Python for CA Professionals

print('Hello, World!')

Hello, World!

print('Welcome to Python for Finance!')
print('This notebook will help CA professionals automate their work.')
print('Let us get started!')

Welcome to Python for Finance!
This notebook will help CA professionals automate their work.
Let us get started!

# This is a comment — Python ignores this line
print('Fiscal Year 2081-82 BS (Mid-July 2024 to Mid-July 2025)')  # You can also add comments at the end of a line

# The line below prints the company name
print('Sunrise Trading Pvt. Ltd.')

# Always write comments to explain WHY you wrote the code, not just WHAT it does

Fiscal Year 2081-82 BS (Mid-July 2024 to Mid-July 2025)
Sunrise Trading Pvt. Ltd.

# Write your code here

cash_balance = 50000
     ↑              ↑
 account name    account value

# Creating variables — storing financial data
revenue = 1500000       # Total revenue: NPR 15,00,000
expenses = 980000       # Total expenses: NPR 9,80,000
tax_rate = 0.30         # Tax rate: 30%
company_name = 'Sunrise Trading Pvt. Ltd.'

# Now we can USE these variables
print(company_name)
print(revenue)
print(expenses)
print(tax_rate)

Sunrise Trading Pvt. Ltd.
1500000
980000
0.3

# Variables can be used in calculations — just like cell references in Excel
profit = revenue - expenses
print('Profit before tax:', profit)

tax_payable = profit * tax_rate
print('Tax payable:', tax_payable)

profit_after_tax = profit - tax_payable
print('Profit after tax:', profit_after_tax)

Profit before tax: 520000
Tax payable: 156000.0
Profit after tax: 364000.0

# Good variable names — descriptive and clear
gross_salary = 1200000
standard_deduction = 50000
hra_exemption = 180000
taxable_income = gross_salary - standard_deduction - hra_exemption

print('Taxable Income:', taxable_income)

# Variables can be updated (reassigned)
gross_salary = 1500000  # Got a raise!
print('Updated salary:', gross_salary)

Taxable Income: 970000
Updated salary: 1500000

# Write your code here

# int — whole numbers (no decimal point)
number_of_invoices = 250
number_of_employees = 48
financial_year = 2025

print('Number of invoices:', number_of_invoices)
print('Type:', type(number_of_invoices))  # Shows the data type

Number of invoices: 250
Type: <class 'int'>

# float — numbers with a decimal point
vat_rate = 13.0          # 13% Nepal VAT rate
income_tax_rate = 25.0   # Nepal corporate income tax rate
interest_rate = 8.5      # 8.5% interest rate
invoice_amount = 47500.75

print('VAT Rate:', vat_rate)
print('Type:', type(vat_rate))
print('Invoice Amount:', invoice_amount)

VAT Rate: 13.0
Type: <class 'float'>
Invoice Amount: 47500.75

# str (string) — text data
# Strings are ALWAYS enclosed in quotes (single '' or double "")

client_name = 'Himalayan Tech Solutions Pvt. Ltd.'
audit_period = 'Shrawan 2081 to Ashadh 2082 BS'
pan_number = '123456789'  # Nepal PAN (9 digits)   # Even numbers stored as text when not used for calculation

print('Client:', client_name)
print('Audit Period:', audit_period)
print('PAN:', pan_number)
print('Type:', type(client_name))

Client: Himalayan Tech Solutions Pvt. Ltd.
Audit Period: Shrawan 2081 to Ashadh 2082 BS
PAN: 123456789
Type: <class 'str'>

# bool — Boolean (True or False only)
# Very useful for status checks and decisions

is_audit_complete = True
is_tax_paid = False
is_listed_company = True

print('Audit complete?', is_audit_complete)
print('Tax paid?', is_tax_paid)
print('Listed company?', is_listed_company)
print('Type:', type(is_audit_complete))

Audit complete? True
Tax paid? False
Listed company? True
Type: <class 'bool'>

# Real-world example: A client record with mixed data types
client_name = 'Ncell Pvt. Ltd.'       # str
client_id = 10045                      # int
revenue_crores = 146767.0             # float (Revenue in crores)
is_fortune500 = True                   # bool

print('--- Client Profile ---')
print('Name:', client_name)
print('ID:', client_id)
print('Revenue (NPR Crores):', revenue_crores)
print('Fortune 500 Company?', is_fortune500)

--- Client Profile ---
Name: Ncell Pvt. Ltd.
ID: 10045
Revenue (NPR Crores): 146767.0
Fortune 500 Company? True

# This is a NUMBER (int)
amount1 = 50000
print(type(amount1))   # <class 'int'>

# This is TEXT (str) — the quotes make it text!
amount2 = '50000'
print(type(amount2))   # <class 'str'>

# What happens when you try to add them?
print(amount1 + amount1)   # 100000 — correct! (number + number)
print(amount2 + amount2)   # '5000050000' — joins text! (text + text)

# Lesson: Always store amounts as numbers (int or float), not as strings

<class 'int'>
<class 'str'>
100000
5000050000

# Basic arithmetic
revenue = 2500000
cost_of_goods = 1600000
operating_expenses = 350000

gross_profit = revenue - cost_of_goods
print('Gross Profit:', gross_profit)

net_profit = gross_profit - operating_expenses
print('Net Profit:', net_profit)

gross_margin = (gross_profit / revenue) * 100
print('Gross Profit Margin:', gross_margin, '%')

