semvec/pypi-packages huggingface dataset:
pdkf 1.1.0
Module for working with PDKF files and Pandas dataframes
Requires Python: >=3.7
Keywords: pdkf

# PDKF
Python package for converting PBKF file to DataFrame (pandas) list.
# Example
It is simple example for using package. This code converts data from file.pdkf to dataframe_dict.
```
import pdkf

dataframe_dict = pdkf.read_pdkf('file.pdkf')
for key, dataframe in dataframe_dict.items():
    print(key)
    print(dataframe.info())
```
# PDKF structure
The file is a ZIP archive. For each dataframe, you need to add two files to the archive: 
* META file;
* CSV or JSON file.
The META file contains the Pandas data types for the dataframe. For example:
#### Service.csv
```
name,purchase_price,description,remainder
sofa_low,10000.00,description,8
armchair_rocking,8000.00,description,14
bed_double,18000.00,description,6
chair_computer, 12000.00,description,2
bed_children,16000.00,description,5
```
#### Service.meta
```
{
    "name":"object",
    "purchase_price":"float",
    "description":"object",
    "remainder":"int64"
}
```
#### ServiceCost.json
```
[
    {
        "sofa_low":[
            {
                "price":"21000.00",
                "date":"2021.01.16"
            },
            {
                "price":"19000.00",
                "date":"2021.02.21"
            }
        ]
    },
    {
        "armchair_rocking":[
            {
                "price":"15000.00",
                "date":"2021.01.16"
            }
        ]
    }
]
```
#### ServiceCost.meta
```
{
    "price":"float",
    "date":"datetime64[ns]"
}
```
maneuverRecognition 0.0.6
ManeuverRecognition is a python package providing functions for the preprocessing of time series data for classification, the creation of predictive LSTM based models and the evaluation of multi class predictions.
Requires Python: >=3.8

# ManeuverRecognition Package

ManeuverRecognition is a python package providing functions for the preprocessing of time series data for classification, the creation of predictive LSTM based  models and the evaluation of multi class predictions.

Its functionalities are demonstrated and the documentation is given here: https://github.com/Schuster-j/maneuverRecognition
camp-ftools 0.0.1
营地字体处理工具库
Requires Python: >=3.8
Keywords: font,font-tools

# camp-font-tools

> 营地字体操作工具

## Contribute

首先安装好 `python` 相关的开发环境

> 工程根目录执行 `pip install --editable .`
> 尝试运行 `ftools --help`

每次有修改代码，都重复执行上面的步骤，确保没有引入额外的错误

## Cli Args

> 见代码注释
modulairy-mail-sender 0.0.2.6
This script enables sending emails asynchronously from Azure Service Bus. Each bus message contains a sender's email address along with SMTP configurations. The script utilizes its SMTP configuration of the message for sending emails.
Requires Python: >=3.8, <4

## Modulairy Mail Sender

This script enables sending emails asynchronously from Azure Service Bus. Each bus message contains a sender's email address along with SMTP configurations. The script utilizes its SMTP configuration of the message for sending emails.

### Parameters (OS Environments)
|Name| Description|
|-- |-- |
|SERVICEBUS_CONNECTION_STR| Connection String for your Azure service bus instance |
|SERVICEBUS_QUEUE_NAME| Queue name for email listening. |


### Model
```json
{
    "sender":"<Your Mail Address>",
    "receiver":"<Recevier Address>",
    "bcc":"<BCC Address>",
    "cc":"<CC Address>",
    "subject":"test",
    "message":"<This is a <strong>TEST</strong> mail.",
    "smtp":{
        "server":"<SMTP Server Host>",
        "port":"<SMTP Server PORT>",
        "username":"<Your Mail Address>",
        "password":"<Your Mail Password>"
    }

}
```
### Installation

```sh
pip install modulairy-mail-sender
```

### Run

```sh
python -m modulairy-mail-sender
```

> **NOTE:** If you send email to multiple addresses, you can join with a comma(,) these mail addresses