semvec/pypi-packages huggingface dataset:
KiwoomDE 0.5.1
Kiwoom Data Engineering
Requires Python: >=3.8


# Kiwoom Data Engineering


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### Overview.
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* KiwoomDE(DataEngineering)
* KiwoomDSc(DataScientist)
* KOA(KiwoomOpenAPI)

1. KiwoomDE
- KiwoomTrader와 KiwoomDSc에게 데이터베이스, 데이터분석 기능을 제공하는 핵심 패키지
- 키움증권OpenAPI 에서 제공하는 데이터를 저장하는 데이터베이스 역할.
- KiwoomTrader, KiwoomDSc 에게 DataStructure, DataIO 를 제공해야 한다.

2. KiwoomTrader
- KOA를 통해 키움서버와 직접적으로 통신하는 역할

3. KiwoomDSc
- KiwoomDE 패키지를 사용하여 데이터분석을 EndUser에게 ???


### ============================================================
### 사용법.
### ============================================================


##### ============================================================
### 주의사항 !!
##### ============================================================


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### Configuration
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myosutils 1.0.0
OS Utils Library

# myosutils Library

This is an example project demonstrating how to publish a python module to PyPI

## Installation

Run the following to install:

```python
pip install myosutils
```

## Usage

```python
from myosutils import raw_mode

with raw_mode(sys.stdin):
	...

```
## Developing Hello World

To install helloworld, along with the tools you need to develop and run tests, run the following in your virtualenv:

```bash
$ pip install -e .[dev]
```
error-propagation 0.0.4
Package that computes error propagation

# Error Propagation

__Every number should have an error, even if it's 0.__

To do so, we have created a python package that makes it easy to propagate errors when performing calculations. 

    (10 ± 3) + (11 ± 4) != (21 ± 7) 

To calculate the error when adding 2 numbers, the L2 norm must be calculated
   
    (10 ± 3) + (11 ± 4) != (21 ± (3 ** 2 + 4 ** 2) ** 0.5) == (21 ± 5)

Calculating the error when multiplying, dividing, exponentiation is significantly harder, but 
still important! Please checkout [this notebook](./docs/derivations.ipynb) for derivations of 
error propagation formulas. 

## How to use
Create a Complex class instance: 

#### Basic Functionality
```python
from error_propagation import Complex
a = Complex(10, 3)
b = Complex(11, 4)

# Compute basic operations
a + b # 21.0 ± 5.0
a - b # -1.0 ± 5.0
a * b # 110.0 ± 51.85556864985669
a / b # 0.9090909090909091 ± 0.42855841859385696
a ** b # 100000000000.0 ± 978367874409.4901

# Compute basic equalities
a == b # False
a != b # True
a < b # True
a <= b # True
a > b # False
a >= b # False
```

#### Numpy and Pandas integration
```python
from error_propagation import Complex, arrays_to_complex
import numpy as np 
import pandas as pd

a = np.array([Complex(3, 4), Complex(8, 3)])
b = np.array([Complex(2, 3), Complex(10, 4)])
a + b # array([5.0 ± 5.0, 18.0 ± 5.0], dtype=object)
a * b # array([6.0 ± 12.041594578792296, 80.0 ± 43.86342439892262], dtype=object)

df = pd.DataFrame({"values": [1, 2], "errors": [3, 4]})
df["complex_numbers"] = arrays_to_complex(values=df["values"], errors=df["errors"])
```

#### Financial functions
```python
from error_propagation import Complex
from error_propagation.finance import npv
result = npv(
            cash=[Complex(10, 2), Complex(11, 3), Complex(12, 1)],
            discount_rate=Complex(0.05, 0.001),
        )

result # 29.867184969225782 ± 3.432196994041631
```

#### Leverage functions
```python
from error_propagation import Complex
a = Complex(10, 3)
b = Complex(11, 4)

Complex.add(a, b) # 21.0 ± 5.0
Complex.sub(a, b) # -1.0 ± 5.0
Complex.mul(a, b) # 110.0 ± 51.85556864985669
Complex.truediv(a, b) # 0.9090909090909091 ± 0.42855841859385696
Complex.pow(a, b) # 100000000000.0 ± 978367874409.4901
```


To see more examples, go to [docs/functionality.md](./docs/functionality.md) and the [tests](./tests)folder. 

## How to install
error-propagation is hosted on PyP
```ssh
pip install error-propagation
```