Removing Suffix From Dataframe Column Names - Python - python-3.x

Question

I am trying to remove a suffix from all columns in a dataframe, however I am getting error messages. Any suggestions would be appreciated.
df = pd.DataFrame(np.random.randint(0,10,size=(10, 4)), columns=list('ABCD'))
df.add_suffix('_x')
def strip_right(df.columns, _x):
if not text.endswith("_x"):
return text
# else
return text[:len(df.columns)-len("_x")]
Error:
def strip_right(tmp, "_x"):
^
SyntaxError: invalid syntax
I've also tried removing the quotations.
def strip_right(df.columns, _x):
if not text.endswith(_x):
return text
# else
return text[:len(df.columns)-len(_x)]
Error:
def strip_right(df.columns, _x):
^
SyntaxError: invalid syntax

Answer 1

Here is a more concrete example:.
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randint(0,10,size=(10, 4)), columns=list('ABCD'))
df = df.add_suffix('_x')
print ("With Suffix")
print(df.head())
def strip_right(df, suffix='_x'):
df.columns = df.columns.str.rstrip(suffix)
strip_right(df)
print ("\n\nWithout Suffix")
print(df.head())
Output:
With Suffix
A_x B_x C_x D_x
0 0 7 0 2
1 5 1 8 5
2 6 2 0 1
3 6 6 5 6
4 8 6 5 8
Without Suffix
A B C D
0 0 7 0 2
1 5 1 8 5
2 6 2 0 1
3 6 6 5 6
4 8 6 5 8

Answer 2

I found a bug in the implementation of the accepted answer. The docs for pandas.Series.str.rstrip() reference str.rstrip(), which states:
"The chars argument is not a suffix; rather, all combinations of its values are stripped."
Instead I had to use pandas.Series.str.replace to remove the actual suffix from my column names. See the modified example below.
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randint(0,10,size=(10, 4)), columns=list('ABCD'))
df = df.add_suffix('_x')
df['Ex_'] = np.random.randint(0,10,size=(10, 1))
df1 = pd.DataFrame(df, copy=True)
print ("With Suffix")
print(df1.head())
def strip_right(df, suffix='_x'):
df.columns = df.columns.str.rstrip(suffix)
strip_right(df1)
print ("\n\nAfter .rstrip()")
print(df1.head())
def replace_right(df, suffix='_x'):
df.columns = df.columns.str.replace(suffix+'$', '', regex=True)
print ("\n\nWith Suffix")
print(df.head())
replace_right(df)
print ("\n\nAfter .replace()")
print(df.head())
Output:
With Suffix
A_x B_x C_x D_x Ex_
0 4 9 2 3 4
1 1 6 5 8 6
2 2 5 2 3 6
3 1 4 7 6 4
4 3 9 3 5 8
After .rstrip()
A B C D E
0 4 9 2 3 4
1 1 6 5 8 6
2 2 5 2 3 6
3 1 4 7 6 4
4 3 9 3 5 8
After .replace()
A B C D Ex_
0 4 9 2 3 4
1 1 6 5 8 6
2 2 5 2 3 6
3 1 4 7 6 4
4 3 9 3 5 8