Ödeme Süreçleri Veri Şablonunuz

import pandas as pd
from datetime import datetime

# CONFIGURATION
INPUT_FILE = 'settlement_detail_report.csv'
OUTPUT_FILE = 'adyen_event_log.csv'
SOURCE_SYSTEM = 'Adyen'

# Load the Adyen Settlement Detail Report
# Skip standard Adyen header rows if present (often reports start on row 1 or have metadata on top)
df = pd.read_csv(INPUT_FILE, keep_default_na=False)

# Define the output list
event_log = []

# Helper function to append events
def add_event(psp_ref, activity, timestamp, attributes):
    if not timestamp or str(timestamp).strip() == '':
        return
    base_event = {
        'PspReference': psp_ref,
        'EventCode': activity,
        'EventDate': timestamp,
        'SourceSystem': SOURCE_SYSTEM,
        'LastDataUpdate': datetime.utcnow().isoformat(),
        'MerchantAccount': attributes.get('Merchant Account', ''),
        'PaymentMethod': attributes.get('Payment Method', ''),
        'PaymentAmount': attributes.get('Gross Credit', 0) if attributes.get('Gross Credit') else attributes.get('Gross Debit', 0) * -1
    }
    # Merge optional attributes
    base_event.update(attributes)
    event_log.append(base_event)

# Iterate through the ledger rows
for index, row in df.iterrows():
    psp_ref = row.get('Psp Reference')
    journal_type = row.get('Type')
    creation_date = row.get('Creation Date') # Used for Auth/Request
    booking_date = row.get('Booking Date')   # Used for Settlement/Financials
    
    # Common attributes for this row
    row_attrs = {
        'MerchantAccount': row.get('Merchant Account'),
        'PaymentMethod': row.get('Payment Method'),
        'RefusalReason': row.get('Refusal Reason', ''),
        'PaymentStatus': journal_type
    }

    # --- 1. DERIVED UPSTREAM ACTIVITIES ---
    # Since this is a settlement report, we infer the Request/Auth from the Creation Date
    # of the first record we see for this PSP Reference. 
    # Logic: If Creation Date exists, implies Request and Auth occurred.
    
    if creation_date:
        # Activity: Payment Request Received
        add_event(psp_ref, 'Payment Request Received', creation_date, row_attrs)
        
        # Activity: Risk Screening Performed (Inferred from existence of transaction)
        # Note: True risk score requires 'Payments Accounting Report', inferred here for process continuity
        add_event(psp_ref, 'Risk Screening Performed', creation_date, row_attrs)
        
        # Activity: Payment Authorised
        # Typically happens immediately after request for successful settled items
        add_event(psp_ref, 'Payment Authorised', creation_date, row_attrs)

    # --- 2. MAPPED FINANCIAL ACTIVITIES ---
    # Map Adyen Ledger Types to Process Activities using Booking Date
    
    if journal_type == 'Settled':
        add_event(psp_ref, 'Payment Settled', booking_date, row_attrs)
        
        # Activity: Payment Sent for Settlement
        # Inferred: Occurs shortly before settlement. 
        # Using Creation Date if Booking Date is distinct, or defaulting to Booking Date.
        sent_date = creation_date if creation_date else booking_date
        add_event(psp_ref, 'Payment Sent for Settlement', sent_date, row_attrs)

    elif journal_type == 'Refunded':
        add_event(psp_ref, 'Refund Initiated', creation_date, row_attrs)
        add_event(psp_ref, 'Refund Settled', booking_date, row_attrs)

    elif journal_type == 'Chargeback':
        add_event(psp_ref, 'Chargeback Notification Received', booking_date, row_attrs)

    elif journal_type == 'ChargebackRetried':
        add_event(psp_ref, 'Chargeback Defended', booking_date, {'IsRework': 'True'})

    elif journal_type == 'ChargebackReversed':
        add_event(psp_ref, 'Chargeback Reversed', booking_date, row_attrs)

    elif journal_type == 'PaidOut':
        # Payouts often group many transactions. 
        # If the report is at transaction level, this row represents the batch payout.
        add_event(psp_ref, 'Payout Confirmed', booking_date, row_attrs)

    elif journal_type == 'CaptureFailed':
        add_event(psp_ref, 'Capture Failed', booking_date, {'IsRework': 'True'})

