Il Suo template per i dati delle richieste Purchase to Pay

WITH REQUISITION_BASE AS (
    SELECT 
        EBAN.MANDT,
        EBAN.BANFN,
        EBAN.BNFPO,
        EBAN.BSART AS RequisitionDocumentType,
        EBAN.ERNAM,
        EBAN.ERDAT,
        EBAN.ERZET,
        EBAN.FRGZU AS CurrentRequisitionStatus,
        EBAKN.KOSTL AS Department,
        EBAKN.GSWRT
    FROM EBAN
    LEFT JOIN EBAKN ON EBAN.MANDT = EBAKN.MANDT AND EBAN.BANFN = EBAKN.BANFN AND EBAN.BNFPO = EBAKN.BNFPO
    WHERE EBAN.ERDAT BETWEEN '{start_date}' AND '{end_date}'
      AND EBAN.BUKRS IN ('{company_codes}')
      AND EBAN.BSART IN ('{document_types}')
),
REQUISITION_AGG AS (
    SELECT 
        MANDT, BANFN, RequisitionDocumentType, ERNAM, ERDAT, ERZET, CurrentRequisitionStatus, Department,
        SUM(GSWRT) AS TotalRequisitionValue
    FROM REQUISITION_BASE
    GROUP BY MANDT, BANFN, RequisitionDocumentType, ERNAM, ERDAT, ERZET, CurrentRequisitionStatus, Department
)
-- 1. Requisition Created
SELECT
    R.BANFN AS PurchaseRequisitionId,
    'Requisition Created' AS ActivityName,
    TO_TIMESTAMP(R.ERDAT || R.ERZET, 'YYYYMMDDHH24MISS') AS EventTime,
    '{source_system_name}' AS SourceSystem,
    CURRENT_TIMESTAMP AS LastDataUpdate,
    R.ERNAM AS User,
    R.Department AS Department,
    R.CurrentRequisitionStatus AS RequisitionStatus,
    R.RequisitionDocumentType AS RequisitionDocumentType,
    R.TotalRequisitionValue AS TotalRequisitionValue
FROM REQUISITION_AGG R

UNION ALL

-- 2. Requisition Amended, Approval Steps, Status Changes from Change Logs
SELECT 
    SUBSTRING(C.OBJECTID, 4, 10) AS PurchaseRequisitionId,
    CASE 
        WHEN C.FNAME = 'FRGZU' AND C.VALUE_NEW > C.VALUE_OLD THEN 'Approval Step Approved'
        WHEN C.FNAME = 'FRGZU' AND C.VALUE_NEW < C.VALUE_OLD THEN 'Approval Step Rejected'
        WHEN C.FNAME = 'FRGZU' AND C.VALUE_NEW = '{final_approval_status_code}' THEN 'Requisition Approved'
        WHEN C.FNAME = 'FRGZU' AND C.VALUE_NEW = '{final_rejection_status_code}' THEN 'Requisition Rejected'
        WHEN C.FNAME = 'FRGSX' AND C.VALUE_OLD IS NOT NULL AND C.VALUE_NEW IS NULL THEN 'Approval Reset'
        WHEN C.FNAME = 'FRGZU' AND C.CHNGIND = 'U' AND C.TCODE LIKE 'ME54%' THEN 'Approval Submitted' 
        WHEN C.FNAME = 'LOEKZ' AND C.VALUE_NEW IN ('L', 'X') THEN 'Requisition Withdrawn'
        WHEN C.FNAME = 'EBAKZ' AND C.VALUE_NEW = 'X' THEN 'Requisition Blocked'
        WHEN C.FNAME = 'STATU' AND C.VALUE_NEW = 'B' THEN 'Purchase Order Created'
        WHEN C.FNAME = 'EREKZ' AND C.VALUE_NEW = 'X' THEN 'Requisition Closed'
        ELSE 'Requisition Amended'
    END AS ActivityName,
    TO_TIMESTAMP(C.UDATE || C.UTIME, 'YYYYMMDDHH24MISS') AS EventTime,
    '{source_system_name}' AS SourceSystem,
    CURRENT_TIMESTAMP AS LastDataUpdate,
    C.USERNAME AS User,
    R.Department AS Department,
    C.VALUE_NEW AS RequisitionStatus,
    R.RequisitionDocumentType AS RequisitionDocumentType,
    R.TotalRequisitionValue AS TotalRequisitionValue
FROM CDHDR C
JOIN CDPOS P ON C.MANDANT = P.MANDANT AND C.OBJECTCLAS = P.OBJECTCLAS AND C.OBJECTID = P.OBJECTID AND C.CHANGENR = P.CHANGENR
JOIN REQUISITION_AGG R ON SUBSTRING(C.OBJECTID, 4, 10) = R.BANFN
WHERE C.OBJECTCLAS = 'BANF'
  AND C.UDATE || C.UTIME <> R.ERDAT || R.ERZET -- Exclude changes made at the exact time of creation
  AND NOT (C.TCODE IN ('ME51', 'ME51N') AND C.UDATE = R.ERDAT) -- Exclude initial creation events from being amendments

