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NumPy

Write fast, memory-efficient numerical code with arrays, broadcasting, vectorization, and linear algebra.

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Write fast, memory-efficient numerical code with arrays, broadcasting, vectorization, and linear algebra.

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Requirements

Target platform: OpenClaw
Install method: Manual import
Extraction: Extract archive
Prerequisites: OpenClaw
Primary doc: SKILL.md

Package facts

Download mode: Yavira redirect
Package format: ZIP package
Source platform: Tencent SkillHub
What's included: SKILL.md, memory-template.md, setup.md

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Release facts

Source: Tencent SkillHub
Verification: Indexed source record
Version: 1.0.0

Provenance

Publisher: ivangdavila
Source page: View original listing
Canonical URL: Open canonical page

Documentation

ClawHub primary doc Primary doc: SKILL.md 22 sections Open source page

Setup

On first use, read setup.md for integration guidelines. Creates ~/numpy/ to store preferences and snippets.

When to Use

User needs numerical computing in Python. Agent handles array operations, mathematical computations, linear algebra, and data manipulation with NumPy.

Architecture

Memory lives in ~/numpy/. See memory-template.md for structure. ~/numpy/ ├── memory.md # Preferences + common patterns used └── snippets/ # User's saved code patterns

Quick Reference

TopicFileSetup processsetup.mdMemory templatememory-template.md

1. Vectorize First

Never use Python loops for array operations. NumPy's vectorized operations are 10-100x faster. # BAD - Python loop result = [] for x in arr: result.append(x * 2) # GOOD - Vectorized result = arr * 2

2. Understand Broadcasting

Broadcasting allows operations on arrays of different shapes. Know the rules: Dimensions align from the right Size-1 dimensions stretch to match Missing dimensions treated as size-1 # Shape (3,1) + (4,) broadcasts to (3,4) a = np.array([[1], [2], [3]]) # (3,1) b = np.array([10, 20, 30, 40]) # (4,) result = a + b # (3,4)

3. Prefer Views Over Copies

Slicing returns views (same memory). Use .copy() only when needed. # View - modifying b changes a b = a[::2] # Copy - independent b = a[::2].copy()

4. Use Appropriate Dtypes

Choose the smallest dtype that fits your data. Saves memory and speeds up computation. # For integers 0-255 arr = np.array(data, dtype=np.uint8) # For floats that don't need double precision arr = np.array(data, dtype=np.float32)

5. Axis Awareness

Most functions accept axis parameter. Know your axes: axis=0: operate along rows (down columns) axis=1: operate along columns (across rows) axis=None or omit: operate on flattened array arr = np.array([[1, 2], [3, 4]]) np.sum(arr, axis=0) # [4, 6] - sum each column np.sum(arr, axis=1) # [3, 7] - sum each row

6. Leverage Built-in Functions

NumPy has optimized functions for common operations. Don't reinvent them. NeedUseElement-wise mathnp.sin, np.exp, np.logStatisticsnp.mean, np.std, np.medianLinear algebranp.dot, np.linalg.*Sortingnp.sort, np.argsortSearchingnp.where, np.searchsorted

Shape Mismatches

# TRAP: Confusing (n,) with (n,1) or (1,n) a = np.array([1, 2, 3]) # shape (3,) b = np.array([[1, 2, 3]]) # shape (1,3) c = np.array([[1], [2], [3]]) # shape (3,1) # FIX: Use reshape or newaxis a.reshape(-1, 1) # (3,1) a[np.newaxis, :] # (1,3)

Silent Type Coercion

# TRAP: Integer array silently truncates floats arr = np.array([1, 2, 3]) # int64 arr[0] = 1.9 # becomes 1, not 1.9! # FIX: Declare dtype upfront arr = np.array([1, 2, 3], dtype=np.float64)

View vs Copy Confusion

# TRAP: Fancy indexing returns copy, slicing returns view arr = np.array([1, 2, 3, 4, 5]) # This is a VIEW (changes affect original) view = arr[1:4] # This is a COPY (independent) copy = arr[[1, 2, 3]]

Broadcasting Surprises

# TRAP: Unexpected broadcasting a = np.array([1, 2, 3]) b = np.array([1, 2]) a + b # ERROR - shapes don't broadcast # TRAP: Accidental broadcasting a = np.zeros((3, 4)) b = np.array([1, 2, 3]) a + b # ERROR - (3,4) and (3,) don't align a + b.reshape(-1, 1) # Works - (3,4) and (3,1)

In-Place Operations

# TRAP: Some operations modify in-place, others don't np.sort(arr) # Returns sorted copy arr.sort() # Sorts in-place # Safe pattern: be explicit arr = np.sort(arr) # Clear intent

Create Arrays

np.zeros((3, 4)) # All zeros np.ones((3, 4)) # All ones np.full((3, 4), 7) # All sevens np.eye(3) # Identity matrix np.arange(0, 10, 2) # [0, 2, 4, 6, 8] np.linspace(0, 1, 5) # [0, 0.25, 0.5, 0.75, 1] np.random.rand(3, 4) # Uniform [0,1) np.random.randn(3, 4) # Normal distribution

Reshape and Stack

arr.reshape(2, 6) # New shape (must match size) arr.flatten() # 1D copy arr.ravel() # 1D view np.concatenate([a, b]) # Join along existing axis np.stack([a, b]) # Join along new axis np.vstack([a, b]) # Stack vertically np.hstack([a, b]) # Stack horizontally

Boolean Indexing

arr = np.array([1, 5, 3, 8, 2]) mask = arr > 3 arr[mask] # [5, 8] arr[arr > 3] = 0 # Replace values > 3 with 0 np.where(arr > 3, 1, 0) # 1 where >3, else 0

Linear Algebra

np.dot(a, b) # Matrix multiplication a @ b # Same (Python 3.5+) np.linalg.inv(a) # Inverse np.linalg.det(a) # Determinant np.linalg.eig(a) # Eigenvalues/vectors np.linalg.solve(a, b) # Solve Ax = b

Security & Privacy

Data that stays local: All computations run locally Code patterns saved in ~/numpy/ This skill does NOT: Send data externally Access files outside ~/numpy/ Require network connectivity

Related Skills

Install with clawhub install <slug> if user confirms: data — data processing workflows math — mathematical computations statistics — statistical analysis

Feedback

If useful: clawhub star numpy Stay updated: clawhub sync

Category context

Agent frameworks, memory systems, reasoning layers, and model-native orchestration.

Source: Tencent SkillHub

Largest current source with strong distribution and engagement signals.

Package contents

Included in package

3 Docs

SKILL.md Primary doc
memory-template.md Docs
setup.md Docs