python - Can I create a new column based on when the value changes in another column? -

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let s have df

print(df)               date_time   b 0   10/08/2016 12:04:56  1  5 1   10/08/2016 12:04:58  1  6 2   10/08/2016 12:04:59  2  3 3   10/08/2016 12:05:00  2  2 4   10/08/2016 12:05:01  3  4 5   10/08/2016 12:05:02  3  6 6   10/08/2016 12:05:03  1  3 7   10/08/2016 12:05:04  1  2 8   10/08/2016 12:05:05  2  4 9   10/08/2016 12:05:06  2  6 10  10/08/2016 12:05:07  3  4 11  10/08/2016 12:05:08  3  2

the values in column ['a'] repeat on time, need column though, have new id each time change, have following df

print(df)                date_time   b  c  0   10/08/2016 12:04:56  1  5  1  1   10/08/2016 12:04:58  1  6  1  2   10/08/2016 12:04:59  2  3  2  3   10/08/2016 12:05:00  2  2  2  4   10/08/2016 12:05:01  3  4  3  5   10/08/2016 12:05:02  3  6  3  6   10/08/2016 12:05:03  1  3  4  7   10/08/2016 12:05:04  1  2  4  8   10/08/2016 12:05:05  2  4  5  9   10/08/2016 12:05:06  2  6  5  10  10/08/2016 12:05:07  3  4  6  11  10/08/2016 12:05:08  3  2  6

is there way python? still new , hoped find me in pandas, have not found yet. in original dataframe values in column ['a'] change on irregular intervals approximately every ten minutes , not every 2 rows in example. has idea how approach task? thank you

you can use shift-cumsum pattern.

df['c'] = (df.a != df.a.shift()).cumsum()  >>> df               date_time   b  c 0  10/08/2016  12:04:56  1  5  1 1  10/08/2016  12:04:58  1  6  1 2  10/08/2016  12:04:59  2  3  2 3  10/08/2016  12:05:00  2  2  2 4  10/08/2016  12:05:01  3  4  3 5  10/08/2016  12:05:02  3  6  3 6  10/08/2016  12:05:03  1  3  4 7  10/08/2016  12:05:04  1  2  4 8  10/08/2016  12:05:05  2  4  5 9  10/08/2016  12:05:06  2  6  5 10 10/08/2016  12:05:07  3  4  6 11 10/08/2016  12:05:08  3  2  6

as side note, popular pattern grouping. example, average b value of each such group:

df.groupby((df.a != df.a.shift()).cumsum()).b.mean()

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