How to count number of identical, sequential values in a column with python/pandas?

Question

Say I have a dataframe such as:

df = pd.DataFrame({'A': [1, 1, 2, 3, 3, 3, 1, 1]})

I'd like to count the number of time the current column value has been seen in a row previous. For the above example, the output would be:

[1, 2, 1, 1, 2, 3, 1, 2]

I know how to group by and cumulative sum all repeating values, but I don't know how to get it to restart at each new value.

ie

df['A'].groupby(df['A']).cumcount() 
# returns [0, 1, 0, 0, 1, 2, 2, 3] which is not what I want.

Answer 1

Try this method:

df.groupby((df['A'] != df['A'].shift()).cumsum()).cumcount() + 1

Output:

0    1
1    2
2    1
3    1
4    2
5    3
6    1
7    2
dtype: int64

Details

Use equality to check between current row and next row, then cumsum to create a new group for each changing in 'A', then groupby and cumcount adding 1 to start at 1 instead of zero.

Break down into steps

Broken up in steps so you can see the progression in the dataframe columns.

df['grp'] = df['A'] != df['A'].shift() 
#for numbers you can use df['A'].diff().ne(0) 
#however using inquality check is more versatile for strings
df['cumgroup'] = df['grp'].cumsum()
df['count'] = df.groupby('cumgroup').cumcount() + 1
df

Output:

   A    grp  cumgroup  count
0  1   True         1      1
1  1  False         1      2
2  2   True         2      1
3  3   True         3      1
4  3  False         3      2
5  3  False         3      3
6  1   True         4      1
7  1  False         4      2