python - Pandas split column name -

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i have test dataframe looks this:

data = pd.dataframe([[0,0,0,3,6,5,6,1],[1,1,1,3,4,5,2,0],[2,1,0,3,6,5,6,1],[3,0,0,2,9,4,2,1]], columns=["id", "sex", "split", "group0low", "group0high", "group1low", "group1high", "trim"])  grouped = data.groupby(['sex','split']).mean()  stacked = grouped.stack().reset_index(level=2) stacked.columns = ['group_level', 'mean']

next, want separate out group_level , stack 2 new factors:

stacked['group'] = stacked.group_level.str[:6] stacked['level'] = stacked.group_level.str[6:]

this works fine. question this:

this works if column names ("group0low", "group0high", "group1low", "group1high") have in common each other.

what if instead column names more "routelow", "routehigh", "landmarklow", "landmarkhigh"? how use str split group_level in case?

this question similar 1 posted here: slice/split string series @ various positions

the difference of column subnames different , have no commonality (whereas in other post had group or class in name). there regex string, or other method, can use stacking?

here way. assumes low/high group ends words low , high respectively, can use .str.endswith() identify rows low/high.

here sample data

df = pd.dataframe('group0low group0high group1low group1high routelow routehigh landmarklow landmarkhigh'.split(), columns=['group_level']) df      group_level 0     group0low 1    group0high 2     group1low 3    group1high 4      routelow 5     routehigh 6   landmarklow 7  landmarkhigh

use np.where, can following

df['level'] = np.where(df['group_level'].str.endswith('low'), 'low', 'high') df['group'] = np.where(df['group_level'].str.endswith('low'), df['group_level'].str[:-3], df['group_level'].str[:-4])  df      group_level level     group 0     group0low   low    group0 1    group0high  high    group0 2     group1low   low    group1 3    group1high  high    group1 4      routelow   low     route 5     routehigh  high     route 6   landmarklow   low  landmark 7  landmarkhigh  high  landmark

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