Gross Profit: 900000
Net Profit: 550000
Gross Profit Margin: 36.0 %

# VAT Calculation (Nepal — single tax, no CGST/SGST split)
invoice_base_amount = 85000   # Base amount before VAT (NPR)
vat_rate = 13.0               # 13% VAT (Nepal standard rate per IRD)

vat_amount = invoice_base_amount * (vat_rate / 100)
invoice_total = invoice_base_amount + vat_amount

print('--- VAT Invoice Summary (Nepal) ---')
print('Base Amount:    NPR ', f'{invoice_base_amount:,}')
print('VAT @ 13%:      NPR ', f'{vat_amount:,}')
print('Invoice Total:  NPR ', f'{invoice_total:,}')
print()
print('Note: Nepal uses a single VAT rate (no CGST/SGST split like India)')
print('VAT Registration required for turnover > NPR 50 Lakhs per year (IRD)')

--- VAT Invoice Summary (Nepal) ---
Base Amount:    NPR  85,000
VAT @ 13%:      NPR  11,050.0
Invoice Total:  NPR  96,050.0

Note: Nepal uses a single VAT rate (no CGST/SGST split like India)
VAT Registration required for turnover > NPR 50 Lakhs per year (IRD)

# Compound Interest Calculation
# Formula: A = P * (1 + r/n)^(n*t)

principal = 500000    # NPR 5,00,000 initial investment
annual_rate = 0.12   # 12% per annum
n = 4                 # Compounded quarterly (4 times a year)
years = 3             # Investment period

# ** is the power operator in Python (same as ^ in Excel/maths)
amount = principal * (1 + annual_rate / n) ** (n * years)

interest_earned = amount - principal

print('--- Fixed Deposit Summary ---')
print('Principal:        NPR ', principal)
print('Rate:             ', annual_rate * 100, '% per annum')
print('Period:           ', years, 'years')
print('Maturity Amount:  NPR ', round(amount, 2))
print('Interest Earned:  NPR ', round(interest_earned, 2))

--- Fixed Deposit Summary ---
Principal:        NPR  500000
Rate:              12.0 % per annum
Period:            3 years
Maturity Amount:  NPR  712880.44
Interest Earned:  NPR  212880.44

# EMI Calculation
# Formula: EMI = [P * r * (1+r)^n] / [(1+r)^n - 1]

loan_amount = 2000000    # NPR 20,00,000 home loan
annual_rate = 8.5        # 8.5% per annum
loan_years = 20          # 20 years

monthly_rate = (annual_rate / 100) / 12   # Convert to monthly rate
n_months = loan_years * 12                 # Total number of months

emi = (loan_amount * monthly_rate * (1 + monthly_rate) ** n_months) / ((1 + monthly_rate) ** n_months - 1)

total_payment = emi * n_months
total_interest = total_payment - loan_amount

print('--- Home Loan EMI Calculator ---')
print('Loan Amount:      NPR ', loan_amount)
print('Interest Rate:    ', annual_rate, '% per annum')
print('Loan Tenure:      ', loan_years, 'years')
print('Monthly EMI:      NPR ', round(emi, 2))
print('Total Payment:    NPR ', round(total_payment, 2))
print('Total Interest:   NPR ', round(total_interest, 2))

--- Home Loan EMI Calculator ---
Loan Amount:      NPR  2000000
Interest Rate:     8.5 % per annum
Loan Tenure:       20 years
Monthly EMI:      NPR  17356.46
Total Payment:    NPR  4165551.52
Total Interest:   NPR  2165551.52

# Write your P&L calculator here

if condition:
    # do this if condition is True
elif another_condition:
    # do this if the above is False but this is True
else:
    # do this if ALL above conditions are False

# Simple if/else — Checking if a company is profitable
net_profit = 250000

if net_profit > 0:
    print('Company is PROFITABLE')
    print('Profit amount: NPR ', net_profit)
else:
    print('Company is at a LOSS')
    print('Loss amount: NPR ', abs(net_profit))   # abs() gives absolute (positive) value

# Try changing net_profit to a negative number and re-run!

Company is PROFITABLE
Profit amount: NPR  250000

# Comparison operators — used to create conditions
# >   greater than
# <   less than
# >=  greater than or equal to
# <=  less than or equal to
# ==  equal to (note: TWO equal signs!)
# !=  not equal to

revenue = 5000000
threshold = 4000000

print(revenue > threshold)    # True
print(revenue < threshold)    # False
print(revenue == threshold)   # False (not equal)
print(revenue != threshold)   # True (they are different)

True
False
False
True

# Nepal Income Tax Slab Calculator (FY 2081-82 BS)
# Source: Inland Revenue Department (IRD), Nepal
taxable_income = 850000   # Change this to test different amounts (in NPR)

# Nepal individual income tax slabs (FY 2081-82)
# Slab 1: Up to NPR 5,00,000        → 1%
# Slab 2: NPR 5,00,001 to 7,00,000  → 10%
# Slab 3: NPR 7,00,001 to 20,00,000 → 20%
# Slab 4: Above NPR 20,00,000       → 30%

print('Taxable Income: NPR ', f'{taxable_income:,}')
print('-' * 45)

if taxable_income <= 500000:
    tax = taxable_income * 0.01
    slab = '1% slab (up to NPR 5 Lakh)'

elif taxable_income <= 700000:
    tax = (500000 * 0.01) + (taxable_income - 500000) * 0.10
    slab = '10% slab (NPR 5L to NPR 7L)'

elif taxable_income <= 2000000:
    tax = (500000 * 0.01) + (200000 * 0.10) + (taxable_income - 700000) * 0.20
    slab = '20% slab (NPR 7L to NPR 20L)'

else:
    tax = (500000 * 0.01) + (200000 * 0.10) + (1300000 * 0.20) + (taxable_income - 2000000) * 0.30
    slab = '30% slab (above NPR 20L)'

print('Applicable Slab:', slab)
print('Income Tax:     NPR ', f'{round(tax, 2):,}')
print('Net Income:     NPR ', f'{round(taxable_income - tax, 2):,}')
print()
print('Note: No cess/surcharge in Nepal. SSF contribution is separate.')

