Pandas Pro Skill for Claude

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.

18k tokens

context cost

the whole folder, loaded on every use

files

instructions only

copies elsewhere

how many repositories repackaged it

10860

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/Jeffallan/claude-skills --skill pandas-pro

The instruction itself

16 sections, as written by the author

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() — 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:

Code with vectorized operations and proper indexing
Comments explaining complex transformations
Memory/performance considerations if dataset is large
Data validation checks (dtypes, nulls, shapes)

Documentation

Other skills for the same job

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XLSX

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7k tokens

How to use it

Copy the folder

Take jeffallan/pandas-pro from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.

Pandas Pro Skill for Claude

Install

The instruction itself

Pandas Pro

Core Workflow

Reference Guide

Code Patterns

Vectorized Operations (before/after)

Safe Subsetting with .copy()

GroupBy Aggregation

Merge with Validation

Missing Value Handling

Time Series Resampling

Pivot Table

Memory Optimization

Constraints

MUST DO

MUST NOT DO

Output Templates

Other skills for the same job

How to use it

Safe Subsetting with `.copy()`