无法正确解析字符串以删除特殊字符

Question

I have one column of a df, which contains strings, which I wish to parse:

df = pd.DataFrame({'name':'apple banana orange'.split(), 'size':"2'20 12:00 456".split()})

which gives

I wish to remove all ' characters, remove :\\d\\d and preserve the pure integers, such that the results looks like as follows:

I have tried to extract the integers prior to ':' and filling the NaN with the original data. While this works for the first row (preserving the original data) and for the second row (correctly removes the ' character), for the last row it somehow casts the data of the first row. My code is

df['size'] = df['size'].str.extract('(\\d*):').fillna(df['size'])

Answer 1

If you only need to test for the ' and the : in the time stamp this will do the job:

df["size"] = df["size"].str.replace("'", "").str.split(":").map(lambda x: x[0])

Output:

     name size
0   apple  220
1  banana   12
2  orange  456

Answer 2

如果我错了，请纠正我，但你不能做.replace('character', '')吗？

Answer 3

Try this...

df['size'] = df['size'].str.replace(r"'", '').str.replace(r'((\d{2}):\d{2})', r'\2', regex=True)

Outputs:

    name    size
0   apple   220
1   banana  12
2   orange  456