semvec/pypi-packages huggingface dataset:
data-transformation-library 0.1.7
The Data Transformation Library is a Python library that provides a set of functions for common data transformation tasks. It is designed to simplify the process of handling and processing data, making it easier for developers to work with various types of data structures.
Requires Python: >=3.11,<4.0

# Data Transformation Library

The Data Transformation Library is a Python library that provides a set of functions for common data transformation tasks. 
It is designed to simplify the process of handling and processing data, making it easier for developers to work with various types of data structures.

# Installation

You can install the library using pip:

<i>pip install data-transformation-library</i>

# Features
## transpose2d

The transpose2d function switches the axes of a 2D tensor, making it easy to transform rows into columns and vice versa.

<i>import data_transformation_library     

input_matrix = [[1, 2, 3], [4, 5, 6]]   
transposed_matrix = data_transformation_library.transpose2d(input_matrix)</i>

## window1d

The window1d function creates overlapping or non-overlapping 1D windows from an input array. This is useful for tasks like time series analysis and data segmentation.

<i>import data_transformation_library   
import numpy as np  

input_array = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])  
windows = data_transformation_library.window1d(input_array, size=3, shift=2, stride=1)</i>

## concolution2d

The convolution2d function performs 2D convolution between an input matrix and a kernel. This is a fundamental operation in image processing and feature extraction.

<i>import data_transformation_library  
import numpy as np  

input_matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])  
kernel = np.array([[0, 1], [2, 3]])  
stride = 1  
result = data_transformation_library.convolution2d(input_matrix, kernel, stride=stride)</i>

## Link to the pip package 

https://pypi.org/project/data-transformation-library/
repleno 0.0.18
Supply chain analytics on Bill Of Materials (BOM) and Material Requirements Planning (MRP)
Requires Python: >=3.0
Keywords: supply chain,bill of materials,material requirements planning,BOM,MRP


# repleno

Repleno allows you to easily answer supply chain questions considering bill of
materials (BOM) structure.

- **PyPI**: https://pypi.org/project/repleno/ 
- **Source code**: 
- **Documentation**: 
- **Bug reports**: 


It answers the following questions:

1. "Which finished/purchased goods are associated with this stock keeping unit (SKU)?"
2. "Based on this master production schedule (MPS), which specific SKU quantities should be ordered from suppliers?"
3. "Using the master requirements planning (MRP) results and current supplier orders, when will there be stock-out periods?"
4. "If I discontinue SKU X, which other SKU's will also need to be discontinued?"

By providing:

- efficient queries about parent-child relationships for SKUs in the BOM
- MRP simulations to analyze the impact of MPS changes on purchase orders to suppliers
- inventory levels over a specific time periods
- identification of collateral SKU's that need to be phased-out along with a specific SKU


Installing:

```bash
python -m pip install requests
```

## Roadmap:

1. Integrate phantom scenarios to `run_mrp` and `get_lineage` and `get_collaterals` methods;
2. issue a warning when a parameter is not being loaded or a column has not been found;
3. get_collaterals function should return a tree, so it can be used to fully deplete the inventory inside it;
4. run special mrp for scenarios: produce all inventory left in the BOM (placing more order to deplete everything or with scrapage);
5. method for propagating any general label from roots to child nodes (from fininshed goods to purchased goods, in supply chain terms);
6. Method for quickly answering the question: if one component goes out of stock, what's the impact on the finished goods?
7. per component, shows how much of its demand impact finished goods. If 1 component goes into only 1 FG, then 100%
8. Add support to add lead times in different units (currently only support calendar days), like weeks or months