    # --- 3. EXCEPTION HANDLING ---
    # These statuses often appear in 'Received payment details' reports but rarely in Settlement
    # unless fees were applied.
    
    elif journal_type == 'Refused': 
        add_event(psp_ref, 'Payment Refused', creation_date, row_attrs)
        
    elif journal_type == 'Cancelled':
        add_event(psp_ref, 'Payment Cancelled', creation_date, row_attrs)

    elif journal_type == 'Error':
        add_event(psp_ref, 'Technical Error Logged', booking_date, {'IsRework': 'True'})

# Convert to DataFrame
events_df = pd.DataFrame(event_log)

# Deduping: Adyen reports may have multiple lines for partial settlements.
# We sort by date and keep unique Activity per PSP Reference if timestamps match exactly.
events_df.drop_duplicates(subset=['PspReference', 'EventCode', 'EventDate'], inplace=True)

# Output
events_df.to_csv(OUTPUT_FILE, index=False)
print(f'Successfully extracted {len(events_df)} events to {OUTPUT_FILE}')

import pandas as pd
import requests
import io
import numpy as np
from datetime import datetime

class=class="hl-string">"hl-comment"># Configuration
API_KEY = class="hl-string">'YOUR_ADYEN_API_KEY'
MERCHANT_ACCOUNT = class="hl-string">'YOUR_MERCHANT_ACCOUNT'
BASE_URL = class="hl-string">'https://management-live.adyen.com/v1'
HEADERS = {class="hl-string">'x-api-key': API_KEY, class="hl-string">'Content-Type': class="hl-string">'application/json'}

class=class="hl-string">"hl-comment"># Placeholder for fetching report URLs via API
class=class="hl-string">"hl-comment"># In production, use GET /companies/{companyId}/reports or /merchants/{merchantId}/reports
class=class="hl-string">"hl-comment"># For this script, we assume the CSV content is loaded into DataFrames

def transform_adyen_data(received_payments_csv_path, settlement_details_csv_path):
    
    class=class="hl-string">"hl-comment"># 1. Load Data
    print(class="hl-string">'Loading reports...')
    df_received = pd.read_csv(received_payments_csv_path)
    df_settled = pd.read_csv(settlement_details_csv_path)

    events = []

    class=class="hl-string">"hl-comment"># 2. Process Received Payment Detail Report (Operational Events)
    class=class="hl-string">"hl-comment"># This report covers: Request, Auth, Refusal, Risk, Capture, Errors
    
    for _, row in df_received.iterrows():
        psp = str(row.get(class="hl-string">'PspReference', class="hl-string">''))
        created_date = pd.to_datetime(row.get(class="hl-string">'CreationDate'))
        amount = row.get(class="hl-string">'Amount', 0)
        status = row.get(class="hl-string">'Status', class="hl-string">'')
        fraud_score = row.get(class="hl-string">'FraudScore', None)
        refusal_reason = row.get(class="hl-string">'RefusalReason', class="hl-string">'')
        
        class=class="hl-string">"hl-comment"># Activity: Payment Request Received
        events.append({
            class="hl-string">'PspReference': psp,
            class="hl-string">'EventCode': class="hl-string">'Payment Request Received',
            class="hl-string">'EventDate': created_date,
            class="hl-string">'PaymentAmount': amount,
            class="hl-string">'PaymentMethod': row.get(class="hl-string">'PaymentMethod', class="hl-string">''),
            class="hl-string">'RefusalReason': None,
            class="hl-string">'SourceSystem': class="hl-string">'Adyen'
        })

