Inventory_automation

derby_inventory_automation/automation.py

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+import automation_functions as af
+import pandas as pd
+import numpy as np 
+import csv
+
+
+
+
+
+
+
+
+updated_inventory = pd.read_csv('updated_inventory_full.csv')
+promotions_list = pd.io.json.read_json("promotionData.json")
+current_inventory = pd.io.json.read_json("old_inventory_nonpromo.json")
+
+## Initial clean and add of upce 
+final_table = af.clean_headers(updated_inventory)
+final_table["upce"] = [af.create_upce(i) for i in final_table["upc"]]
+
+##add crv, and sugar tax and check for taxable, adds column for sugar_tax and images column. 
+final_table = af.crv_column(final_table)
+final_table = af.find_sugar(final_table).drop(["index"],axis=1)
+final_table = af.get_taxable(final_table)
+
+
+## Extract new products 
+def extract_new_products():
+    newest_products = final_table[(~final_table["upc"].isin(current_inventory["upc"]))&(~final_table["upc"].isin(promotions_list["upc"]))]
+    newest_products["contains_promotion"]= False
+    newest_products.to_json("newest_products.json",orient = "records")
+    return newest_products
+
+## Extract changed promotions 
+def extract_changed_promo():
+    updated_promotions = final_table[final_table["upc"].isin(promotions_list["upc"])]
+    merged_promotions = updated_promotions.merge(promotions_list,left_on="upc",right_on = "upc")
+    changed_prices = merged_promotions[merged_promotions["sell_price_x"]!=merged_promotions["sell_price_y"]]
+    final_changed_promotions = updated_promotions[updated_promotions["upc"].isin(changed_prices["upc"])]
+    final_changed_promotions.to_json("changed_promotions.json",orient = "records")
+    return final_changed_promotions
+
+
+##Extract changed product pricing excluding promotions 
+def extract_product_changes():
+    exclude_promos = final_table[~final_table["upc"].isin(promotions_list["upc"])]
+    exclude_promos_images = current_inventory[current_inventory["upc"].isin(exclude_promos["upc"])][["img","upc"]]
+    exclude_promos = exclude_promos.merge(exclude_promos_images, left_on=["upc"],right_on=["upc"])
+    merged_promotions = current_inventory.merge(exclude_promos,left_on=["upc",],right_on = ["upc"])
+    changed_prices = merged_promotions[round(merged_promotions["sell_price_x"],2)!=round(merged_promotions["sell_price_y"],2)]
+    final_changed_products = exclude_promos[exclude_promos["upc"].isin(changed_prices["upc"])]
+    final_changed_products["contains_promotion"] = False
+    final_changed_products["img"]= final_changed_products["img_y"]
+    final_changed_products = final_changed_products.drop(["img_x","img_y"],axis=1)
+    final_changed_products.to_csv("updated_changes.csv")
+    return final_changed_products
+
+
+
+
+extract_product_changes()
+
+
+
+

