Update app.py

app.py

@@ -1,70 +1,338 @@
 import streamlit as st
 import pandas as pd
+import numpy as np
 import matplotlib.pyplot as plt
+import seaborn as sns
+import matplotlib.dates as mdates
 import plotly.express as px
+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')
 
-df = pd.read_csv(r'FY2021_merged_file.csv', dtype={"Fiscal Week": "string", 
-                                            "Fiscal Year": "category", 
-                                            "Chain Code": "category", 
-                                            "Store": "category", 
-                                            "Address": "string", 
-                                            "Postal Code": "float", 
-                                            "City": "category", 
-                                            "State": "category", 
-                                            "Container Code": "category", 
-                                            "Sales Item Category": "category", 
-                                            "units sold":"float", 
-                                            "SalePrice":"float", 
-                                            "sales $":"float"})
+# 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'
 
-df["Postal Code"] = df["Postal Code"].convert_dtypes()
-df["units sold"] = df["units sold"].convert_dtypes()
+# Two columns for the card buttons
+col1, col2, col3, col4 = st.columns(4)
 
-# Extract fiscal year and week from the 'Fiscal Week' column for sorting
-df['Fiscal Year'] = df['Fiscal Week'].apply(lambda x: int(x.split(' ')[1]))  # Extract year as an integer
-df['Week Number'] = df['Fiscal Week'].apply(lambda x: int(x.split('Week ')[1]))  # Extract week as an integer
+# Define buttons for plot categories, update session state when clicked
+with col1:
+    if st.button("Sales Volume Trend for Item Category"):
+        st.session_state['active_card'] = 'card1'
 
-# Sort the DataFrame by fiscal year and week number
-df = df.sort_values(by=['Fiscal Year', 'Week Number'])
+with col2:
+    if st.button("Sales Volume & Unit Price Correlation for Item Category and Container Code"):
+        st.session_state['active_card'] = 'card2'
 
-# 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)
+with col3:
+    if st.button("Price vs Sales Trend by Year"):
+        st.session_state['active_card'] = 'card3'
 
-# 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)
+with col4:
+    if st.button("Total Sales Volume by Price Band"):
+        st.session_state['active_card'] = 'card4'
 
 
-# df['Fiscal Week'] = df['Fiscal Week'].apply(lambda x: x.replace('FY 20', 'FY').replace('Week ', 'W'))
+########################################### 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'])
 
-# Sort by 'Fiscal Week'
-# df = df.sort_values(by='Fiscal Week')
+    # 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)
 
-st.title('Sales Data Dashboard')
+    # 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']))
+
+    # 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
+    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('Select Item Type', 
+                                                                df['Itemtype'].unique(),
+                                                                index=list(df['Itemtype'].unique()).index(st.session_state['selected_item_type']))
+
+    st.subheader("Sales Volume & Unit Price Correlation for Container Codes")
+
+    # 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()
+
+    # 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()
+
+    # Add columns for date and promo to data
+    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['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']]
+
+    # Find the top 3 container codes based on total SalesVolume
+    top_3_containers = filtered_df.groupby('Containercode', observed=True)['SalesVolume'].sum().nlargest(3).index
+
+    # Filter the data for only the top 3 container codes
+    top_container_data = filtered_df[filtered_df['Containercode'].isin(top_3_containers)]
+
+    # Group by Year, Week, Dt, and Containercode and aggregate SalesVolume and UnitPrice
+    agg_df = top_container_data.groupby(['Containercode', 'Year', 'Week', 'Dt'], observed=True).agg({
+        'SalesVolume': 'sum',
+        'UnitPrice': 'mean'
+    }).reset_index()
+
+    # Loop through the top 3 container codes and create separate plots
+    for container in top_3_containers:
+        container_data = agg_df[agg_df['Containercode'] == container]
+
+        # Create a new figure for each container code
+        fig, (axd, axp) = plt.subplots(2, 1, figsize=(10, 6))
+
+        # Plot SalesVolume
+        sns.lineplot(data=container_data, x='Dt', y='SalesVolume', ax=axd)
+        axd.set_title(f"SalesVolume - {container}")
+        axd.grid(True, linestyle='--', color='gray', alpha=0.7)
+
+        # Plot mean line for SalesVolume
+        axd.axhline(container_data['SalesVolume'].mean(), ls="--", color="r")
+        axd.xaxis.set_major_locator(mdates.MonthLocator(interval=2))
+        axd.set_xticklabels([])
+
+        # Plot UnitPrice
+        sns.lineplot(data=container_data, x='Dt', y='UnitPrice', ax=axp, color='green', errorbar='sd')
+        axp.set_title(f"UnitPrice - {container}")
+        axp.grid(True, linestyle='--', color='gray', alpha=0.7)
+
+        # Plot mean line for UnitPrice
+        axp.axhline(container_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)]  
 
-state = st.selectbox('Select State', df['State'].unique())
-feature = st.selectbox('Select Feature for Grouping', ['Chain Code', 'Sales Item Category', 'Fiscal Week'])
+        # 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])
 
-# Filter the dataframe based on selections
-filtered_df = df[df['State'] == state]
+        # 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)
 
-# Plot based on user's selection
-if feature == 'Sales Item Category':
-    st.subheader(f'Sales Data for {state} - Grouped by Sales Item Category')
-    group_data = filtered_df.groupby(['Fiscal Week Short', 'Sales Item Category'])['units sold'].sum().reset_index()
-    fig = px.bar(group_data, x='Fiscal Week Short', y='units sold', color='Sales Item Category',
-                 title=f'Units Sold over Fiscal Week in {state} by Sales Item Category',
-                 labels={'Units Sold': 'Units Sold'})
+    # Adjust the layout so titles and labels don't overlap
+    plt.tight_layout()
 
-elif feature == 'Chain Code':
-    st.subheader(f'Sales Data for {state} - Grouped by Chain Code')
-    group_data = filtered_df.groupby(['Fiscal Week Short', 'Chain Code'])['units sold'].sum().reset_index()
-    fig = px.bar(group_data, x='Fiscal Week Short', y='units sold', color='Chain Code',
-                 title=f'Units Sold over Fiscal Week in {state} by Chain Code',
-                 labels={'Units Sold': 'Units Sold'})
+    # Display the plot in Streamlit
+    st.pyplot(fig)
 
-print(df.head(5))
-# Display the interactive plot
-st.plotly_chart(fig)
+###############################################################################################