> ## Documentation Index > Fetch the complete documentation index at: https://docs.declaw.ai/llms.txt > Use this file to discover all available pages before exploring further. # Data Analysis in a Sandbox > Upload a CSV dataset into a Declaw sandbox, execute a Python analysis script that computes revenue statistics, and read the JSON results back to the host process. ## What You'll Learn * How to upload a dataset file with `sbx.files.write()` * How to write and execute a Python analysis script inside the sandbox * How to read structured JSON results back using `sbx.files.read()` * The upload-compute-download pattern for safe, isolated data processing ## Prerequisites * Declaw running locally or in the cloud (see [Deployment](/deployment/overview)) * `DECLAW_API_KEY` and `DECLAW_DOMAIN` set in your environment ## Code Walkthrough ### 1. Define the dataset and analysis script Both the CSV data and the analysis script are defined as Python strings and uploaded to the sandbox filesystem: ```python theme={null} SALES_CSV = """\ date,product,region,quantity,unit_price 2024-01-05,Widget A,North,120,9.99 2024-01-05,Widget B,South,85,14.50 2024-01-12,Widget A,South,200,9.99 ... """ ANALYSIS_SCRIPT = """\ import csv, json from collections import defaultdict rows = [] with open("/home/user/data/sales.csv") as f: reader = csv.DictReader(f) for row in reader: row["quantity"] = int(row["quantity"]) row["unit_price"] = float(row["unit_price"]) row["revenue"] = row["quantity"] * row["unit_price"] rows.append(row) total_revenue = sum(r["revenue"] for r in rows) rev_by_product = defaultdict(float) for r in rows: rev_by_product[r["product"]] += r["revenue"] top_product = max(rev_by_product, key=rev_by_product.get) results = { "total_records": len(rows), "total_revenue": round(total_revenue, 2), "top_product": top_product, "revenue_by_product": {k: round(v, 2) for k, v in sorted(rev_by_product.items())}, } with open("/home/user/data/results.json", "w") as f: json.dump(results, f, indent=2) print("Analysis complete. Results written to /home/user/data/results.json") """ ``` ### 2. Upload, execute, and read back ```python theme={null} from declaw import Sandbox import json sbx = Sandbox.create(template="python", timeout=300) try: # Upload the dataset and analysis script sbx.files.write("/home/user/data/sales.csv", SALES_CSV) sbx.files.write("/home/user/data/analyze.py", ANALYSIS_SCRIPT) # Run the analysis inside the sandbox result = sbx.commands.run("python3 /home/user/data/analyze.py") print(result.stdout) # "Analysis complete. Results written to ..." # Read the computed results back to the host results_json = sbx.files.read("/home/user/data/results.json") data = json.loads(results_json) print(f"Total revenue: ${data['total_revenue']:,.2f}") print(f"Top product: {data['top_product']}") for product, rev in data["revenue_by_product"].items(): print(f" {product}: ${rev:,.2f}") finally: sbx.kill() ``` ### 1. Define the dataset and analysis script ```typescript theme={null} import { Sandbox } from "@declaw/sdk"; const SALES_CSV = `date,product,region,quantity,unit_price 2024-01-05,Widget A,North,120,9.99 2024-01-05,Widget B,South,85,14.50 ... `; const ANALYSIS_SCRIPT = `import csv, json from collections import defaultdict rows = [] with open("/home/user/data/sales.csv") as f: reader = csv.DictReader(f) for row in reader: row["quantity"] = int(row["quantity"]) row["unit_price"] = float(row["unit_price"]) row["revenue"] = row["quantity"] * row["unit_price"] rows.append(row) total_revenue = sum(r["revenue"] for r in rows) rev_by_product = defaultdict(float) for r in rows: rev_by_product[r["product"]] += r["revenue"] results = { "total_revenue": round(total_revenue, 2), "top_product": max(rev_by_product, key=rev_by_product.get), "revenue_by_product": {k: round(v, 2) for k, v in sorted(rev_by_product.items())}, } with open("/home/user/data/results.json", "w") as f: json.dump(results, f, indent=2) print("Analysis complete.") `; ``` ### 2. Upload, execute, and read back ```typescript theme={null} const sbx = await Sandbox.create({ template: "python", timeout: 300 }); try { // Upload dataset and script await sbx.files.write("/home/user/data/sales.csv", SALES_CSV); await sbx.files.write("/home/user/data/analyze.py", ANALYSIS_SCRIPT); // Run the analysis const result = await sbx.commands.run("python3 /home/user/data/analyze.py"); console.log(result.stdout); if (result.exitCode !== 0) { console.error(result.stderr); return; } // Read results back const resultsJson = await sbx.files.read("/home/user/data/results.json"); const data = JSON.parse(resultsJson); console.log(`Total revenue: $${data.total_revenue.toLocaleString()}`); console.log(`Top product: ${data.top_product}`); for (const [product, rev] of Object.entries(data.revenue_by_product)) { console.log(` ${product}: $${(rev as number).toLocaleString()}`); } } finally { await sbx.kill(); } ``` ## Expected Output ``` --- Creating Sandbox --- Sandbox created: sbx-abc123 --- Uploading Dataset --- Uploaded: sales.csv --- Running Analysis --- stdout: Analysis complete. Results written to /home/user/data/results.json --- Summary --- Total records: 15 Total revenue: $27,897.40 Total quantity: 1,640 Avg order value: $1,859.83 Top product: Widget A Revenue by product: Widget A: $11,680.30 Widget B: $8,265.00 Widget C: $7,952.10 Revenue by region: East: $7,012.20 North: $9,865.50 South: $11,019.70 ``` ## Pattern Notes **Why run the analysis inside the sandbox instead of locally?** * The analysis script could be untrusted (for example, user-submitted code in a data pipeline) * The dataset may contain sensitive data that should not be processed on the host * Sandbox isolation ensures the script cannot access host files, environment variables, or network resources **Scaling the pattern:** * Use `sbx.files.write_files()` to upload multiple files in a single API call * Use `sbx.commands.run_stream()` for long-running analyses where you want incremental output * Add `allow_internet_access=False` to block all outbound network access during analysis