Pandas 通过多个字符串分隔符将列拆分为多列

Question

I have a dataframe:

id    info
1     Name: John Age: 12 Sex: Male
2     Name: Sara Age: 22 Sex: Female
3     Name: Mac Donald Age: 32 Sex: Male

I'm looking to split the info column into 3 columns such that i get the final output as:

id  Name      Age   Sex
1   John      12   Male
2   Sara      22   Female
3 Mac Donald  32   Male

I tried using pandas split function.

df[['Name','Age','Sex']] = df.info.split(['Name'])

I might have to do this multiple times to get desired output.

Is there a better way to achieve this?

PS: The info column also contains NaN values

Answer 1

Using Regex with named groups.

Ex:

df = pd.DataFrame({"Col": ['Name: John Age: 12 Sex: Male', 'Name: Sara Age: 22 Sex: Female', 'Name: Mac Donald Age: 32 Sex: Male']})
df = df['Col'].str.extract(r"Name:\s*(?P<Name>[A-Za-z\s]+)\s*Age:\s*(?P<Age>\d+)\s*Sex:\s*(?P<Sex>Male|Female)") # Or if spacing is standard use df['Col'].str.extract(r"Name: (?P<Name>[A-Za-z\s]+) Age: (?P<Age>\d+) Sex: (?P<Sex>Male|Female)")
print(df)

Output:

          Name Age     Sex
0        John   12    Male
1        Sara   22  Female
2  Mac Donald   32    Male

Answer 2

The regex is pretty tough to write / read, so you could replace with , for where you want separate into new columns and use str.split() and pass expand=True . You will need to set the result back to three new columns that you create with df[['Name', 'Age', 'Sex']] :

df[['Name', 'Age', 'Sex']] = (df['info'].replace(['Name: ', ' Age: ', ' Sex: '], ['',',',','], regex=True)
                              .str.split(',', expand=True))
df

Out[1]: 
   id                                info        Name Age     Sex
0   1        Name: John Age: 12 Sex: Male        John  12    Male
1   2      Name: Sara Age: 22 Sex: Female        Sara  22  Female
2   3  Name: Mac Donald Age: 32 Sex: Male  Mac Donald  32    Male

Answer 3

A quick oneliner can be

df[['Name', 'Age', 'Sex']] = df['info'].str.split('\s?\w+:\s?', expand=True).iloc[:, 1:]

Split using someword: and then add new columns.

Answer 4

  def process_row(row):
        items = row.info.split(' ')
        row['Name']=str(items[1]).strip()
        row['Age']=str(items[3]).strip()
        row['Sex']=str(items[5]).strip()
        return row

  df=pd.DataFrame({"info": ['Name: John Age: 12 Sex: Male', 'Name: Sara Age: 22 Sex: 
     Female', 'Name: Mac Donald Age: 32 Sex: Male']})
  df['Name']=pd.NA #empty cell
  df['Age']=pd.NA #empty cell
  df['Sex']=pd.NA #empty cell

  df[['info','Name','Age','Sex']]=df.apply(process_row, axis=1, result_type="expand")