Taxable Income: NPR  850,000
---------------------------------------------
Applicable Slab: 20% slab (NPR 7L to NPR 20L)
Income Tax:     NPR  55,000.0
Net Income:     NPR  795,000.0

Note: No cess/surcharge in Nepal. SSF contribution is separate.

# Loan Eligibility Check — using 'and' / 'or' for multiple conditions
age = 35
annual_income = 720000     # NPR 7.2 lakhs
credit_score = 720
existing_emi = 8000        # Monthly existing EMI

# Monthly income
monthly_income = annual_income / 12

# EMI should not exceed 40% of monthly income (FOIR rule)
max_emi = monthly_income * 0.40

print('--- Loan Eligibility Assessment ---')
print('Monthly Income: NPR ', round(monthly_income, 2))
print('Max Allowable EMI (40%): NPR ', round(max_emi, 2))
print()

if age >= 21 and age <= 60 and credit_score >= 700 and existing_emi < max_emi:
    print('Result: ELIGIBLE for loan ✓')
elif credit_score < 700:
    print('Result: NOT ELIGIBLE — Low credit score')
elif existing_emi >= max_emi:
    print('Result: NOT ELIGIBLE — Existing EMI too high')
else:
    print('Result: NOT ELIGIBLE — Age criteria not met')

--- Loan Eligibility Assessment ---
Monthly Income: NPR  60000.0
Max Allowable EMI (40%): NPR  24000.0

Result: ELIGIBLE for loan ✓

# Write your D/E ratio checker here
total_debt = 4500000
total_equity = 3000000

# Calculate D/E ratio


# Add your if/elif/else here

# String basics
company = 'CloudNepal Pvt. Ltd.'
city = 'Kathmandu'

# String length — how many characters?
print('Company name length:', len(company))

# Convert case
print(company.upper())       # WIPRO LIMITED
print(company.lower())       # wipro limited

# Combine strings (concatenation) using +
full_description = company + ', ' + city
print(full_description)

Company name length: 20
CLOUDNEPAL PVT. LTD.
cloudnepal pvt. ltd.
CloudNepal Pvt. Ltd., Kathmandu

# f-strings — The best way to create formatted text
# Put 'f' before the opening quote, then use {} to insert variable values

client_name = 'Unilever Nepal Ltd.'
audit_year = 2025
fee = 850000
audit_status = 'In Progress'

# Without f-string (old way — clunky)
message_old = 'Client: ' + client_name + ', Year: ' + str(audit_year)
print(message_old)

# With f-string (modern way — clean and readable)
message = f'Client: {client_name}, Year: {audit_year}'
print(message)

# You can even do calculations inside {}
print(f'Audit Fee: NPR {fee:,}')        # {:,} adds comma formatting
print(f'Status: {audit_status}')
print(f'Fee in Lakhs: NPR {fee/100000:.2f}L')  # :.2f means 2 decimal places

Client: Unilever Nepal Ltd., Year: 2025
Client: Unilever Nepal Ltd., Year: 2025
Audit Fee: NPR 850,000
Status: In Progress
Fee in Lakhs: NPR 8.50L

# Creating a formatted financial report header using f-strings
company_name = 'Bottlers Nepal Ltd.'
report_type = 'Statement of Profit and Loss'
period = 'Year ended 16th July 2025 (Ashadh 2082 BS)'
currency = 'NPR (NPR in Lakhs)'

# Using the * operator to repeat characters — useful for formatting
separator = '=' * 55

print(separator)
print(f'  {company_name}')
print(f'  {report_type}')
print(f'  {period}')
print(f'  All figures in: {currency}')
print(separator)

=======================================================
  Bottlers Nepal Ltd.
  Statement of Profit and Loss
  Year ended 16th July 2025 (Ashadh 2082 BS)
  All figures in: NPR (NPR in Lakhs)
=======================================================

# Useful string methods for data cleaning (very common in practice!)

# Often, data imported from Excel/CSV has extra spaces or inconsistent case
messy_client_name = '  infosys limited  '  # Extra spaces
print('Before cleaning:', messy_client_name)

# .strip() removes leading and trailing spaces
clean_name = messy_client_name.strip()
print('After strip():', clean_name)

# .title() makes first letter of each word uppercase
formatted_name = clean_name.title()
print('After title():', formatted_name)

# .replace() — find and replace text
description = 'Sales Receipts - Cash - 2024'
updated = description.replace('Cash', 'Online')
print('Updated:', updated)

# Check if a string contains something
print('Contains 2024?', '2024' in description)   # True
print('Contains 2025?', '2025' in description)   # False

Before cleaning:   infosys limited  
After strip(): infosys limited
After title(): Infosys Limited
Updated: Sales Receipts - Online - 2024
Contains 2024? True
Contains 2025? False

# Creating lists
monthly_revenue = [120000, 135000, 98000, 145000, 162000, 178000,
                   155000, 142000, 168000, 191000, 203000, 215000]

expense_categories = ['Salaries', 'Rent', 'Utilities', 'Marketing', 'Depreciation']

clients = ['NTC', 'Ncell', 'Fusemachines', 'InfoDevelopers', 'Deerwalk Services Pvt. Ltd.']

print('Monthly Revenue:', monthly_revenue)
print('Expense Categories:', expense_categories)
print('Clients:', clients)
print()
print('Number of months of data:', len(monthly_revenue))   # len() gives length
print('Number of clients:', len(clients))

Monthly Revenue: [120000, 135000, 98000, 145000, 162000, 178000, 155000, 142000, 168000, 191000, 203000, 215000]
Expense Categories: ['Salaries', 'Rent', 'Utilities', 'Marketing', 'Depreciation']
Clients: ['NTC', 'Ncell', 'Fusemachines', 'InfoDevelopers', 'Deerwalk Services Pvt. Ltd.']

