使用Python Pandas按不同的字符分割字符串

Question

how to slice a string in dataframe, start from left, based on different characters, such as ' /- . , I only want the first time this character shows up.

key   name
1   McDonald's
2   CVS/PHARMACY
3   CVS/Store
4   WAL-MART
5   AMAZON.CO

expect result:

key   name            for_Group
1   McDonald's        McDonald
2   CVS/PHARMACY         CVS
3   CVS/Store            CVS
4   WAL-MART             WAL
5   AMAZON.CO          AMAZON

I'm not sure if this need to use regular expression?

Answer 1

Option 1
str.split with expand=True

df['for_group'] = df.name.str.split(r"[\'\/\-\.]", expand=True)[0]

   key          name for_group
0    1    McDonald's  McDonald
1    2  CVS/PHARMACY       CVS
2    3     CVS/Store       CVS
3    4      WAL-MART       WAL
4    5     AMAZON.CO    AMAZON

Option 2 (Best option)
str.extract (I personally prefer this one, it matches until it finds one of your desired stop characters)

df.name.str.extract(r'(.*?)[\'\/\-\.]', expand=False)

0    McDonald
1         CVS
2         CVS
3         WAL
4      AMAZON

The second option here is much faster:

df = pd.concat([df]*10000)

%timeit df.name.str.split(r"[\'\/\-\.]", expand=True)[0]
141 ms ± 1.8 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

%timeit df.name.str.extract(r'(.*)[\'\/\-\.]', expand=False)
72.6 ms ± 397 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

Answer 2

Method 1

You can use the below regular expression, which means a word character (az etc.) repeated one or more times. This returns an array and you can take the first element off it.

import re
df['for_group'] = df['name'].apply(lambda x: re.findall(r"[\w]+", x)[0])

A faster approach to regular expression would be to use .search() as pointed out by @user3483203

df['for_group'] = df['name'].apply(lambda x: re.search(r"[\w]+", x).group())

Method 2

Similarly, you can use:

df['for_group'] = df.name.str.split('\W+').apply(lambda x: x[0])

Output:

    key          name for_group
0    1    McDonald's  McDonald
1    2  CVS/PHARMACY       CVS
2    3     CVS/Store       CVS
3    4      WAL-MART       WAL
4    5     AMAZON.CO    AMAZON