Extract substring from string in dataframe

Question

I have the following ddataframe:

                             Company Name        Time Expectation
0                Asta Funding Inc. (ASFI)  9:35 AM ET           -
1                       BlackBerry (BBRY)  7:00 AM ET     ($0.03)
2                    Carnival Corp. (CCL)  9:15 AM ET       $0.09
3                      Carnival PLC (CUK)  0:00 AM ET           -

I would like to have the company symbols in their own seperate column instead of inside the Company Name column. Right now I just have it iterate over the company names, and a RE pulls the symbols, puts it into a list, and then I apply it to the new column, but I'm wondering if there is a cleaner/easier way.

I'm new to the whole map reduce lambda stuff.

for company in df['Company Name']:
    ticker = re.search("\(.*\)",company).group(0)
    ticker = ticker[1:len(ticker)-1]
    tickers.append(ticker)

Solution 1

Regex search is built into the Series class in pandas. You can find the documentation here. In your case, you could use

df['ticker'] = df['Company Name'].str.extract("\((.*)\)") 

Solution 2

You can use the fact that str operates elementwise on a whole series. I assume that the company's symbol will always be at the end of the company name and surrounded by parantheses:

df['Company Symbol'] = df['Company Name'].str.rstrip(')').str.split('(').str[1] # Make new column
df['Company Name'] = df['Company Name'].str.replace(r'\(.*?\)$', '') # Remove symbol from company name

Comments

[nicholas.reichel]

I cannot get it to work still. I'm using r"..." but it still is saying ValueError: This pattern contains no groups to capture.

[halex]

@mobone Change the regex to "\((.*)\)", note the added parantheses around .* to make this matched part a group