Python: strip pair-wise column names

Question

I have a DataFrame with columns that look like this:

df=pd.DataFrame(columns=['(NYSE_close, close)','(NYSE_close, open)','(NYSE_close, volume)', '(NASDAQ_close, close)','(NASDAQ_close, open)','(NASDAQ_close, volume)'])

df:
(NYSE_close, close) (NYSE_close, open) (NYSE_close, volume) (NASDAQ_close, close) (NASDAQ_close, open) (NASDAQ_close, volume)

I want to remove everything after the underscore and append whatever comes after the comma to get the following:

df:
NYSE_close  NYSE_open  NYSE_volume  NASDAQ_close  NASDAQ_open  NASDAQ_volume

I tried to strip the column name but it replaced it with nan. Any suggestions on how to do that?

Thank you in advance.

Asked By: MathMan 99

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Source

Answer 1

You could use re.sub to extract the appropriate parts of the column names to replace them with:

import re

df=pd.DataFrame(columns=['(NYSE_close, close)','(NYSE_close, open)','(NYSE_close, volume)', '(NASDAQ_close, close)','(NASDAQ_close, open)','(NASDAQ_close, volume)'])
df.columns = [re.sub(r'(([^_]+_)w+, (w+))', r'12', c) for c in df.columns]

Output:

Empty DataFrame
Columns: [NYSE_close, NYSE_open, NYSE_volume, NASDAQ_close, NASDAQ_open, NASDAQ_volume]
Index: []

Answered By: Nick

Answer 2

You could:

import re

def cvt_col(x):
    s = re.sub('[()_,]', ' ', x).split()
    return s[0] + '_' + s[2] 

df.rename(columns = cvt_col)

Empty DataFrame
Columns: [NYSE_close, NYSE_open, NYSE_volume, NASDAQ_close, NASDAQ_open, NASDAQ_volume]
Index: []

Answered By: jch

Answer 3

Use a list comprehension, twice:

step1 = [ent.strip('()').split(',') for ent  in df]

df.columns = ["_".join([left.split('_')[0], right.strip()]) 
              for left, right  in step1]

df

Empty DataFrame
Columns: [NYSE_close, NYSE_open, NYSE_volume, NASDAQ_close, NASDAQ_open, NASDAQ_volume]
Index: []

Answered By: sammywemmy

Python: strip pair-wise column names

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