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Author: f3ax

data_science_tools/SQL-DEMO.py

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+import marimo
+
+__generated_with = "0.12.8"
+app = marimo.App(width="medium")
+
+
+@app.cell
+def _():
+    import marimo as mo
+    import pandas as pd
+    import random
+
+    # Create a simple dataset
+    data = {
+        'product': ['Widget A', 'Widget B', 'Widget C', 'Widget D', 'Widget E'],
+        'category': ['Electronics', 'Office', 'Electronics', 'Kitchen', 'Office'],
+        'price': [random.randint(10, 100) for _ in range(5)],
+        'quantity': [random.randint(1, 50) for _ in range(5)]
+    }
+
+    # Create a pandas DataFrame
+    products_df = pd.DataFrame(data)
+
+    # Display the dataframe
+    products_df
+    return data, mo, pd, products_df, random
+
+
+@app.cell
+def _():
+    select_price = 30
+    return (select_price,)
+
+
+@app.cell
+def _(mo, products_df, select_price):
+    # Using SQL to query our products dataframe
+    result = mo.sql(f"SELECT * FROM products_df WHERE price > {select_price}")
+    # Display the result
+    result
+    return (result,)
+
+
+@app.cell
+def _(mo, products_df):
+    # Create a dropdown for category selection
+    category_filter = mo.ui.dropdown(
+        products_df['category'].unique().tolist(),
+        label="Filter by category",
+        value=products_df['category'].unique().tolist()[0]
+    )
+
+    # Display the dropdown
+    category_filter
+    return (category_filter,)
+
+
+@app.cell
+def _(category_filter, mo, products_df):
+    # Query that reacts to the dropdown selection
+    filtered_result = mo.sql(f"""
+        -- This query will update automatically when the dropdown value changes
+        SELECT * FROM products_df 
+        WHERE category = '{category_filter.value}'
+    """)
+
+    # Display the filtered results
+    filtered_result
+    return (filtered_result,)
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    import matplotlib.pyplot as plt
+
+    # Create a price range slider
+    price_range = mo.ui.slider(
+        0, 
+        100, 
+        value=50, 
+        label="Maximum price"
+    )
+
+    # Display the slider
+    price_range
+    return plt, price_range
+
+
+@app.cell(hide_code=True)
+def _(mo, plt, price_range, products_df):
+    # Query with price filter
+    price_filtered = mo.sql(f"""
+        SELECT * FROM products_df 
+        WHERE price <= {price_range.value}
+    """)
+
+    # Create a visualization
+    plt.figure(figsize=(10, 5))
+    plt.bar(price_filtered['product'], price_filtered['price'])
+    plt.xlabel('Product')
+    plt.ylabel('Price ($)')
+    plt.title(f'Products with price <= ${price_range.value}')
+    plt.xticks(rotation=45)
+    plt.gcf()
+
+    return (price_filtered,)
+
+
+@app.cell
+def _(cars, mo):
+    # Query external CSV file directly
+    cars = mo.sql("""
+        -- Download a CSV and create an in-memory table
+        CREATE OR REPLACE TABLE cars AS
+        FROM 'https://datasets.marimo.app/cars.csv';
+    
+        -- Query the table
+        SELECT Origin, AVG(MPG_City) as avg_city_mpg, AVG(MPG_Highway) as avg_highway_mpg 
+        FROM cars
+        GROUP BY Origin
+        ORDER BY avg_city_mpg DESC;
+    """)
+
+    # Display the results
+    cars
+    return (cars,)
+
+
+@app.cell
+def _(cars, mo):
+    # Create a dropdown for selecting car origin
+    origin_dropdown = mo.ui.dropdown.from_series(
+        mo.sql("SELECT DISTINCT Origin FROM cars")["Origin"], 
+        value="Asia"
+    )
+
+    # Display the dropdown
+    origin_dropdown
+    return (origin_dropdown,)
+
+
+@app.cell
+def _(cars, mo, origin_dropdown):
+    # Query that filters based on selection
+    filtered_cars = mo.sql(f"""
+        SELECT * FROM cars
+        WHERE Origin = '{origin_dropdown.value}'
+        ORDER BY MPG_Highway DESC
+        LIMIT 10
+    """)
+
+    # Create statistics summary
+    mo.hstack([
+        mo.stat(label="Total cars", value=str(len(filtered_cars))),
+        mo.stat(label="Avg MPG Highway", value=f"{filtered_cars['MPG_Highway'].mean():.1f}"),
+        mo.stat(label="Avg MPG City", value=f"{filtered_cars['MPG_City'].mean():.1f}")
+    ])
+    return (filtered_cars,)
+
+
+if __name__ == "__main__":
+    app.run()