# 🚀 Quick Start Guide

Welcome to the Advanced Code Interpreter Sandbox! This guide will help you get started in just a few minutes.

## 📋 Table of Contents
1. [Your First Code](#1-your-first-code)
2. [Installing Packages](#2-installing-packages)
3. [Working with Files](#3-working-with-files)
4. [Data Visualization](#4-data-visualization)
5. [Examples to Try](#5-examples-to-try)

---

## 1. Your First Code 💻

### Basic Print Statement
```python
print("Hello, World!")
print("Welcome to the Code Interpreter Sandbox!")
print(2 + 2)
```

### Working with Variables
```python
name = "Python"
version = 3.11
print(f"I'm using {name} {version}")
```

### Using Pre-installed Libraries
```python
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# Create a simple array
arr = np.array([1, 2, 3, 4, 5])
print(f"Array: {arr}")
print(f"Mean: {np.mean(arr)}")
```

**Try it:** Copy any of these examples into the Code Executor tab and click "Run Code" ▶️

---

## 2. Installing Packages 📦

### Method 1: Using Package Manager Tab
1. Go to **Package Manager** tab
2. Enter package name: `requests`
3. Click **Install** 📥
4. Wait for success message

### Method 2: Install via Code
```python
import subprocess
import sys

# Install a package
subprocess.run([sys.executable, "-m", "pip", "install", "faker"])
print("Package installed!")
```

### Popular Packages to Try
- `faker` - Generate fake data
- `wordcloud` - Create word clouds
- `geopandas` - Geospatial analysis
- `sqlalchemy` - Database ORM

---

## 3. Working with Files 📁

### Upload a File
1. Go to **File Manager** tab
2. Click **Upload File**
3. Select any CSV, JSON, or text file
4. Click **Upload** 📤

### Access Your Files in Code
```python
# List all files
import os
print("Files in workspace:", os.listdir('.'))

# Read a file
with open('your_file.txt', 'r') as f:
    content = f.read()
    print(content)
```

### Create and Save Files
```python
# Create a new file
with open('my_data.txt', 'w') as f:
    f.write("This is my data\n")
    f.write("Line 2\n")
    f.write("Line 3\n")

print("File created successfully!")
```

---

## 4. Data Visualization 📊

### Basic Matplotlib Plot
```python
import matplotlib.pyplot as plt
import numpy as np

# Create data
x = np.linspace(0, 10, 100)
y = np.sin(x)

# Create plot
plt.figure(figsize=(10, 6))
plt.plot(x, y)
plt.title('Sine Wave')
plt.xlabel('x')
plt.ylabel('sin(x)')
plt.grid(True)
plt.show()
```

### Interactive Plot with Plotly
```python
import plotly.express as px
import numpy as np

# Create scatter plot
x = np.random.randn(500)
y = np.random.randn(500)
fig = px.scatter(x=x, y=y, title='Random Scatter Plot')
fig.show()
```

### Data Analysis Example
```python
import pandas as pd
import numpy as np

# Create sample data
data = {
    'Month': ['Jan', 'Feb', 'Mar', 'Apr', 'May'],
    'Sales': [100, 120, 150, 110, 200]
}
df = pd.DataFrame(data)

# Analyze
print(df)
print(f"\nAverage sales: {df['Sales'].mean():.2f}")
print(f"Total sales: {df['Sales'].sum()}")

# Plot
import matplotlib.pyplot as plt
plt.bar(df['Month'], df['Sales'])
plt.title('Monthly Sales')
plt.show()
```

---

## 5. Examples to Try 🎯

### Example 1: Data Analysis
```python
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

# Generate sample data
np.random.seed(42)
dates = pd.date_range('2023-01-01', periods=30, freq='D')
values = np.random.randn(30).cumsum()

# Create DataFrame
df = pd.DataFrame({'date': dates, 'value': values})

# Display
print(df.head(10))
print(f"\nStatistics:\n{df['value'].describe()}")

# Plot
plt.figure(figsize=(12, 6))
plt.plot(df['date'], df['value'])
plt.title('Time Series Data')
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()
```

