semvec/pypi-packages huggingface dataset:
abcard 0.1.4
Primarily used for developing binary classification models and generating reports for production work. 
Requires Python: >=3.9

# abcard  

**abcard** is a Python package primarily used for developing binary classification models and generating reports for production work.  
It supports Logit and LGBM models, can generate PDF model reports and production-ready deployment code.  
It emphasizes concise and efficient API calls, rich visualizations, and retention of the development process.  


# Installation  

**abcard** requires **Python 3.9 or later**, install using **pip** with:  

`pip install abcard`  

Additional dependencies: numpy, pandas, tqdm, statsmodels, scikit-learn, matplotlib, PyMuPDF, lightgbm  


# Main Features  

from abcard import Frame, LogitFrame, LGBMCFrame, Report, ModReport, LogitReport, LGBMCReport  

train: 'pandas.DataFrame datasets'  
test: 'pandas.DataFrame datasets'  
oot: 'pandas.DataFrame datasets'  
flag: 'target label (y)'  
time: 'name of the time column, optional'  
exclude: 'column names to be excluded, optional'  
Mod: 'Logit, LGBM models'  

df = LogitFrame(flag = flag, time = time, exclude = exclude) # Initial sample set field configuration.  
df = LGBMCFrame(flag = flag, time = time, exclude = exclude) # Initial sample set field configuration.  
df.set_samp(train, 'train') # Set the sample dataset.  
df.get_samp(test, 'test') # Get the sample dataset.  
df.del_samp(oot, 'oot') # Delete the sample dataset.  

df.describe_sample() # Descriptive analysis of samples.  
df.describe_feature() # Descriptive analysis of fearures.  
df.chi2_split() # Perform chi-square binning on all features.  

df.mergebins() # Merge bins manually.  

df.drop_nan(nan = 0.9) # Various feature filtering methods starting with 'drop'.  

df.transform() # Convert the sample set into bins or WOE.  

train_set = df.get_xy(label = 'train') # Get the X and y for model training or prediction.  
df.get_metric() # Retrieve the model's evaluation metrics on all sample sets.  
df.scorecard() # Calculate Logistic Regression Scorecard for Selected Features.  

df._mod = Mod  # A fully trained model.  

rep = LGBMCReport(df) # Initialize a PyMuPDF Document object for a LGBMClassifier model Frame object.  
rep.design()  
rep.describe_feature()  
rep.bins() # Generate a chapter for the feature binning results.  
rep.filter() # Generate a chapter for the feature filtering results.  
rep.modres() # Generate a chapter containing the model results.  
rep.analysis('train') # Generate a chapter containing the model analysis and evaluation for the specified samples.  
rep.plotcuts(cores = 4) # Generate a chapter containing the model binning plots.  
rep.code() # Generate a chapter containing the deployment code.  
rep.log()  
rep.save("model_name_report.pdf")  
rep.close()  


# License and Copyright  

**abcard** is available under [open-source AGPL](https://www.gnu.org/licenses/agpl-3.0.html) and commercial license agreements.
zhixinpy 0.2.22
Excel Ribbon via Python
Requires Python: <3.14,>=3.11

# zhixinpy

[![PyPI version](https://img.shields.io/pypi/v/zhixinpy.svg)](https://pypi.org/project/zhixinpy/)
[![Python Version](https://img.shields.io/pypi/pyversions/zhixinpy.svg)](https://pypi.org/project/zhixinpy/)


## 简介

zhixinpy 是一款功能强大的 Excel 插件，专为办公自动化设计。它通过 Python 提供丰富的 Excel 功能扩展，包括 Excel 宏、Excel 函数、在线更新和功能授权管理等。