UNION ALL

-- 3. Approval Step Started (Inferred)
SELECT 
    SUBSTRING(C.OBJECTID, 4, 10) AS PurchaseRequisitionId,
    'Approval Step Started' AS ActivityName,
    TO_TIMESTAMP(C.UDATE || C.UTIME, 'YYYYMMDDHH24MISS') AS EventTime,
    '{source_system_name}' AS SourceSystem,
    CURRENT_TIMESTAMP AS LastDataUpdate,
    C.USERNAME AS User,
    R.Department AS Department,
    P.VALUE_OLD AS RequisitionStatus,
    R.RequisitionDocumentType AS RequisitionDocumentType,
    R.TotalRequisitionValue AS TotalRequisitionValue
FROM CDHDR C
JOIN CDPOS P ON C.MANDANT = P.MANDANT AND C.OBJECTCLAS = P.OBJECTCLAS AND C.OBJECTID = P.OBJECTID AND C.CHANGENR = P.CHANGENR
JOIN REQUISITION_AGG R ON SUBSTRING(C.OBJECTID, 4, 10) = R.BANFN
WHERE C.OBJECTCLAS = 'BANF' AND P.FNAME = 'FRGZU' AND P.CHNGIND = 'U'
  AND P.VALUE_OLD IS NOT NULL AND P.VALUE_NEW IS NOT NULL AND P.VALUE_NEW > P.VALUE_OLD; -- This logic infers that a step starts just before it is approved

import pandas as pd
from pyrfc import Connection
import datetime

class=class="hl-string">"hl-comment"># --- 1. Configuration Settings ---
class=class="hl-string">"hl-comment"># Replace with your SAP system connection details
SAP_CONNECTION_PARAMS = {
    class="hl-string">"ashost": class="hl-string">"your_sap_host",      class=class="hl-string">"hl-comment"># Application server host
    class="hl-string">"sysnr": class="hl-string">"00",                 class=class="hl-string">"hl-comment"># System number
    class="hl-string">"client": class="hl-string">"100",               class=class="hl-string">"hl-comment"># Client
    class="hl-string">"user": class="hl-string">"your_rfc_user",       class=class="hl-string">"hl-comment"># RFC user
    class="hl-string">"passwd": class="hl-string">"your_password"      class=class="hl-string">"hl-comment"># Password
}

class=class="hl-string">"hl-comment"># Date range for the extraction (YYYYMMDD format)
date_from = class="hl-string">'20230101'
date_to = class="hl-string">'20231231'

source_system = class="hl-string">"SAP ECC"
last_data_update = datetime.datetime.now().isoformat()

class=class="hl-string">"hl-comment"># --- 2. Helper function for RFC_READ_TABLE ---
def rfc_read_table(conn, table_name, fields, options):
    class="hl-string">"""Fetches data from an SAP table using RFC_READ_TABLE."""
    try:
        result = conn.call(class="hl-string">'RFC_READ_TABLE',
                           QUERY_TABLE=table_name,
                           DELIMITER=class="hl-string">',',
                           FIELDS=fields,
                           OPTIONS=options)
        
        data = result[class="hl-string">'DATA']
        if not data:
            return pd.DataFrame(columns=[field[class="hl-string">'FIELDNAME'] for field in fields])

        records = [row[class="hl-string">'WA'].strip().split(class="hl-string">',') for row in data]
        df = pd.DataFrame(records, columns=[field[class="hl-string">'FIELDNAME'] for field in fields])
        class=class="hl-string">"hl-comment"># Trim whitespace from all cells
        df = df.apply(lambda x: x.str.strip() if x.dtype == class="hl-string">"object" else x)
        return df
    except Exception as e:
        print(fclass="hl-string">"Error reading table {table_name}: {e}")
        return pd.DataFrame(columns=[field[class="hl-string">'FIELDNAME'] for field in fields])

