QUICKSTART.md

🚀 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
2. Installing Packages
3. Working with Files
4. Data Visualization
5. Examples to Try

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1. Your First Code 💻

Basic Print Statement

print("Hello, World!")
print("Welcome to the Code Interpreter Sandbox!")
print(2 + 2)

Working with Variables

name = "Python"
version = 3.11
print(f"I'm using {name} {version}")

Using Pre-installed Libraries

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" ▶️

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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

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

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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

# 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

# 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!")

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4. Data Visualization 📊

Basic Matplotlib Plot

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

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

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()

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5. Examples to Try 🎯

Example 1: Data Analysis

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

# 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

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

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)}")

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🎓 Learning Resources

Tutorials

• NumPy: numpy.org/learn
• Pandas: pandas.pydata.org/docs/getting_started
• Matplotlib: matplotlib.org/stable/tutorials
• Scikit-learn: 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

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💡 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

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🆘 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

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🎉 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! 🚀

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📞 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