semvec/pypi-packages huggingface dataset:
GHLPy 0.0.3
A basic python wrapper for gohighlevel
Keywords: python,ghl,gohighlevel,api,wrapper

# GHL v1 Python Wrapper
> A GHL Python wrapper for persomal use, doesn't contain all the methods as of now.

<br/>

## Table of Content 

- [Installation](#installation)
- [Requirements](#requirements)
- [Available Methods](#available-methods)
- [Example](#example)
- [Contribute](#contribute)

<br/>

### Installation
---
```
pip install GHLpy
```

<br/>
### Requirements
---
> Used runtime: Python 3.8

```
requests
json
```

<br/>

### Available methods
---

```
 'add_contactTag', 'bookAppointment', 'contact_appointments', 'create_contactByName', 'delete_appointmentById', 'endpoint', 'get_appointmentById', 'get_calendarBookedSlots', 'get_calendarFreeSlots', 'get_calendars', 'headers', 'lookup_contactByEmail', 'lookup_contactById', 'remove_contactTag', 'token', 'update_appointmentById', 'update_appointmentStatusById'

```

There is no documentation as of now for this package, you can use help() to get information

```python
from GHL import GHLPy

print(help(GHLPy))
```

The official API Reference for GHL v1 Endpoint is extensive and detailed as well. It can be found [here](https://public-api.gohighlevel.com/)

<br/>

### Example
---

To get information on calendars associated:
```python
from GHL import GHLPy

client = GHLPy("mytoken")

r = client.get_calendars(type="service") #type="teams" Default: services

print(r)
```

<br/>

### Contribute
---

Well, this was just for personal use, but if you wish to contribute, you are welcome to make PRs 

<br/>

>To be updated
<br/>
Have Fun!
lianyhaii 0.0.143
A package to win data competition
Requires Python: >=3.6

# lianyhaii

# usage
## 第一步：安装与测试
`pip install lianyhaii`  
`import lianyhaii`  
`print(lianyhaii.__version__)`  
## 第二步：准备数据、特征、参数
```python
import pandas as pd 
import numpy  as np 
import lianyhaii
import warnings 
import sys 
warnings.filterwarnings('ignore')

## 定义数据集、label、训练参数、特征名
train = pd.DataFrame({
    'y':(np.random.random(size=500)>0.5).astype(int)
})
test = pd.DataFrame({
    'y':(np.random.random(size=500)>0.5).astype(int)
})
for i in range(10):
    train[f'x{i}'] = np.random.random(size=500)
    test[f'x{i}'] = np.random.random(size=500)
label = 'y'
lgb_params = {
    'objective': 'binary',
    'boosting_type': 'gbdt',
    'metric': 'auc',
    'early_stopping_rounds': 50,
    'verbose':-1,
}
base_features = [f'x{i}' for i in range(10)]
```
以上是送进模型的主要准备工作，接下来会轻松不少

## 第三步：快速模型测试
```python
mt = make_test(train,test,base_features=base_features,new_features=[],
            m_score=[[0.0,]],label=label,metrices=['auc'],log_tool=None)
mt.init_CV(seed=412,CV_type='skFold',n_split=5)
oof,pred = mt.lgb_test(lgb_params=lgb_params)
## 得到oof和pred方便后续调整或者提交
```

# 获奖经历
> 2021 山东赛 公积金贷款逾期预测 A榜 rank2/xxx B榜 rank6/xxx  
> 2021 梧桐杯 5G潜客识别 B榜 rank7/xxx  
> 2022 招商银行Fintech  rank23/xxx   -> [开源](example/competition_solution/zhaohang_B.md)
> 2022 搜狐校园 情感分析 × 推荐排序 算法大赛 rank5/xxx  -> [开源](example/sohu2022/sohu2022.md)