Pandas replace string values in a column which has multiple variations-CodePudding

I am working with this csv file. It's a small dataset of laptop information.

laptops = pd.read_csv('laptops.csv',encoding="Latin-1")
laptops["Operating System"].value_counts()

Which gives:

Windows      1125
No OS          66
Linux          62
Chrome OS      27
macOS          13
Mac OS          8
Android         2
Name: Operating System, dtype: int64

I want to merge the variations of macOS and Mac OS under a single value "macOS".

I have tried this, which works.

mapping_dict = {
    'Android': 'Android',
    'Chrome OS': 'Chrome OS',
    'Linux': 'Linux',
    'Mac OS': 'macOS',
    'No OS': 'No OS',
    'Windows': 'Windows',
    'macOS': 'macOS'
}

laptops["Operating System"] = laptops["Operating System"].map(mapping_dict)

laptops["Operating System"].value_counts()

Windows      1125
No OS          66
Linux          62
Chrome OS      27
macOS          21
Android         2
Name: Operating System, dtype: int64

Is this is the only way or the best way of doing it? Assume such requirement might arise for multiple values (and not just macOS).

CodePudding user response：

You can simply do

laptops['Operating System'] = laptops['Operating System'].replace('Mac OS', 'macOS')

CodePudding user response：

laptops['Operating System'] = laptops['Operating System'].str.replace(r'(Mac OS|MAC OS|MC OS)', 'macOS', regex=True)