Pandas dataframe: change string values based on condition with regex

Question

I have a dataframe with numbers represented as strings. I need to remove the parentheses, if they exist, and add a negative sign. For example, (30) should become -30.

Positive numbers should not change.

df = pd.DataFrame({'a':['19','(30)','(1000)'],
                   'b':['(202)','200', '100'],
                   'c':['101','(30)', '40']})

        a      b     c
0      19  (202)   101
1    (30)    200  (30)
2  (1000)    100    40

For the regular expression, I can do this on a single value:

neg_pat = r'\((\d+)\)'
num = '(30)'
new_num = '-' + re.search(neg_pat, num).group(1)
print(new_num)
-30

Now, how can I apply this to the dataframe? I've used apply() and lambda expressions before but I'm stuck on putting this together.

Does this answer your question? Python- replace negative numbers shown as a string with parentheses into a float — Matt
– Matt, Commented Sep 22, 2021 at 19:16
Almost but not quite. BENY replied with a good answer. Thanks. — Chuck
– Chuck, Commented Sep 22, 2021 at 19:40

BENY · Accepted Answer · 2021-09-22 19:13:06Z

2

You can do with replace

out = df.replace({'\((.*)\)':'-\\1'},regex=True).astype(int)
Out[280]: 
      a    b    c
0    19 -202  101
1   -30  200  -30
2 -1000  100   40

answered Sep 22, 2021 at 19:13

BENY

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1 Comment

Chuck Over a year ago

That's a new one - I never saw a replace with curly brackets but I get it. Thank you.

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