Update app.py

app.py

@@ -5,46 +5,92 @@ import matplotlib.pyplot as plt
 import seaborn as sns
 import matplotlib.dates as mdates
 import plotly.express as px
+import plotly.graph_objects as go
 import re
 from datetime import datetime, timedelta
 import warnings
-# Load the data
-df = pd.read_csv(r"fy21-24.csv", 
-                 dtype={"FyWeek": "string",
-                        "Fy": "category", 
-                        "Chaincode": "category", 
-                        "Store": "category", 
-                        "Address": "string", 
-                        "Zipcode": "float", 
-                        "City": "category", 
-                        "State": "category", 
-                        "Containercode": "category", 
-                        "Itemtype": "category", 
-                        "SalesVolume":"float", 
-                        "UnitPrice":"float", 
-                        "Sales":"float"})
-
-# Convert columns
-df["Zipcode"] = df["Zipcode"].convert_dtypes()
-df["SalesVolume"] = df["SalesVolume"].convert_dtypes()
-
-# Title for the app
-st.title('Sales Data Dashboard')
-
-# Initialize session state for storing which card was clicked and item type
+import time
+import dask.dataframe as dd
+
+@st.cache_data
+def date_from_week(year, week):
+    # Assuming the fiscal year starts in August and the week starts from August 1st
+    base_date = pd.to_datetime((year - 1).astype(str) + '-08-01')
+    dates = base_date + pd.to_timedelta((week - 1) * 7, unit='days')
+    return dates
+
+@st.cache_data
+def load_data(active_card):
+    # st.write(f"{active_card}")
+    # Define columns common to multiple cards if there are any
+    common_cols = ['FyWeek', 'Itemtype', 'Chaincode', 'State', 'SalesVolume', 'UnitPrice', 'Sales']
+
+    # Columns specific to cards
+    card_specific_cols = {
+        'card1': ['FyWeek', 'State', 'Itemtype', 'Chaincode', 'SalesVolume'],
+        'card2': ['FyWeek', 'Fy', 'State','Store','Address','Zipcode','City','Itemtype', 'Chaincode', 'Containercode', 'SalesVolume', 'UnitPrice', 'Sales'],
+    }
+
+    # Choose columns based on the active card
+    required_columns = card_specific_cols.get(active_card, common_cols)
+
+    # Define the data types for efficient memory usage
+    dtype_spec = {
+        'FyWeek': 'string',
+        'Fy': 'category',  # Add data type for 'Fy' if it's used
+        'Itemtype': 'category',
+        'Chaincode': 'category',
+        'State': 'category',
+        "Store": "category", 
+        'Containercode': 'category',
+        "Address": "string",
+        "Zipcode": "float",
+        "City": "category", 
+        'SalesVolume': 'float',
+        'UnitPrice': 'float',
+        'Sales': 'float'
+    }
+
+    # Read only the necessary columns
+    # st.write(required_columns)
+    ddf = dd.read_csv("fy21-24.csv", usecols=required_columns, dtype=dtype_spec)
+    df = ddf.compute()
+
+
+    if active_card in ['card2']:
+        df = df.groupby(['FyWeek', 'Fy', 'Chaincode', 'Store', 'Address', 'Zipcode', 'City', 'State', 'Containercode', 'Itemtype'], observed=True).agg({
+        'SalesVolume': 'sum',
+        'UnitPrice': 'mean',
+        'Sales': 'sum'
+        }).reset_index()
+        df[['FY', 'Week']] = df['FyWeek'].str.split(' Week ', expand=True)
+        df['Week'] = df['Week'].astype(int)  # Convert 'Week' to int
+        df['Year'] = df['FY'].str[2:].astype(int)  # Extract year part and convert to int
+        df['Dt'] = date_from_week(df['Year'], df['Week'])
+
+    # st.write(df.columns)
+    return df
+
+# Display logo
+st.image("bonnie.png", width=150)  # Adjust width as needed
+
+# Display title
+st.title("Bonnie Plants Pricing & Sales Analytics Dashboard")
+
+# Close the div for logo and title
+st.markdown('</div>', unsafe_allow_html=True)
+
+#  Initialize session state for storing which card was clicked and item type
 if 'active_card' not in st.session_state:
     st.session_state['active_card'] = None
 if 'selected_item_type' not in st.session_state:
     st.session_state['selected_item_type'] = 'CORE'  # Set default to 'CORE'
-# Initialize session state for storing the selected state and feature
-if 'selected_state' not in st.session_state:
-    st.session_state['selected_state'] = df['State'].unique()[0]  # Default to the first state
+
 if 'selected_feature' not in st.session_state:
     st.session_state['selected_feature'] = 'Chaincode'  # Default to 'Chain Code'
 
