pandas-pro
Performs pandas DataFrame operations for data analysis, manipulation, and transformation. Use when working with pandas DataFrames, data cleaning, aggregation, merging, or time series analysis. Invoke for data manipulation tasks such as joining DataFrames on multiple keys, pivoting tables, resampling time series, handling NaN values with interpolation or forward-fill, groupby aggregations, type conversion, or performance optimization of large datasets.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/jeffallan/claude-skills --skill pandas-pro- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads pandas-pro automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
Security audits
Independent scans of the skill's instructions and bundled files.
SKILL.md
The instructions your agent reads when the skill is triggered.
Pandas Pro
Expert pandas developer specializing in efficient data manipulation, analysis, and transformation workflows with production-grade performance patterns.
Core Workflow
- Assess data structure — Examine dtypes, memory usage, missing values, data quality:
print(df.dtypes) print(df.memory_usage(deep=True).sum() / 1e6, "MB") print(df.isna().sum()) print(df.describe(include="all")) - Design transformation — Plan vectorized operations, avoid loops, identify indexing strategy
- Implement efficiently — Use vectorized methods, method chaining, proper indexing
- Validate results — Check dtypes, shapes, null counts, and row counts:
assert result.shape[0] == expected_rows, f"Row count mismatch: {result.shape[0]}" assert result.isna().sum().sum() == 0, "Unexpected nulls after transform" assert set(result.columns) == expected_cols - Optimize — Profile memory, apply categorical types, use chunking if needed
Reference Guide
Load detailed guidance based on context:
| Topic | Reference | Load When |
|---|---|---|
| DataFrame Operations | references/dataframe-operations.md | Indexing, selection, filtering, sorting |
| Data Cleaning | references/data-cleaning.md | Missing values, duplicates, type conversion |
| Aggregation & GroupBy | references/aggregation-groupby.md | GroupBy, pivot, crosstab, aggregation |
| Merging & Joining | references/merging-joining.md | Merge, join, concat, combine strategies |
| Performance Optimization | references/performance-optimization.md | Memory usage, vectorization, chunking |
Code Patterns
Vectorized Operations (before/after)
# ❌ AVOID: row-by-row iteration
for i, row in df.iterrows():
df.at[i, 'tax'] = row['price'] * 0.2
# ✅ USE: vectorized assignment
df['tax'] = df['price'] * 0.2
Safe Subsetting with .copy()
# ❌ AVOID: chained indexing triggers SettingWithCopyWarning
df['A']['B'] = 1
# ✅ USE: .loc[] with explicit copy when mutating a subset
subset = df.loc[df['status'] == 'active', :].copy()
subset['score'] = subset['score'].fillna(0)
GroupBy Aggregation
summary = (
df.groupby(['region', 'category'], observed=True)
.agg(
total_sales=('revenue', 'sum'),
avg_price=('price', 'mean'),
order_count=('order_id', 'nunique'),
)
.reset_index()
)
Merge with Validation
merged = pd.merge(
left_df, right_df,
on=['customer_id', 'date'],
how='left',
validate='m:1', # asserts right key is unique
indicator=True,
)
unmatched = merged[merged['_merge'] != 'both']
print(f"Unmatched rows: {len(unmatched)}")
merged.drop(columns=['_merge'], inplace=True)
Missing Value Handling
# Forward-fill then interpolate numeric gaps
df['price'] = df['price'].ffill().interpolate(method='linear')
# Fill categoricals with mode, numerics with median
for col in df.select_dtypes(include='object'):
df[col] = df[col].fillna(df[col].mode()[0])
for col in df.select_dtypes(include='number'):
df[col] = df[col].fillna(df[col].median())
Time Series Resampling
daily = (
df.set_index('timestamp')
.resample('D')
.agg({'revenue': 'sum', 'sessions': 'count'})
.fillna(0)
)
Pivot Table
pivot = df.pivot_table(
values='revenue',
index='region',
columns='product_line',
aggfunc='sum',
fill_value=0,
margins=True,
)
Memory Optimization
# Downcast numerics and convert low-cardinality strings to categorical
df['category'] = df['category'].astype('category')
df['count'] = pd.to_numeric(df['count'], downcast='integer')
df['score'] = pd.to_numeric(df['score'], downcast='float')
print(df.memory_usage(deep=True).sum() / 1e6, "MB after optimization")
Constraints
MUST DO
- Use vectorized operations instead of loops
- Set appropriate dtypes (categorical for low-cardinality strings)
- Check memory usage with
.memory_usage(deep=True) - Handle missing values explicitly (don't silently drop)
- Use method chaining for readability
- Preserve index integrity through operations
- Validate data quality before and after transformations
- Use
.copy()when modifying subsets to avoid SettingWithCopyWarning
MUST NOT DO
- Iterate over DataFrame rows with
.iterrows()unless absolutely necessary - Use chained indexing (
df['A']['B']) — use.loc[]or.iloc[] - Ignore SettingWithCopyWarning messages
- Load entire large datasets without chunking
- Use deprecated methods (
.ix,.append()— usepd.concat()) - Convert to Python lists for operations possible in pandas
- Assume data is clean without validation
Output Templates
When implementing pandas solutions, provide:
- Code with vectorized operations and proper indexing
- Comments explaining complex transformations
- Memory/performance considerations if dataset is large
- Data validation checks (dtypes, nulls, shapes)
Maintained by @jeffallan, Principal Consultant at Synergetic Solutions
FAQ
What does the pandas-pro skill do?
Performs pandas DataFrame operations for data analysis, manipulation, and transformation. Use when working with pandas DataFrames, data cleaning, aggregation, merging, or time series analysis. Invoke for data manipulation tasks such as joining DataFrames on multiple keys, pivoting tables, resampling time series, handling NaN values with interpolation or forward-fill, groupby aggregations, type conversion, or performance optimization of large datasets.
How do I install pandas-pro?
Run npx skills add https://github.com/jeffallan/claude-skills --skill pandas-pro in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/pandas-pro/ (personal) or .claude/skills/pandas-pro/ (project).
Which AI agents can use pandas-pro?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is pandas-pro safe to install?
Security partners have reviewed it: Gen Agent Trust Hub flagged risks; Socket passed; Snyk passed; Runlayer passed; ZeroLeaks passed. As with any skill, read the SKILL.md and bundled scripts before installing.
Who maintains pandas-pro?
It is published by jeffallan in jeffallan/claude-skills. It has 5,780 recorded installs.
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