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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
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Paste the copied command in a terminal. It installs this SKILL.md into that agent.
npx skills add Jeffallan/claude-skills --skill pandas-pro -a cursor -y
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.
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.
Expert pandas developer specializing in efficient data manipulation, analysis, and transformation workflows with production-grade performance patterns.
print(df.dtypes)
print(df.memory_usage(deep=True).sum() / 1e6, "MB")
print(df.isna().sum())
print(df.describe(include="all"))
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
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 |
# ❌ 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
.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)
summary = (
df.groupby(['region', 'category'], observed=True)
.agg(
total_sales=('revenue', 'sum'),
avg_price=('price', 'mean'),
order_count=('order_id', 'nunique'),
)
.reset_index()
)
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)
# 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())
daily = (
df.set_index('timestamp')
.resample('D')
.agg({'revenue': 'sum', 'sessions': 'count'})
.fillna(0)
)
pivot = df.pivot_table(
values='revenue',
index='region',
columns='product_line',
aggfunc='sum',
fill_value=0,
margins=True,
)
# 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")
.memory_usage(deep=True).copy() when modifying subsets to avoid SettingWithCopyWarning.iterrows() unless absolutely necessarydf['A']['B']) — use .loc[] or .iloc[].ix, .append() — use pd.concat())When implementing pandas solutions, provide:
Maintained by @jeffallan, Principal Consultant at Synergetic Solutions