Finding average age of incidents in a datetime series - python-3.x

Question

I'm new to Stackoverflow and fairly fresh with Python (some 5 months give or take), so apologies if I'm not explaining this too clearly!
I want to build up a historic trend of the average age of outstanding incidents on a daily basis.
I have two dataframes.
df1 contains incident data going back 8 years, with the two most relevant columns being "opened_at" and "resolved_at" which contains datetime values.
df2 contains a column called date with the full date range from 2012-06-13 to now.
The goal is to have df2 contain the number of outstanding incidents on each date (as of 00:00:00) and the average age of all those deemed outstanding.
I know it's possible to get all rows that exist between two dates, but I believe I want the opposite and find where each date row in df2 exists between dates in opened_at and resolved_at in df1

Answer 1

(It would be helpful to have some example code containing an anonymized/randomized short extract of your data to try solutions on)
This is unlikely to be the most efficient solution, but I believe you could do:
df2["incs_open"] = 0 # Ensure the column exists
for row_num in range(df2.shape[0]):
df2.at[row_num, "incs_open"] = sum(
(df1["opened_at"] < df2.at[row_num, "date"]) &
(df2.at[row_num, "date"] < df1["opened_at"])
)
(This assumes you haven't set an index on the data frame other than the default one)
For the second part of your question, the average age, I believe you can calculate that in the body of the loop like this:
open_incs = (df1["opened_at"] < df2.at[row_num, "date"]) & \
(df2.at[row_num, "date"] < df1["opened_at"])
ages_of_open_incs = df2.at[row_num, "date"] - df1.loc[open_incs, "opened_at"]
avg_age = ages_of_open_incs.mean()
You'll hit some weirdnesses about rounding and things. If an incident was opened last night at 3am, what is its age "today" -- 1 day, 0 days, 9 hours (if we take noon as the point to count from), etc. -- but I assume once you've got code that works you can adjust that to taste.