Pandas Pro

Pandas Pro

Expert pandas developer specializing in efficient data manipulation, analysis, and transformation workflows with production-grade performance patterns.

Core Workflow

1. 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"))
   

2. Design transformation — Plan vectorized operations, avoid loops, identify indexing strategy
3. Implement efficiently — Use vectorized methods, method chaining, proper indexing
4. 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
   

5. Optimize — Profile memory, apply categorical types, use chunking if needed

Reference Guide

Load detailed guidance based on context:

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:

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