pandas 熊猫修剪数据帧中的前导和尾随空白
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Pandas trim leading & trailing white space in a dataframe
提问by S.Gu
develop a function that Trims leading & trailing white space.
开发一个修剪前导和尾随空白的功能。
here is a simple sample, but real file contains far more complex rows and columns.
这是一个简单的示例，但实际文件包含更复杂的行和列。
df=pd.DataFrame([["A b ",2,3],[np.nan,2,3],\
[" random",43,4],[" any txt is possible "," 2 1",22],\
["",23,99],[" help ",23,np.nan]],columns=['A','B','C'])
the result should eliminate all leading & trailing white space, but retain the space inbetween the text.
结果应该消除所有前导和尾随空格，但保留文本之间的空间。
df=pd.DataFrame([["A b",2,3],[np.nan,2,3],\
["random",43,4],["any txt is possible","2 1",22],\
["",23,99],["help",23,np.nan]],columns=['A','B','C'])
Mind that the function needs to cover all possible situations. thank you
请注意，该功能需要涵盖所有可能的情况。谢谢你
回答by jezrael
I think need check if values are strings, because mixed values in column - numeric with strings and for each string call strip:
我认为需要检查值是否为字符串，因为列中的混合值 - 带有字符串的数字以及每个字符串调用strip：
df = df.applymap(lambda x: x.strip() if isinstance(x, str) else x)
print (df)
A B C
0 A b 2 3.0
1 NaN 2 3.0
2 random 43 4.0
3 any txt is possible 2 1 22.0
4 23 99.0
5 help 23 NaN
If columns have same dtypes, not get NaNs like in your sample for numeric values in column B:
如果列具有相同的 dtypes，则不会NaN像您的示例中那样为列中的数值获取s B：
cols = df.select_dtypes(['object']).columns
df[cols] = df[cols].apply(lambda x: x.str.strip())
print (df)
A B C
0 A b NaN 3.0
1 NaN NaN 3.0
2 random NaN 4.0
3 any txt is possible 2 1 22.0
4 NaN 99.0
5 help NaN NaN
