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)

Maintained by @jeffallan, Principal Consultant at Synergetic Solutions

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