semvec/pypi-packages huggingface dataset:
trepublic2json 0.3.7
Parse and convert Invoice PDFs from Trade republic to JSON.
Requires Python: >=3.8
Keywords: json,parser,pdf,trade republic



<p align="center"><img src="https://github.com/ucyo/trepublic2json/raw/main/assets/logo.svg" alt="Logo for transformation of trade republic reports to json" width="200" height="100" ></p>
<h1 align="center"> trepublic2json</h1>

This is a library for parsing reports and invoices from [Trade Republic](traderepublic.com) to JSON. 
The library provides the command line tool: `trepublic2json`.
It only takes the path to the folder of pdf files as an argument and returns a single json with information from all pdf files.
 
An example output with a folder containing a single pdf file.
```json
[
    {
        "account_num": "AB01234567890123456",
        "date": "2001-01-01",
        "depot_num": "0123456789",
        "depot_owner": "John Doe",
        "filename": "/path/to/pdfs/ab012345678901234567890123456789.pdf",
        "isin": "GB0123456789",
        "name": "A Company that Manufactures Everything (ACME)",
        "order_id": "c829-8f27",
        "order_type": "buy",
        "share_price": 0.07,
        "shares": 900.0,
        "total_check_amount": -64.0,
        "total_share_price": 63.0,
        "total_share_price_calc": 63.00000000000001,
        "transaction_id": "o832-a928"
    }
]
```

## Install

You can install `trepublic2json` using either pypi:

```bash
pip install trepublic2json
```

Or from source using [`rye`](https://rye-up.com/guide/):

```bash
git clone git@github.com:ucyo/trepublic2json.git && cd trepublic2json && rye install .
```

## Usage

Once the package is installed you can use the command-line tool `trepublic2json`:

```bash
> trepublic2json --help
usage: trepublic2json [-h] path

positional arguments:
  path        Path of the pdf files to parse.

options:
  -h, --help  show this help message and exit
```
DataPrepKitz 0.1.0
A package for data preprocessing tasks
Requires Python: >=3.6

# DataPrepKitz

DataPrepKitz is a Python package that provides a convenient set of functions for data preprocessing tasks. It simplifies the process of reading data from various file formats, generating data summaries, handling missing values, and encoding categorical data.

## Features

- **Data Reading**: Read data from CSV, Excel (XLS, XLSX), and JSON files.
- **Data Summary**: Generate key statistical summaries, including information about data types, missing values, and categorical value counts.
- **Handling Missing Values**: Impute missing values with the column mean or replace them with zeros.
- **Categorical Data Encoding**: Encode categorical data using techniques like label encoding, ordinal encoding, and one-hot encoding.

## Installation

You can install DataPrepKitz using pip:
pip install DataPrepKitz
## Usage

Here's a basic example of how to use the DataPrepKitz:

```python
from data_prep_kit import DataPrepKitz

# Create a DataPrepKitz instance with the file path
data_prep = DataPrepKitz('path/to/your/data/file.csv')

# Summarize the data
data_prep.summarize()

# Drop duplicates
data_prep.drop_duplicates()

# Drop empty columns
data_prep.drop_empty_columns()

# Impute missing values with column mean
data_prep.impute_avg()

# Encode categorical columns using one-hot encoding
data_prep.encode_categorical(['category_col'], 'one-hot')
```
## Usage
DataPrepKitz relies on the following Python packages:

pandas
numpy
scikit-learn
These dependencies will be installed automatically when you install DataPrepKitz.

License
DataPrepKitz is released under the MIT License.

This README.md file provides an overview of the DataPrepKitz package, including its features, installation instructions, a basic usage example, information about dependencies. You can customize it further by adding more details or examples as needed.
PrintScript 1.3.0
A package for making print scripts
Requires Python: >=3.8

# PrintScript

## Write less, do more


```python
import PrintScript.zpl as ZPL

# specify your image file path
imagePath = "/path/to/your/image.png"

with open("example.zpl", "wb") as file:

    # create a script generator
    generator = ZPL.Generator()

    # set parameters
    generator.setPrintWidth(576) \
             .setLabelLength(800) \
             .setLabelShift(0) \
             .setLabelHomePosition((0, 0)) \
             .addGraphicField((0, 0), (576, -1), imagePath)

    # generate the script
    script = generator.makeScript()

    # write it to the file
    file.write(script)
```