I have a table that looks like so
Class | Health (R) | Pension (R) | Health (E) | Pension (E) |
---|---|---|---|---|
10 | 1.00 | 2.50 | 1.25 | 3.00 |
11 | 1.00 | 2.50 | 1.25 | 3.00 |
12 | 1.00 | 2.50 | 1.25 | 3.00 |
The rows with (E) and (R) are created dynamically with a pandas pivot function. That means I won't always know the name of the columns to directly specify it in a typical function like this:
df = df['column', 'column', 'column']
df.filter(like = "(R)")
The filter function above works fantastic but, the only issue is that I have to also bring in the class column from the table.
I have tried using.
df.filter(regex = r"(Class|(R)")
This one gives me the Class but will also for some reason pull any of the columns that have () in them.
What is the best way to extract the class and the columns with (R) to create a new dataframe?
Thank you in advance!
You need to escape your parentheses, and you don't need any on the outside:
df.filter(regex=r'Class|\(R\)')
Or without a regex:
df[df.columns[df.columns.str.contains('(R)')].union(['Class'])]
Output:
Class Health (R) Pension (R)
0 ... ... ...