メニューを変更

create_object.py

@@ -2,58 +2,22 @@ import plotly.graph_objects as go
 import plotly.express as px
 
 # 分析メニュー
-analysis_menu_list = ["ABC分析", "バスケット分析","時系列分析"]
+analysis_menu_list = ["時系列分析","バスケット分析"]
 
 # 分析メニューごとのSQL
 def create_sql(analysis_menu, country, start_date, end_date):
-    if analysis_menu == "ABC分析":
+    if analysis_menu == "時系列分析":
         sql = f"""
-        WITH
-        t_base AS(
-            -- 商品コードごとの売上（単価×個数）の合計値を算出
-            -- 期間を、2011年1～6月に絞る
-            SELECT
-            StockCode, Description,
-            SUM(UnitPrice * Quantity) AS SalesTotal
-            FROM df
-            WHERE CAST(InvoiceDate AS DATE) BETWEEN DATETIME '{start_date}' AND DATETIME '{end_date}'
-            AND {country}
-            GROUP BY StockCode, Description
-        ),
-        t_standard AS(
-            -- 全体の売上のうち、70%を占める売上額・90%を占める売上額を算出
-            SELECT
-            SUM(SalesTotal) AS Sum_SalesTotal,
-            FROM t_base
-        ),
-        t_cumulative AS(
-            -- 売上を降順（高い順）でソートし、先頭からの累計売上額を算出
-            SELECT
-            StockCode,
-            Description,
-            SalesTotal,
-            SUM(SalesTotal) OVER (ORDER BY SalesTotal DESC) AS SalesCumulative
-            FROM t_base
-            ORDER BY SalesTotal DESC
-        )
-
         SELECT
-            StockCode,
-            Description,
-            SalesTotal,
-            SalesCumulative,
-            SalesCumulative / Sum_SalesTotal AS Percentage_SalesCumulative,
-            -- 累計売上額が売上合計の70%以下の場合はランクA、90%以下の場合はランクB、それ以降はランクCとしてランク付け
-            CASE
-            WHEN SalesCumulative / Sum_SalesTotal <= 0.7 THEN 'A'
-            WHEN SalesCumulative / Sum_SalesTotal <= 0.9 THEN 'B'
-            ELSE 'C'
-            END AS SalesRank
-
-        FROM t_cumulative
-        FULL OUTER JOIN t_standard
-        ON TRUE
-        ORDER BY SalesTotal desc
+        CAST(InvoiceDate AS DATE) AS YearMonthDate,
+        COUNT(DISTINCT CustomerID) AS Num_of_Purchaser,
+        SUM(Quantity) AS Total_of_Amount,
+        SUM(UnitPrice * Quantity) AS SalesTotal
+        FROM df
+        WHERE CAST(InvoiceDate AS DATE) BETWEEN DATETIME '{start_date}' AND DATETIME '{end_date}'
+        AND {country}
+        GROUP BY YearMonthDate
+        ORDER BY YearMonthDate
         """
 
     elif analysis_menu == "バスケット分析":
@@ -128,33 +92,20 @@ def create_sql(analysis_menu, country, start_date, end_date):
         Description_B in (SELECT Description FROM t_purchaser ORDER BY Num_of_Purchaser DESC LIMIT 10)
         """
 
-    elif analysis_menu == "時系列分析":
-        sql = f"""
-        SELECT
-        CAST(InvoiceDate AS DATE) AS YearMonthDate,
-        COUNT(DISTINCT CustomerID) AS Num_of_Purchaser,
-        SUM(Quantity) AS Total_of_Amount,
-        SUM(UnitPrice * Quantity) AS SalesTotal
-        FROM df
-        WHERE CAST(InvoiceDate AS DATE) BETWEEN DATETIME '{start_date}' AND DATETIME '{end_date}'
-        AND {country}
-        GROUP BY YearMonthDate
-        ORDER BY YearMonthDate
-        """
-
     return sql
 
 
 # 分析メニューごとのグラフ
 def create_graph(analysis_menu, df):
-    if analysis_menu == "バスケット分析":
+    if analysis_menu == "時系列分析":
+        # 折れ線グラフ
+        fig = px.line(df, x='YearMonthDate', y='Total_of_Amount')
+    
+    elif analysis_menu == "バスケット分析":
         # ヒートマップ
         df = df.sort_values(["Description_A","Description_B"], ascending=[True, False]).reset_index()
         fig = go.Figure([go.Heatmap(z=df.CombinedSalesRate,
                                  x=df.Description_A.values,
                                  y=df.Description_B.values)])
 
-    elif analysis_menu == "時系列分析":
-        # 折れ線グラフ
-        fig = px.line(df, x='YearMonthDate', y='Total_of_Amount')
     return fig