-# Two columns for the card buttons
-col1, col2, col3, col4 = st.columns(4)
-
+# Card selection buttons
+col1, col2 = st.columns(2)
 # Define buttons for plot categories, update session state when clicked
 with col1:
     if st.button("Sales Volume Trend for Item Category"):
@@ -54,67 +100,48 @@ with col2:
     if st.button("Sales Volume & Unit Price Correlation for Item Category and Container Code"):
         st.session_state['active_card'] = 'card2'
 
-with col3:
-    if st.button("Price vs Sales Trend by Year"):
-        st.session_state['active_card'] = 'card3'
+start_time=time.time()
+# st.write(st.session_state['active_card'])
+df = load_data(st.session_state['active_card'])
+time_taken = time.time() - start_time
+st.write(f"Data loaded in {time_taken:.2f} seconds")
 
-with col4:
-    if st.button("Total Sales Volume by Price Band"):
-        st.session_state['active_card'] = 'card4'
 
+# Initialize session state for storing the selected state and feature
+if 'selected_state' not in st.session_state:
+    st.session_state['selected_state'] = df['State'].unique()[0]  # Default to the first state
 
-########################################### CARD #1 ####################################################
+############################################ CARD #1 ####################################################
 if st.session_state['active_card'] == 'card1':
-    # Create short fiscal week display
-    df['Fiscal Year'] = df['FyWeek'].apply(lambda x: int(x.split(' ')[1]))  
-    df['Week Number'] = df['FyWeek'].apply(lambda x: int(x.split('Week ')[1]))
-    df = df.sort_values(by=['Fiscal Year', 'Week Number'])
-
-    # Reformat 'Fiscal Week' for display (e.g., 'FY21W51')
-    df['Fiscal Week Short'] = df.apply(lambda x: f"FY{x['Fiscal Year']%100}W{x['Week Number']}", axis=1)
-
-    # Ensure the short fiscal week column is treated as a categorical variable and sorted by the order of appearance
-    df['Fiscal Week Short'] = pd.Categorical(df['Fiscal Week Short'], categories=df['Fiscal Week Short'].unique(), ordered=True)
-    # Dropdown for selecting the state (using session_state)
-    st.session_state['selected_state'] = st.selectbox('Select State', df['State'].unique(),
-                                                    index=list(df['State'].unique()).index(st.session_state['selected_state']))
-
-    # Dropdown for selecting the feature for grouping (using session_state)
-    st.session_state['selected_feature'] = st.selectbox('Select Feature for Grouping', 
-                                                        ['Chaincode', 'Itemtype', 'FyWeek'], 
-                                                        index=['Chaincode', 'Itemtype', 'FyWeek'].index(st.session_state['selected_feature']))
-
+    # st.write("Processing card1...")
+    # Dropdown for selecting the state
+    selected_state = st.selectbox('Select State', df['State'].unique())
+    # Dropdown for selecting the feature for grouping
+    selected_feature = st.selectbox('Select Feature for Grouping', ['Chaincode', 'Itemtype',])
+    
     # Filter the dataframe based on selected state
-    filtered_df = df[df['State'] == st.session_state['selected_state']]
-
-    # Plot based on user's selected feature
-    if st.session_state['selected_feature'] == 'Itemtype':
-        st.subheader(f'Sales Data for {st.session_state["selected_state"]} - Grouped by Item Type')
-        group_data = filtered_df.groupby(['FyWeek', 'Itemtype'])['SalesVolume'].sum().reset_index()
-        fig = px.bar(group_data, x='FyWeek', y='SalesVolume', color='Itemtype',
-                    title=f'Sales Volume over Fiscal Week in {st.session_state["selected_state"]} by Item Type',
-                    labels={'SalesVolume': 'Sales Volume'})
-
-    elif st.session_state['selected_feature'] == 'Chaincode':
-        st.subheader(f'Sales Data for {st.session_state["selected_state"]} - Grouped by Chain Code')
-        group_data = filtered_df.groupby(['FyWeek', 'Chaincode'])['SalesVolume'].sum().reset_index()
-        fig = px.bar(group_data, x='FyWeek', y='SalesVolume', color='Chaincode',
-                    title=f'Sales Volume over Fiscal Week in {st.session_state["selected_state"]} by Chain Code',
-                    labels={'SalesVolume': 'Sales Volume'})
-
-    elif st.session_state['selected_feature'] == 'FyWeek':
-        st.subheader(f'Sales Data for {st.session_state["selected_state"]} - Grouped by Fiscal Week')
-        group_data = filtered_df.groupby(['FyWeek'])['SalesVolume'].sum().reset_index()
-        fig = px.bar(group_data, x='FyWeek', y='SalesVolume',
-                    title=f'Sales Volume over Fiscal Week in {st.session_state["selected_state"]}',
-                    labels={'SalesVolume': 'Sales Volume'})
-
-    # Display the interactive plot
+    filtered_df = df[df['State'] == selected_state]
+    
+    # Time the grouping operation
+    start_time = time.time()
+    group_data = filtered_df.groupby(['FyWeek', selected_feature],observed=True)['SalesVolume'].sum().reset_index()
+    time_taken = time.time() - start_time
+
+    # Plotting
+    fig = px.bar(group_data, x='FyWeek', y='SalesVolume', color=selected_feature,
+                 title=f'Sales Volume over Fiscal Week in {selected_state} by {selected_feature}',
+                 labels={'SalesVolume': 'Sales Volume', 'Fiscal Week Short': 'Fiscal Week'})
     st.plotly_chart(fig)
+
 ##########################################################################################################
 
