Pandas Python Regex : error: nothing to repeat

Question

I have a dataframe with a couple of strange characters, "*" and "-".

import pandas as pd
import numpy as np

data = {'year': [2010, 2011, 2012, 2011, 2012, 2010, 2011, 2012],
        'team': ['Bears', 'Bears', 'Bears', 'Packers', 'Packers', 'Lions',     'Lions', 'Lions'],
        'wins': [11, '*', 10, '-', 11, 6, 10, 4],
        'losses': [5, 8, 6, 1, 5, 10, 6, 12]}
football = pd.DataFrame(data, columns=['year', 'team', 'wins', 'losses'])

I would like to replace the strange characters with '0.00' but I get an error -

error: nothing to repeat

I understand this is linked to regex but I still dont know how to overcome the issue.

the code I use to replace the characters:

football.replace(['*','-'], ['0.00','0.00'], regex=True).astype(np.float64)

Toto · Accepted Answer · 2015-02-19 12:39:22Z

7

* is a special character in regex, you have to escape it:

football.replace(['\*','-'], ['0.00','0.00'], regex=True).astype(np.float64)

or use a character class:

football.replace([*-], '0.00', regex=True).astype(np.float64)

answered Feb 19, 2015 at 12:39

Toto

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Comments

Antti Haapala · Accepted Answer · 2015-02-19 12:39:33Z

2

Do

football.replace(['*','-'], ['0.00','0.00'], regex=False)

That is, there is no need to use regular expression for a simple case of matching just 1 character or another;

or if you want to use regular expression, do note that * is a special character; if you want to match values that are '*' or '-' exactly, use

football.replace('^[*-]$', '0.00', regex=True)

answered Feb 19, 2015 at 12:39

Antti Haapala

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Comments

Cory Kramer · Accepted Answer · 2015-02-19 12:39:37Z

1

You could use a list comprehension within a dict comprehension to do this

>>> {key: [i if i not in {'*','-'} else '0.00' for i in values] for key, values in data.items()}
{'year': [2010, 2011, 2012, 2011, 2012, 2010, 2011, 2012],
 'wins': [11, '0.00', 10, '0.00', 11, 6, 10, 4],
 'losses': [5, 8, 6, 1, 5, 10, 6, 12],
 'team': ['Bears', 'Bears', 'Bears', 'Packers', 'Packers', 'Lions', 'Lions', 'Lions']}

This would be done to clean up data before you make a DataFrame.

answered Feb 19, 2015 at 12:39

Cory Kramer

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1 Comment

Boosted_d16 Over a year ago

Thanks for the effort dude.

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