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| import streamlit as st | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| import plotly.express as px | |
| 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"}) | |
| df["Postal Code"] = df["Postal Code"].convert_dtypes() | |
| df["units sold"] = df["units sold"].convert_dtypes() | |
| # 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 | |
| # Sort the DataFrame by fiscal year and week number | |
| 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) | |
| # df['Fiscal Week'] = df['Fiscal Week'].apply(lambda x: x.replace('FY 20', 'FY').replace('Week ', 'W')) | |
| # Sort by 'Fiscal Week' | |
| # df = df.sort_values(by='Fiscal Week') | |
| st.title('Sales Data Dashboard') | |
| state = st.selectbox('Select State', df['State'].unique()) | |
| feature = st.selectbox('Select Feature for Grouping', ['Chain Code', 'Sales Item Category', 'Fiscal Week']) | |
| # Filter the dataframe based on selections | |
| filtered_df = df[df['State'] == state] | |
| # 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'}) | |
| 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'}) | |
| print(df.head(5)) | |
| # Display the interactive plot | |
| st.plotly_chart(fig) | |