.1405641833068148:dbabbfc871684ff3e0956dbc755a81c7_69e2087d6857dd8141c6e412.69e213016857dd8141c6e4b2.69e213007c9eb83a68b2cc6b:Trae CN.T(2026/4/17 19:01:21)

.gitignore

@@ -0,0 +1 @@
+__pycache__/dashboard.cpython-311.pyc

dashboard.py

@@ -12,12 +12,24 @@
 st.markdown('<style>div.block-container{padding-top:1rem;}</style>',unsafe_allow_html=True)
 
 
-os.chdir(r"C:\\STUDY\\PROGRAMS\\PYTHON\\DMDW_Project")   
-df = pd.read_csv("SuperStoreDataSet.csv", encoding="ISO-8859-1")
+script_dir = os.path.dirname(__file__)
+csv_path = os.path.join(script_dir, "SuperStoreDataSet.csv")
+df = pd.read_csv(csv_path, encoding="ISO-8859-1")
 
 
 col1,col2 = st.columns((2)) 
-df["Order Date"] = pd.to_datetime(df["Order Date"])
+order_date_str = df["Order Date"].copy()
+df["Order Date"] = pd.to_datetime(order_date_str, errors='coerce')
+
+date_formats = ['%m/%d/%Y', '%m-%d-%Y', '%m/%d/%y', '%m-%d-%y']
+for fmt in date_formats:
+    na_mask = df["Order Date"].isna()
+    if na_mask.any():
+        df.loc[na_mask, "Order Date"] = pd.to_datetime(
+            order_date_str[na_mask], 
+            format=fmt, 
+            errors='coerce'
+        )
 
 #GETTING THE MIN AND MAX DATE
 startDate = pd.to_datetime(df["Order Date"]).min()
@@ -37,7 +49,7 @@
 
 #CREATE FOR REGION
 st.sidebar.header("CHOOSE YOUR FILTER: ")
-region = st.sidebar.multiselect("CHOOSE YOU REGION",df["Region"].unique())
+region = st.sidebar.multiselect("CHOOSE YOU REGION", df["Region"].unique())
 
 if not region:
     df2 = df.copy()
@@ -46,35 +58,27 @@
 
 
 #CREATE FOR STATE
-state = st.sidebar.multiselect("CHOOSE YOUR STATE" ,df2["State"].unique())
+state = st.sidebar.multiselect("CHOOSE YOUR STATE", df2["State"].unique())
 
 if not state:
     df3 = df2.copy()
 else:
-    df3=df[df["State"].isin(state)]
+    df3 = df2[df2["State"].isin(state)]
 
 #CREATE FOR CITY
-city = st.sidebar.multiselect("CHOOSE YOUR CITY" ,df2["City"].unique())
+city = st.sidebar.multiselect("CHOOSE YOUR CITY", df3["City"].unique())
 
 #FILTER THE DATA BASED ON REGION, STATE AND CITY
+filtered_df = df.copy()
 
-if not region and not state and not city:
-    filtered_df = df 
-elif not state and not city:
-    filtereddf = df[df["Region"].isin(region)]
-
-elif not region and not state:
-    filtered_df = df[df["State"].isin(state)]
-elif state and city:
-    filtered_df = df3[df["State"].isin(state) & df3["City"].isin(city)]
-elif region and city:
-    filtered_df = df3[df["Region"].isin(region) & df3["City"].isin(city)]    
-elif region & state:
-    filtered_df = df3[df["Region"].isin(region) & df3["State"].isin(state)]    
-elif city:
-    filtered_df = df3[df3["City"].isin(city)]
-else:
-    filtered_df = df3[df3["Region"].isin(region) & df3["State"].isin(state) & df["City"].isin(city)] 
+if region:
+    filtered_df = filtered_df[filtered_df["Region"].isin(region)]
+
+if state:
+    filtered_df = filtered_df[filtered_df["State"].isin(state)]
+
+if city:
+    filtered_df = filtered_df[filtered_df["City"].isin(city)] 
 
 category_df =filtered_df.groupby(by=["Category"],as_index =False )["Sales"].sum()
 
@@ -99,9 +103,9 @@
 
 with cl2:
     with st.expander("REGION VIEWDATA"):
-        region = filtered_df.groupby(by = "Region", as_index = False)["Sales"].sum()
-        st.write(region.style.background_gradient(cmap="Oranges"))
-        csv = region.to_csv(index = False).encode('utf-8')
+        region_sales = filtered_df.groupby(by = "Region", as_index = False)["Sales"].sum()
+        st.write(region_sales.style.background_gradient(cmap="Oranges"))
+        csv = region_sales.to_csv(index = False).encode('utf-8')
         st.download_button("Download Data", data = csv, file_name = "Region.csv", mime = "text/csv",help = 'CLICK HERE TO DOWNLOAD CSV FILE')
 
 

requirements.txt

@@ -0,0 +1,3 @@
+streamlit>=1.20.0
+plotly>=5.10.0
+pandas>=1.4.0