Number of months of data: 12
Number of clients: 5

# Accessing items in a list — indexing
# IMPORTANT: Python counts from 0, not 1!
# Index:   0       1      2      3       4
clients = ['NTC', 'Ncell', 'Fusemachines', 'InfoDevelopers', 'Deerwalk Services Pvt. Ltd.']

print('First client:', clients[0])    # TCS
print('Second client:', clients[1])   # Ncell
print('Last client:', clients[-1])    # -1 always gives the last item!
print('Second last:', clients[-2])

# Slicing — getting a range of items [start:end] (end is NOT included)
print('First three clients:', clients[0:3])   # Ncell, NTC, Fusemachines
print('Last two clients:', clients[-2:])

First client: NTC
Second client: Ncell
Last client: Deerwalk Services Pvt. Ltd.
Second last: InfoDevelopers
First three clients: ['NTC', 'Ncell', 'Fusemachines']
Last two clients: ['InfoDevelopers', 'Deerwalk Services Pvt. Ltd.']

# Useful list operations for financial data
monthly_revenue = [120000, 135000, 98000, 145000, 162000, 178000,
                   155000, 142000, 168000, 191000, 203000, 215000]

print('--- Revenue Analysis ---')
print('Total Annual Revenue: NPR ', sum(monthly_revenue))     # sum() adds all values
print('Highest Month:        NPR ', max(monthly_revenue))     # max() finds highest
print('Lowest Month:         NPR ', min(monthly_revenue))     # min() finds lowest
print('Average Monthly:      NPR ', sum(monthly_revenue) / len(monthly_revenue))
print()

# Adding items to a list
clients = ['NTC', 'Ncell', 'Fusemachines']
clients.append('Accenture')    # Add to the end
print('Updated clients:', clients)

# Sorting
invoices = [45000, 12000, 78000, 34000, 91000, 23000]
invoices.sort()                # Sort in ascending order
print('Sorted invoices:', invoices)
invoices.sort(reverse=True)    # Sort in descending order
print('Descending order:', invoices)

--- Revenue Analysis ---
Total Annual Revenue: NPR  1912000
Highest Month:        NPR  215000
Lowest Month:         NPR  98000
Average Monthly:      NPR  159333.33333333334

Updated clients: ['NTC', 'Ncell', 'Fusemachines', 'Accenture']
Sorted invoices: [12000, 23000, 34000, 45000, 78000, 91000]
Descending order: [91000, 78000, 45000, 34000, 23000, 12000]

Think of it like a mini spreadsheet row:
Column name  →  Value
'name'       →  'NTC'
'revenue'    →  2200000
'sector'     →  'IT'

# A dictionary — like a client record
client = {
    'name': 'Nepal Telecom (NTC)',
    'pan': '123456789',  # Nepal PAN — 9-digit numeric (IRD registration)
    'sector': 'Information Technology',
    'annual_revenue': 220000000000,   # NPR 2.2 lakh crores
    'employees': 614000,
    'is_listed': True
}

# Accessing values using the key (like looking up a column name)
print('Client Name:', client['name'])
print('PAN:', client['pan'])
print('Sector:', client['sector'])
print('Listed?', client['is_listed'])

Client Name: Nepal Telecom (NTC)
PAN: 123456789
Sector: Information Technology
Listed? True

# Adding, updating, and removing from a dictionary
invoice = {
    'invoice_no': 'INV-2025-001',
    'client': 'ABC Pvt Ltd',
    'date': '15-Apr-2025',
    'amount': 250000,
    'vat_rate': 18
}

# Add a new key-value pair
invoice['vat_amount'] = invoice['amount'] * invoice['vat_rate'] / 100
invoice['total'] = invoice['amount'] + invoice['vat_amount']

# Update an existing value
invoice['status'] = 'Paid'

# Print all key-value pairs
print('--- Invoice Details ---')
for key, value in invoice.items():
    print(f'{key:15}: {value}')

--- Invoice Details ---
invoice_no     : INV-2025-001
client         : ABC Pvt Ltd
date           : 15-Apr-2025
amount         : 250000
vat_rate       : 18
vat_amount     : 45000.0
total          : 295000.0
status         : Paid

# List of dictionaries — like a table in Excel
# Each dictionary is one row, the keys are column names

transactions = [
    {'date': '01-Apr-2025', 'description': 'Sales Revenue',    'debit': 0,      'credit': 500000},
    {'date': '03-Apr-2025', 'description': 'Office Rent',      'debit': 45000,  'credit': 0},
    {'date': '05-Apr-2025', 'description': 'Sales Revenue',    'debit': 0,      'credit': 320000},
    {'date': '10-Apr-2025', 'description': 'Salaries Paid',    'debit': 180000, 'credit': 0},
    {'date': '15-Apr-2025', 'description': 'Utility Bill',     'debit': 12000,  'credit': 0},
]

print('--- Transaction Ledger ---')
print(f'{"Date":<15} {"Description":<20} {"Debit":>12} {"Credit":>12}')
print('-' * 62)
for txn in transactions:
    debit_str  = f"NPR {txn['debit']:>10,}" if txn['debit'] > 0 else ''
    credit_str = f"NPR {txn['credit']:>10,}" if txn['credit'] > 0 else ''
    print(f"{txn['date']:<15} {txn['description']:<20} {debit_str:>12} {credit_str:>12}")

