semvec/pypi-packages huggingface dataset:
PyFinOps 0.3.2
A comprehensive toolkit for various financial calculations and tools.
Requires Python: >=3.6

# PyFinOps

FinToolkit is a comprehensive Python package for various financial calculations and tools. It includes:

- **SIP Calculator**: Calculate the future value of Systematic Investment Plan (SIP) investments.
- **Compound Interest Calculator**: Calculate the future value of investments with compound interest.

## Installation

You can install PyFinOps via pip:

```bash
pip install PyFinOps
```

# How to use:
## SIP Calculator
Calculate the future value of a Systematic Investment Plan (SIP) investment.

```bash
from pyfinops import SIPCalculator

# Create an instance of the calculator
sip_calculator = SIPCalculator(monthly_investment=500, annual_rate=12, years=10)
# Calculate the future value of the SIP investment
future_value = sip_calculator.calculate_future_value()
print(f"Future Value of SIP: ${future_value}")
# Display SIP details including total investment and interest earned
sip_calculator.display_sip_details()
```

## Compound Interest Calculator
Calculate the future value of an investment using compound interest.

```bash
from pyfinops import CompoundInterestCalculator

# Create an instance of the calculator
compound_interest_calculator = CompoundInterestCalculator(principal=1000, annual_rate=5, times_compounded=12, years=10)

# Calculate the future value of the investment
future_value = compound_interest_calculator.calculate_future_value()
print(f"Future Value of Investment: ${future_value}")

# Display investment details including principal, future value, and total interest earned
compound_interest_calculator.display_investment_details()

```

## Command line usage

```
sip-calculator --monthly-investment 500 --annual-rate 12 --years 10

compound-interest --principal 1000 --annual-rate 5 --times-compounded 12 --years 10
rwdq 0.1.3
A tool for querying real-world graph database
Requires Python: >=3.9

**Description**:

This tool is used to extract specific graphs in a database(must have the same columns and attributes with ours).

**Motivation**:

In GNN dataset like OGB and TUDataset, we can directly use the dataset e.g. MUTAG, Cora, etc. However, in some cases, we need to extract specific graphs from a database to support specific tasks or experiments or do some stress tests. Take the GNN based subgraph counting for example, there is a method that cannot support very dense graphs or graphs has some dense and star-shaped part, so we need to filter the graphs to get proper ones. This tool is designed for this purpose.

**Usage**:

* python main.py --config my_config.json --database my_database.db
* data_objects = run_query('config.json', 'rwd.db')

The configuration file should be a JSON file specifying the query parameters.

**License**:

This project is licensed under the MIT License - see the LICENSE file for details.