remove_unwanted_string_benchmark.py

    import perfplot 
    import pandas as pd
    import re

    p1 = re.compile(r'\D')
    p2 = re.compile(r'\d+')
    
    def eumiro(df):
        return df.assign(
            result=df['result'].map(lambda x: x.lstrip('+-').rstrip('aAbBcC')))
        
    def coder375(df):
        return df.assign(
            result=df['result'].replace(r'\D', r'', regex=True))
    
    def monkeybutter(df):
        return df.assign(result=df['result'].map(lambda x: x[1:-1]))
    
    def wes(df):
        return df.assign(result=df['result'].str.lstrip('+-').str.rstrip('aAbBcC'))
    
    def cs1(df):
        return df.assign(result=df['result'].str.replace(r'\D', ''))
    
    def cs2_ted(df):
        # `str.extract` based solution, similar to @Ted Petrou's. so timing together.
        return df.assign(result=df['result'].str.extract(r'(\d+)', expand=False))
    
    def cs1_listcomp(df):
        return df.assign(result=[p1.sub('', x) for x in df['result']])
    
    def cs2_listcomp(df):
        return df.assign(result=[p2.search(x)[0] for x in df['result']])

    def cs_eumiro_listcomp(df):
        return df.assign(
            result=[x.lstrip('+-').rstrip('aAbBcC') for x in df['result']])

    def cs_mb_listcomp(df):
        return df.assign(result=[x[1:-1] for x in df['result']])
 

    df_ = pd.DataFrame({
       'time': ['09:00', '10:00', '11:00', '12:00', '13:00'],
       'result': ['+52A', '+62B', '+44a', '+30b', '-110a']
    })
    
    kernels = [
        eumiro, coder375, monkeybutter, wes, cs1, cs2_ted, 
        cs1_listcomp, cs2_listcomp, cs_eumiro_listcomp, cs_mb_listcomp
    ]
    
    perfplot.show(
        setup=lambda n: pd.concat([df_] * n, ignore_index=True),
        kernels=kernels,
        labels=[k.__name__ for k in kernels],
        n_range=[2**k for k in range(1, 15)],
        xlabel='N',
        logx=True,
        logy=True,
        equality_check=lambda x, y: (x == y).all(axis=None),)