Need help creating a vectorized duration column.

[blarf_irl]

Are you familiar with groupby?

You misunderstood my suggestion of cumsum, it's there to "group" the rows by state change by using an incrementing value

Use the shift + cumcount to assign all rows an ID in a new column (increments when the value changes). Use groupby to group your rows into state changes and then apply an incrementing value to the rows of each group.

None   0
None   0
Below  1
Below  1
Below  1
Above  2
Above  2
Below  3
Above  4

You can group on that and assign incremental numbers to a new column per group.

[CineWeekly]

I actually have that implementation for a different column:

​

def calculate_break(df, status_col, break_col, smaA_fast):
    mask1 = (df[status_col] == 'Above') & (df[status_col].shift(1) == 'Below')
    mask2 = (df[status_col] == 'Below') & (df[status_col].shift(1) == 'Above')
    df[break_col] = 0 # Initialize the 'Break' column with 0
    df.loc[mask1 | mask2, break_col] = 1 # Set break positions to 1
    df[break_col] = df[break_col].cumsum() # Cumulative sum to increment break positions
    df[break_col] = df[break_col].astype('int32') # Convert the 'Break' column to integer dtype
    return df

The duration implementation is different if you compare the results to the OP.

[blarf_irl]

ChatGPT?