class=class="hl-string">"hl-comment"># --- 3. Main Extraction Logic ---
def main():
    print(class="hl-string">"Starting SAP data extraction...")
    try:
        conn = Connection(**SAP_CONNECTION_PARAMS)
    except Exception as e:
        print(fclass="hl-string">"Failed to connect to SAP: {e}")
        return

    class=class="hl-string">"hl-comment"># --- EBAN (Purchase Requisitions) ---
    print(class="hl-string">"Extracting EBAN...")
    eban_fields = [{class="hl-string">'FIELDNAME': class="hl-string">'BANFN'}, {class="hl-string">'FIELDNAME': class="hl-string">'BNFPO'}, {class="hl-string">'FIELDNAME': class="hl-string">'BSART'}, {class="hl-string">'FIELDNAME': class="hl-string">'ERNAM'}, {class="hl-string">'FIELDNAME': class="hl-string">'ERDAT'}, {class="hl-string">'FIELDNAME': class="hl-string">'ERFZEIT'}, {class="hl-string">'FIELDNAME': class="hl-string">'PREIS'}, {class="hl-string">'FIELDNAME': class="hl-string">'MENGE'}, {class="hl-string">'FIELDNAME': class="hl-string">'KOSTL'}, {class="hl-string">'FIELDNAME': class="hl-string">'FRGZU'}, {class="hl-string">'FIELDNAME': class="hl-string">'FRGSX'}]
    eban_options = [fclass="hl-string">"ERDAT >= '{date_from}' AND ERDAT <= '{date_to}'"]
    df_eban = rfc_read_table(conn, class="hl-string">'EBAN', eban_fields, eban_options)
    if df_eban.empty:
        print(class="hl-string">"No requisitions found in EBAN for the given date range. Exiting.")
        conn.close()
        return

    df_eban[class="hl-string">'PREIS'] = pd.to_numeric(df_eban[class="hl-string">'PREIS'], errors=class="hl-string">'coerce').fillna(0)
    df_eban[class="hl-string">'MENGE'] = pd.to_numeric(df_eban[class="hl-string">'MENGE'], errors=class="hl-string">'coerce').fillna(0)
    df_eban[class="hl-string">'TotalRequisitionValue'] = df_eban[class="hl-string">'PREIS'] * df_eban[class="hl-string">'MENGE']
    df_eban_grouped = df_eban.groupby(class="hl-string">'BANFN').agg(
        TotalRequisitionValue=(class="hl-string">'TotalRequisitionValue', class="hl-string">'sum'),
        RequisitionDocumentType=(class="hl-string">'BSART', class="hl-string">'first'),
        Department=(class="hl-string">'KOSTL', class="hl-string">'first')
    ).reset_index()
    df_eban = df_eban.merge(df_eban_grouped, on=class="hl-string">'BANFN', suffixes=(class="hl-string">'', class="hl-string">'_agg'))

    class=class="hl-string">"hl-comment"># --- EKPO/EKKO (Purchase Orders) ---
    print(class="hl-string">"Extracting EKPO and EKKO...")
    banfn_list = class="hl-string">"'" + class="hl-string">"', '".join(df_eban[class="hl-string">'BANFN'].unique()) + class="hl-string">"'"
    ekpo_fields = [{class="hl-string">'FIELDNAME': class="hl-string">'BANFN'}, {class="hl-string">'FIELDNAME': class="hl-string">'BNFPO'}, {class="hl-string">'FIELDNAME': class="hl-string">'EBELN'}]
    ekpo_options = [fclass="hl-string">"BANFN IN ({banfn_list})"]
    df_ekpo = rfc_read_table(conn, class="hl-string">'EKPO', ekpo_fields, ekpo_options)

    ekko_fields = [{class="hl-string">'FIELDNAME': class="hl-string">'EBELN'}, {class="hl-string">'FIELDNAME': class="hl-string">'AEDAT'}]
    if not df_ekpo.empty:
        ebeln_list = class="hl-string">"'" + class="hl-string">"', '".join(df_ekpo[class="hl-string">'EBELN'].unique()) + class="hl-string">"'"
        ekko_options = [fclass="hl-string">"EBELN IN ({ebeln_list})"]
        df_ekko = rfc_read_table(conn, class="hl-string">'EKKO', ekko_fields, ekko_options)
        df_po = pd.merge(df_ekpo, df_ekko, on=class="hl-string">'EBELN')
    else:
        df_po = pd.DataFrame(columns=[class="hl-string">'BANFN', class="hl-string">'BNFPO', class="hl-string">'EBELN', class="hl-string">'AEDAT'])

