import pandas as pd
import numpy as np
import xml.etree.ElementTree as ET
import csv
from xml.dom import minidom
from datetime import datetime
import logging
from pathlib import Path
from enum import Enum, auto
ACCOUNT_INFO = ['Account', 'Make', 'Site', 'Origin', 'SalesChannel', 'CostCarrier', 'CostAccountingString']
class GCHR:
def __init__(self, project_name) -> None:
self.base_dir = f'/home/robert/projekte/python/gcstruct/Kunden/{project_name}/'
self.account_translation = self.base_dir + 'Kontenrahmen_uebersetzt.csv'
self.account_bookings = Path(self.base_dir).glob('GuV_Bilanz_Salden*.csv')
self.first_month_of_financial_year = '01'
pd.set_option('display.max_rows', 500)
pd.set_option('display.float_format', lambda x: '%.2f' % x)
def set_bookkeep_period(self, year, month):
self.current_year = year
self.current_month = month
period = f'{year}-{month}'
prot_file = self.base_dir + f'protokoll_{period}.log'
logging.basicConfig(
filename=prot_file,
filemode='w',
encoding='utf-8',
level=logging.DEBUG
)
self.debug_file = self.base_dir + f'debug_{period}.csv'
self.account_ignored = self.base_dir + f'ignoriert_{period}.csv'
self.account_invalid = self.base_dir + f'ungueltig_{period}.csv'
self.last_year = str(int(self.current_year) - 1)
self.last_year2 = str(int(self.current_year) - 2)
self.next_year = str(int(self.current_year) + 1)
def header(self, makes, sites):
return {
'Country': 'DE',
'MainBmCode': sites[0]['Standort_HBV'],
'Month': self.current_month,
'Year': self.current_year,
'Currency': 'EUR',
'NumberOfMakes': len(makes),
'NumberOfSites': len(sites),
'ExtractionDate': datetime.now().strftime('%d.%m.%Y'),
'ExtractionTime': datetime.now().strftime('%H:%M:%S'),
'BeginFiscalYear': self.first_month_of_financial_year
}
def bookkeep_filter(self):
period = [self.current_year + str(i).zfill(2) for i in range(1, 13)]
if self.first_month_of_financial_year != '01':
if self.first_month_of_financial_year > self.current_month:
period = [self.last_year + str(i).zfill(2) for i in range(1, 13)] + period
else:
period = period + [self.next_year + str(i).zfill(2) for i in range(1, 13)]
fm = int(self.first_month_of_financial_year)
period = period[fm - 1:fm + 12]
period = [self.current_year + '00'] + period
rename_to = ['OpeningBalance'] + ['Period' + str(i).zfill(2) for i in range(1, 13)]
return dict(zip(period, rename_to))
def extract_acct_info(self, df: pd.DataFrame):
acct_info = ['Marke', 'Standort', 'Konto_Nr', 'Kostenstelle', 'Absatzkanal', 'Kostenträger']
df['Konto_Nr_SKR51'] = df.index
df[acct_info] = df['Konto_Nr_SKR51'].str.split('-', 6, expand=True)
return df
def export_period(self, year, month):
self.set_bookkeep_period(year, month)
# Übersetzungstabelle laden
df_translate = pd.read_csv(self.account_translation, decimal=',', sep=';', encoding='latin-1',
converters={i: str for i in range(0, 200)})
logging.info(df_translate.shape)
df_translate['duplicated'] = df_translate.duplicated()
logging.info(df_translate[df_translate['duplicated']])
df_translate = df_translate[['Konto_Nr_Händler', 'Konto_Nr_SKR51', 'Marke',
'Marke_HBV', 'Standort', 'Standort_HBV']]
