Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Data Analysis
Pandas
Analyze, transform, and clean DataFrames with efficient patterns for filtering, grouping, merging, and pivoting.
skill openclawclawhub Free
Analyze, transform, and clean DataFrames with efficient patterns for filtering, grouping, merging, and pivoting.
⬇ 0 downloads ★ 0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, memory-template.md, setup.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.1
Provenance
- Publisher
- ivangdavila
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Setup
On first use, create ~/pandas/ and read setup.md for initialization. User preferences are stored in ~/pandas/memory.md — users can view or edit this file anytime.
When to Use
User needs to work with tabular data in Python. Agent handles DataFrame operations, data cleaning, aggregations, merges, pivots, and exports.
Architecture
Memory lives in ~/pandas/. See memory-template.md for structure. ~/pandas/ ├── memory.md # User preferences and common patterns └── snippets/ # Saved code patterns (optional)
Quick Reference
TopicFileSetup processsetup.mdMemory templatememory-template.md
1. Use Vectorized Operations
NEVER iterate with for loops over DataFrame rows Use .apply() only when vectorized alternatives don't exist Prefer df['col'].str.method() over apply(lambda x: x.method())
2. Chain Methods for Readability
# Good: method chaining result = (df .query('age > 30') .groupby('city') .agg({'salary': 'mean'}) .reset_index()) # Bad: intermediate variables everywhere filtered = df[df['age'] > 30] grouped = filtered.groupby('city') result = grouped.agg({'salary': 'mean'}).reset_index()
3. Handle Missing Data Explicitly
Always check df.isna().sum() before analysis Choose strategy: dropna(), fillna(), or interpolation Document WHY missing values exist before removing them
4. Use Categorical for Repeated Strings
# Memory savings for columns with few unique values df['status'] = df['status'].astype('category') df['country'] = df['country'].astype('category')
5. Merge with Validation
# Always specify how and validate result = pd.merge( df1, df2, on='id', how='left', validate='m:1' # Many-to-one: catch unexpected duplicates )
6. Prefer query() for Complex Filters
# Readable df.query('age > 30 and city == "NYC" and salary < 100000') # Hard to read df[(df['age'] > 30) & (df['city'] == 'NYC') & (df['salary'] < 100000)]
7. Set Index When Appropriate
# Faster lookups, cleaner merges df = df.set_index('user_id') user_data = df.loc[12345] # O(1) lookup
Common Traps
SettingWithCopyWarning → Use .loc[] for assignment: df.loc[mask, 'col'] = value Slow loops → Replace iterrows() with vectorized ops or apply() Memory explosion → Use dtype in read_csv(): pd.read_csv(f, dtype={'id': 'int32'}) Silent data loss → Check shape before/after merge: print(f"Before: {len(df1)}, After: {len(result)}") Index confusion → Use reset_index() after groupby() to get clean DataFrame Chained indexing → df['a']['b'] fails silently; use df.loc[:, ['a', 'b']]
Security & Privacy
Data storage: User preferences stored in ~/pandas/memory.md All DataFrame operations run locally No data is sent externally This skill does NOT: Upload data to any service Access files outside ~/pandas/ and the working directory Modify source data files without explicit instruction User control: View stored preferences: cat ~/pandas/memory.md Clear all data: rm -rf ~/pandas/
Related Skills
Install with clawhub install <slug> if user confirms: data-analysis — general data analysis patterns csv — CSV file handling sql — database queries excel-xlsx — Excel file operations
Feedback
If useful: clawhub star pandas Stay updated: clawhub sync
Category context
Data access, storage, extraction, analysis, reporting, and insight generation.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
3 Docs
- SKILL.md
- memory-template.md
- setup.md