    class=class="hl-string">"hl-comment"># --- CDHDR/CDPOS (Change Documents) ---
    print(class="hl-string">"Extracting CDHDR and CDPOS...")
    cdhdr_fields = [{class="hl-string">'FIELDNAME': class="hl-string">'OBJECTID'}, {class="hl-string">'FIELDNAME': class="hl-string">'CHANGENR'}, {class="hl-string">'FIELDNAME': class="hl-string">'USERNAME'}, {class="hl-string">'FIELDNAME': class="hl-string">'UDATE'}, {class="hl-string">'FIELDNAME': class="hl-string">'UTIME'}]
    cdhdr_options = [fclass="hl-string">"OBJECTCLAS = 'BANF' AND UDATE >= '{date_from}' AND UDATE <= '{date_to}'"]
    df_cdhdr = rfc_read_table(conn, class="hl-string">'CDHDR', cdhdr_fields, cdhdr_options)
    
    df_cdpos = pd.DataFrame()
    if not df_cdhdr.empty:
        class=class="hl-string">"hl-comment"># Process changenr in chunks to avoid overly long SQL queries
        changenr_chunks = [df_cdhdr[class="hl-string">'CHANGENR'][i:i + 100] for i in range(0, df_cdhdr.shape[0], 100)]
        cdpos_fields = [{class="hl-string">'FIELDNAME': class="hl-string">'CHANGENR'}, {class="hl-string">'FIELDNAME': class="hl-string">'TABNAME'}, {class="hl-string">'FIELDNAME': class="hl-string">'FNAME'}, {class="hl-string">'FIELDNAME': class="hl-string">'VALUE_NEW'}, {class="hl-string">'FIELDNAME': class="hl-string">'VALUE_OLD'}]
        cdpos_frames = []
        for chunk in changenr_chunks:
            changenr_list = class="hl-string">"'" + class="hl-string">"', '".join(chunk) + class="hl-string">"'"
            cdpos_options = [fclass="hl-string">"OBJECTCLAS = 'BANF' AND CHANGENR IN ({changenr_list}) AND TABNAME = 'EBAN'"]
            cdpos_frames.append(rfc_read_table(conn, class="hl-string">'CDPOS', cdpos_fields, cdpos_options))
        if cdpos_frames:
            df_cdpos = pd.concat(cdpos_frames, ignore_index=True)

    conn.close()
    print(class="hl-string">"Extraction from SAP complete. Starting transformation...")

    class=class="hl-string">"hl-comment"># --- 4. Event Log Generation ---
    events = []

    class=class="hl-string">"hl-comment"># Activity: Requisition Created
    df_created = df_eban[[class="hl-string">'BANFN', class="hl-string">'ERNAM', class="hl-string">'ERDAT', class="hl-string">'ERFZEIT', class="hl-string">'RequisitionDocumentType', class="hl-string">'TotalRequisitionValue', class="hl-string">'Department']].drop_duplicates(subset=[class="hl-string">'BANFN'])
    for _, row in df_created.iterrows():
        events.append({
            class="hl-string">'PurchaseRequisitionId': row[class="hl-string">'BANFN'],
            class="hl-string">'ActivityName': class="hl-string">'Requisition Created',
            class="hl-string">'EventTime': fclass="hl-string">"{row['ERDAT']} {row['ERFZEIT']}",
            class="hl-string">'User': row[class="hl-string">'ERNAM'],
            class="hl-string">'RequisitionDocumentType': row[class="hl-string">'RequisitionDocumentType'],
            class="hl-string">'TotalRequisitionValue': row[class="hl-string">'TotalRequisitionValue'],
            class="hl-string">'Department': row[class="hl-string">'Department'],
            class="hl-string">'RequisitionStatus': class="hl-string">'Created'
        })

    class=class="hl-string">"hl-comment"># Merge change documents
    df_changes = pd.DataFrame()
    if not df_cdhdr.empty and not df_cdpos.empty:
        df_changes = pd.merge(df_cdhdr, df_cdpos, on=class="hl-string">'CHANGENR')
        df_changes[class="hl-string">'PurchaseRequisitionId'] = df_changes[class="hl-string">'OBJECTID'].str[:10]
        df_changes[class="hl-string">'EventTime'] = pd.to_datetime(df_changes[class="hl-string">'UDATE'] + class="hl-string">' ' + df_changes[class="hl-string">'UTIME'], format=class="hl-string">'%Y%m%d %H%M%S')

