Claude for data analysis: how Anthropic’s assistant handles files, charts, code, and context

Claude supports exploratory data work with a mix of large context, reproducible code, and structured file workflows.

Claude is not just a text-based chatbot—it is also a file-ready, analysis-capable assistant with features tailored for structured data, charts, and spreadsheets. In 2025, both Claude 4 Opus and Claude 4 Sonnet models provide enterprise-scale context windows (200,000 tokens and 1 million tokens respectively) along with powerful tools for processing, interpreting, and visualizing data files. Anthropic’s solution is designed for researchers, analysts, and technical users who need a secure, explainable, and reproducible way to work with tabular data, structured documents, or even full PDF reports.

Claude achieves this through a layered ecosystem: interactive chart tools inside the chat interface, a code execution environment via API, and a separate developer shell (“Claude Code”) that automates multi-step data pipelines. Together, these components position Claude as a quiet powerhouse for both casual exploration and advanced analysis.

The Claude chat interface accepts spreadsheets and builds insights through guided steps.

Claude’s default chat interface includes an “Analysis” tool when you upload an Excel or CSV file. This tool runs in a secure browser-based JavaScript sandbox, which is different from the code-execution Python runtime (available via API and Claude Code).

The Analysis tool supports:

• File formats: CSV, TSV, and XLSX

• Size limit: ~30 MB (UI), 32 MB (API)

• Actions available: summary statistics, missing value maps, column profiling, correlation detection, and automated chart generation

Charts are displayed as scalable vector graphics (SVGs) and remain editable in future prompts. The tool interprets natural-language commands like:

“Create a scatter plot of monthly revenue vs churn, color-coded by region.”

It will generate both the chart and a narrative description of trends, along with options to filter or segment the dataset further. You can ask follow-up queries to clean columns, extract substrings, format date fields, or even summarize trends by category.

Claude Code provides terminal-level control for full data pipelines and reports.

Anthropic’s Claude Code experience offers a POSIX-style command-line environment with Claude embedded in the shell. This tool is ideal for technical users working locally or through an IDE terminal. It supports full project workflows using Python 3.11, pandas, polars, matplotlib, and custom environment setup via prompts.

Example use cases

Prompt	Action performed
“Load customers.csv and drop duplicate entries”	Python snippet written and executed
“Plot 30-day rolling average of sales, grouped by product”	Chart is generated and saved to a reports/ folder
“Write the cleaned DataFrame to cleaned_customers.csv and commit it”	File written, Git commit made
“Install openpyxl and update requirements.txt”	Virtual environment updated programmatically

Claude Code supports file editing, GitHub pull requests, cron jobs, and templated reporting, making it a complete environment for analysts who need automation and reproducibility.

The Claude Files API supports large datasets, multi-document uploads, and detailed PDF extraction.

The Files API allows developers and enterprise users to upload datasets and long reports directly to Claude’s backend. These files are parsed and made available for query using the /v1/messages endpoint. This is ideal for document-based or spreadsheet-based analysis at scale.

File handling specs

Capability	Limit
File size (single)	500 MB
Daily upload (Pro)	25 files, 50 MB each
Daily upload (Max)	100 files, 100 MB each
Storage per org	100 GB
Accepted formats	CSV, TSV, XLSX, PDF, Markdown, LaTeX, code, image (OCR via beta)

Claude can read entire PDFs, including charts and tables. For scanned documents, it converts each page to text and image format and can reference specific page numbers or data cells in answers.

The PDF ingestion system currently supports 100-page limits and 32 MB max per PDF per request, though multiple PDFs can be chained through context-aware prompts.

Pricing tiers and limits affect the scope of analysis.

Claude’s ability to handle long-form data, code execution, and embedded files scales with the user’s subscription tier.

Claude data analysis tiers – comparison table

Plan	Context window	Files/day	Max per file	Sandbox access	Monthly price
Free	32,000 tokens	3	25 MB	UI (browser-only)	$0
Pro	200,000 tokens	25	50 MB	Full (no API)	$20
Max	200K (chat) + 1M (Sonnet API)	100	100 MB	API + CLI + Claude Code	$200
Enterprise	1 million tokens	500	100 MB	Extended + API	From $40/seat

Opus 4.1 API usage is metered at $15 per million tokens (input) and $75 per million (output). Sonnet 4 offers reduced pricing at $5 input / $25 output.

What Claude does best—and where limitations still apply.

Claude’s tooling is ideal for the following use cases:

• Exploratory data review of spreadsheets or CSVs

• Automated charting with follow-up tweaks

• Natural-language transformation of structured files

• Terminal-style automation of data tasks

• Contextual linking of narrative summaries to actual file content

However, limitations remain:

• No Python runtime inside the chat UI: the sandbox uses JavaScript; Python requires API or Claude Code.

• No external internet access during code execution (sandboxed environment)

• Chart interactivity is limited—no live filters or mouse-over tooltips yet.

• No built-in database querying; users must first convert data to files.

Also, Claude's strict rate limits for sandbox execution and file analysis (especially for free and Pro plans) require planning in professional workflows.

Claude’s approach favors explainable analysis, one step at a time.

Anthropic’s Claude is not a black-box analyst. It prefers to walk through each analytical step with clarity, showing raw code, returned values, and narrative interpretations. For users who want fast, accurate, transparent insights from structured data—without hallucinated results or risky shortcuts—Claude’s environment is reliable, reproducible, and audit-friendly.

Where ChatGPT excels in fast transformations and Gemini in in-sheet commands, Claude offers a hybrid between technical precision and conversational interface, making it particularly suited to research-heavy, code-assisted analysis workflows.

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