row = {
'Konto_Nr_Händler': '01-01-0861-00-00-00',
'Konto_Nr_SKR51': '01-01-0861-00-00-00',
'Marke': '01',
'Marke_HBV': '',
'Standort': '01',
'Standort_HBV': ''
}
df_translate.append(row, ignore_index=True)
# df_translate.drop(columns=['duplicated'], inplace=True)
df_translate.drop_duplicates(inplace=True)
df_translate.set_index('Konto_Nr_Händler')
# Kontensalden laden
df2 = []
for csv_file in self.account_bookings:
df2.append(pd.read_csv(csv_file, decimal=',', sep=';', encoding='latin-1', converters={0: str, 1: str}))
df_bookings = pd.concat(df2)
# Kontensalden auf gegebenen Monat filtern
filter_from = self.current_year + self.first_month_of_financial_year
filter_prev = self.last_year + self.first_month_of_financial_year
if self.first_month_of_financial_year > self.current_month:
filter_from = self.last_year + self.first_month_of_financial_year
filter_prev = self.last_year2 + self.first_month_of_financial_year
filter_to = self.current_year + self.current_month
filter_opening = self.current_year + '00'
filter_prev_opening = self.last_year + '00'
prev_year_closed = True
df_opening_balance = df_bookings[(df_bookings['Bookkeep Period'] == filter_opening)]
if df_opening_balance.shape[0] == 0:
df_opening_balance = df_bookings[(df_bookings['Bookkeep Period'] == filter_prev_opening) |
((df_bookings['Bookkeep Period'] >= filter_prev) &
(df_bookings['Bookkeep Period'] < filter_from))].copy()
df_opening_balance['Bookkeep Period'] = filter_opening
prev_year_closed = False
# df_opening_balance = df_opening_balance.merge(df_translate, how='inner', on='Konto_Nr_Händler')
df_opening_balance = df_opening_balance[(df_opening_balance['Konto_Nr_Händler'].str.contains(r'-[013]\d\d+-'))]
df_opening_balance['amount'] = (df_opening_balance['Debit Amount'] + df_opening_balance['Credit Amount']).round(2)
# df_opening_balance.drop(columns=['Debit Amount', 'Credit Amount', 'Debit Quantity', 'Credit Quantity'], inplace=True)
# df_opening_balance = df_opening_balance.groupby(['Marke', 'Standort']).sum()
opening_balance = df_opening_balance['amount'].sum().round(2)
logging.info('Gewinn/Verlustvortrag')
logging.info(opening_balance)
if not prev_year_closed:
row = {
'Konto_Nr_Händler': '01-01-0861-00-00-00',
'Bookkeep Period': filter_opening,
'Debit Amount': opening_balance,
'Credit Amount': 0,
'Debit Quantity': 0,
'Credit Quantity': 0,
'amount': opening_balance
}
df_opening_balance = df_opening_balance.append(row, ignore_index=True)
df_bookings = df_bookings[(df_bookings['Bookkeep Period'] >= filter_from) & (df_bookings['Bookkeep Period'] <= filter_to)]
df_bookings['amount'] = (df_bookings['Debit Amount'] + df_bookings['Credit Amount']).round(2)
df_stats = df_bookings.copy()
# df_stats = df_stats[df_stats['Konto_Nr_Händler'].str.match(r'-[24578]\d\d\d-')]
df_stats['Konto_Nr_Händler'] = df_stats['Konto_Nr_Händler'].str.replace(r'-(\d\d\d+)-', r'-\1_STK-', regex=True)
df_stats['amount'] = (df_bookings['Debit Quantity'] + df_bookings['Credit Quantity']).round(2)
df_bookings = pd.concat([df_opening_balance, df_bookings, df_stats])
