This is an adaptation of a question posed by @ScalaBoy here and answered by @timgeb, the question is the same, except it is about a prefix not suffix:
Given pandas DataFrame, how can I add the prefix "new_" to all columns except two columns Id and Name?
import pandas as pd
data = [[1,'Alex',22,'single'],[2,'Bob',32,'married'],[3,'Clarke',23,'single']]
df = pd.DataFrame(data,columns=['Id','Name','Age','Status'])
Use add_prefix. This function is applied to the whole dataframe. To hide columns you dont need renamed, set them as index. After renaming you can reset back the index. Code below
df=df.set_index(['Id', 'Name']).add_prefix('new_').reset_index()
Id Name new_Age new_Status
0 1 Alex 22 single
1 2 Bob 32 married
2 3 Clarke 23 single
You can use rename method:
df.rename({col:'new_'+ col for col in df.columns[~df.columns.isin(['Id','Name'])]}, axis=1)
df.rename(columns = lambda col: f"new_{col}"
if col not in ('Name', 'Id')
else col
)
Id Name new_Age new_Status
0 1 Alex 22 single
1 2 Bob 32 married
2 3 Clarke 23 single
You could achieve it with regex; however, in my opinion, it is not as clear as the previous solution:
new_col = df.columns.str.replace(pat = r"\b(?!Id|Name\b).+",
repl = lambda m: f"new_{m[0]}",
regex=True)
df.set_axis(new_col, axis=1) # or df.columns = new_col
Id Name new_Age new_Status
0 1 Alex 22 single
1 2 Bob 32 married
2 3 Clarke 23 single