    if not df_changes.empty:
        class=class="hl-string">"hl-comment"># Activities from Change Logs
        for _, row in df_changes.iterrows():
            activity = None
            status = None

            class=class="hl-string">"hl-comment"># Approval-related activities
            if row[class="hl-string">'FNAME'] in [class="hl-string">'FRGZU', class="hl-string">'FRGSX']:
                if row[class="hl-string">'VALUE_OLD'].strip() == class="hl-string">'' and row[class="hl-string">'VALUE_NEW'].strip() != class="hl-string">'':
                    activity = class="hl-string">'Approval Submitted'
                    status = class="hl-string">'In Approval'
                elif row[class="hl-string">'VALUE_OLD'] != class="hl-string">'' and row[class="hl-string">'VALUE_NEW'] == class="hl-string">'':
                    activity = class="hl-string">'Approval Reset'
                    status = class="hl-string">'In Progress'
                else:
                    class=class="hl-string">"hl-comment"># Logic to differentiate approve/reject might need config specifics
                    activity = class="hl-string">'Approval Step Approved' if row[class="hl-string">'VALUE_NEW'] > row[class="hl-string">'VALUE_OLD'] else class="hl-string">'Approval Step Rejected'
                    status = class="hl-string">'In Approval'
            class=class="hl-string">"hl-comment"># Final Approval/Rejection
            elif row[class="hl-string">'FNAME'] == class="hl-string">'FRGZU':
                 class=class="hl-string">"hl-comment"># This logic assumes specific final approval/rejection codes. Adjust if needed.
                 if row[class="hl-string">'VALUE_NEW'] in [class="hl-string">'X', class="hl-string">'Y', class="hl-string">'Z']: class=class="hl-string">"hl-comment"># Placeholder for final approval states
                     activity = class="hl-string">'Requisition Approved'
                     status = class="hl-string">'Approved'
                 elif row[class="hl-string">'VALUE_NEW'] in [class="hl-string">'R', class="hl-string">'S']: class=class="hl-string">"hl-comment"># Placeholder for final rejection states
                     activity = class="hl-string">'Requisition Rejected'
                     status = class="hl-string">'Rejected'
            class=class="hl-string">"hl-comment"># Other Amendments
            elif row[class="hl-string">'FNAME'] not in [class="hl-string">'FRGZU', class="hl-string">'FRGSX', class="hl-string">'LOEKZ', class="hl-string">'EBAKZ']:
                activity = class="hl-string">'Requisition Amended'
                status = class="hl-string">'Amended'
            class=class="hl-string">"hl-comment"># Blocked
            elif row[class="hl-string">'FNAME'] == class="hl-string">'EBAKZ' and row[class="hl-string">'VALUE_NEW'] == class="hl-string">'B':
                activity = class="hl-string">'Requisition Blocked'
                status = class="hl-string">'Blocked'
            class=class="hl-string">"hl-comment"># Withdrawn
            elif row[class="hl-string">'FNAME'] == class="hl-string">'LOEKZ' and row[class="hl-string">'VALUE_NEW'] == class="hl-string">'L':
                activity = class="hl-string">'Requisition Withdrawn'
                status = class="hl-string">'Withdrawn'
            class=class="hl-string">"hl-comment"># Closed
            elif row[class="hl-string">'FNAME'] == class="hl-string">'EBAKZ' and row[class="hl-string">'VALUE_NEW'] == class="hl-string">'L':
                activity = class="hl-string">'Requisition Closed'
                status = class="hl-string">'Closed'

            if activity:
                events.append({
                    class="hl-string">'PurchaseRequisitionId': row[class="hl-string">'PurchaseRequisitionId'],
                    class="hl-string">'ActivityName': activity,
                    class="hl-string">'EventTime': row[class="hl-string">'EventTime'].strftime(class="hl-string">'%Y-%m-%d %H:%M:%S'),
                    class="hl-string">'User': row[class="hl-string">'USERNAME'],
                    class="hl-string">'RequisitionStatus': status
                })

    class=class="hl-string">"hl-comment"># Activity: Purchase Order Created
    if not df_po.empty:
        df_po_first = df_po.sort_values(class="hl-string">'AEDAT').drop_duplicates(class="hl-string">'BANFN', keep=class="hl-string">'first')
        for _, row in df_po_first.iterrows():
            events.append({
                class="hl-string">'PurchaseRequisitionId': row[class="hl-string">'BANFN'],
                class="hl-string">'ActivityName': class="hl-string">'Purchase Order Created',
                class="hl-string">'EventTime': fclass="hl-string">"{row['AEDAT']} 00:00:00", class=class="hl-string">"hl-comment"># EKKO only has date
                class="hl-string">'User': None, class=class="hl-string">"hl-comment"># User info not directly in EKKO/EKPO for this event
                class="hl-string">'RequisitionStatus': class="hl-string">'PO Created'
            })
            
