How to assign a group number to each ID (n=1,2,3…..)?

Question

I want to assign a number to each group. I tried to do

df['group_n'] = df.groupby('ID').ngroup()

but it gives me an error msg:

SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

If i do, df[‘group_n’] = df.groupby(‘ID’).ngroup().add(1)

I get _n in descending order (meaning C:3, B:2, A:1) is there a way to preserve that order but have group_n start from 0?

My current table:

ID   date   sender   
C    Jan20     3         
C    Feb20     7         
C    Mar20     12        
C    Apr20     15        
B    Mar20     1         
B    May20     10        
B    Jun20     15        
...
A    Jan21     10        
A    Feb21     12        
A    Mar21     20     
A    Apr21     5

desired table:

ID   date   sender   group_n
C    Jan20     3         1
C    Feb20     7         1
C    Mar20     12        1
C    Apr20     15        1
B    Mar20     1         2
B    May20     10        2
B    Jun20     15        2
A    Jan21     10        3
A    Feb21     12        3
A    Mar21     20        3
A    Apr21     5         3

Thank you in advance!

Asked By: Olive

||

Source

Answer 1

Use:

df['group_n'] = pd.factorize(df['ID'])[0] + 1

Or:

df['group_n'] = df.groupby('ID', sort=False).ngroup().add(1)

print(df)

ID   date   sender   group_n
A    Jan20     3         1
A    Feb20     7         1
A    Mar20     12        1
A    Apr20     15        1
B    Mar20     1         2
B    May20     10        2
B    Jun20     15        2
C    Jan21     10        3
C    Feb21     12        3
C    Mar21     20        3
C    Apr21     5         3

Answered By: ansev

How to assign a group number to each ID (n=1,2,3…..)?

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