如何从pandas数据框中特定列的所有值中删除所有非数字字符?

问题描述：

我有一个看起来像这样的数据框:

I have a dataframe which looks like this:

     A       B           C
1   red78   square    big235
2   green   circle    small123
3   blue45  triangle  big657

我需要能够从C列的所有行中删除非数字字符，以便数据框看起来像这样:

I need to be able to remove the non-numeric characters from all the rows in column C so that my dataframe looks like:

     A       B           C
1   red78   square    235
2   green   circle    123
3   blue45  triangle  657

我尝试使用以下命令，但得到了错误的预期字符串或缓冲区:

I tried using the following but get the error expected string or buffer:

import re
dfOutput.imgID = dfOutput.imgID.apply(re.sub('[^0-9]','', dfOutput.imgID), axis = 0)

我该怎么办?

创建数据框的代码:

dfObject = pd.DataFrame()
dfObject.set_value(1, 'A', 'red78')
dfObject.set_value(1, 'B', 'square')
dfObject.set_value(1, 'C', 'big235')
dfObject.set_value(2, 'A', 'green')
dfObject.set_value(2, 'B', 'circle')
dfObject.set_value(2, 'C', 'small123')
dfObject.set_value(3, 'A', 'blue45')
dfObject.set_value(3, 'B', 'triangle')
dfObject.set_value(3, 'C', 'big657')

答

使用str.extract并传递正则表达式模式以仅提取数字部分:

Use str.extract and pass a regex pattern to extract just the numeric parts:

In[40]:
dfObject['C'] = dfObject['C'].str.extract('(\d+)', expand=False)
dfObject

Out[40]: 
        A         B    C
1   red78    square  235
2   green    circle  123
3  blue45  triangle  657

如果需要，可以强制转换为int:

If needed you can cast to int:

dfObject['C'] = dfObject['C'].astype(int)

如何从pandas数据框中特定列的所有值中删除所有非数字字符?

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