Identify consecutive same values in Pandas Dataframe, with a Groupby

Question

I have the following dataframe df:

data={\'id\':[1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,2],
      \'value\':[2,2,3,2,2,2,3,3,3,3,1,4,1,1,1,4,4,1,1,1,1,1]}         


        

Answer 1

You can try this; 1) Create an extra group variable with df.value.diff().ne(0).cumsum() to denote the value changes; 2) use transform('size') to calculate the group size and compare with three, then you get the flag column you need:

df['flag'] = df.value.groupby([df.id, df.value.diff().ne(0).cumsum()]).transform('size').ge(3).astype(int) 
df

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Break downs:

1) diff is not equal to zero (which is literally what df.value.diff().ne(0) means) gives a condition True whenever there is a value change:

df.value.diff().ne(0)
#0      True
#1     False
#2      True
#3      True
#4     False
#5     False
#6      True
#7     False
#8     False
#9     False
#10     True
#11     True
#12     True
#13    False
#14    False
#15     True
#16    False
#17     True
#18    False
#19    False
#20    False
#21    False
#Name: value, dtype: bool

2) Then cumsum gives a non descending sequence of ids where each id denotes a consecutive chunk with same values, note when summing boolean values, True is considered as one while False is considered as zero:

df.value.diff().ne(0).cumsum()
#0     1
#1     1
#2     2
#3     3
#4     3
#5     3
#6     4
#7     4
#8     4
#9     4
#10    5
#11    6
#12    7
#13    7
#14    7
#15    8
#16    8
#17    9
#18    9
#19    9
#20    9
#21    9
#Name: value, dtype: int64

3) combined with id column, you can group the data frame, calculate the group size and get the flag column.