Rename column names partially with lambda functions-CodePudding

I want to replace some characters in column names at scale and I was able to do it through columns with str.replace(). However, I want to know if I could do this through lambda functions so I could bring them into my other pandas workflow instead of doing it independently.

dat.columns = (
    dat.columns
    .str.replace(r"park_1_city", "us1state")
    .str.replace(r"park_2_city", "us2state")
    .str.replace(r"park_3_city", "us3state")
    .str.replace(r"us1tree", "us1garden")
    .str.replace(r"us2tree", "us2garden")
    .str.replace(r"us3tree", "us3garden")
)

CodePudding user response：

Simply do:

your_function = lambda col: col  # Or whatever you would like to do with the names
dat.columns = [your_function(col) for col in dat.columns]

You can also use any normal function, instead of a lambda, of course.

CodePudding user response：

Use dictionary for replace subtrings, here \d match digit and \1 same value in Series.replace for possible pass dictionary:

dat = pd.DataFrame(columns=['park_1_city','park_2_city','park_3_city',
                            'us1tree','us2tree','us30tree'])
    

d = {r"park_(\d )_city": r"us\1state",  r"us(\d )tree": r"us\1garden"}
dat.columns = dat.columns.to_series().replace(d, regex=True)
print (dat)
Empty DataFrame
Columns: [us1state, us2state, us3state, us1garden, us2garden, us30garden]
Index: []