CODEWITHSUNDEEP
pythonpandas
Burgertron
Burgertron

Reputation: 76

How to best calculate average issue age for a range of historic dates (pandas)

Objective: I need to show the trend in ageing of issues. e.g. for each date in 2021 show the average age of the issues that were open as at that date.

Starting data (Historic issue list):. "df"

ref Created resolved
a1 20/4/2021 15/5/2021
a2 21/4/2021 15/6/2021
a3 23/4/2021 15/7/2021

Endpoint: "df2"

Date Avg_age
1/1/2021 x
2/1/2021 y
3/1/2021 z

where x,y,z are the calculated averages of age for all issues open on the Date.

Tried so far: I got this to work in what feels like a very poor way.

  1. create a date range (pd.date_range(start,finish,freq="D")
  2. I loop through the dates in this range and for each date I filter the "df" dataframe (boolean filtering) to show only issues live on the date in question. Then calc age (date - created) and average for those. Each result appended to a list.
  3. once done I just convert the list into a series for my final result, which I can then graph or whatever.
    hist_dates = pd.date_range(start="2021-01-01",end="2021-12-31"),freq="D")
    result_list = []
    for each_date in hist_dates:
        f1=df.Created < each_date      #filter1
        f2=df.Resolved >= each_date    #filter2
        df['Refdate'] = each_date      #make column to allow refdate-created  
        df['Age']= (df.Refdate - df.Created)
        results_list.append(df[f1 & f2]).Age.mean())

Problems: This works, but it feels sloppy and it doesn't seem fast. The current data-set is small, but I suspect this wouldn't scale well. I'm trying not to solve everything with loops as I understand it is a common mistake for beginners like me.

Upvotes: 1

Views: 278

Answers (1)

Lukas Thaler
Lukas Thaler

Reputation: 2720

I'll give you two solutions: the first one is step-by-step for you to understand the idea and process, the second one replicates the functionality in a much more condensed way, skipping some intermediate steps

First, create a new column that holds your issue age, i.e. df['age'] = df.resolved - df.Created (I'm assuming your columns are of datetime type, if not, use pd.to_datetime to convert them)

You can then use groupby to group your data by creation date. This will internally slice your dataframe into several pieces, one for each distinct value of Created, grouping all values with the same creation date together. This way, you can then use aggregation on a creation date level to get the average issue age like so

# [['Created', 'age']] selects only the columns you are interested in
df[['Created', 'age']].groupby('Created').mean()

With an additional fourth data point [a4, 2021/4/20, 2021/4/30] (to enable some proper aggregation), this would end up giving you the following Series with the average issue age by creation date:

                        age
Created                    
2021-04-20 17 days 12:00:00
2021-04-21 55 days 00:00:00
2021-04-23 83 days 00:00:00

A more condensed way of doing this is by defining a custom function and apply it to each creation date grouping

def issue_age(s: pd.Series):
    return (s['resolved'] - s['Created']).mean()

df.groupby('Created').apply(issue_age)

This call will give you the same Series as before.

Upvotes: 1

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