🎯 Replace Nested IF Statements with Python Functions

import pandas as pd

def classify_fund(code):
    """Map fund codes to fund names - clean and maintainable"""
    funds = {
        11: 'Fund_A',
        12: 'Fund_B', 
        13: 'Fund_C',
        14: 'Fund_D',
        15: 'Fund_E',
        16: 'Fund_F',
        17: 'Fund_G',
        18: 'Fund_H'
    }
    return funds.get(code, 'Unknown')  # Return 'Unknown' if code not found

# Apply to entire column instantly
df['Fund_Name'] = df['Fund_Code'].apply(classify_fund)

print("Done!")
print(df[['Fund_Code', 'Fund_Name']].head())

funds = {
    11: 'Fund_A',
    12: 'Fund_B',
    # ...
}

return funds.get(code, 'Unknown')

df['Fund_Name'] = df['Fund_Code'].apply(classify_fund)

import pandas as pd

# Sample data
data = {
    'Account': ['1200-11-001', '1300-12-002', '1400-13-003', '1500-99-004'],
    'Fund_Code': [11, 12, 13, 99],
    'Amount': [5000, 3000, 2000, 1000]
}

df = pd.DataFrame(data)

print("BEFORE:")
print(df)

# Classification function
def classify_fund(code):
    """Map fund codes to fund names"""
    funds = {
        11: 'Fund_A',
        12: 'Fund_B',
        13: 'Fund_C',
        14: 'Fund_D',
        15: 'Fund_E',
        16: 'Fund_F',
        17: 'Fund_G',
        18: 'Fund_H'
    }
    return funds.get(code, 'Unknown')

# Apply classification
df['Fund_Name'] = df['Fund_Code'].apply(classify_fund)

print("\nAFTER:")
print(df)

# Summary by fund
print("\nSUMMARY:")
print(df.groupby('Fund_Name')['Amount'].sum())

def classify_account(row):
    """
    Classify accounts based on multiple conditions:
    - Account number range
    - Fund code  
    - Amount threshold
    """
    account_num = int(row['Account'][:4])  # First 4 digits
    fund_code = row['Fund_Code']
    amount = row['Amount']
    
    # Revenue accounts (1000-1999)
    if 1000 <= account_num < 2000:
        if amount > 10000:
            return 'Major Revenue'
        else:
            return 'Minor Revenue'
    
    # Expense accounts (2000-2999)
    elif 2000 <= account_num < 3000:
        if fund_code in [11, 12, 13]:
            return 'Operating Expense'
        else:
            return 'Capital Expense'
    
    # Asset accounts (3000-3999)
    elif 3000 <= account_num < 4000:
        return 'Asset'
    
    # Liability accounts (4000-4999)
    elif 4000 <= account_num < 5000:
        return 'Liability'
    
    else:
        return 'Other'

# Apply to entire dataframe
df['Category'] = df.apply(classify_account, axis=1)

import pandas as pd

# Load mapping from Excel
mapping = pd.read_excel('fund_mappings.xlsx')
print("Mapping table:")
print(mapping)

# Convert to dictionary
fund_dict = dict(zip(mapping['Fund_Code'], mapping['Fund_Name']))

# Classification function (now uses the loaded mapping)
def classify_fund(code):
    return fund_dict.get(code, 'Unknown')

# Apply
df['Fund_Name'] = df['Fund_Code'].apply(classify_fund)

funds = {
    11: 'Fund_A',
    12: 'Fund_B',
    13: 'Fund_C',
    # ...
    18: 'Fund_H',
    19: 'Fund_I'  # ← Add one line. Done.
}

def get_budget_category(account_num):
    """Classify accounts into budget categories and subcategories"""
    
    categories = {
        # Format: (start, end): (category, subcategory)
        (1000, 1099): ('Revenue', 'Tax Revenue'),
        (1100, 1199): ('Revenue', 'Investment Income'),
        (1200, 1299): ('Revenue', 'Other Revenue'),
        (2000, 2099): ('Expense', 'Personnel'),
        (2100, 2199): ('Expense', 'Operations'),
        (2200, 2299): ('Expense', 'Capital'),
        (3000, 3999): ('Asset', 'Current Assets'),
        (4000, 4999): ('Liability', 'Current Liabilities'),
    }
    
    account_num = int(account_num[:4])  # First 4 digits
    
    for (start, end), (cat, subcat) in categories.items():
        if start <= account_num <= end:
            return cat, subcat
    
    return 'Unknown', 'Unknown'

# Apply and split into two columns
df[['Category', 'Subcategory']] = df['Account'].apply(
    lambda x: pd.Series(get_budget_category(x))
)

print(df[['Account', 'Category', 'Subcategory']])

import pandas as pd

# Your data
df = pd.read_excel('your_data.xlsx')

# Define your classification
def classify(code):
    mapping = {
        'A': 'Category 1',
        'B': 'Category 2',
        'C': 'Category 3',
        # Add your categories
    }
    return mapping.get(code, 'Unknown')

# Apply it
df['Category'] = df['Code'].apply(classify)

# Save results
df.to_excel('classified_data.xlsx', index=False)