Add Sales Data Analysis app to community_gallery

community_gallery/sales_data/README.md

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+# 📊 Sales Data Analysis App
+
+This is an interactive data analysis app. It allows users to explore and visualize sales data through an intuitive web-based interface.
+
+## 🔧 Features
+
+- **Dynamic Table Viewer**: Use a slider to control how many rows of the dataset to preview.
+- **Interactive Histogram**: Select between `Quantity_Sold` or `Sales_Amount` to visualize their distributions.
+- **Sales Filtering**: Toggle a checkbox to view only high-performing sales (Sales Amount > 5000).
+- **Scatter Plot Visualization**: Explore the relationship between `Quantity_Sold` and `Sales_Amount`, colored by product category.
+- **Error Handling**: Gracefully handles missing or misconfigured datasets with alerts.
+
+## 🧾 Dataset Description
+
+This app expects a dataset named `sales_data.csv` with the following columns:
+
+| Column                | Description                          |
+|-----------------------|--------------------------------------|
+| `Product_ID`          | Product identifier                   |
+| `Sale_Date`           | Date of the sale                     |
+| `Sales_Rep`           | Sales representative name            |
+| `Region`              | Sales region                         |
+| `Sales_Amount`        | Total revenue from the sale          |
+| `Quantity_Sold`       | Units sold                           |
+| `Product_Category`    | Category (e.g., Furniture, Food)     |
+| `Unit_Cost`           | Cost per unit                        |
+| `Unit_Price`          | Selling price per unit               |
+| `Customer_Type`       | Customer status (New or Returning)   |
+| `Discount`            | Discount applied (e.g., 0.08 = 8%)   |
+| `Payment_Method`      | Cash, Credit Card, Bank Transfer...  |
+| `Sales_Channel`       | Online or Retail                     |
+| `Region_and_Sales_Rep`| Combined label for visualizations    |
+
+

community_gallery/sales_data/data/sample.csv

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+item,quantity,value
+Item A,10,100
+Item B,5,60
+Item C,8,80
+Item D,12,150
+Item E,7,90
+Item F,9,110
+Item G,4,50
+Item H,11,130
+Item I,6,70
+Item J,3,40

community_gallery/sales_data/hello.py

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+from preswald import connect, get_df, table, text, plotly, slider, selectbox, separator, alert, checkbox
+import plotly.express as px
+
+# Connect to Preswald data sources
+connect()
+df = get_df("sales_data")  # Load dataset
+
+# Display title
+text("# Sales Data Analysis App")
+
+# Check if df is None before proceeding
+if df is None:
+    alert("Error: Dataset 'sales_data' not found. Ensure it is correctly configured in preswald.toml.", type="error")
+else:
+    # Debugging: Print available columns
+    # table(df.head())
+    text(f"Columns in dataset: {', '.join(df.columns)}")
+
+    # Ensure DataFrame is loaded and contains required columns
+    if not df.empty and all(col in df.columns for col in ['Quantity_Sold', 'Sales_Amount', 'Product_ID', 'Product_Category']):
+
+        separator()  # Add visual break
+
+        # Add slider to control number of rows displayed
+        num_rows = slider("Select number of rows to display", min_val=5, max_val=50, step=5, default=10)
+        table(df.head(num_rows))
+
+        separator()  # Add another visual break
+
+        # Add selectbox for column selection
+        column_choice = selectbox("Choose a column to visualize", options=['Quantity_Sold', 'Sales_Amount'])
+
+        # Display histogram based on selected column
+        fig_hist = px.histogram(df, x=column_choice, title=f"Distribution of {column_choice}")
+        plotly(fig_hist)
+
+        separator()  # Add another visual break
+
+        # Add checkbox for toggling between full dataset and filtered dataset
+        show_filtered = checkbox("Show only high sales data (Sales Amount > 5000)")
+
+        if show_filtered:
+            df_filtered = df[df['Sales_Amount'] > 5000]
+            table(df_filtered.head(num_rows))
+        else:
+            table(df.head(num_rows))
+
+        # Create scatter plot with hover labels (removing static text labels)
+        fig = px.scatter(df, x='Quantity_Sold', y='Sales_Amount', hover_name='Product_ID',
+                         color='Product_Category',
+                         title='Quantity Sold vs. Sales Amount',
+                         labels={'Quantity_Sold': 'Quantity Sold', 'Sales_Amount': 'Sales Amount'})
+
+        # Update traces for better readability
+        fig.update_traces(marker=dict(size=6, opacity=0.6))
+
+        # Style the plot
+        fig.update_layout(template='plotly_white')
+
+        # Show the plot
+        plotly(fig)
+
+    else:
+        alert("Error: Data failed to load or missing required columns. Please check the dataset.", type="error")