+
 ########################################### CARD #2 ####################################################
-# Card 2: Sales Volume & Unit Price Correlation plot for Item Category and Container Code
+
+
+
+
+
 if st.session_state['active_card'] == 'card2':
     # Dropdown to select item type (using session_state)
     st.session_state['selected_item_type'] = st.selectbox(
@@ -124,33 +151,18 @@ if st.session_state['active_card'] == 'card2':
     # Dropdown to select the grouping category (container code, chain code, or state)
     group_by_option = st.selectbox('Group by', ['Containercode', 'Chaincode', 'State'])
 
-    st.subheader(f"Sales Volume & Unit Price Correlation for {group_by_option}")
-
-    # Group the dataframe and prepare for plotting
-    df = df.groupby(['FyWeek', 'Fy', 'Chaincode', 'Store', 'Address', 'Zipcode', 'City', 'State', 'Containercode', 'Itemtype'], observed=True).agg({
-        'SalesVolume': 'sum',
-        'UnitPrice': 'mean',
-        'Sales': 'sum'
-    }).reset_index()
+    # Multi-select checkbox to select multiple years
+    selected_years = st.multiselect('Select Year(s)', [2021, 2022, 2023, 2024], default=[2021])
 
-    # Function to extract date from fiscal week
-    def dt_from_fy_week(fyweek):
-        fy, w = re.findall(r'\d+', fyweek)
-        week1_start = datetime.strptime("{}-08-01".format(int(fy) - 1), "%Y-%m-%d")
-        return (week1_start + timedelta(weeks=int(w) - 1)).date()
+    st.subheader(f"Sales Volume & Unit Price Correlation for {group_by_option} in {', '.join(map(str, selected_years))}")
 
-    # Add columns for date and promo to data
-    df['Dt'] = df['FyWeek'].apply(dt_from_fy_week)
+    # Convert 'Dt' column to datetime
     df['Dt'] = pd.to_datetime(df['Dt'], errors='coerce')
     df['Promo'] = np.where(df['Dt'].dt.month.astype(str).isin(['3', '4', '5', '6']), 'Promo', 'NoPromo')
     df["Promo"] = df["Promo"].astype("category")
-    
-    # Split FyWeek into fiscal year and week number
-    df['Week'] = df['FyWeek'].str.split().str[-1].astype(int)
-    df['Year'] = df['FyWeek'].str.split().str[1].astype(int)
 
