Initial

.gitignore

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+data.db

convert.py

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+import pandas as pd
+import numpy as np
+import sqlite3
+import time
+
+start = time.time()
+
+data = pd.read_excel("data.xlsx")
+df = pd.DataFrame()
+
+years = []
+months = []
+level2s = []
+level3s = []
+skus = []
+quantities = []
+revenues = []
+costs = []
+descriptions = []
+
+for index,row in data.iterrows():
+    if type(row["FiscalYearMonth"]) == str:
+        years.append(int(row["FiscalYearMonth"][0:4]))
+        months.append(int(row["FiscalYearMonth"][5:]))
+        level2s.append(row["Level2"])
+        level3s.append(row["Level3"])
+        skus.append(row["MaterialEntered"])
+        quantities.append(int(row["Quantity"]))
+        revenues.append(row["SalesRevenue"])
+        costs.append(row["CostOfGoodsSold"])
+        descriptions.append(row["ProductDescription"])
+
+
+df["year"] = years
+df["month"] = months
+df["level2"] = level2s
+df["level3"] = level3s
+df["sku"] = skus
+df["quantity"] = quantities
+df["revenue"] = revenues
+df["cost"] = costs
+df["description"] = descriptions
+
+conn = sqlite3.connect("data.db")
+df.to_sql("data", conn, if_exists='replace')
+
+end = time.time()
+
+print(end-start)

get_top.py

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+get_top = """
+    SELECT
+      description,
+      sku,
+      level2,
+      level3,
+      rev,
+      qty,
+      tren
+    from
+      (
+        SELECT
+        *,
+          ROW_NUMBER () OVER (
+            PARTITION BY level2
+            ORDER BY
+              SUM(revenue) DESC
+          ) rownum,
+          SUM(revenue) OVER (PARTITION BY level2) total,
+          ROUND(SUM(revenue), 2) as rev,
+          SUM(quantity) as qty,
+          tren
+        FROM
+          (
+            select
+              *,
+              cast(past as float) / 3 as past,
+              round(
+                cast(present as float) /(cast(past as float) / 3.0),
+                2
+              ) as tren
+            from
+              (
+                select
+                  *,
+                  sum(quantity) FILTER (
+                    WHERE
+                      year = {0}
+                      and month between {1}
+                      and {2}
+                  ) over (PARTITION BY description) as past,
+                  sum(quantity) FILTER (
+                    WHERE
+                      year = {3}
+                      and month = {4}
+                  ) over (PARTITION BY description) as present
+                from
+                  data
+              )
+          )
+        where
+          year = {3}
+          and month = {4}
+        GROUP BY
+          description
+        ORDER BY
+          total DESC
+      )
+    WHERE
+      rownum <= 10
+      and revenue > 0
+    order by
+      (sum(revenue) over (
+        partition by level2
+      )) desc;
+""".format(2022,1,2,2022,3)
+
+import sqlite3
+
+conn = sqlite3.connect("data.db")
+
+res = conn.execute(get_top)
+print(res.description)
+for row in res.fetchall():
+    print(row)