### Example 2: Web Scraping
```python
# Install requests and beautifulsoup4 first via Package Manager
import requests
from bs4 import BeautifulSoup

# Get a webpage
url = "https://httpbin.org/html"
response = requests.get(url)
soup = BeautifulSoup(response.content, 'html.parser')

# Extract title
title = soup.find('title')
print(f"Page title: {title.text if title else 'No title found'}")

# Get headings
headings = soup.find_all(['h1', 'h2', 'h3'])
print(f"\nFound {len(headings)} headings:")
for h in headings:
    print(f"  - {h.text}")
```

### Example 3: Machine Learning
```python
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split

# Generate data
X, y = make_classification(n_samples=1000, n_features=4, n_classes=2, random_state=42)

# Split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train model
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

# Predict
accuracy = model.score(X_test, y_test)
print(f"Model accuracy: {accuracy:.4f}")

# Feature importance
importance = model.feature_importances_
print(f"\nFeature importance: {importance}")
```

### Example 4: API Integration
```python
import requests
import json

# Call an API
response = requests.get("https://api.github.com/users/octocat")
data = response.json()

# Display key info
print(f"User: {data.get('login', 'N/A')}")
print(f"Name: {data.get('name', 'N/A')}")
print(f"Public repos: {data.get('public_repos', 0)}")
print(f"Followers: {data.get('followers', 0)}")
```

---

## 🎓 Learning Resources

### Tutorials
- **NumPy**: [numpy.org/learn](https://numpy.org/learn/)
- **Pandas**: [pandas.pydata.org/docs/getting_started](https://pandas.pydata.org/docs/getting_started)
- **Matplotlib**: [matplotlib.org/stable/tutorials](https://matplotlib.org/stable/tutorials)
- **Scikit-learn**: [scikit-learn.org/stable/tutorial](https://scikit-learn.org/stable/tutorial)

### Example Scripts
Check the `examples/` folder for complete scripts:
- `data_analysis_example.py` - Comprehensive data analysis
- `ml_example.py` - Machine learning demonstrations
- `visualization_example.py` - Advanced plotting examples

---

## 💡 Pro Tips

1. **Use variables**: Values persist between code executions
2. **Install first**: Install packages before using them
3. **Check output mode**: Switch between stdout, stderr, or both
4. **Save outputs**: Use File Manager to save important results
5. **Check Session Info**: Monitor your session status
6. **Explore packages**: Try installing different packages to see what's available
7. **Use comments**: Add `# comments` to document your code
8. **Handle errors**: Use try/except blocks for robust code

---

## 🆘 Troubleshooting

### Package Installation Fails
- Check package name (must match PyPI)
- Some packages need system dependencies
- Try installing one at a time

### Code Doesn't Run
- Check for syntax errors
- Verify variable names
- Ensure packages are installed
- Check output mode setting

### Can't Upload File
- File size must be < 100MB
- Supported formats: CSV, JSON, TXT, etc.

### Performance Issues
- Reduce data size
- Avoid infinite loops
- Clear variables you don't need
- Restart session if needed

---

## 🎉 You're Ready!

You now know the basics of the Code Interpreter Sandbox. Start experimenting with your own ideas!

### What to Try Next:
- [ ] Analyze your own data file
- [ ] Create a custom visualization
- [ ] Build a machine learning model
- [ ] Scrape data from the web
- [ ] Create a web dashboard

**Happy Coding! 🚀**

---

## 📞 Need Help?

1. Check the **Session Info** tab for system status
2. Review the full **README.md** for detailed documentation
3. Explore the **examples/** folder for inspiration
4. Use the **Package Manager** to install more libraries

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**Made with ❤️ using Gradio and HuggingFace Spaces**