我有一个如下所示的数据框：

# Create an example dataframe about a fictional army
raw_data = {'regiment': ['Nighthawks', 'Nighthawks', 'Nighthawks', 'Nighthawks'],
            'company': ['1st', '1st', '2nd', '2nd'],
            'deaths': ['kkk', 52, '25', 616],
            'battles': [5, '42', 2, 2],
            'size': ['l', 'll', 'l', 'm']}
df = pd.DataFrame(raw_data, columns = ['regiment', 'company', 'deaths', 'battles', 'size'])

我的目标是将数据帧内的每个字符串转换为大写,以便它看起来像这样：

注意：所有数据类型都是对象,不得更改;输出必须包含所有对象.我想避免逐个转换每一列……我想在整个数据框中做一般的.

到目前为止我尝试过的是这样做但没有成功

df.str.upper()

解决方法:

astype()将每个系列转换为dtype对象(字符串),然后在转换后的系列上调用str()方法以字面上获取字符串并在其上调用函数upper().请注意,在此之后,所有列的dtype都将更改为object.

In [17]: df
Out[17]: 
     regiment company deaths battles size
0  Nighthawks     1st    kkk       5    l
1  Nighthawks     1st     52      42   ll
2  Nighthawks     2nd     25       2    l
3  Nighthawks     2nd    616       2    m

In [18]: df.apply(lambda x: x.astype(str).str.upper())
Out[18]: 
     regiment company deaths battles size
0  NIGHTHAWKS     1ST    KKK       5    L
1  NIGHTHAWKS     1ST     52      42   LL
2  NIGHTHAWKS     2ND     25       2    L
3  NIGHTHAWKS     2ND    616       2    M

您可以稍后使用to_numeric()将“战斗”列再次转换为数字：

In [42]: df2 = df.apply(lambda x: x.astype(str).str.upper())

In [43]: df2['battles'] = pd.to_numeric(df2['battles'])

In [44]: df2
Out[44]: 
     regiment company deaths  battles size
0  NIGHTHAWKS     1ST    KKK        5    L
1  NIGHTHAWKS     1ST     52       42   LL
2  NIGHTHAWKS     2ND     25        2    L
3  NIGHTHAWKS     2ND    616        2    M

In [45]: df2.dtypes
Out[45]: 
regiment    object
company     object
deaths      object
battles      int64
size        object
dtype: object