--- Transaction Ledger ---
Date            Description                 Debit       Credit
--------------------------------------------------------------
01-Apr-2025     Sales Revenue                     NPR    500,000
03-Apr-2025     Office Rent          NPR     45,000             
05-Apr-2025     Sales Revenue                     NPR    320,000
10-Apr-2025     Salaries Paid        NPR    180,000             
15-Apr-2025     Utility Bill         NPR     12,000

for item in collection:
    # do something with item

# Simple for loop — going through a list
clients = ['NTC', 'Ncell', 'Fusemachines', 'InfoDevelopers', 'Deerwalk Services Pvt. Ltd.']

print('--- Client List ---')
for client in clients:
    print('Client:', client)

--- Client List ---
Client: NTC
Client: Ncell
Client: Fusemachines
Client: InfoDevelopers
Client: Deerwalk Services Pvt. Ltd.

# Batch GST calculation on multiple invoices
invoice_amounts = [85000, 42000, 125000, 67500, 98000, 33000]
vat_rate = 13.0

total_gst_collected = 0   # We'll accumulate GST here

print(f"{'Invoice Amount':>17} | {'GST (13%)':>12} | {'Total Amount':>14}")
print('-' * 50)

for amount in invoice_amounts:
    gst = amount * vat_rate / 100
    total = amount + gst
    total_gst_collected += gst   # Same as: total_gst_collected = total_gst_collected + gst
    print(f'NPR {amount:>15,} | NPR {gst:>11,.2f} | NPR {total:>12,.2f}')

print('-' * 50)
print(f'Total GST Liability: NPR {total_gst_collected:,.2f}')

   Invoice Amount |    GST (13%) |   Total Amount
--------------------------------------------------
NPR          85,000 | NPR   11,050.00 | NPR    96,050.00
NPR          42,000 | NPR    5,460.00 | NPR    47,460.00
NPR         125,000 | NPR   16,250.00 | NPR   141,250.00
NPR          67,500 | NPR    8,775.00 | NPR    76,275.00
NPR          98,000 | NPR   12,740.00 | NPR   110,740.00
NPR          33,000 | NPR    4,290.00 | NPR    37,290.00
--------------------------------------------------
Total GST Liability: NPR 58,565.00

# range() — loop a specific number of times
# Useful when you don't have a list but know how many times to repeat

print('Quarterly Revenue Projections (10% growth each year):')
base_revenue = 1000000   # NPR 10 lakhs starting revenue
growth_rate = 0.10

for year in range(1, 6):   # range(1, 6) gives: 1, 2, 3, 4, 5
    projected = base_revenue * (1 + growth_rate) ** year
    print(f'Year {year}: NPR {projected:>12,.2f}')

Quarterly Revenue Projections (10% growth each year):
Year 1: NPR 1,100,000.00
Year 2: NPR 1,210,000.00
Year 3: NPR 1,331,000.00
Year 4: NPR 1,464,100.00
Year 5: NPR 1,610,510.00

# while loop — keeps going as long as a condition is True
# Use when you don't know HOW MANY times to repeat

# How many years to double your investment at 8% per annum? (Rule of 72)
investment = 100000   # Starting amount
target = 200000       # Double the investment
rate = 0.08           # 8% annual return
year = 0

print('Investment Growth at 8% p.a.:')
while investment < target:
    investment = investment * (1 + rate)   # Grow by 8%
    year += 1   # Count the year
    print(f'Year {year}: NPR {investment:>12,.2f}')

print(f'\nYour investment doubled in {year} years!')
print(f'(Rule of 72 predicts: {round(72/8)} years)')

Investment Growth at 8% p.a.:
Year 1: NPR   108,000.00
Year 2: NPR   116,640.00
Year 3: NPR   125,971.20
Year 4: NPR   136,048.90
Year 5: NPR   146,932.81
Year 6: NPR   158,687.43
Year 7: NPR   171,382.43
Year 8: NPR   185,093.02
Year 9: NPR   199,900.46
Year 10: NPR   215,892.50

Your investment doubled in 10 years!
(Rule of 72 predicts: 9 years)

salaries = [480000, 720000, 960000, 1200000, 1440000, 2400000]

salaries = [480000, 720000, 960000, 1200000, 1440000, 2400000]

# Write your loop here

def function_name(input1, input2):
    # Do something with the inputs
    result = input1 + input2
    return result      # Send the answer back

# Defining a simple function
def calculate_gst(base_amount, rate=18):
    """
    Calculates GST on a given amount.
    base_amount: The pre-GST invoice amount
    rate: GST rate in % (default is 13%)
    Returns: (vat_amount, total_amount)
    """
    vat_amount = base_amount * rate / 100
    total_amount = base_amount + vat_amount
    return vat_amount, total_amount

# Calling the function — use it like Excel's VLOOKUP
gst, total = calculate_gst(85000)         # Uses default 13%
print(f'GST: NPR {gst:,}  |  Total: NPR {total:,}')

gst, total = calculate_gst(50000, 5)      # 5% GST
print(f'GST: NPR {gst:,}  |  Total: NPR {total:,}')

gst, total = calculate_gst(200000, 28)    # 28% GST
print(f'GST: NPR {gst:,}  |  Total: NPR {total:,}')

GST: NPR 15,300.0  |  Total: NPR 100,300.0
GST: NPR 2,500.0  |  Total: NPR 52,500.0
GST: NPR 56,000.0  |  Total: NPR 256,000.0

