column transformation in python

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O have this table where the type column shows 3 levels of information. I want to transform the second and third level to be in separated column.

number    type  
10       type 1
10        bottom
10        up
10          1
10          2
10          3
20       type 2
20        bottom
20        up
20          1
20          2
20          3

The expected result is as below:

number     type    description    detail
10        type 1   bottom         bottom
10        type 1   up               1
10        type 1   up               2
10        type 1   up               3
20        type 2   bottom         bottom
20        type 2   up               1
20        type 2   up               2
20        type 2   up               3

is there any way I can do to make it with python?

Thanks in advance

pandas python
1 Answers

You can use pandas str.extract with ffill:

df['type_new'] = df['type'].str.extract(('(type.*)')).ffill()
df['detail'] = df['type'].str.extract('(bottom|[0-9])').ffill()
df['description'] = df['type'].str.extract('(bottom|up)').ffill()

Finally use masking to get only the required rows, and rename columns if needed:

df = df[df['type'].isin(df['detail'].values)].reset_index(drop=True)[['number', 'type_new', 'description', 'detail']].rename(columns={'type_new':'type'})

OUTPUT:

   number    type description  detail
0      10  type 1      bottom  bottom
1      10  type 1          up       1
2      10  type 1          up       2
3      10  type 1          up       3
4      20  type 2      bottom  bottom
5      20  type 2          up       1
6      20  type 2          up       2
7      20  type 2          up       3
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