I've tried searching around and can't figure out an easy way to do this, so I'm hoping your expertise can help.
I have a pandas data frame with two columns
import numpy as np
import pandas as pd
pd.options.display.width = 1000
testing = pd.DataFrame({'NAME':[
'FIRST', np.nan, 'NAME2', 'NAME3',
'NAME4', 'NAME5', 'NAME6'], 'FULL_NAME':['FIRST LAST', np.nan, 'FIRST LAST', 'FIRST NAME3', 'FIRST NAME4 LAST', 'ANOTHER NAME', 'LAST NAME']})
which gives me
FULL_NAME NAME
0 FIRST LAST FIRST
1 NaN NaN
2 FIRST LAST NAME2
3 FIRST NAME3 NAME3
4 FIRST NAME4 LAST NAME4
5 ANOTHER NAME NAME5
6 LAST NAME NAME6
what I'd like to do is take the values from the 'NAME' column and remove then from the 'FULL NAME' column if it's there. So the function would then return
FULL_NAME NAME NEW
0 FIRST LAST FIRST LAST
1 NaN NaN NaN
2 FIRST LAST NAME2 FIRST LAST
3 FIRST NAME3 NAME3 FIRST
4 FIRST NAME4 LAST NAME4 FIRST LAST
5 ANOTHER NAME NAME5 ANOTHER NAME
6 LAST NAME NAME6 LAST NAME
So far, I've defined a function below and am using the apply method. This runs rather slow on my large data set though and I'm hoping there's a more efficient way to do it. Thanks!
def address_remove(x):
try:
newADDR1 = re.sub(x['NAME'], '', x[-1])
newADDR1 = newADDR1.rstrip()
newADDR1 = newADDR1.lstrip()
return newADDR1
except:
return x[-1]
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