How to check that rows start with or contain same value when grouped by other column in pandas-CodePudding

I have a dataframe that I'd like to group by id and the common 'top level' string with the count of 'lower-level' strings it includes. For example,

    id name
    1  AA-BB-CC-DD
    1  AA-BB-CC
    1  AA-BB-CC-DD-EE
    1  AA-BB-UU-VV
    1  AA-BB-UU
    1  FF-MM-NN
    1  FF-MM-NN-PP
    2  XX-YY
    2  XX-ZZ
    2  XX-ZZ-AA

for id 1 the first top level of column name is AA, second BB, third CC, etc. The common 'highest' level for id 1 in the data frame is AA-BB-CC.

The desired output is

    id  name      count
    1   AA-BB-CC  3
    1   AA-BB-UU  2
    1   FF-MM-NN  2
    2   XX-YY     1
    2   XX-ZZ     2

Thank you.

CodePudding user response：

IIUC, you can form groups using the first level, then groupby.agg using a custom function:

group = df['name'].str.extract('^([^-] )', expand=False)

def min_string(s):
    return min(s, key=lambda x: x.count('-'))

out = (df
 .groupby(['id', group], as_index=False)
 .agg(name=('name', min_string),
      count=('name', 'count')
     )
)

output:

   id      name  count
0   1  AA-BB-CC      3
1   1  FF-MM-NN      2
2   2     XX-YY      3

update: handle minimal set of first level(s)

change min_string to:

def min_string(s):
    return '-'.join(x[0] for x in zip(*s.str.split('-'))
                    if len(set(x)) == 1)

example input:

   id            name
0   1     AA-BB-CC-DD
1   1        AA-BB-CC
2   1  AA-BB-CC-DD-EE
3   1     AA-BB-UU-VV
4   1        AA-BB-UU
5   1        FF-MM-NN
6   1     FF-MM-NN-PP
7   2           XX-YY
8   2           XX-ZZ
9   2        XX-ZZ-AA

output:

   id      name  count
0   1     AA-BB      5
1   1  FF-MM-NN      2
2   2        XX      3

update2: maximal common subgroup

def make_groups(s, sep='-'):
    d = {}
    s = s.str.split(sep).sort_values()
    prev = s.iloc[0]
    for idx, val in s.items():
        if val[:len(prev)] != prev:
            prev = val
        d[idx] = sep.join(prev)
    return pd.Series(d, index=s.index)

group = df.groupby('id', group_keys=False)['name'].apply(make_groups)

out = (df
 .groupby(['id', group], as_index=False)
 .agg(name=('name', min_string),
      count=('name', 'count')
     )
)

output:

   id      name  count
0   1  AA-BB-CC      3
1   1  AA-BB-UU      2
2   1  FF-MM-NN      2
3   2     XX-YY      1
4   2     XX-ZZ      2

used input:

   id            name
0   1     AA-BB-CC-DD
1   1        AA-BB-CC
2   1  AA-BB-CC-DD-EE
3   1     AA-BB-UU-VV
4   1        AA-BB-UU
5   1        FF-MM-NN
6   1     FF-MM-NN-PP
7   2           XX-YY
8   2           XX-ZZ
9   2        XX-ZZ-AA