def inactive_customer_cohorts(startDate, endDate):
try:
    print("startDate : ", startDate)
    print("endDate : ", endDate)

    df = data_util.get_customer_scans_df(startDate, endDate)
    columns = ['userId', 'createdAt']

    df = df[columns]
    df['scanQuater'] = ('Q' + df['createdAt'].dt.quarter.astype(str) + '/' + df['createdAt'].dt.year.astype(str))

    quartersMap = dict(zip(df['scanQuater'].unique(), range(len(df['scanQuater'].unique()))))

    scanQuarterUsers = df.groupby('scanQuater')['userId'].apply(list).reset_index()
    scanQuarterUsers['length'] = scanQuarterUsers['userId'].apply(len)


    quarterUsers = {row['scanQuater']: row['userId'] for _, row in scanQuarterUsers.iterrows()}
    quarters = list(quartersMap.keys())

    cumulativePreviousUserIds = set()
    previousQuarterUserMap = {}
    
    data = []
    for i in range(len(quarters)):
        currentQuarter = quarters[i]
        if currentQuarter in quarterUsers:
            nonUniquecurrentUserIds = quarterUsers[currentQuarter]
            currentUserIds = set(quarterUsers[currentQuarter])
            currentCount = len(currentUserIds)
            nonUniqueCurrentCount = len(nonUniquecurrentUserIds)
         
            if cumulativePreviousUserIds:
                uniqueCommonUserIds = set()
                quarterCommonCounts = {}
                for prevQuarter, prevUserIds in previousQuarterUserMap.items():
                    specificCommonUserIds = currentUserIds.intersection(prevUserIds)
                    for userId in specificCommonUserIds:
                        if userId not in uniqueCommonUserIds:
                            uniqueCommonUserIds.add(userId)
                            if prevQuarter in quarterCommonCounts:
                                quarterCommonCounts[prevQuarter] += 1
                            else:
                                quarterCommonCounts[prevQuarter] = 1

                previousQuartersSummary = ", ".join([f"{count} from {quarter}" for quarter, count in quarterCommonCounts.items()])
                totalSpecificCommonCount = len(uniqueCommonUserIds)
                newUsers = currentCount - totalSpecificCommonCount

                data.append({
                        "quarter": currentQuarter,
                        "scanCount": nonUniqueCurrentCount,
                        "uniqueCounts": currentCount,
                        "common": totalSpecificCommonCount,
                        "new": newUsers,
                        "common_breakdown": previousQuartersSummary
                    })
            else:
                data.append({
                    "quarter": currentQuarter,
                    "scanCount": nonUniqueCurrentCount,
                    "uniqueCounts": currentCount,
                    "common": 0,
                    "new": currentCount,
                    "common_breakdown": ""
                })

            cumulativePreviousUserIds.update(currentUserIds)
            previousQuarterUserMap[currentQuarter] = currentUserIds
    df = pd.DataFrame(data)
    print(df)
    return df   
except Exception as e:
    common_util.error_logs(e, 'inactive_customer_cohorts')
    raise e
            

This is the code I’m trying to run, whenever I run this code normally without any flask or postman, I get a proper output. But whenever I try to run it through postman, I get the following error :

     {
     "status": 500,
     "message": "maximum recursion depth exceeded",
     "data": {}
     }

Please let me know what should I do to avoid getting this error. Let me know if more info is needed. Thank you.