AmortaPy

Convenient Amortization Schedule Calculations

About

AmortyPy is built on top of pandas and plotly-express

• Pandas is used to display the amortization table as a DataFrame and allow for futher data analysis and manipulation.
• plotly-express provides a useful interactive graph for visualizing and interacting with the data.
• I developed the package as a way for me to easily model and visualize my mortage homeloan.

Disclaimer

• The package is used to model and provide an informational analysis.
• This package does not provide financial advice. Loan decsions should consider other external factors and should not be made on this model alone.
• AmortaPy does not accept any liability for decisions made based on its use.

Quick Start

Checkout the Docs folder for example notebooks

Install AmortaPy

Currently only available via Github install.

pip install git+https://github.com/b3mery/AmortaPy.git

Generating a Amortization Table DataFrame:

• Amortization DataFrame

  import AmortaPy as ap
  
  input_interest_rate = 0.0394
  input_loan = 515000
  input_years = 30
  repayment_freq = 12 # monthly
  
  period_int_rate = input_interest_rate/repayment_freq
  periods = input_years*repayment_freq
  
  df = ap.generate_amortization_table(period_int_rate, input_loan, periods)

• Amortization DataFrame with additional payments per peirod

  df = ap.generate_amortization_table(period_int_rate, input_loan, periods, additional_payment_per_period=50)

Generatating an Amortization Object

• Create an amortization schedule

  import AmortaPy as ap
  
  input_interest_rate = 0.0394
  input_loan = 515000
  input_years = 30
  input_repayment_method = 'weekly'
  
  loan = ap.generate_amortization_schedule(input_interest_rate, input_loan, input_years, input_repayment_method)

• Get a quick summary

  loan # if in Jupyter Notebook
  print(loan) # if in Python file/cmd

• Tap into the amortization table dataframe

  loan.amortization_schedule.head()

• Use the Setter Methods to recalculate the schedule. Setters default to inplace updates. Meaning the instance will be updated. If you want to reterive a copy set inplace=False

  # Update the years of the Amortization object and preview the table head again
  loan.set_years(20).amortization_schedule.head()
  
  # Update some attribute and have a new object returned
  loan2 = loan.set_nominal_annual_interest_rate(0.045, inplace=False)

• AmortaPy Classes support dot notation. This means you can chain commands

  loan2.set_nominal_annual_interest_rate(0.028).set_repayment_frequency_periods('monthly')

• Like how the inplace argument of False returns a copy of the object you can also explicitly create a copy.

  loan3 = loan.copy()

Graph Visualization

To visualize the Amortization Schedule in a graph you will need to manually install plotly-express

pip install plotly-express

• Once installed you can simply tap into the figure properties.

  loan.period_balances_chart.show()

  

  loan.period_repayments_chart.show()

  

• You can also create a stacked graph manually using the plot_stacked_bar_chart API which is was created to be reusable with the Amortization class.

• More custom graphs should be created using plotly or plotly-express directly