# Nepal Income Tax Calculator (FY 2081-82 BS)
# IRD-compliant individual income tax rates

def calculate_nepal_income_tax(taxable_income, status='individual'):
    """
    Calculate Nepal income tax per IRD slabs (FY 2081-82 BS).
    status: 'individual' (single) or 'couple' (additional NPR 1L exemption)
    """
    extra_exemption = 100000 if status == 'couple' else 0
    adjusted = max(taxable_income - extra_exemption, 0)

    # Nepal progressive slab
    if adjusted <= 500000:
        tax = adjusted * 0.01
    elif adjusted <= 700000:
        tax = 5000 + (adjusted - 500000) * 0.10
    elif adjusted <= 2000000:
        tax = 5000 + 20000 + (adjusted - 700000) * 0.20
    else:
        tax = 5000 + 20000 + 260000 + (adjusted - 2000000) * 0.30

    # Karmachari Sanchaya Kosh (EPF) is separate — not income tax
    return round(tax, 2)


# Test the function
income = 1200000

tax_individual = calculate_nepal_income_tax(income, 'individual')
tax_couple     = calculate_nepal_income_tax(income, 'couple')

print(f'Taxable Income:  NPR {income:,}')
print()
print(f"{'Particulars':<28} {'Individual':>12} {'Couple':>12}")
print('-' * 55)
print(f"{'Income Tax':<28} NPR {tax_individual:>9,} NPR {tax_couple:>9,}")
print(f"{'Effective Rate':<28} {tax_individual/income*100:>11.2f}% {tax_couple/income*100:>10.2f}%")
print(f"{'Net Take-home':<28} NPR {income-tax_individual:>9,} NPR {income-tax_couple:>9,}")
print()
print(f'Couple gets NPR {tax_individual - tax_couple:,} tax saving (NPR 1L extra exemption at 20% bracket)')
print()
print('Source: Inland Revenue Department (IRD), Nepal — FY 2081-82 BS')

Taxable Income:  NPR 1,200,000

Particulars                    Individual       Couple
-------------------------------------------------------
Income Tax                   NPR 125,000.0 NPR 105,000.0
Effective Rate                     10.42%       8.75%
Net Take-home                NPR 1,075,000.0 NPR 1,095,000.0

Couple gets NPR 20,000.0 tax saving (NPR 1L extra exemption at 20% bracket)

Source: Inland Revenue Department (IRD), Nepal — FY 2081-82 BS

# Financial Ratios Calculator — all ratios in one place
def financial_ratios(revenue, gross_profit, net_profit, current_assets,
                     current_liabilities, total_debt, total_equity):
    """Calculate key financial ratios and return a summary."""

    gross_margin    = (gross_profit / revenue) * 100
    net_margin      = (net_profit / revenue) * 100
    current_ratio   = current_assets / current_liabilities
    de_ratio        = total_debt / total_equity
    roe             = (net_profit / total_equity) * 100

    print('--- Financial Ratios Analysis ---')
    print(f'Gross Profit Margin : {gross_margin:.2f}%')
    print(f'Net Profit Margin   : {net_margin:.2f}%')
    print(f'Current Ratio       : {current_ratio:.2f}x')
    print(f'Debt-to-Equity      : {de_ratio:.2f}x')
    print(f'Return on Equity    : {roe:.2f}%')

    # Quick health check
    print()
    print('Quick Assessment:')
    print(' Liquidity:', 'Good ✓' if current_ratio >= 1.5 else 'Monitor ⚠')
    print(' Leverage :', 'Safe ✓' if de_ratio <= 2.0 else 'High Risk ✗')
    print(' Profit   :', 'Good ✓' if net_margin >= 10 else 'Low ⚠')


# Call the function with sample data
financial_ratios(
    revenue            = 5000000,
    gross_profit       = 1800000,
    net_profit         = 620000,
    current_assets     = 2200000,
    current_liabilities= 1100000,
    total_debt         = 1800000,
    total_equity       = 2500000
)

--- Financial Ratios Analysis ---
Gross Profit Margin : 36.00%
Net Profit Margin   : 12.40%
Current Ratio       : 2.00x
Debt-to-Equity      : 0.72x
Return on Equity    : 24.80%

Quick Assessment:
 Liquidity: Good ✓
 Leverage : Safe ✓
 Profit   : Good ✓

# Write your depreciation_schedule function here



# Call your function
# depreciation_schedule('Computer Equipment', 150000, 10000, 5)

# Mini Financial Report Generator
# Uses: variables, data types, lists, dicts, loops, functions, f-strings, conditionals

def generate_pl_report(company_info, pl_data):
    """Generates a simple Profit & Loss statement."""

    separator = '=' * 55
    thin_line = '-' * 55

    # Header
    print(separator)
    print(f"  {company_info['name']}")
    print(f"  Statement of Profit & Loss")
    print(f"  For the year ended {company_info['year_end']}")
    print(f"  (All amounts in NPR )")
    print(separator)

    # Revenue section
    total_revenue = sum(pl_data['revenue'].values())
    print(f"\n  {'I. REVENUE':}")  
    for item, amount in pl_data['revenue'].items():
        print(f"     {item:<35} {amount:>12,}")
    print(thin_line)
    print(f"     {'Total Revenue':<35} {total_revenue:>12,}")

    # Expenses section
    total_expenses = sum(pl_data['expenses'].values())
    print(f"\n  {'II. EXPENEPSES'}")  
    for item, amount in pl_data['expenses'].items():
        print(f"     {item:<35} {amount:>12,}")
    print(thin_line)
    print(f"     {'Total Expenses':<35} {total_expenses:>12,}")

