def get(self):
ts = time.time()
MSalesDetailTxns.insert_data_from_csv() # to database
#create_and_dump_sales_from_csv() # to pickle
te = time.time()
return {
'Time taken for import sales master':
"{} min".format(round((te - ts) / 60, 2))
}
def get(self, client_id):
error = None
try:
MSalesDetailTxns.delete_client_data(client_id)
ItemMaster.delete_client_data(client_id)
delete_clients_mongo_collections(client_id)
delete_client_pickle_files(client_id)
except Exception as e:
error = str(e)
if error:
return {"error": error}
return {"cleaned":True}, 200
def load_n_process_salesdetail_data(client_id):
sales_details = sales_table.fetch_all_by_client(client_id) #from database
# sales_details = read_dataframe_from_pickle(client_id, data_name='sales') #from pickle
sales_details['Datetimestamp'] = pd.to_datetime(
sales_details['Datetimestamp'])
sales_details['item_len'] = sales_details['Item_code'].apply(
lambda x: len(x))
sales_details = sales_details[sales_details['item_len'] > 2].copy()
# itemcode2name = {}
# for code, name in zip(sales_details.Item_code, sales_details.Item_name):
# itemcode2name[code] = name
# #itemname2code[name] = code
#return sales_details, itemcode2name
return sales_details
def get_daily_sales_data(client_id):
required_col = ['Datetimestamp', 'Total_sales']
sales = sales_table.fetch_all_by_client(client_id)[
required_col] # from database
# sales = read_dataframe_from_pickle(client_id, data_name='sales')[required_col] # from pickle
sales['Datetimestamp'] = pd.to_datetime(sales['Datetimestamp'])
#print(sales.head())
sales['date'] = sales['Datetimestamp'].dt.date.astype('datetime64[ns]')
daily_sales = sales.groupby('date',
as_index=False).agg({'Total_sales': np.sum})
daily_sales['month'] = daily_sales.date.dt.month.astype(np.int8)
daily_sales['week'] = daily_sales.date.dt.week.astype(np.int8)
#daily_sales['month'] = daily_sales['month'].apply(lambda x: calendar.month_abbr[x])
daily_sales['year'] = daily_sales.date.dt.year.astype(np.int16)
return daily_sales
def get_item_wise_sales_data(client_id, year):
required_cols = [
'Datetimestamp', 'Item_code', 'Item_name', 'Sale_quantity',
'Unitcost_price', 'Unitsale_price', 'Total_sales'
]
sales = sales_table.fetch_all_by_client(client_id)[
required_cols] #from database
# sales = read_dataframe_from_pickle(client_id, data_name='sales')[required_cols] #from pickle
sales['Datetimestamp'] = pd.to_datetime(sales['Datetimestamp'])
sales['month'] = sales['Datetimestamp'].dt.month.astype(np.int8)
sales['year'] = sales['Datetimestamp'].dt.year.astype(np.int16)
sales['profit'] = round(
((sales['Unitsale_price'] - sales['Unitcost_price']) *
sales['Sale_quantity']), 2)
sales = sales[sales['year'] >= year - 1]
return sales
def create_itemcodes_list(client_id):
required_cols = ['Sales_id', 'Item_code', 'Item_name']
saleid_items = sales_table.fetch_all_by_client(client_id) #from database
# saleid_items = read_dataframe_from_pickle(client_id, data_name='sales') #from pickle
saleid_items = saleid_items[required_cols]
saleid_items['vitem_len'] = saleid_items['Item_code'].apply(
lambda x: len(x))
saleid_items = saleid_items[saleid_items['vitem_len'] > 2]
items_groups = saleid_items[['Sales_id', 'Item_code', 'Item_name']].groupby(['Sales_id'], as_index=False). \
agg(lambda x : ','.join(x))
items_groups['Item_code'] = items_groups['Item_code'].apply(
lambda x: x.split(','))
items_groups['Item_code'] = items_groups['Item_code'].apply(
lambda x: list(set(x)))
