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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)
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