    class=class="hl-string">"hl-comment"># Dummy activities for Approval Step Started (inferred before approval)
    class=class="hl-string">"hl-comment"># This is a simplified inference. Real logic can be much more complex.
    approval_events = [e for e in events if e[class="hl-string">'ActivityName'] == class="hl-string">'Approval Step Approved']
    for event in approval_events:
        event_time = pd.to_datetime(event[class="hl-string">'EventTime'])
        class=class="hl-string">"hl-comment"># Infer class="hl-string">'started' event one minute before class="hl-string">'approved' for demonstration
        start_time = event_time - datetime.timedelta(minutes=1)
        events.append({
            class="hl-string">'PurchaseRequisitionId': event[class="hl-string">'PurchaseRequisitionId'],
            class="hl-string">'ActivityName': class="hl-string">'Approval Step Started',
            class="hl-string">'EventTime': start_time.strftime(class="hl-string">'%Y-%m-%d %H:%M:%S'),
            class="hl-string">'User': None, class=class="hl-string">"hl-comment"># Approver user is not known until they approve
            class="hl-string">'RequisitionStatus': class="hl-string">'In Approval'
        })

    class=class="hl-string">"hl-comment"># --- 5. Final Formatting ---
    if not events:
        print(class="hl-string">"No events generated. Exiting.")
        return

    df_event_log = pd.DataFrame(events)
    df_event_log = df_event_log.merge(df_eban_grouped, left_on=class="hl-string">'PurchaseRequisitionId', right_on=class="hl-string">'BANFN', how=class="hl-string">'left')
    df_event_log[class="hl-string">'EventTime'] = pd.to_datetime(df_event_log[class="hl-string">'EventTime'])
    df_event_log = df_event_log.sort_values(by=[class="hl-string">'PurchaseRequisitionId', class="hl-string">'EventTime'])

    class=class="hl-string">"hl-comment"># Fill forward attributes within each case
    df_event_log[class="hl-string">'RequisitionDocumentType'] = df_event_log.groupby(class="hl-string">'PurchaseRequisitionId')[class="hl-string">'RequisitionDocumentType_agg'].transform(lambda x: x.ffill().bfill())
    df_event_log[class="hl-string">'TotalRequisitionValue'] = df_event_log.groupby(class="hl-string">'PurchaseRequisitionId')[class="hl-string">'TotalRequisitionValue_agg'].transform(lambda x: x.ffill().bfill())
    df_event_log[class="hl-string">'Department'] = df_event_log.groupby(class="hl-string">'PurchaseRequisitionId')[class="hl-string">'Department_agg'].transform(lambda x: x.ffill().bfill())

    class=class="hl-string">"hl-comment"># Add standard columns
    df_event_log[class="hl-string">'SourceSystem'] = source_system
    df_event_log[class="hl-string">'LastDataUpdate'] = last_data_update

    class=class="hl-string">"hl-comment"># Select and rename final columns
    final_columns = {
        class="hl-string">'PurchaseRequisitionId': class="hl-string">'PurchaseRequisitionId',
        class="hl-string">'ActivityName': class="hl-string">'ActivityName',
        class="hl-string">'EventTime': class="hl-string">'EventTime',
        class="hl-string">'SourceSystem': class="hl-string">'SourceSystem',
        class="hl-string">'LastDataUpdate': class="hl-string">'LastDataUpdate',
        class="hl-string">'User': class="hl-string">'User',
        class="hl-string">'Department': class="hl-string">'Department',
        class="hl-string">'RequisitionStatus': class="hl-string">'RequisitionStatus',
        class="hl-string">'RequisitionDocumentType': class="hl-string">'RequisitionDocumentType',
        class="hl-string">'TotalRequisitionValue': class="hl-string">'TotalRequisitionValue'
    }
    df_event_log = df_event_log[final_columns.keys()].rename(columns=final_columns)
    
    class=class="hl-string">"hl-comment"># Save to CSV
    df_event_log.to_csv(class="hl-string">'event_log_pr.csv', index=False)
    print(class="hl-string">"Event log successfully created: event_log_pr.csv")

if __name__ == class="hl-string">'__main__':
    main()