def create_or_load_item_monthly_sales_profit_mat(client_id):
# now = datetime.datetime.now()
# year = now.year
year = get_current_or_max_year(client_id)
cur_sale_name = f'{year}_sales_mat'
prev_sale_name = f'{year-1}_sales_mat'
cur_profit_name = f'{year}_profit_mat'
prev_profit_name = f'{year-1}_profit_mat'
if (not does_collection_exist(cur_sale_name, client_id)
or not does_collection_exist(prev_sale_name, client_id)):
df = get_item_wise_sales_data(client_id, year)
print("In item_analyis load data complete")
item2idx, idx2item, code2name = load_item_dict(client_id)
curr_df, prev_df = df[df['year'] == year], df[df['year'] == year - 1]
create_collection_item_sales(cur_sale_name, curr_df, item2idx,
client_id)
create_collection_item_sales(prev_sale_name, prev_df, item2idx,
client_id)
print(f'{prev_sale_name}')
create_collection_item_profit(cur_profit_name, curr_df, item2idx,
client_id)
create_collection_item_profit(prev_profit_name, prev_df, item2idx,
client_id)
print(f'{prev_profit_name}')
else:
item2idx, idx2item, code2name = load_item_dict(client_id)
return item2idx, idx2item, code2name
def process_item_analysis(client_id):
try:
create_or_load_item_monthly_sales_profit_mat(client_id)
except Exception as e:
logging.error(
f"while creating itemwise_sales_profit_mat for {client_id} in item_analysis{e}"
)
try:
_, _, _ = load_item_dict(client_id)
except Exception as e:
logging.error(
f'while creating pickle code2name, idx2code for {client_id}in item_analysis {e}'
)
logging.info(
f'pickle for code2name, idx2code for {client_id} in item_analysis exists'
)
# now = datetime.datetime.now()
# year = now.year
year = get_current_or_max_year(client_id)
cur_sale_name = f'{year}_sales_mat'
prev_sale_name = f'{year-1}_sales_mat'
cur_profit_name = f'{year}_profit_mat'
prev_profit_name = f'{year-1}_profit_mat'
data_names = [
cur_sale_name, prev_sale_name, cur_profit_name, prev_profit_name
]
collection_exist = True
for data_name in data_names:
if does_collection_exist(data_name, client_id):
collection_exist = collection_exist and True
return collection_exist
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
item2idx, idx2item, code2name = load_item_dict(client_id)
return sales_details, code2name
def process_abc_analysis(client_id):
try:
get_saleswise_abc_data(client_id)
except Exception as e:
logging.error(
f"while creating sales_abc_df for {client_id} in abc_analysis{e}")
try:
get_profitwise_abc_data(client_id)
except Exception as e:
logging.error(
f"while creating profit_abc for {client_id} in abc_analysis{e}")
try:
_, _, _ = load_item_dict(client_id)
except Exception as e:
logging.error(
f'while creating pickle code2name, idx2code for {client_id}in abc_analysis {e}'
)
return (does_collection_exist('sales_abc', client_id)
and does_collection_exist('profit_abc', client_id))
def get_saleswise_abc_data(client_id):
data_name = 'sales_abc'
if not does_collection_exist(data_name, client_id):
#df, code2name = load_n_process_salesdetail_data(client_id)
df = load_n_process_salesdetail_data(client_id)
item2idx, idx2item, code2name = load_item_dict(client_id)
itemwise_sales = df.groupby(['Item_code'], as_index=False). \
agg({'Unitsale_price':np.average, 'Sale_quantity': np.sum})
itemwise_sales['totalsales'] = itemwise_sales[
'Unitsale_price'] * itemwise_sales['Sale_quantity']
itemwise_sales = itemwise_sales.sort_values(by='totalsales',
ascending=False)
itemwise_sales['cumulativesales'] = itemwise_sales[
'totalsales'].cumsum()
itemwise_sales['%sales'] = (itemwise_sales['cumulativesales'] /
itemwise_sales['totalsales'].sum())
itemwise_sales['ABC_category'] = itemwise_sales['%sales']. \
apply(lambda x: 'A' if x < 0.71
else 'B' if (x >= 0.71 and x < 0.91)
else 'C')
itemwise_sales.insert(
1, 'Item_name',
itemwise_sales['Item_code'].apply(lambda x: code2name[x]))
create_collection_and_insert_datafrme(data_name, itemwise_sales,
client_id)
logging.info(
f'created sales_abc Mongo collection for {client_id}in abc analysis'
)
else:
itemwise_sales = get_dataframe_collection(data_name, client_id)
logging.info(
f'sales_abc Mongo collection for {client_id} in abc_analysis exists'
)
return itemwise_sales