        class=class="hl-string">"hl-comment"># Activity: Risk Screening Performed
        class=class="hl-string">"hl-comment"># Inferred if a fraud score was calculated
        if pd.notna(fraud_score):
            events.append({
                class="hl-string">'PspReference': psp,
                class="hl-string">'EventCode': class="hl-string">'Risk Screening Performed',
                class="hl-string">'EventDate': created_date + pd.Timedelta(seconds=1), class=class="hl-string">"hl-comment"># Slight offset
                class="hl-string">'PaymentAmount': amount,
                class="hl-string">'SourceSystem': class="hl-string">'Adyen'
            })

        class=class="hl-string">"hl-comment"># Activity: Payment Authorised / Refused / Error
        if status == class="hl-string">'Authorised':
            events.append({
                class="hl-string">'PspReference': psp,
                class="hl-string">'EventCode': class="hl-string">'Payment Authorised',
                class="hl-string">'EventDate': created_date + pd.Timedelta(seconds=2),
                class="hl-string">'PaymentAmount': amount,
                class="hl-string">'SourceSystem': class="hl-string">'Adyen'
            })
        elif status == class="hl-string">'Refused':
            events.append({
                class="hl-string">'PspReference': psp,
                class="hl-string">'EventCode': class="hl-string">'Payment Refused',
                class="hl-string">'EventDate': created_date + pd.Timedelta(seconds=2),
                class="hl-string">'PaymentAmount': amount,
                class="hl-string">'RefusalReason': refusal_reason,
                class="hl-string">'SourceSystem': class="hl-string">'Adyen'
            })
        elif status == class="hl-string">'Error':
            events.append({
                class="hl-string">'PspReference': psp,
                class="hl-string">'EventCode': class="hl-string">'Technical Error Logged',
                class="hl-string">'EventDate': created_date + pd.Timedelta(seconds=2),
                class="hl-string">'PaymentAmount': amount,
                class="hl-string">'RefusalReason': row.get(class="hl-string">'AcquirerResponse', class="hl-string">'Technical Error'),
                class="hl-string">'SourceSystem': class="hl-string">'Adyen'
            })
        elif status == class="hl-string">'Cancelled':
            events.append({
                class="hl-string">'PspReference': psp,
                class="hl-string">'EventCode': class="hl-string">'Payment Cancelled',
                class="hl-string">'EventDate': created_date + pd.Timedelta(seconds=5),
                class="hl-string">'PaymentAmount': amount,
                class="hl-string">'SourceSystem': class="hl-string">'Adyen'
            })

    class=class="hl-string">"hl-comment"># 3. Process Settlement Detail Report (Financial Events)
    class=class="hl-string">"hl-comment"># This report covers: Capture, Settlement, Refunds, Chargebacks, Payouts
    
    for _, row in df_settled.iterrows():
        psp = str(row.get(class="hl-string">'PspReference', class="hl-string">''))
        journal_type = row.get(class="hl-string">'JournalType', class="hl-string">'')
        process_date = pd.to_datetime(row.get(class="hl-string">'CreationDate')) class=class="hl-string">"hl-comment"># Or ProcessingDate
        amount = row.get(class="hl-string">'NetCredit', 0) if pd.notna(row.get(class="hl-string">'NetCredit')) else row.get(class="hl-string">'GrossCredit', 0)
        
        class=class="hl-string">"hl-comment"># Map JournalType to Activities
        activity_name = None
        
        if journal_type == class="hl-string">'SentForSettlement':
            activity_name = class="hl-string">'Payment Sent for Settlement'
        elif journal_type == class="hl-string">'Settled':
            activity_name = class="hl-string">'Payment Settled'
        elif journal_type == class="hl-string">'SentForRefund' or journal_type == class="hl-string">'Refunded':
            activity_name = class="hl-string">'Refund Initiated'
        elif journal_type == class="hl-string">'SettledRefund':
            activity_name = class="hl-string">'Refund Settled'
        elif journal_type == class="hl-string">'Chargeback':
            activity_name = class="hl-string">'Chargeback Notification Received'
        elif journal_type == class="hl-string">'SecondChargeback' or journal_type == class="hl-string">'Defense':
            activity_name = class="hl-string">'Chargeback Defended'
        elif journal_type == class="hl-string">'ChargebackReversed':
            activity_name = class="hl-string">'Chargeback Reversed'
        elif journal_type == class="hl-string">'CaptureFailed':
            activity_name = class="hl-string">'Capture Failed'
        elif journal_type == class="hl-string">'PaidOut':
            class=class="hl-string">"hl-comment"># PaidOut is usually batch level, if psp is present, map it
            activity_name = class="hl-string">'Payout Confirmed'
        
