Run the Code in Notebook

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Step 1: Run Individual Code Blocks

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Method 1: Hover Menu Run Button

1. Hover Over Code Block: Hover over any code block
2. Click Run Button: Click the play button (▶️) that appears in the hover menu
3. Code Executes: Code runs and output streams in below the block
4. Status Updates: Green checkmark shows successful execution

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Method 2: Keyboard Shortcut

1. Select Code Block: Click on the code block you want to run
2. Press Shift+Enter: Use the keyboard shortcut to execute
3. Code Executes: Code runs and cursor moves to next block
4. View Results: See streaming output in real-time

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Step 2: Run All Code Blocks

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Run All Feature

1. Click “Run All”: Use the “Run All” button in the top-right toolbar
2. Sequential Execution: All codeNo Dragging Required blocks execute in order from top to bottom
3. Progress Tracking: Watch as each block executes in sequence
4. Complete Execution: All blocks finish with results displayed

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Clear All Outputs

• Click “Clear All Outputs”: Remove all execution results
• Fresh Start: Start with clean output areas
• Re-run: Execute blocks again as needed

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Step 3: Handle Exceptions and Errors

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Exception Display

• Detailed Error Messages: Full exception details are displayed
• Traceback Information: Complete stack trace for debugging
• Error Context: Shows exactly where the error occurred
• Helpful Details: Error messages provide debugging guidance

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AI-Powered Debugging

• “Resolve in GAIA” Button: Click to get AI assistance with errors
• Smart Suggestions: AI provides code fixes and explanations
• Context-Aware Help: AI understands your notebook context
• Learn More: See AI Coding in Notebook for detailed AI features

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Step 4: Code Quality Features

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Real-Time Code Linting

• Syntax Checking: Python syntax is checked after each line change
• Error Highlighting: Syntax errors are highlighted immediately
• Best Practices: Suggestions for code improvements
• Instant Feedback: Get feedback as you type

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Autocompletion (Coming Soon)

• Tab Key Trigger: Press Tab to trigger code completion
• Context-Aware: Suggestions based on notebook context
• Smart Predictions: AI-powered code completion
• Variable Recognition: Understands variables in your notebook

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Step 5: Reorder Code Blocks

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Easy Block Reordering

1. Use Arrow Buttons: Click up/down arrow buttons in block toolbar
2. Shift Blocks: Move blocks up or down without dragging
3. Visual Feedback: See blocks move in real-time
4. Maintain Order: Keep logical flow of your analysis

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Block Management

• Shift Up: Move block to previous position
• Shift Down: Move block to next position
• Dragging: Dragging block for reordering in a longer range
• Preserve Output: Block outputs move with the code

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Step 6: Streaming Execution

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Background Execution

• Streaming Output: Results stream in real-time from kernel
• Leave and Return: Close workspace, return hours later
• Persistent Execution: Long-running code continues in background
• Output Preservation: All results are saved and displayed

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Long-Running Tasks

# Example: Long-running analysis
import time
import pandas as pd

# Load large dataset
print("Loading large dataset...")
data = pd.read_parquet('/workspace/data/massive_dataset.parquet')

# Process data in chunks
print("Processing data...")
for i in range(100):
    chunk = data[i*1000:(i+1)*1000]
    # Complex processing here
    time.sleep(1)  # Simulate processing time
    print(f"Processed chunk {i+1}/100")

print("Analysis complete!")

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Benefits of Streaming

• No Time Pressure: Start long tasks and walk away
• Resource Efficiency: Use compute resources effectively
• Progress Monitoring: Check progress when you return
• Reliable Execution: No interruption from browser closure

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Step 7: Code Execution Examples

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Basic Code Execution

# Simple data analysis
import pandas as pd
import numpy as np

# Load data
data = pd.read_csv('/workspace/data/sample.csv')
print(f"Dataset shape: {data.shape}")

# Basic statistics
print(f"Mean: {data['value'].mean()}")
print(f"Standard deviation: {data['value'].std()}")

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Library Installation

# Install required libraries
!pip install earthengine-api geemap geopandas

# Libraries are installed and ready to use
import ee
import geemap
import geopandas as gpd

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Error Handling Example

try:
    # Attempt to load data
    data = pd.read_csv('/workspace/data/missing_file.csv')
except FileNotFoundError as e:
    print(f"Error: {e}")
    print("Please check if the file exists in the workspace")

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Best Practices

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Code Organization

• Logical Flow: Arrange blocks in logical order
• Clear Comments: Add comments to explain your code
• Modular Code: Break complex tasks into smaller blocks
• Error Handling: Use try-catch blocks for robust code

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Execution Strategy

• Test Small: Run small code blocks first
• Incremental Development: Build analysis step by step
• Save Results: Save intermediate results for long tasks
• Monitor Resources: Keep track of memory and CPU usage

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Performance Tips

• Use Streaming: Leverage background execution for long tasks
• Efficient Code: Write optimized, vectorized code
• Batch Processing: Process data in manageable chunks
• Resource Management: Clean up large variables when done

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Troubleshooting

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Common Issues

• Code Not Running: Check if VM is started and ready
• Syntax Errors: Use the real-time linting to catch errors
• Long Execution: Use streaming execution for background tasks
• Memory Issues: Clear outputs or restart VM if needed

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Solutions

• Restart VM: Stop and restart if VM becomes unresponsive
• Clear Outputs: Use “Clear All Outputs” to free memory
• AI Debugging: Use GAIA for help with complex errors
• Contact Support: Get help for persistent issues

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Next Steps

1. AI Assistance: Learn about AI Coding in Notebook for debugging
2. Install Libraries: Check Supported Python Libraries
3. Start Machine: Learn about Starting Your Machine
4. Publish Results: Publish Your Notebook to share your work

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Get Expert Help