Item_codes_list = items_groups.Item_code.tolist()
return Item_codes_list
def get_code2name_dict(client_id):
saleid_items = sales_table.fetch_all_by_client(client_id)[[
'Item_code', 'Item_name'
]] #from database
# saleid_items = read_dataframe_from_pickle(client_id, data_name='sales')[['Item_code', 'Item_name']] #from pickle
saleid_items['vitem_len'] = saleid_items['Item_code'].apply(
lambda x: len(x))
saleid_items = saleid_items[saleid_items['vitem_len'] > 2]
# mst_item = download_columns_from_db(['Item_code', 'Item_name'], item_table, client_id)
# mst_item = read_dataframe_from_pickle(client_id, data_name='item')
code2name = {}
# for i in range(mst_item.shape[0]):
# code, name = mst_item.Item_code[i], mst_item.Item_name[i]
# if len(code) > 2:
# code2name[code] = name
for code, name in zip(saleid_items.Item_code, saleid_items.Item_name):
#if code not in code2name:
code2name[code] = name
return code2name
def get(self, objectid):
if FS.exists({"_id":ObjectId(objectid)})==True:
data = []
m =FS.get(ObjectId(objectid))
metadata= (m.metadata)
client_obj_id= (metadata['client'])
#print(client_obj_id)
if metadata['category']== "item":
outputdata =FS.get(ObjectId(objectid)).read()
for i in (str(outputdata)).split("\\n"):
byedata = bytes(i, 'utf-8')
d = (byedata.decode("utf-8"))
fresh= (str((d.strip('\\r'))) )
if len (fresh.split(','))>2:
data.append(fresh.split(','))
#print(data[0])
try:
#print('item_delete')
client_id = str(client_obj_id) # to convert bson objectId to str
#print(client_id)
if ItemMaster.does_client_exist(client_id):
ItemMaster.delete_client_data(client_id)
delete_clients_mongo_collections(client_id)
delete_client_pickle_files(client_id)
ItemMaster.insert_data_for_items_by_row(data, client_obj_id) #to database
#creat_and_dump_dataframe(data, metadata['category'], client_obj_id) #to pikcle
#set isProcessed to True in myfiles.files collection
metadata.setdefault('isProcessed', True)
metadata['isProcessed']=True
files_coll = nosqldb['myfiles.files']
files_coll.update_one(
{'_id':ObjectId(objectid)}, {"$set": {'metadata': metadata}}
)
except Exception as e:
print (str(e))
#error = e
return {"status": False}, 404
return True, 200
elif metadata['category']=="sales":
outputdata =FS.get(ObjectId(objectid)).read()
for i in (str(outputdata)).split("\\n"):
byedata = bytes(i, 'utf-8')
d = (byedata.decode("utf-8"))
fresh= (str((d.strip('\\r'))) )
if len (fresh.split(','))>4:
data.append(fresh.split(','))
try:
client_id = str(client_obj_id) # to convert bson objectId to str
#print(client_id)
if MSalesDetailTxns.does_client_exist(client_id):
MSalesDetailTxns.delete_client_data(client_id)
delete_clients_mongo_collections(client_id)
delete_client_pickle_files(client_id)
MSalesDetailTxns.insert_data_for_sales_by_row(data, client_obj_id) #to database
#creat_and_dump_dataframe(data, metadata['category'], client_obj_id) #to pickle
#set isProcessed to True in myfiles.files collection
metadata.setdefault('isProcessed', True)
metadata['isProcessed']=True
files_coll = nosqldb['myfiles.files']
files_coll.update_one(
{'_id':ObjectId(objectid)}, {"$set": {'metadata': metadata}}
)
except Exception as e:
print (str(e))
return { "status": False}, 404
return True, 200
else:
return {"status": False}, 404
return {"status": False}, 400
def load_data_dataframe(client_id):
df = sales_table.fetch_all_by_client(client_id) # from database
#df = read_dataframe_from_pickle(client_id, data_name='sales') # from pikcle
return df