    # Profit calculations
    pbt = total_revenue - total_expenses
    tax = pbt * company_info['tax_rate'] if pbt > 0 else 0
    pat = pbt - tax

    print(f"\n{separator}")
    print(f"  {'Profit Before Tax':<38} {pbt:>12,}")
    print(f"  {'Tax @ {:.0f}%'.format(company_info['tax_rate']*100):<38} {round(tax):>12,}")
    print(separator)
    print(f"  {'PROFIT AFTER TAX':<38} {round(pat):>12,}")
    print(separator)

    # Ratios
    print(f"\n  Key Ratios:")
    print(f"  Net Profit Margin : {pat/total_revenue*100:.2f}%")
    print(f"  Tax Efficiency    : {pat/pbt*100:.2f}%" if pbt > 0 else '')
    status = 'Profitable ✓' if pat > 0 else 'Loss Making ✗'
    print(f"  Financial Status  : {status}")


# ---- Data ----
company_info = {
    'name': 'Sunrise Manufacturing Pvt. Ltd.',
    'year_end': '16th July 2025 (Ashadh 2082 BS)',
    'tax_rate': 0.25
}

pl_data = {
    'revenue': {
        'Revenue from Operations': 8500000,
        'Other Income':             125000,
    },
    'expenses': {
        'Cost of Materials':       4200000,
        'Employee Benefits':       1250000,
        'Finance Costs':            185000,
        'Depreciation':             320000,
        'Other Expenses':           480000,
    }
}

generate_pl_report(company_info, pl_data)

=======================================================
  Sunrise Manufacturing Pvt. Ltd.
  Statement of Profit & Loss
  For the year ended 16th July 2025 (Ashadh 2082 BS)
  (All amounts in NPR )
=======================================================

  I. REVENUE
     Revenue from Operations                8,500,000
     Other Income                             125,000
-------------------------------------------------------
     Total Revenue                          8,625,000

  II. EXPENEPSES
     Cost of Materials                      4,200,000
     Employee Benefits                      1,250,000
     Finance Costs                            185,000
     Depreciation                             320,000
     Other Expenses                           480,000
-------------------------------------------------------
     Total Expenses                         6,435,000

=======================================================
  Profit Before Tax                         2,190,000
  Tax @ 25%                                   547,500
=======================================================
  PROFIT AFTER TAX                          1,642,500
=======================================================

  Key Ratios:
  Net Profit Margin : 19.04%
  Tax Efficiency    : 75.00%
  Financial Status  : Profitable ✓

# Exercise 1 — Working Capital Analysis
# Tip: Use a dictionary for assets and liabilities, then loop through them

invoices = [
    {'client': 'Alpha Ltd',   'amount': 125000, 'days': 25},
    {'client': 'Beta Corp',   'amount': 85000,  'days': 65},
    {'client': 'Gamma Inc',   'amount': 210000, 'days': 45},
    {'client': 'Delta Pvt',   'amount': 55000,  'days': 95},
    {'client': 'Epsilon Ltd', 'amount': 175000, 'days': 15},
    {'client': 'Zeta Co',     'amount': 92000,  'days': 130},
]

# Exercise 2 — Debtors Ageing Analysis
invoices = [
    {'client': 'Alpha Ltd',   'amount': 125000, 'days': 25},
    {'client': 'Beta Corp',   'amount': 85000,  'days': 65},
    {'client': 'Gamma Inc',   'amount': 210000, 'days': 45},
    {'client': 'Delta Pvt',   'amount': 55000,  'days': 95},
    {'client': 'Epsilon Ltd', 'amount': 175000, 'days': 15},
    {'client': 'Zeta Co',     'amount': 92000,  'days': 130},
]

# Write your ageing analysis here

# Exercise 3 — Housing Allowance Exemption Calculator
# Tip: Use the min() function to find the minimum of three values

# SOLUTION — Exercise 1: Working Capital Analysis

current_assets = {
    'Cash':              85000,
    'Debtors':          320000,
    'Inventory':        475000,
    'Prepaid Expenses':  25000,
}

current_liabilities = {
    'Creditors':        280000,
    'Short-term Loans': 150000,
    'Accrued Expenses':  45000,
}

total_ca = sum(current_assets.values())
total_cl = sum(current_liabilities.values())
working_capital = total_ca - total_cl
current_ratio = total_ca / total_cl
quick_ratio = (total_ca - current_assets['Inventory']) / total_cl

print('=== Working Capital Analysis ===')
print('\nCurrent Assets:')
for item, amt in current_assets.items():
    print(f'  {item:<25} NPR {amt:>10,}')
print(f'  {"Total Current Assets":<25} NPR {total_ca:>10,}')

print('\nCurrent Liabilities:')
for item, amt in current_liabilities.items():
    print(f'  {item:<25} NPR {amt:>10,}')
print(f'  {"Total Current Liab.":<25} NPR {total_cl:>10,}')

print(f'\nWorking Capital:  NPR {working_capital:,}')
print(f'Current Ratio:    {current_ratio:.2f}x', '✓' if current_ratio >= 1.5 else '⚠ Below ideal')
print(f'Quick Ratio:      {quick_ratio:.2f}x', '✓' if quick_ratio >= 1.0 else '⚠ Below ideal')

=== Working Capital Analysis ===

Current Assets:
  Cash                      NPR     85,000
  Debtors                   NPR    320,000
  Inventory                 NPR    475,000
  Prepaid Expenses          NPR     25,000
  Total Current Assets      NPR    905,000

Current Liabilities:
  Creditors                 NPR    280,000
  Short-term Loans          NPR    150,000
  Accrued Expenses          NPR     45,000
  Total Current Liab.       NPR    475,000