df_bookings = df_bookings[df_bookings['amount'] != 0.00]
bk_filter = self.bookkeep_filter()
period_no = list(bk_filter.keys()).index(filter_to) + 1
# Spalten konvertieren
df_bookings['period'] = df_bookings['Bookkeep Period'].apply(lambda x: bk_filter[x])
logging.info('df_bookings: ' + str(df_bookings.shape))
# Join auf Übersetzung
df_combined = df_bookings.merge(df_translate, how='inner', on='Konto_Nr_Händler')
logging.info('df_combined: ' + str(df_combined.shape))
# Hack für fehlende Markenzuordnung
df_combined['Fremdmarke'] = (df_combined['Marke_HBV'].str.match(r'^0000'))
df_combined['Marke'] = np.where(df_combined['Fremdmarke'], '99', df_combined['Marke'])
df_combined['Standort_egal'] = (df_combined['Standort_HBV'].str.match(r'^\d\d_'))
df_combined['Standort_HBV'] = np.where(df_combined['Fremdmarke'] | df_combined['Standort_egal'], '0000', df_combined['Standort_HBV'])
makes = df_combined[['Marke', 'Marke_HBV']].drop_duplicates().sort_values(by=['Marke'])
sites = df_combined[['Marke', 'Standort', 'Standort_HBV']].drop_duplicates().sort_values(by=['Marke', 'Standort'])
# df_combined.to_csv(account_invalid, decimal=',', sep=';', encoding='latin-1', index=False)
# Gruppieren
# df_grouped = df_combined.groupby(['Konto_Nr_SKR51', 'period']).sum()
df = df_combined.pivot_table(index=['Konto_Nr_SKR51'], columns=['period'], values='amount',
aggfunc=np.sum, margins=True, margins_name='CumulatedYear')
logging.info('df_pivot: ' + str(df.shape))
df = self.extract_acct_info(df)
# df = df_translate.reset_index(drop=True).drop(columns=['Kostenträger_Ebene']).drop_duplicates()
logging.info(df.shape)
logging.info(df.columns)
logging.info(df.head())
# df = df.merge(df_translate, how='inner', on='Konto_Nr_SKR51')
logging.info('df: ' + str(df.shape))
df['Bilanz'] = (df['Konto_Nr'].str.match(r'^[013]'))
df['Kontoart'] = np.where(df['Bilanz'], '1', '2')
df['Kontoart'] = np.where(df['Konto_Nr'].str.contains('_STK'), '3', df['Kontoart'])
df['Kontoart'] = np.where(df['Konto_Nr'].str.match(r'^[9]'), '3', df['Kontoart'])
df['Konto_1'] = (df['Konto_Nr'].str.slice(0, 1))
# Hack für fehlende Markenzuordnung
df = df.merge(makes, how='left', on='Marke')
df['Marke'] = np.where(df['Marke_HBV'].isna(), '99', df['Marke'])
df_debug = df.drop(columns=['Bilanz'])
logging.info(df_debug.groupby(['Kontoart']).sum())
logging.info(df_debug.groupby(['Kontoart', 'Konto_1']).sum())
logging.info(df_debug.groupby(['Konto_Nr']).sum())
df_debug.groupby(['Konto_Nr']).sum().to_csv(self.debug_file, decimal=',', sep=';', encoding='latin-1')
# Bereinigung GW-Kostenträger
df['GW_Verkauf_1'] = (df['Konto_Nr'].str.match(r'^[78]0')) & (df['Kostenstelle'].str.match(r'^[^1]\d'))
df['Kostenstelle'] = np.where(df['GW_Verkauf_1'] == True, '11', df['Kostenstelle'])
df['GW_Verkauf_2'] = (df['Konto_Nr'].str.match(r'^[78]1')) & (df['Kostenstelle'].str.match(r'^[^2]\d'))
df['Kostenstelle'] = np.where(df['GW_Verkauf_2'] == True, '21', df['Kostenstelle'])
df['GW_Verkauf_3'] = (df['Konto_Nr'].str.match(r'^[78]3')) & (df['Kostenstelle'].str.match(r'^[^3]\d'))
df['Kostenstelle'] = np.where(df['GW_Verkauf_3'] == True, '31', df['Kostenstelle'])