community_gallery/sales_data/preswald.toml

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+[project]
+title = "Preswald Project"
+version = "0.1.0"
+port = 8501
+slug = "my-project-262319"  # Required: Unique identifier for your project
+entrypoint = "hello.py"    # Required: Main script to run when executing preswald run
+
+[branding]
+name = "Preswald Project"
+logo = "images/logo.png"
+favicon = "images/favicon.ico"
+primaryColor = "#F89613"
+
+[data.sales_data]
+type = "csv"
+path = "data/sales_data.csv"
+
+[logging]
+level = "INFO"  # Options: DEBUG, INFO, WARNING, ERROR, CRITICAL
+format = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"

community_gallery/sales_data/pyproject.toml

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+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[project]
+name = "preswald-app"
+version = "0.1.0"
+description = "A Preswald application"
+requires-python = ">=3.8"
+dependencies = [
+    "preswald"
+]
+
+[tool.hatch.build.targets.wheel]
+packages = ["."]
+
+[tool.black]
+line-length = 88
+target-version = ['py38']
+
+[tool.isort]
+profile = "black"
+multi_line_output = 3

community_gallery/sales_data/readme.md

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+# 📊 Sales Data Analysis App
+
+This is an interactive data analysis app. It provides visualizations and controls for exploring sales performance data, including quantities sold, sales amounts, product categories, and more.
+
+## 🔧 Features
+
+- **Dynamic Table Viewer**: Use a slider to control how many rows of the dataset to preview.
+- **Interactive Histogram**: Select between `Quantity_Sold` or `Sales_Amount` to visualize their distributions.
+- **Sales Filtering**: Toggle a checkbox to view only high-performing sales (Sales Amount > 5000).
+- **Scatter Plot Visualization**: Explore the relationship between `Quantity_Sold` and `Sales_Amount`, colored by product category.
+- **Error Handling**: Gracefully handles missing or misconfigured datasets with alerts.
+
+## 📁 Dataset Requirements
+
+
+| Column              | Description                         |
+|---------------------|-------------------------------------|
+| `Product_ID`        | Product identifier                  |
+| `Sale_Date`         | Date of the sale                    |
+| `Sales_Rep`         | Sales representative name           |
+| `Region`            | Sales region                        |
+| `Sales_Amount`      | Total revenue from the sale         |
+| `Quantity_Sold`     | Units sold                          |
+| `Product_Category`  | Category (e.g., Furniture, Food)     |
+| `Unit_Cost`         | Cost per unit                       |
+| `Unit_Price`        | Selling price per unit              |
+| `Customer_Type`     | Customer status (New or Returning)  |
+| `Discount`          | Discount applied (e.g., 0.08 = 8%)  |
+| `Payment_Method`    | Cash, Credit Card, Bank Transfer... |
+| `Sales_Channel`     | Online or Retail                    |
+| `Region_and_Sales_Rep` | Combined label for visualizations |
+
+

community_gallery/sales_data/secrets.toml

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+[data.postgres]
+password = ""
+