Working Capital:  NPR 430,000
Current Ratio:    1.91x ✓
Quick Ratio:      0.91x ⚠ Below ideal

# SOLUTION — Exercise 2: Debtors Ageing Analysis

invoices = [
    {'client': 'Alpha Ltd',   'amount': 125000, 'days': 25},
    {'client': 'Beta Corp',   'amount': 85000,  'days': 65},
    {'client': 'Gamma Inc',   'amount': 210000, 'days': 45},
    {'client': 'Delta Pvt',   'amount': 55000,  'days': 95},
    {'client': 'Epsilon Ltd', 'amount': 175000, 'days': 15},
    {'client': 'Zeta Co',     'amount': 92000,  'days': 130},
]

buckets = {
    'Current (0-30 days)':        0,
    '1-2 Months (31-60 days)':    0,
    '2-3 Months (61-90 days)':    0,
    'Overdue >3 Months (91+ days)': 0
}

print(f"{'Client':<15} {'Amount':>12} {'Days':>6} {'Category':<30}")
print('-' * 68)

for inv in invoices:
    if inv['days'] <= 30:
        category = 'Current (0-30 days)'
        flag = ''
    elif inv['days'] <= 60:
        category = '1-2 Months (31-60 days)'
        flag = ''
    elif inv['days'] <= 90:
        category = '2-3 Months (61-90 days)'
        flag = '⚠'
    else:
        category = 'Overdue >3 Months (91+ days)'
        flag = '🔴 HIGH RISK'

    buckets[category] += inv['amount']
    print(f"{inv['client']:<15} NPR {inv['amount']:>11,} {inv['days']:>5}d  {category} {flag}")

print('\n=== Ageing Summary ===')
total = 0
for category, amount in buckets.items():
    print(f'{category:<35} NPR {amount:>10,}')
    total += amount
print(f'{"TOTAL OUTSTANDING":<35} NPR {total:>10,}')

Client                Amount   Days Category                      
--------------------------------------------------------------------
Alpha Ltd       NPR     125,000    25d  Current (0-30 days) 
Beta Corp       NPR      85,000    65d  2-3 Months (61-90 days) ⚠
Gamma Inc       NPR     210,000    45d  1-2 Months (31-60 days) 
Delta Pvt       NPR      55,000    95d  Overdue >3 Months (91+ days) 🔴 HIGH RISK
Epsilon Ltd     NPR     175,000    15d  Current (0-30 days) 
Zeta Co         NPR      92,000   130d  Overdue >3 Months (91+ days) 🔴 HIGH RISK

=== Ageing Summary ===
Current (0-30 days)                 NPR    300,000
1-2 Months (31-60 days)             NPR    210,000
2-3 Months (61-90 days)             NPR     85,000
Overdue >3 Months (91+ days)        NPR    147,000
TOTAL OUTSTANDING                   NPR    742,000

# SOLUTION — Exercise 3: Housing Allowance Exemption Calculator

def calculate_hra_exemption(basic_salary, hra_received, rent_paid, is_metro):
    """Calculate Professional/Education Deduction under Income Tax (Nepal Income Tax)."""

    # Method 1: Actual Housing Allowance received
    limit1 = hra_received

    # Method 2: 50% of Basic (metro) or 40% (outside Kathmandu Valley)
    limit2 = basic_salary * 0.50 if is_metro else basic_salary * 0.40

    # Method 3: Rent paid minus 10% of Basic
    limit3 = max(0, rent_paid - basic_salary * 0.10)   # Cannot be negative

    # Exemption = minimum of all three
    exemption = min(limit1, limit2, limit3)

    city_type = 'Metro' if is_metro else 'Non-Metro'
    print(f'--- Housing Allowance Exemption Calculation ({city_type}) ---')
    print(f'Basic Salary:           NPR {basic_salary:>10,}')
    print(f'Housing Allowance Received:           NPR {hra_received:>10,}')
    print(f'Rent Paid:              NPR {rent_paid:>10,}')
    print()
    print(f'Limit 1 (Actual Housing Allowance):   NPR {limit1:>10,}')
    print(f'Limit 2 (% of Basic):   NPR {limit2:>10,}')
    print(f'Limit 3 (Rent - 10%B):  NPR {limit3:>10,}')
    print(f'Housing Allowance Exemption (min):    NPR {exemption:>10,}')
    print(f'Housing Allowance Taxable:            NPR {hra_received - exemption:>10,}')
    print()
    return exemption


# Test Case 1
calculate_hra_exemption(600000, 240000, 216000, True)

# Test Case 2
calculate_hra_exemption(480000, 144000, 180000, False)

--- Housing Allowance Exemption Calculation (Metro) ---
Basic Salary:           NPR    600,000
Housing Allowance Received:           NPR    240,000
Rent Paid:              NPR    216,000

Limit 1 (Actual Housing Allowance):   NPR    240,000
Limit 2 (% of Basic):   NPR  300,000.0
Limit 3 (Rent - 10%B):  NPR  156,000.0
Housing Allowance Exemption (min):    NPR  156,000.0
Housing Allowance Taxable:            NPR   84,000.0

--- Housing Allowance Exemption Calculation (Non-Metro) ---
Basic Salary:           NPR    480,000
Housing Allowance Received:           NPR    144,000
Rent Paid:              NPR    180,000

Limit 1 (Actual Housing Allowance):   NPR    144,000
Limit 2 (% of Basic):   NPR  192,000.0
Limit 3 (Rent - 10%B):  NPR  132,000.0
Housing Allowance Exemption (min):    NPR  132,000.0
Housing Allowance Taxable:            NPR   12,000.0






132000.0