df['GW_Verkauf_4'] = (df['Konto_Nr'].str.match(r'^[78]4')) & (df['Kostenstelle'].str.match(r'^[^4]\d'))
df['Kostenstelle'] = np.where(df['GW_Verkauf_4'] == True, '41', df['Kostenstelle'])
df['GW_Verkauf_5'] = (df['Konto_Nr'].str.match(r'^[78]5')) & (df['Kostenstelle'].str.match(r'^[^5]\d'))
df['Kostenstelle'] = np.where(df['GW_Verkauf_5'] == True, '51', df['Kostenstelle'])
df['GW_Verkauf_50'] = (df['Konto_Nr'].str.match(r'^[78]')) & (df['Kostenstelle'].str.match(r'^2'))
df['Kostenträger'] = np.where(df['GW_Verkauf_50'] == True, '52', df['Kostenträger'])
df['Kostenträger'] = np.where((df['GW_Verkauf_50'] == True) & (df['Marke'] == '01'), '50', df['Kostenträger'])
df['GW_Stk_50'] = (df['Konto_Nr'].str.match(r'^9130')) & (df['Kostenstelle'].str.match(r'^2'))
df['Kostenträger'] = np.where(df['GW_Stk_50'] == True, '52', df['Kostenträger'])
df['Kostenträger'] = np.where((df['GW_Stk_50'] == True) & (df['Marke'] == '01'), '50', df['Kostenträger'])
df['Kostenträger'] = np.where(df['Bilanz'] == True, '00', df['Kostenträger'])
df['Konto_5er'] = (df['Konto_Nr'].str.match('^5')) | (df['Konto_Nr'].str.match('^9143'))
df['Absatzkanal'] = np.where(df['Konto_5er'] == True, '99', df['Absatzkanal'])
df['Teile_30_60'] = (df['Konto_Nr'].str.match(r'^[578]')) & \
(df['Kostenstelle'].str.match(r'^[3]')) & \
(df['Kostenträger'].str.match(r'^[^6]'))
df['Kostenträger'] = np.where(df['Teile_30_60'] == True, '60', df['Kostenträger'])
df['Service_40_70'] = (df['Konto_Nr'].str.match(r'^[578]')) & \
(df['Kostenstelle'].str.match(r'^[4]')) & \
(df['Kostenträger'].str.match(r'^[^7]'))
df['Kostenträger'] = np.where(df['Service_40_70'] == True, '70', df['Kostenträger'])
from_label = ['Marke', 'Standort', 'Konto_Nr', 'Kostenstelle', 'Absatzkanal', 'Kostenträger']
to_label = ['Make', 'Site', 'Account', 'Origin', 'SalesChannel', 'CostCarrier']
df = df.rename(columns=dict(zip(from_label, to_label)))
makes = makes.rename(columns=dict(zip(from_label, to_label))).to_dict(orient='records')
sites = sites.rename(columns=dict(zip(from_label, to_label))).to_dict(orient='records')
df['CostAccountingString'] = df['Make'] + df['Site'] + df['Origin'] + \
df['SalesChannel'] + df['CostCarrier']
df['IsNumeric'] = (df['CostAccountingString'].str.isdigit()) & (df['Account'].str.isdigit()) & (df['Account'].str.len() == 4)
df_invalid = df[df['IsNumeric'] == False]
df_invalid.to_csv(self.account_invalid, decimal=',', sep=';', encoding='latin-1', index=False)
export_csv = self.base_dir + 'export_' + self.current_year + '-' + self.current_month + '.csv'
xmlfile = export_csv[:-4] + '.xml'
df.to_csv(export_csv, decimal=',', sep=';', encoding='latin-1', index=False)
df = df[df['IsNumeric'] != False].groupby(ACCOUNT_INFO, as_index=False).sum()
# Infos ergänzen
df['Decimals'] = 2
# df['OpeningBalance'] = 0.0
logging.info(df.shape)
export_file = self.export_xml(df.to_dict(orient='records'), bk_filter, period_no, makes, sites, xmlfile)
# Join auf Übersetzung - nicht zugeordnet
df_ignored = df_bookings.merge(df_translate, how='left', on='Konto_Nr_Händler')
