Hugging Face's logo

Spaces:
milindkamat0507
/
STTutorial
Sleeping

App Files Community
STTutorial
File size: 4,653 Bytes 
a97cd9f
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
 
import streamlit as st
import pandas as pd
import numpy as np
import plotly.graph_objects as go
from datetime import datetime, timedelta

st.set_page_config(layout="wide", page_title="Business Analytics Dashboard Tutorial")

# Sample business data generation
def generate_sales_data():
    dates = pd.date_range(start='2024-01-01', end='2024-12-31', freq='D')
    np.random.seed(42)
    
    df = pd.DataFrame({
        'Date': dates,
        'Sales': np.random.normal(1000, 200, len(dates)),
        'Region': np.random.choice(['North', 'South', 'East', 'West'], len(dates)),
        'Product': np.random.choice(['Electronics', 'Clothing', 'Food', 'Books'], len(dates)),
        'Customer_Type': np.random.choice(['Retail', 'Wholesale'], len(dates)),
    })
    df['Profit'] = df['Sales'] * np.random.uniform(0.15, 0.25, len(df))
    return df

# Main Navigation
st.title("📊 Business Analytics & Data Analysis Tutorial")

# Generate sample data
df_sales = generate_sales_data()

# Dashboard filters
col1, col2, col3 = st.columns(3)
with col1:
    selected_region = st.multiselect(
        "Select Region",
        df_sales['Region'].unique(),
        default=df_sales['Region'].unique()[0]
    )
with col2:
    date_range = st.date_input(
        "Select Date Range",
        value=(df_sales['Date'].min(), df_sales['Date'].max())
    )
with col3:
    product_type = st.selectbox(
        "Select Product",
        ['All'] + list(df_sales['Product'].unique())
    )

# Filter data based on selections
mask = (df_sales['Region'].isin(selected_region)) & \
       (df_sales['Date'] >= pd.Timestamp(date_range[0])) & \
       (df_sales['Date'] <= pd.Timestamp(date_range[1]))

if product_type != 'All':
    mask &= (df_sales['Product'] == product_type)

filtered_df = df_sales[mask]

# KPI Metrics
st.subheader("Key Performance Indicators")
kpi1, kpi2, kpi3, kpi4 = st.columns(4)

with kpi1:
    st.metric(
        "Total Sales",
        f"${filtered_df['Sales'].sum():,.0f}",
        f"{((filtered_df['Sales'].sum() / df_sales['Sales'].sum()) - 1) * 100:.1f}%"
    )
with kpi2:
    st.metric(
        "Average Daily Sales",
        f"${filtered_df['Sales'].mean():,.0f}",
        f"{((filtered_df['Sales'].mean() / df_sales['Sales'].mean()) - 1) * 100:.1f}%"
    )
with kpi3:
    st.metric(
        "Total Profit",
        f"${filtered_df['Profit'].sum():,.0f}",
        f"{((filtered_df['Profit'].sum() / df_sales['Profit'].sum()) - 1) * 100:.1f}%"
    )
with kpi4:
    st.metric(
        "Profit Margin",
        f"{(filtered_df['Profit'].sum() / filtered_df['Sales'].sum() * 100):.1f}%"
    )

# Sales Trends
st.subheader("Sales Trends Analysis")
daily_sales = filtered_df.groupby('Date')[['Sales', 'Profit']].sum().reset_index()

# Create the figure ensuring dates are in datetime format
fig = go.Figure()

fig.add_trace(go.Scatter(
    x=daily_sales['Date'].dt.strftime('%Y-%m-%d'),  # Convert to string format
    y=daily_sales['Sales'],
    name='Sales',
    line=dict(color='blue')
))

fig.add_trace(go.Scatter(
    x=daily_sales['Date'].dt.strftime('%Y-%m-%d'),  # Convert to string format
    y=daily_sales['Profit'],
    name='Profit',
    line=dict(color='green')
))

fig.update_layout(
    title='Daily Sales and Profit Trends',
    xaxis_title='Date',
    yaxis_title='Amount ($)',
    xaxis=dict(
        type='category',  # Use category type for x-axis
        tickangle=45
    )
)

st.plotly_chart(fig, use_container_width=True)

# Regional Analysis
st.subheader("Regional Performance")
regional_data = filtered_df.groupby('Region').agg({
    'Sales': 'sum',
    'Profit': 'sum'
}).reset_index()

fig_region = go.Figure(data=[
    go.Bar(name='Sales', x=regional_data['Region'], y=regional_data['Sales']),
    go.Bar(name='Profit', x=regional_data['Region'], y=regional_data['Profit'])
])

fig_region.update_layout(
    barmode='group',
    title='Sales and Profit by Region'
)

st.plotly_chart(fig_region, use_container_width=True)

# Product Analysis
if product_type == 'All':
    st.subheader("Product Performance")
    product_data = filtered_df.groupby('Product').agg({
        'Sales': 'sum',
        'Profit': 'sum'
    }).reset_index()

    fig_product = go.Figure(data=[
        go.Bar(name='Sales', x=product_data['Product'], y=product_data['Sales']),
        go.Bar(name='Profit', x=product_data['Product'], y=product_data['Profit'])
    ])

    fig_product.update_layout(
        barmode='group',
        title='Sales and Profit by Product'
    )

    st.plotly_chart(fig_product, use_container_width=True)

# Footer
st.markdown("---")
st.markdown(f"Dashboard last updated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")