사용 방법
- 설치 명령을 복사하고 자신의 에이전트 환경에 설치하기 전에 소스 패키지를 확인하세요.
- 필요한 도구, 인증 정보, 지원 환경은 원본 문서에서 확인하세요.
- 입력과 원하는 산출물을 설명하고 출처와 결과를 대조하세요.
npx skills add https://github.com/jeffallan/claude-skills --skill pandas-pro시작 전 확인
- 설치에는 모델 구독, API 한도, 파일 접근 권한이 포함되지 않습니다.
- 별점 수와 설치 수는 활동 지표이며 독립적인 품질·보안 검증이 아닙니다.
원본 Skill 문서
원본 저장소의 제목, 예시, 코드, 표, 링크, 이미지를 유지해 표시합니다.
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:
python
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:
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- 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 iteration
for i, row in df.iterrows():
df.at[i, 'tax'] = row['price'] * 0.2
# ✅ USE: vectorized assignment
df['tax'] = df['price'] * 0.2Safe Subsetting with .copy()
python
# ❌ 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
python
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
python
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
python
# 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
python
daily = (
df.set_index('timestamp')
.resample('D')
.agg({'revenue': 'sum', 'sessions': 'count'})
.fillna(0)
)Pivot Table
python
pivot = 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 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