df_ignored = df_ignored[df_ignored['Konto_Nr_SKR51'].isna()] # [['Konto_Nr_Händler', 'Bookkeep Period', 'amount', 'quantity']]
if not df_ignored.empty:
df_ignored = df_ignored.pivot_table(index=['Konto_Nr_Händler'], columns=['period'], values='amount',
aggfunc=np.sum, margins=True, margins_name='CumulatedYear')
df_ignored.to_csv(self.account_ignored, decimal=',', sep=';', encoding='latin-1')
return export_file
def export_xml(self, records, bk_filter, period_no, makes, sites, export_file):
record_elements = ACCOUNT_INFO + ['Decimals'] + \
list(bk_filter.values())[:period_no] + ['CumulatedYear']
root = ET.Element('HbvData')
h = ET.SubElement(root, 'Header')
for k, v in self.header(makes, sites).items():
ET.SubElement(h, k).text = str(v)
make_list = ET.SubElement(root, 'MakeList')
for m in makes:
e = ET.SubElement(make_list, 'MakeListEntry')
ET.SubElement(e, 'Make').text = m['Make']
ET.SubElement(e, 'MakeCode').text = m['Marke_HBV']
bm_code_list = ET.SubElement(root, 'BmCodeList')
for s in sites:
e = ET.SubElement(bm_code_list, 'BmCodeEntry')
ET.SubElement(e, 'Make').text = s['Make']
ET.SubElement(e, 'Site').text = s['Site']
ET.SubElement(e, 'BmCode').text = s['Standort_HBV']
record_list = ET.SubElement(root, 'RecordList')
for row in records:
record = ET.SubElement(record_list, 'Record')
for e in record_elements:
child = ET.SubElement(record, e)
field = row.get(e, 0.0)
if str(field) == 'nan':
field = '0'
elif type(field) is float:
field = '{:.0f}'.format(field * 100)
child.text = str(field)
with open(export_file, 'w', encoding='utf-8') as fwh:
fwh.write(minidom.parseString(ET.tostring(root)).toprettyxml(indent=' '))
# with open(export_file, 'wb') as fwh:
# fwh.write(ET.tostring(root))
return export_file
def convert_to_row(self, node):
return [child.text for child in node]
def convert_xml_to_csv(self, xmlfile, csvfile):
record_list = ET.parse(xmlfile).getroot().find('RecordList')
header = [child.tag for child in record_list.find('Record')]
bookings = [self.convert_to_row(node) for node in record_list.findall('Record')]
with open(csvfile, 'w') as fwh:
cwh = csv.writer(fwh, delimiter=';')
cwh.writerow(header)
cwh.writerows(bookings)
return True
def convert_csv_to_xml(self, csvfile, xmlfile):
makes = [{'Make': '01', 'Marke_HBV': '1844'}]
sites = [{'Make': '01', 'Site': '01', 'Marke_HBV': '1844'}]
with open(csvfile, 'r', encoding='latin-1') as frh:
csv_reader = csv.DictReader(frh, delimiter=';')
self.export_xml(csv_reader, self.bookkeep_filter(), 1, makes, sites, xmlfile)
class Kunden(Enum):
Altermann = auto()
Barth_und_Frey = auto()
Hannuschka = auto()
Koenig_und_Partner = auto()
Luchtenberg = auto()
Russig_Neustadt_deop01 = auto()
Russig_Neustadt_deop02 = auto()
Siebrecht = auto()
if __name__ == '__main__':
gchr = GCHR(Kunden.Russig_Neustadt_deop02.name)
export_file = gchr.export_period('2022', '04')
# convert_xml_to_csv(export_file, export_file[:-4] + '.csv')
# convert_xml_to_csv('DE2119_2105_150621_080944.xml', 'DE2119_2105_150621_080944.csv')
# convert_csv_to_xml(base_dir + '../SKR51/maximum.csv', base_dir + '../maximum_2022-01.xml')