Question

这是问题here的后续跟进：如何使用函数修改数据框？假设我想对.upper()

中的值进行调用a

df = pd.DataFrame({'a':['london','newyork','berlin'],
                   'b':['uk','usa','germany'],
                   'c':[7,8,9]})

df1 = df[['a', 'b']]

def doSomething(x):
    return x.a

print (df1.apply(doSomething, axis=1))
0     london
1    newyork
2     berlin
dtype: object

call `.upper()` on values in `a`:
return 
0     LONDON
1     NEWYORK
2     BERLIN
dtype: object

Answer 1

您可以为列a调用函数：

def doSomething(x):
    return x.upper()

print (df1.a.apply(doSomething))
0     LONDON
1    NEWYORK
2     BERLIN
Name: a, dtype: object

print (df1.a.apply(lambda x: x.upper()))
0     LONDON
1    NEWYORK
2     BERLIN
Name: a, dtype: object

它也适用于：

def doSomething(x):
    return x.a.upper()

print (df1.apply(doSomething, axis=1))
0     LONDON
1    NEWYORK
2     BERLIN
dtype: object

但更好的是使用str.upper，它与NaN值完美配合：

print (df1.a.str.upper())
0     LONDON
1    NEWYORK
2     BERLIN
Name: a, dtype: object

如果需要添加新列：

df['c'] = df1.a.str.upper()
print (df)
         a        b        c
0   london       uk   LONDON
1  newyork      usa  NEWYORK
2   berlin  germany   BERLIN

使用函数修改pandas数据帧

1 个答案: