Mode d’emploi
- Copiez la commande et examinez le paquet avant de l’installer dans votre environnement agent.
- Consultez le document original pour les outils, identifiants et environnements requis.
- Décrivez les entrées et le livrable souhaité ; comparez la sortie aux sources.
npx skills add https://github.com/jeffallan/claude-skills --skill pandas-proAvant de commencer
- L’installation n’inclut ni abonnement au modèle, ni quota API, ni accès aux fichiers.
- Étoiles et installations reflètent l’activité, pas un test indépendant de qualité ou de sécurité.
Document Skill original
Rendu depuis le dépôt source en conservant titres, exemples, code, tableaux, liens et images.
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