-    # Filter the dataframe based on the selected item type
-    filtered_df = df[df['Itemtype'] == st.session_state['selected_item_type']]
+    # Filter the dataframe based on the selected item type and selected years
+    filtered_df = df[(df['Itemtype'] == st.session_state['selected_item_type']) & (df['Dt'].dt.year.isin(selected_years))]
 
     # Find the top 3 values based on total SalesVolume in the selected grouping category
     top_3_values = filtered_df.groupby(group_by_option, observed=True)['SalesVolume'].sum().nlargest(3).index
@@ -158,183 +170,78 @@ if st.session_state['active_card'] == 'card2':
     # Filter the data for only the top 3 values
     top_group_data = filtered_df[filtered_df[group_by_option].isin(top_3_values)]
 
-    # Group by Year, Week, Dt, and the selected category and aggregate SalesVolume and UnitPrice
+    # Aggregate data
     agg_df = top_group_data.groupby([group_by_option, 'Year', 'Week', 'Dt'], observed=True).agg({
         'SalesVolume': 'sum',
         'UnitPrice': 'mean'
     }).reset_index()
 
-    # Loop through the top 3 values and create separate plots
+    # Create a new column 'week-year' for X-axis labels
+    agg_df['week-year'] = agg_df['Dt'].dt.strftime('%U-%Y')
+
+    # Loop through the top 3 values and create separate plots using Plotly
     for value in top_3_values:
         value_data = agg_df[agg_df[group_by_option] == value]
+        # Assuming you have 'value_data' from your previous code
+        mean_sales_volume = value_data['SalesVolume'].mean()
+        mean_unit_price = value_data['UnitPrice'].mean()
+
+        # Create a Plotly figure
+        fig = go.Figure()
+
+        # Add SalesVolume trace
+        fig.add_trace(go.Scatter(
+            x=value_data['week-year'],
+            y=value_data['SalesVolume'],
+            mode='lines+markers',
+            name='SalesVolume',
+            line=dict(color='blue'),
+            hovertemplate='SalesVolume: %{y}<br>Week-Year: %{x}'
+        ))
+
+        # Add UnitPrice trace on a secondary Y-axis
+        fig.add_trace(go.Scatter(
+            x=value_data['week-year'],
+            y=value_data['UnitPrice'],
+            mode='lines+markers',
+            name='UnitPrice',
+            line=dict(color='green'),
+            yaxis='y2',
+            hovertemplate='UnitPrice: %{y}<br>Week-Year: %{x}'
+        ))
+        # Add mean line for SalesVolume
+        fig.add_shape(type="line",
+                    x0=value_data['week-year'].min(), x1=value_data['week-year'].max(),
+                    y0=mean_sales_volume, y1=mean_sales_volume,
+                    line=dict(color="blue", width=2, dash="dash"),
+                    xref='x', yref='y')
+
+        # Add mean line for UnitPrice (on secondary Y-axis)
+        fig.add_shape(type="line",
+                    x0=value_data['week-year'].min(), x1=value_data['week-year'].max(),
+                    y0=mean_unit_price, y1=mean_unit_price,
+                    line=dict(color="green", width=2, dash="dash"),
+                    xref='x', yref='y2')
+
+        # Update layout for dual axes
+        fig.update_layout(
+            template='plotly_white',
+            title=f"SalesVolume and UnitPrice - {value} ({group_by_option})",
+            xaxis_title='Week-Year',
+            yaxis_title='Sales Volume',
+            yaxis2=dict(title='UnitPrice', overlaying='y', side='right'),
+            legend=dict(x=0.9, y=1.15),
+            hovermode="x unified",  # Show both values in a tooltip
+            height=600,
+            margin=dict(l=50, r=50, t=50, b=50)
+        )
+
+        # Rotate X-axis labels
+        fig.update_xaxes(tickangle=90)
+
+        # Display the Plotly figure in Streamlit
+        st.plotly_chart(fig, use_container_width=True)
 
-        # Create a new figure for each group
-        fig, (axd, axp) = plt.subplots(2, 1, figsize=(10, 6))
 
-        # Plot SalesVolume
-        sns.lineplot(data=value_data, x='Dt', y='SalesVolume', ax=axd)
-        axd.set_title(f"SalesVolume - {value} ({group_by_option})")
-        axd.grid(True, linestyle='--', color='gray', alpha=0.7)
 
