为数据框的选定列应用select_dtypes [英] Applying select_dtypes for selected columns of a dataframe

问题描述

我有一些同时包含浮点数和字符串的列.我希望能够选择这些列并根据其数据类型应用不同的掩码.

I have a few columns that have both floats and strings. I want to be able to select these columns and apply different masks according to their data type.

我已经找到了select_dtypes()方法，但是它在整个数据帧上运行，我需要的是能够进行列选择.例如:

I have found select_dtypes() method but it runs over the entire dataframe what I need is to be able to do column selection. For example:

 df['A'].select_dtypes(exclude=[np.number]) 

现在，当我尝试执行此操作时，我会得到

Right now when I try to do this I get

AttributeError: 'Series' object has no attribute 'select_dtypes'

要提供更多详细信息，假设我有这样的数据框:

To give more details let's say I have such dataframe:

df = pd.DataFrame([
[-1, 3, 0],
[5, 2, 1],
[-6, 3, 2],
[7, '<blank>', 3 ],     
['<blank>', 2, 4],
['<blank>', '<blank>', '<blank>']], columns='A B C'.split())

我跑步时

df.select_dtypes(exclude=[np.number]) 

它没有给我一个错误，但是什么也没有发生，因为它找不到除np.number

It doesn't give me an error but also nothing happens since it did not find any column which contains only one dtype other than np.number

最后，我想用dtype选择创建一个遮罩，例如

In the end I want to create a mask with dtype selection such as

mask=  df['A'].select_dtypes(exclude=[np.number]) 

注意:我需要不更改此字符串，因为在下一步中，我将将此数据帧呈现到html表，因此这些< blank >字符串将给我空格.

Note : I need this strings not to be changed because at a further step I will render this dataframe to html table so these < blank > strings will give me white spaces.

推荐答案

您可以定义将转换应用于数字的函数，然后根据转换是否成功进行过滤:

You can define a function to apply conversion to numeric, and then filter according to whether the conversion is successful:

def filter_type(s, num=True):
    s_new = pd.to_numeric(s, errors='coerce')
    if num:
        return s[s_new.notnull()]
    else:
        return s[s_new.isnull()]

res = filter_type(df['A'], num=False)

print(res)

4    <blank>
5    <blank>
Name: A, dtype: object

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