## 主要功能

zhixinpy 提供多种功能模块，帮助用户提升 Excel 工作效率：

### 日历函数 (Calendar)

提供日期和日历相关的函数，帮助处理日期计算、工作日判断等操作。

### 图表功能 (Chart)

基于 plotly、matplotlib 和 seaborn 等库，提供强大的数据可视化能力，支持在 Excel 中创建各类专业图表。

### 文本处理 (Text)

提供文本处理函数，如正则表达式提取、文本转换等功能。

### 数据转换 (Converter)

支持各种数据格式转换功能，如大小写转换、日期格式转换等。

### 智能查找 (Lookup)

提供基于 NLP 的智能查找功能，支持模糊匹配和相似度搜索。

### 实时数据 (RTD)

支持实时数据更新，如股票行情等信息的实时获取。

### 数据分析 (Series)

提供时间序列分析功能，支持使用 Prophet 等算法进行数据预测。

## 安装方法

```bash
pip install zhixinpy
zhixinpy install
```

### 系统要求

- Python 3.11 或更高版本 (小于 3.13)
- Windows 操作系统
- 64位Microsoft Excel 2010 或更高版本

## 使用示例

### 在 Excel 中使用函数

```python
# 使用智能查找功能
=nlp_vlookup(查找值, 表格区域, 返回列索引, 是否包含分数, 是否返回所有匹配项, 阈值)

# 使用时间序列预测
=prophet(数据数组, 日期列, 值列, 预测周期)

# 使用正则表达式提取文本
=regexExtract(文本, 正则表达式, 分隔符)
```

### 使用 Ribbon 界面

zhixinpy 提供了 Excel Ribbon 界面，可以方便地访问各种功能，如发送邮件、配置设置等。


## 作者

- Morgan Yuan

## 文档

更多详细文档请访问：[https://zhixin.readthedocs.io](https://zhixin.readthedocs.io)


## 发布历史

### 0.2.18 (2025-06-19)
- 优化了图表功能。

### 0.2.12 (2025-05-22)
- 优化了帮助文档。

### 0.2.11 (2025-05-15)
- 修复了一些已知问题。

### 0.2.10 (2025-05-15)
- 初始发布。
dartboard-ia 0.2.2
Internet Archive upload management tool
Requires Python: >=3.8

# dartboard
generalized Internet Archive upload target

dartboard is [available on PyPi](https://pypi.org/project/dartboard-ia) as `dartboard-ia`!

## Configuration
There are two ways you can provide dartboard with Internet Archive credentials to be able to upload:
- If you use the IA CLI (and you've run `ia configure`) dartboard will automatically use those credentials
- Otherwise, you can create a config json file (see below) and pass the location of it with `--config-path` (default: `./config.json`)

```json
{
  "s3_key": "abcdefg",
  "s3_secret": "abcdefg"
}
```

## Usage instructions
You can upload a directory with
```bash
dartboard path/to/directory
```
dartboard will try to upload to the item with the same identifier as the name of the directory. If the item does not already exist on IA, you must specify the metadata by placing an `__ia_meta.json` file in the directory:
```json
{
  "collection": "opensource",
  "mediatype": "data",
  "title": "My title",
  "description": "My description",
  "foo": "bar"
}
```
You are required to specify, at minimum, a `collection` and `mediatype` to create a new item.

If the item already exists, dartboard will try to upload the files to it even if `__ia_meta.json` isn't present. If it is present, dartboard WILL attempt to diff the metadata and make the necessary changes once it has finished uploading the new files.

You can also specify dartboard settings for that item with an `__uploader_meta.json` file:
```java
{
  "setUploadState": false, // if enabled, dartboard will set an upload-state:uploading key on the item, and change it to upload-state:uploaded when done
  "setScanner": true, // if disabled, dartboard will not add "dartboard (vX.Y.Z)" to the scanner field
  "sendSizeHint": false, // if enabled, dartboard will send IA a size hint for the item, based on the size of files in the directory. Only enable this for new items, and only if you know that every file you want to upload to this item is already in the directory
  "derive": true // if disabled, dartboard will not queue a derive task once it has finished uploading
}
```
(Values shown above are the defaults. The comments in JSON are only to make reading easier - don't include them when running the actual command)