Splitting the columns and naming in pandas

Question

I have a data file that contains the columns of time interval such as 0-10,11-20,21-30 …., 81-90. There are also additional two columns FH and SH. The following sample dataframe represent part of my data:

df = pd.DataFrame()
df['Team'] = ['A','B','C']
df['0-10'] = ['4-0','2-2','3-2']
df ['11-20']= ['2-1','2-2','3-0']
df ['21-30'] = ['2-1','1-1','2-2']
df ['FH'] = ['5-3','6-6','5-5']
df ['SH'] = ['2-3','3-2','3-3']

What I wanted to do is to split the element under the time intervals (0-10,11-20, 21-30)
that means i will have two different columns for each time interval [‘0-10’] to [‘0-10F’ and ‘0-10A’] and the value under ‘0-10F’ will be 4 and ‘0-10A’ will be 0 for Team A,
I will do the same for other time interval ’11-20′ to ’11-20F’ and ’11-20A’
I could write code for each column seperately as follow:

df ['0-10F'] = df['0-10'].str.split('-').str[0]
df ['0-10A'] = df['0-10'].str.split('-').str[1]
df ['11-20F'] = df['11-20'].str.split('-').str[0]
df ['11-20A'] = df['11-20'].str.split('-').str[1]
df ['21-30F'] = df['21-30'].str.split('-').str[0]
df ['21-30A'] = df['21-30'].str.split('-').str[1]

Is there any better way to write it for all columns with one generic codes. The following is the expected output:

Asked By: Zephyr

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Source

Answer 1

Use DataFrame.filter for columns names with -, loop by each column and create new DataFrame with Series.str.split and if necessary convert values to integers:

for c in df.filter(like='-').columns:
    df[[f'{c}F', f'{c}A']] = df[c].str.split('-', expand=True).astype(int)

print (df)
  Team 0-10 11-20 21-30   FH   SH  0-10F  0-10A  11-20F  11-20A  21-30F  
0    A  4-0   2-1   2-1  5-3  2-3      4      0       2       1       2   
1    B  2-2   2-2   1-1  6-6  3-2      2      2       2       2       1   
2    C  3-2   3-0   2-2  5-5  3-3      3      2       3       0       2   

   21-30A  
0       1  
1       1  
2       2

Answered By: jezrael

Splitting the columns and naming in pandas

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