-        # Plot mean line for SalesVolume
-        axd.axhline(value_data['SalesVolume'].mean(), ls="--", color="r")
-        axd.xaxis.set_major_locator(mdates.MonthLocator(interval=2))
-        axd.set_xticklabels([])
-
-        # Plot UnitPrice
-        sns.lineplot(data=value_data, x='Dt', y='UnitPrice', ax=axp, color='green', errorbar='sd')
-        axp.set_title(f"UnitPrice - {value} ({group_by_option})")
-        axp.grid(True, linestyle='--', color='gray', alpha=0.7)
-
-        # Plot mean line for UnitPrice
-        axp.axhline(value_data['UnitPrice'].mean(), ls="--", color="r")
-        axp.xaxis.set_major_locator(mdates.MonthLocator(interval=2))
-        axp.tick_params(axis='x', rotation=90)
-
-        # Adjust layout for each figure
-        plt.tight_layout()
-
-        # Display the plot in Streamlit
-        st.pyplot(fig)
-###############################################################################################
-
-########################################### CARD #3 ####################################################
-# Check which card was selected using session state
-if st.session_state['active_card'] == 'card3':
-    # Dropdown for selecting the Item Type
-    st.session_state['selected_item_type'] = st.selectbox('Select Item Type', df['Itemtype'].unique(), 
-                                                          index=list(df['Itemtype'].unique()).index(st.session_state['selected_item_type']))
-    df = df.groupby(['FyWeek','Fy','Chaincode','Store','Address','Zipcode','City','State','Containercode','Itemtype'],observed=True).agg({
-    'SalesVolume': 'sum',
-    'UnitPrice':'mean',
-    'Sales': 'sum'}).reset_index()
-    # add promo and date columns to data
-    def dt_from_fy_week(fyweek):
-
-        fy, w = re.findall(r'\d+', fyweek)
-
-        week1_start = datetime.strptime("{}-08-01".format(int(fy)-1), "%Y-%m-%d")
-
-        return (week1_start + timedelta(weeks=int(w)-1)).date()
-
-    df['Dt'] = df['FyWeek'].apply(dt_from_fy_week)
-    df['Dt'] = pd.to_datetime(df['Dt'], errors='coerce')
-    df['Promo'] = np.where(df['Dt'].dt.month.astype(str).isin(['3','4','5','6']) , 'Promo', 'NoPromo')
-    df["Promo"] = df["Promo"].astype("category")
-    # Split FyWeek into fiscal year and week number
-    df['Week'] = df['FyWeek'].str.split().str[-1].astype(int)
-    # df_21['Year'] = df_21['Fy'].str.extract(r'(\d+)').astype(int)
-    df['Year'] = df['FyWeek'].str.split().str[1].astype(int)
-
-    # Define the fiscal years
-    years = ["FY 2021", "FY 2022", "FY 2023", "FY 2024"]
-
-    # Set up a 2x2 grid of subplots for the four years
-    fig, axs = plt.subplots(2, 2, figsize=(12, 8))
-
-    # Loop through each year and create a plot in the grid
-    for i, fy in enumerate(years):
-        ax = axs.flat[i]
-
-        # Plot Promo data
-        sns.regplot(data=df[(df["Itemtype"] == st.session_state['selected_item_type']) & (df["Fy"] == fy) & (df["Promo"] == "Promo")],
-                    x="UnitPrice", y="SalesVolume", lowess=True, ci=None, marker='.', line_kws=dict(color="r"), ax=ax, label="Promo")
-
-        # Plot NoPromo data
-        sns.regplot(data=df[(df["Itemtype"] == st.session_state['selected_item_type']) & (df["Fy"] == fy) & (df["Promo"] == "NoPromo")],
-                    x="UnitPrice", y="SalesVolume", lowess=True, ci=None, marker='x', line_kws=dict(color="g"), ax=ax, label="NoPromo")
-
-        # Set the title of each subplot
-        ax.set_title(f"{st.session_state['selected_item_type']} - {fy}")
-        ax.legend(loc="best")
-
-    # Set the overall title for the figure
-    fig.suptitle(f"Price vs SalesVolume for {st.session_state['selected_item_type']} across years")
-
-    # Adjust layout to prevent overlap
-    fig.tight_layout(rect=[0, 0, 1, 0.95])
-
-    # Display the plot
-    st.pyplot(fig)
-
-###############################################################################################
-
-########################################### CARD #4 ####################################################
-if st.session_state['active_card'] == 'card4':
-    # Define the fiscal years
