python - 使用函数修改 Pandas 数据框

Question

这是问题 here 的跟进:如何使用函数修改数据框？假设我想对 a

中的值调用 .upper()

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 a 列调用函数:

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