        class=class="hl-string">"hl-comment"># Logic for class="hl-string">'Payment Captured' 
        class=class="hl-string">"hl-comment"># Adyen often auto-captures. If we see Settled, we assume Captured occurred prior if not explicitly logged.
        class=class="hl-string">"hl-comment"># Here we rely on class="hl-string">'SentForSettlement' as the trigger for Capture success in many flows.
        if journal_type == class="hl-string">'SentForSettlement':
             events.append({
                class="hl-string">'PspReference': psp,
                class="hl-string">'EventCode': class="hl-string">'Payment Captured',
                class="hl-string">'EventDate': process_date - pd.Timedelta(seconds=1),
                class="hl-string">'PaymentAmount': amount,
                class="hl-string">'SourceSystem': class="hl-string">'Adyen'
            })

        if activity_name:
            events.append({
                class="hl-string">'PspReference': psp,
                class="hl-string">'EventCode': activity_name,
                class="hl-string">'EventDate': process_date,
                class="hl-string">'PaymentAmount': amount,
                class="hl-string">'SourceSystem': class="hl-string">'Adyen',
                class="hl-string">'MerchantAccount': row.get(class="hl-string">'MerchantAccount', MERCHANT_ACCOUNT),
                class="hl-string">'PaymentMethod': row.get(class="hl-string">'PaymentMethod', class="hl-string">'')
            })

    class=class="hl-string">"hl-comment"># 4. Create DataFrame
    df_events = pd.DataFrame(events)
    
    class=class="hl-string">"hl-comment"># 5. Post-Processing & Cleanup
    if not df_events.empty:
        class=class="hl-string">"hl-comment"># Ensure standard columns
        df_events[class="hl-string">'LastDataUpdate'] = datetime.now().isoformat()
        df_events[class="hl-string">'TotalCycleTime'] = None class=class="hl-string">"hl-comment"># Calculated in ProcessMind
        df_events[class="hl-string">'IsRework'] = df_events[class="hl-string">'EventCode'].apply(lambda x: 1 if class="hl-string">'Chargeback' in x or class="hl-string">'Refused' in x else 0)
        df_events[class="hl-string">'PaymentStatus'] = df_events.sort_values(class="hl-string">'EventDate').groupby(class="hl-string">'PspReference')[class="hl-string">'EventCode'].transform(class="hl-string">'last')
        
        class=class="hl-string">"hl-comment"># Filter invalid rows (empty PspReference)
        df_events = df_events[df_events[class="hl-string">'PspReference'] != class="hl-string">'nan']
        df_events = df_events[df_events[class="hl-string">'PspReference'] != class="hl-string">'']

        class=class="hl-string">"hl-comment"># Sort
        df_events = df_events.sort_values([class="hl-string">'PspReference', class="hl-string">'EventDate'])
        
    return df_events

class=class="hl-string">"hl-comment"># Execution Entry Point
class=class="hl-string">"hl-comment"># Replace paths with actual file locations or API download streams
if __name__ == class="hl-string">'__main__':
    class=class="hl-string">"hl-comment"># Example usage
    df_log = transform_adyen_data(class="hl-string">'received_payment_detail_report.csv', class="hl-string">'settlement_detail_report.csv')
    
    class=class="hl-string">"hl-comment"># Export
    df_log.to_csv(class="hl-string">'ProcessMind_Adyen_Log.csv', index=False, date_format=class="hl-string">'%Y-%m-%d %H:%M:%S')
    print(fclass="hl-string">'Extraction Complete. Generated {len(df_log)} events.')