Matlab Analyze DataSAFE

The MATLAB Agentic Toolkit brings proven MATLAB capabilities to AI agents, making engineering and scientific workflows agent-ready.

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---
name: matlab-analyze-data
description: Analyze data using MATLAB. Use when the task involves tables, timetables, time-series data, numeric arrays, sensor matrices, or gridded data — including but not limited to exploring, row filtering, sorting, cleaning, transforming, aggregating, smoothing, padding, trimming, and answering questions about data. MATLAB provides extensive, easy-to-use built-in functions for these workflows with no additional products required.
license: https://www.mathworks.com/content/dam/mathworks/license/pmrl/license.md
metadata:
  author: MathWorks
  version: "1.5"
---

# MATLAB Data Analysis

Generate idiomatic MATLAB code for tabular data analysis tasks using tables and timetables.

## When to Use

- Any task involving tabular data: exploring, cleaning, transforming, or aggregating tables
- Time-series analysis: resampling, synchronizing, trend detection, smoothing
- Answering questions about data in tables (top-N, filtering, group comparisons)
- Data cleaning: missing values, outliers, type conversion, normalization

## When NOT to Use

- The task has no data context (no tables, timetables, arrays, or date/time values to work with)
- The primary goal is visualization or plotting, not data analysis (use `matlab-build-chart` instead)
- The task is purely symbolic math, simulation, or app building
- Reading or writing files — CSV, Excel, Parquet, MAT-file (use `matlab-import-export-data` instead)
- Data doesn't fit in memory or requires out-of-core processing (use `matlab-choose-big-data-solution` instead)
- Storing or retrieving passwords, tokens, or API keys (use `matlab-secure-credentials` instead)
- Data lives in a relational database (use `matlab-use-database` or `matlab-use-duckdb` instead)
- Building an ML training pipeline from labeled signal files (use `matlab-prepare-signal-data` instead)
- Training classifiers or comparing ML model accuracy on tabular data (use `matlab-classify-tabular-data` instead)
- Engineering or selecting features to optimize a supervised ML model (use `matlab-engineer-tabular-features` instead)
- Code migration or replacing deprecated APIs (use `matlab-modernize-code` instead)

## Routing: Read references on all relevant topics

This skill covers core MATLAB functions for tabular, time-series, and array-based data workflows — including numeric arrays, sensor matrices, and gridded data. These functions work natively with `table`, `timetable`, and numeric arrays, handle missing data correctly, and are performance-optimized. Prefer the modern functions recommended here (e.g., `groupsummary`, `datetime`, `fillmissing`, `smoothdata2`) over legacy alternatives (e.g., `accumarray`, `nanmean`, `datenum`). Override only if the user explicitly requests otherwise.

**Each section below links to a reference file. ALWAYS read the reference file for every relevant topic before writing code.** Reference files contain correct syntax, common pitfalls, and "Avoid" patterns that prevent silent bugs. Skipping the reference risks using a deprecated approach or hitting a known pitfall. After reading, state `Reference files read: [list]` at the top of your response.

### Key Functions — Available From

Most functions in this skill are available in R2023a or earlier. The following require a newer release:

| Function | Available From | Purpose |
|----------|---------------|---------|
| `paddata`, `trimdata`, `resize` | R2023b | Pad, trim, or resize arrays to target length |
| `smoothdata2` | R2023b | Smooth 2-D gridded data over rectangular windows |
| `kde` | R2023b | Kernel density estimate for univariate data |
| `clip` | R2024a | Clamp values to a range |
| `islocalmax2`, `islocalmin2` | R2024a | Detect local extrema in 2-D gridded data |
| `summary` (enhanced) | R2024b | Supports arrays (numeric, datetime, duration, logical); adds `Statistics`, `DataVariables`, `Detail` name-value args |
| `isapprox` | R2024b | Tolerance-aware floating-point comparison (use instead of `==` for computed values) |
| `isbetween` (numeric) | R2024b | Check elements within a numeric range |
| `numunique` | R2025a | Count distinct values in a variable |
| `allbetween` | R2025a | Validate all values are within a range |
| `allunique` | R2025a | Validate all values are unique |

---

## Getting Oriented with Data

When data is already in a workspace variable, start by understanding its structure and contents before writing analysis code.

**Topics:**
- Summarizing structure, shape, and variable types
- Assessing how much data is missing and where
- Understanding distributions (numeric quartiles, categorical value counts)
- Finding correlations and relationships between variables
- Checking for duplicates, unique keys, and cardinality
- Exploring time range, regularity, and temporal patterns

**Functions:** `summary`, `head`, `size`, `jsonencode`, `anymissing`, `allfinite`, `ismissing`, `groupcounts`, `numunique`, `allunique`, `kde`, `corrcoef`, `pivot`, `unique`, `isregular`, `isuniform`, `retime`

**Read:** [exploration.md](references/exploration.md)

---

## Data Types

Use modern MATLAB types instead of legacy alternatives. Modern types are faster, more readable, and work better with table functions.

**Topics:**
- Dates and times (parsing, arithmetic, timezones, extracting year/month/day/hour)
- Elapsed time and calendar offsets
- Text data (comparing, searching, splitting, editing strings)
- Categorical data (ordinal rankings, merging/renaming/reordering levels)

**Functions:** `datetime`, `dateshift`, `year`, `month`, `day`, `weekday`, `quarter`, `hour`, `timeofday`, `ymd`, `hms`, `hours`, `days`, `minutes`, `seconds`, `calmonths`, `caldays`, `string`, `matches`, `contains`, `startsWith`, `extractAfter`, `extractBefore`, `replace`, `erase`, `strip`, `split`, `count`, `categorical`, `mergecats`, `renamecats`, `removecats`, `reordercats`, `countcats`

**Read:** [data-types.md](references/data-types.md)

---

## Tables and Timetables

Tables are the primary container fo

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

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