Hi! I'm a research assistant trying to use this code for our own project. However, our data is large, so I'm rewriting ui_calculator.py in a way that takes advantage of column/dataframe operations (as opposed to the current approach of calculating everything one row at a time via list comprehension).
Why is there a distinction made between earnings_history and base_period?
Per observation, earnings_history is a list [q1, q2, q3, q4, 0] . I interpret this as the earnings history (q1-q4) plus a 0 to represent the current quarter where the individual is unemployed. This is from the definition of calc_weekly_state_quarterly().
Then, in the definition of calc_weekly_state(), we create base_period = earnings_hist[-5:-1], which is the same as [q1, q2, q3, q4]. From here, we only use base_period. Why add the 0 just to ignore it completely?
Somewhat related, I notice we index [-5:-1] when [:4] is equivalent; is the intended result a reversing ordering like [q4, q3, q2, q1]?
Hi! I'm a research assistant trying to use this code for our own project. However, our data is large, so I'm rewriting
ui_calculator.pyin a way that takes advantage of column/dataframe operations (as opposed to the current approach of calculating everything one row at a time via list comprehension).Why is there a distinction made between
earnings_historyandbase_period?Per observation,
earnings_historyis a list[q1, q2, q3, q4, 0]. I interpret this as the earnings history (q1-q4) plus a 0 to represent the current quarter where the individual is unemployed. This is from the definition ofcalc_weekly_state_quarterly().Then, in the definition of
calc_weekly_state(), we createbase_period = earnings_hist[-5:-1], which is the same as[q1, q2, q3, q4]. From here, we only usebase_period. Why add the 0 just to ignore it completely?Somewhat related, I notice we index
[-5:-1]when[:4]is equivalent; is the intended result a reversing ordering like[q4, q3, q2, q1]?