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npx skills add https://github.com/jeffallan/claude-skills --skill pandas-proWorks with Paperclip
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Pandas Pro drops into any Paperclip agent that handles this kind of work. Assign it to a specialist inside a pre-configured PaperclipOrg company and the skill becomes available on every heartbeat — no prompt engineering, no tool wiring.
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SKILL.md178 linesExpandCollapse
---name: pandas-prodescription: 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.license: MITmetadata: author: https://github.com/Jeffallan version: "1.1.0" domain: data-ml triggers: pandas, DataFrame, data manipulation, data cleaning, aggregation, groupby, merge, join, time series, data wrangling, pivot table, data transformation role: expert scope: implementation output-format: code related-skills: python-pro--- # Pandas Pro Expert pandas developer specializing in efficient data manipulation, analysis, and transformation workflows with production-grade performance patterns. ## Core Workflow 1. **Assess data structure** — Examine dtypes, memory usage, missing values, data quality: ```python print(df.dtypes) print(df.memory_usage(deep=True).sum() / 1e6, "MB") print(df.isna().sum()) print(df.describe(include="all")) ```2. **Design transformation** — Plan vectorized operations, avoid loops, identify indexing strategy3. **Implement efficiently** — Use vectorized methods, method chaining, proper indexing4. **Validate results** — Check dtypes, shapes, null counts, and row counts: ```python 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 ```5. **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) ```python# ❌ AVOID: row-by-row iterationfor i, row in df.iterrows(): df.at[i, 'tax'] = row['price'] * 0.2 # ✅ USE: vectorized assignmentdf['tax'] = df['price'] * 0.2``` ### Safe Subsetting with `.copy()` ```python# ❌ AVOID: chained indexing triggers SettingWithCopyWarningdf['A']['B'] = 1 # ✅ USE: .loc[] with explicit copy when mutating a subsetsubset = df.loc[df['status'] == 'active', :].copy()subset['score'] = subset['score'].fillna(0)``` ### GroupBy Aggregation ```pythonsummary = ( 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 ```pythonmerged = 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 ```python# Forward-fill then interpolate numeric gapsdf['price'] = df['price'].ffill().interpolate(method='linear') # Fill categoricals with mode, numerics with medianfor 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 ```pythondaily = ( df.set_index('timestamp') .resample('D') .agg({'revenue': 'sum', 'sessions': 'count'}) .fillna(0))``` ### Pivot Table ```pythonpivot = df.pivot_table( values='revenue', index='region', columns='product_line', aggfunc='sum', fill_value=0, margins=True,)``` ### Memory Optimization ```python# Downcast numerics and convert low-cardinality strings to categoricaldf['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()` — use `pd.concat()`)- Convert to Python lists for operations possible in pandas- Assume data is clean without validation ## Output Templates When implementing pandas solutions, provide:1. Code with vectorized operations and proper indexing2. Comments explaining complex transformations3. Memory/performance considerations if dataset is large4. Data validation checks (dtypes, nulls, shapes)