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Tag: rolling-computation

Pandas lagged rolling average on aggregate data with multiple groups and missing dates

I’d like to calculate a lagged rolling average on a complicated time-series dataset. Consider the toy example as follows: This results in the following DataFrame: Now I’d like to add a column representing the average weight per fruit for the previous 7 days: wgt_per_frt_prev_7d. It should be defined as the sum of all the fruit weights divided by the sum

Apply function to each unique value of column seperately

I have a dataframe with more than 500 cities which look like this city value datetime london 23 2022-03-25 17:59:18 dubai 12 2022-03-25 17:59:36 berlin 5 2022-03-25 17:59:42 london 25 2022-03-25 18:01:18 dubai 12 2022-03-25 18:02:18 berlin 5 2022-03-25 18:03:18 I have a function called rolling_mean which creates a new column ‘rolling_mean’ which calculates the last hour rolling average. However

Getting Rolling Sum per Group

I have a dataframe like this: I would like to get the Sum of the last three months (excluding the current month), per Product_ID. Therefore I tried this: My code is failing, because it does not only calculate it per product, but it will give me also numbers for other products (let’s say Product 2, quarter 1: gives me the

Datetime rolling count per category in Pandas

Starting from a DataFrame with a date and user column, I’d like to add a third count_past_5_days column to indicate the rolling count of occurrences of each row’s user during the past 5 days: date user count_past_5_days 2020-01-01 abc 1 2020-01-01 def 1 2020-01-02 abc 2 2020-01-03 abc 3 2020-01-04 abc 4 2020-01-04 def 2 2020-01-04 ghi 1 2020-01-05 abc

non fixed rolling window

I am looking to implement a rolling window on a list, but instead of a fixed length of window, I would like to provide a rolling window list: Something like this: and the result would be: 6.67 is calculated as average of 3 elements 10, 2, 8. I implemented a slow solution, and every idea is welcome to make it