derby_inventory_automation/automation_functions.py

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+import pandas as pd
+import numpy as np 
+import csv
+
+
+
+def clean_headers(df):
+    df["Price"] = [float(i)/100 for i in df["cents"]]
+    df = df.rename(columns = {"Department":"department","Name":"product_name","Fee Multiplier": "crv_by_05_cents","Price":"sell_price"})
+    df = df[["upc","department","product_name","crv_by_05_cents","sell_price","size"]]
+    return df
+
+
+def create_upce(upca2):
+    string = ''
+    length=len(str(upca2))
+    upca=str(upca2)
+    list = [ "5", "6", "7", "8" ,"9"]
+    upca= "0"+ upca
+    if len(str(upca))!=12:
+        return "0"
+    if upca[10] in list and upca[5]!="0" and upca[6]=="0"and upca[7]=="0"and upca[8]=="0" and upca[9]=="0":
+        string+=upca[0]
+        string+=upca[1]
+        string+=upca[2]
+        string+=upca[3]
+        string+=upca[4]
+        string+=upca[5]
+        string+=upca[10]
+        string+=upca[11]
+
+    if upca[5]=="0" and upca[7]=="0"and upca[8]=="0"and upca[9]=="0" and upca[4]!="0":
+        string+=upca[0]
+        string+=upca[1]
+        string+=upca[2]
+        string+=upca[3]
+        string+=upca[4]
+        string+=upca[10]
+        string+="4"
+        string+=upca[11]
+
+    if upca[4]=="0" and upca[5]=="0"and upca[6]=="0"and upca[7]=="0" and upca[3]=="1" or upca[3]=="2" or upca[3]=="0":
+        string+=upca[0]
+        string+=upca[1]
+        string+=upca[2]
+        string+=upca[8]
+        string+=upca[9]
+        string+=upca[10]
+        string+=upca[3]
+        string+=upca[11]
+    lister2=["3","4","5","6","7","8","9"]
+    if upca[3] in lister2 and upca[4]=="0" and upca[5]=="0" and upca[6]=="0" and upca[7]=="0" and upca[8]=="0":
+        string+=upca[0]
+        string+=upca[1]
+        string+=upca[2]
+        string+=upca[3]
+        string+=upca[9]
+        string+=upca[10]
+        string+="3"
+        string+=upca[11]
+
+    return string
+
+
+## df = crv_column(df)
+def crv_column(df):
+    has_crv=[]
+    departments = ["Soda","Water/Juices","Energy","Sports Drinks","Drinks","Teas/coffees"]
+    for i in range(len(df["upc"])):
+        if df["department"][i] in departments:
+            has_crv.append(True)
+
+        elif df["department"][i] not in departments: 
+            has_crv.append(False)
+    df["has_crv"]=has_crv
+    return df
+
+
+## returns the full table, including those without sugar 
+def find_sugar (df): 
+    ##table66 = table[(table["department"]=="Soda") | (table["department"]=="Water/Juices") | (table["department"]=="Sports Drinks")| (table["department"]=="Energy")].reset_index()
+    table4 = df[(df["department"]=="Soda") | (df["department"]=="Water/Juices") | (df["department"]=="Sports Drinks") | (df["department"]=="Energy") | (df["department"]=="Drinks")| (df["department"]=="Kombucha")].reset_index()
+    sugar_column=[]
+    for i in range(len(table4["product_name"])):
+        if "diet" in table4["product_name"][i] or "Diet" in table4["product_name"][i] or "Sugar" in  table4["product_name"][i] or "sugar" in table4["product_name"][i] or "Fresh" in table4["product_name"][i] or "Zero" in table4["product_name"][i] or "zero" in table4["product_name"][i] or "water" in table4["product_name"][i] or "Water" in table4["product_name"][i] or "Organic" in table4["product_name"][i] or "organic" in table4["product_name"][i] or "real" in table4["product_name"][i] or "Real" in table4["product_name"][i] or "Geyser" in table4["product_name"][i]:
+            sugar_column.append(False)
+        else: 
+            sugar_column.append(True)
+    table4["has_sugar_tax"] = sugar_column
+
+    df_not_sugar= df[~df["upc"].isin(table4["upc"])]
+    df_not_sugar["has_sugar_tax"]=False
+    combined= df_not_sugar.append(table4)
+
+    return combined
+
+
+def add_images(df): 
+    df["img"]= ""
+    return df
+
+
+##Gets whats taxable and whats not and returns a table with all the data, including a column to indicate taxabilty 
+def get_taxable(df): 
+    table44 = df[(df["department"]=="Energy")  |(df["department"]=="Pets")  |(df["department"]=="Soda")  |  (df["department"]=="Household Essentials")|(df["department"]=="Health & Wellness")]
+    table44["has_sales_tax"]=True
+
+    other_department = df[~df["upc"].isin(table44["upc"])]
+    other_department["has_sales_tax"]=False
+
+    finalized= table44.append(other_department)
+    finalized["img"]=""
+    finalized["sugar_tax"]=0
+
+    return finalized
+
+
+

derby_inventory_automation/changed_promotions.json

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+[{"upc":12000016721,"department":"Teas\/coffees","product_name":"Starbucks Frappuccino Caramel Intense Chilled Coffee Drink","crv_by_05_cents":1.0,"sell_price":4.94,"size":"13.70 oz","upce":"01267201","has_crv":true,"has_sugar_tax":false,"has_sales_tax":false,"img":"","sugar_tax":0},{"upc":28400199612,"department":"Chips","product_name":"Lays BBQ Chips","crv_by_05_cents":1.0,"sell_price":4.09,"size":"7.75 oz","upce":"","has_crv":false,"has_sugar_tax":false,"has_sales_tax":false,"img":"","sugar_tax":0},{"upc":28400199636,"department":"Chips","product_name":"Lays Sour Cream & Onion","crv_by_05_cents":1.0,"sell_price":4.09,"size":"7.75 Oz","upce":"","has_crv":false,"has_sugar_tax":false,"has_sales_tax":false,"img":"","sugar_tax":0}]