sales-data-analysis/.cursor/rules/analysis_patterns.md

Common Analysis Patterns

⭐ RECOMMENDED: Use Utilities

Always prefer analysis_utils.py and config.py over manual implementations:

• Consistent formatting
• Fewer errors
• Easier maintenance
• Standardized output

Standard Script Structure (Using Utilities)

RECOMMENDED: Use analysis_utils.py and config.py for consistency:

# 1. IMPORTS
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from data_loader import load_sales_data, validate_data_structure
from validate_revenue import validate_revenue
from analysis_utils import (
    get_ltm_period_config, get_annual_data, calculate_annual_metrics,
    get_millions_formatter, setup_revenue_chart, save_chart,
    format_currency, print_annual_summary, sort_mixed_years,
    apply_exclusion_filters
)
from config import (
    DATA_FILE, OUTPUT_DIR, CHART_SIZES, ensure_directories,
    get_data_path, REVENUE_COLUMN, COMPANY_NAME
)

# 2. LOAD DATA (ALWAYS use data_loader)
df = load_sales_data(get_data_path())

# 3. VALIDATE DATA STRUCTURE
is_valid, msg = validate_data_structure(df)
if not is_valid:
    print(f"ERROR: {msg}")
    return

# 4. APPLY EXCLUSION FILTERS (if configured)
df = apply_exclusion_filters(df)

# 5. SETUP LTM (if doing annual comparisons and LTM is enabled)
ltm_start, ltm_end = get_ltm_period_config()

# 6. DATA PREPARATION
# Convert columns, filter data, create derived columns

# 7. ANALYSIS LOGIC
# Use calculate_annual_metrics() for annual aggregations

# 8. VISUALIZATIONS
# Use setup_revenue_chart() and save_chart() from analysis_utils

# 9. VALIDATION
validate_revenue(df, "Analysis Name")

Annual Aggregation Pattern

RECOMMENDED: Use calculate_annual_metrics() from analysis_utils.py:

from analysis_utils import calculate_annual_metrics, get_ltm_period_config
from config import REVENUE_COLUMN

ltm_start, ltm_end = get_ltm_period_config()

def calculate_metrics(year_data):
    """Calculate metrics for a single year"""
    return {
        'Revenue': year_data[REVENUE_COLUMN].sum(),
        # ... other metrics
    }

annual_df = calculate_annual_metrics(df, calculate_metrics, ltm_start, ltm_end)

Chart Formatting Pattern

ALWAYS use this pattern for revenue charts:

from analysis_utils import setup_revenue_chart, save_chart
from config import CHART_SIZES

fig, ax = plt.subplots(figsize=CHART_SIZES['medium'])

# Divide data by 1e6 BEFORE plotting
ax.plot(data / 1e6, ...)
# OR
ax.bar(x, values / 1e6, ...)

# Apply formatter automatically
setup_revenue_chart(ax)

# Save chart
save_chart(fig, 'chart_name.png')
plt.close()

Mixed Type Handling

When dealing with year columns that may contain mixed int/str types (e.g., "2025 (LTM 9/2025)"):

from analysis_utils import sort_mixed_years

# Sort DataFrame by year
df_sorted = sort_mixed_years(df, year_col='Year')

# For chart labels
years = df_sorted['Year'].tolist()
x_pos = range(len(years))
ax.set_xticks(x_pos)
ax.set_xticklabels(years, rotation=45, ha='right')

Price Calculation Pattern

from analysis_utils import calculate_price_per_unit
from config import QUANTITY_COLUMN, REVENUE_COLUMN

# Calculate average price per unit (excludes outliers automatically)
price_per_unit = calculate_price_per_unit(df, QUANTITY_COLUMN, REVENUE_COLUMN)

Exclusion Filters Pattern

If you need to exclude specific segments (e.g., test accounts, business units):

from analysis_utils import apply_exclusion_filters

# Configure in config.py:
# EXCLUSION_FILTERS = {
#     'enabled': True,
#     'exclude_by_column': 'Country',
#     'exclude_values': ['KVT', 'Test']
# }

df = apply_exclusion_filters(df)

Using Configuration Values

ALWAYS use config values instead of hardcoding:

from config import (
    REVENUE_COLUMN,      # Use this instead of 'USD' or 'Amount'
    CUSTOMER_COLUMN,     # Use this instead of 'Customer'
    DATE_COLUMN,         # Use this instead of 'InvoiceDate'
    COMPANY_NAME,        # Use this for titles
    ANALYSIS_YEARS,      # Use this for year filtering
    CHART_SIZES,         # Use this for figure sizes
)