-    years = ['FY 2021', 'FY 2022', 'FY 2023', 'FY 2024']
-    df = df.groupby(['FyWeek','Fy','Chaincode','Store','Address','Zipcode','City','State','Containercode','Itemtype'],observed=True).agg({
-    'SalesVolume': 'sum',
-    'UnitPrice':'mean',
-    'Sales': 'sum'}).reset_index()
-
-    # Dropdown for selecting the Item Type (using session_state)
-    st.session_state['selected_item_type'] = st.selectbox('Select Item Type', 
-                                                                df['Itemtype'].unique(),
-                                                                index=list(df['Itemtype'].unique()).index(st.session_state['selected_item_type']))
-
-    # Set up a 2x2 grid of subplots for the four years
-    fig, axes = plt.subplots(2, 2, figsize=(16, 12))
-    axes = axes.flatten()  # To access axes easily in a loop
-
-    # Loop through each year and plot the data
-    for i, year in enumerate(years):
-        # print(st.session_state['selected_item_type'])
-        # Filter data for the specific year and item type selected
-        cage_data = df[(df['Itemtype'] == st.session_state['selected_item_type']) & (df['Fy'] == year)]
-        cage_data['Itemtype'] = cage_data['Itemtype'].cat.remove_unused_categories()
-        cage_data['Containercode'] = cage_data['Containercode'].cat.remove_unused_categories()
-        # print(cage_data['Itemtype'].unique())
-        relevant_container_codes = cage_data['Containercode'].unique()
-        print(relevant_container_codes)
-        # Calculate price bands
-        lower_band = cage_data['UnitPrice'].quantile(0.25)
-        median_band = cage_data['UnitPrice'].quantile(0.50)
-        higher_band = cage_data['UnitPrice'].quantile(0.75)
-
-        # Get data for each price band
-        lower_band_data = cage_data[cage_data['UnitPrice'] <= lower_band]
-        median_band_data = cage_data[(cage_data['UnitPrice'] > lower_band) & (cage_data['UnitPrice'] <= median_band)]
-        higher_band_data = cage_data[cage_data['UnitPrice'] > higher_band]
-        # print(lower_band_data['Containercode'].unique())
-        # Aggregate SalesVolume and average UnitPrice for each container code in each pricing band
-        lower_band_agg = lower_band_data.groupby('Containercode',observed=True).agg(
-            total_sales_volume=('SalesVolume', 'sum'),
-            avg_unit_price=('UnitPrice', 'mean')
-        ).reset_index()
-
-        median_band_agg = median_band_data.groupby('Containercode',observed=True).agg(
-            total_sales_volume=('SalesVolume', 'sum'),
-            avg_unit_price=('UnitPrice', 'mean')
-        ).reset_index()
-
-        higher_band_agg = higher_band_data.groupby('Containercode',observed=True).agg(
-            total_sales_volume=('SalesVolume', 'sum'),
-            avg_unit_price=('UnitPrice', 'mean')
-        ).reset_index()
-
-        # Add the price band labels
-        lower_band_agg['PriceBand'] = 'Lower Band'
-        median_band_agg['PriceBand'] = 'Median Band'
-        higher_band_agg['PriceBand'] = 'Higher Band'
-
-        # Combine the data for plotting
-        combined_data = pd.concat([lower_band_agg, median_band_agg, higher_band_agg])
-        combined_data = combined_data[combined_data['Containercode'].isin(relevant_container_codes)]  
-
-        # Plot Total Sales Volume for each price band in the current subplot
-        sns.barplot(x='Containercode', y='total_sales_volume', hue='PriceBand', data=combined_data, ax=axes[i])
-
-        # Set the title and customize x-axis for each subplot
-        axes[i].set_title(f"Total Sales Volume by Container Code and Price Band for {year}")
-        axes[i].set_xlabel('Container Code')
-        axes[i].set_ylabel('Total Sales Volume')
-        axes[i].tick_params(axis='x', rotation=45)
-
-    # Adjust the layout so titles and labels don't overlap
-    plt.tight_layout()
-
-    # Display the plot in Streamlit
-    st.pyplot(fig)
-
-###############################################################################################
+##########################################################################################################