

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

谢谢

推荐答案

code>应该适合你：

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]

这篇关于python大 pandas 数据帧如果没有迭代思想数据帧的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！