如何迭代 pandas 数据框并创建新列

2022-01-20 00:00:00 python pandas loops multiple-columns conditional-statements

问题描述

我有一个有 2 列的 pandas 数据框.我想遍历它的行并基于第 2 列中的字符串我想在新创建的第 3 列中添加一个字符串.我试过了:

I have a pandas dataframe that has 2 columns. I want to loop through it's rows and based on a string from column 2 I would like to add a string in a newly created 3th column. I tried:

for i in df.index:
    if df.ix[i]['Column2']==variable1:
        df['Column3'] = variable2
    elif df.ix[i]['Column2']==variable3:
        df['Column3'] = variable4

print(df)

但生成的数据框在第 3 列中只有变量 2.

But the resulting dataframe has in column 3 only Variable2.

有什么想法我还能做到这一点吗?

Any ideas how else I could do this?

解决方案

我认为你可以使用 double numpy.where，什么比循环更快:

I think you can use double numpy.where, what is faster as loop:

df['Column3'] = np.where(df['Column2']==variable1, variable2, 
                np.where(df['Column2']==variable3, variable4))

如果两个条件都为False，则需要添加变量:

And if need add variable if both conditions are False:

df['Column3'] = np.where(df['Column2']==variable1, variable2, 
                np.where(df['Column2']==variable3, variable4, variable5))

示例:

df = pd.DataFrame({'Column2':[1,2,4,3]})
print (df)
   Column2
0        1
1        2
2        4
3        3

variable1 = 1
variable2 = 2
variable3 = 3
variable4 = 4
variable5 = 5

df['Column3'] = np.where(df['Column2']==variable1, variable2, 
                np.where(df['Column2']==variable3, variable4, variable5))

print (df)
   Column2  Column3
0        1        2
1        2        5
2        4        5
3        3        4

另一个解决方案，谢谢<代码>乔恩克莱门茨:

Another solution, thanks Jon Clements:

df['Column4'] = df.Column2.map({variable1: variable2, variable3:variable4}).fillna(variable5)
print (df)
   Column2  Column3  Column4
0        1        2      2.0
1        2        5      5.0
2        4        5      5.0
3        3        4      4.0

相关文章