semvec/pypi-packages huggingface dataset:
sqlang 0.2
Generate SQL programmatically with function calls
Requires Python: >=3.6

# SQLANG
*Generate SQL with functional calls.*

Use base set of tokens:

```
from sqlang import SQL, tokens

S = SQL(tokens)

expr = S.SELECT([S.FIELD('id')], S.FROM(S.TABLE('example')))
print(S(expr))

 "SELECT id FROM example;"
```

Add your own expression tokens:

```
from sqlang.sql import SQL as SQL_
from sqlang.sql import token_list, token_list_item, tokens

t2 = token_list(
    tokens,
    token_list_item('REPLACE', lambda e, *args: f"REPLACE({e(args[0])}, {e(args[1])}, {e(args[2])})"),
    token_list_item('WHERE', lambda e, *args: f"WHERE {e(args[0])}"),
    token_list_item('NOT_LIKE', lambda e, *args: f"{e(args[0])} NOT LIKE {e(args[1])}"),
)

SQL = SQL_(t2)
```
PyXGBoost 1.0.9
pyXgboost,github:https://github.com/303844828/PyXGBoost.git

Introduction
------------

xgboost for pyspark

python3.5 spark2.4.x xgboost0.9

python install
--------------

pip install PyXGBoost

code examples:
--------------

.. code:: python

    from pyspark.sql import SparkSession
    from PyXGBoost import PyXGBoostClassifier, PyXGBoostClassificationModel
    spark = SparkSession \
        .builder \
        .appName("pyspark xgboost") \
        .getOrCreate()

    df=spark.read.csv("src/main/resources/iris.csv",schema="sepal_length double, sepal_width double, petal_length double,petal_width double,label int")
    df=df.fillna(0)
    #same as xgboost param map
    params0 = {
        "objective" :"binary:logistic"
        , "eta" : 0.01
        , "max_depth" : 6
        , "min_child_weight" : 50
        , "colsample_bytree" : 0.5
        , "silent" : 0
        , "seed" : 12345
    }

    xgb=PyXGBoostClassifier(params0)

    xgb.set_num_round(11) \
        .set_num_workers(11)
        
    feature_names=["sepal_length","sepal_width","petal_length","petal_width"]
    xgbModel=xgb.train(df,feature_names, "label")
    xgbModel.saveOverwrite("hdfs://xxxx")
    #xgbModel.write().overwrite().save("hdfs://xxxx")
    xgbModel=PyXGBoostClassificationModel.load("hdfs://xxxx")
    result_df=xgbModel.transform(df,feature_names)

download jar
------------

https://github.com/303844828/PyXGBoost/blob/master/src/main/build/pyspark-xgboost-1.0-SNAPSHOT.jar

submit
------

.. code:: shell

    spark-submit --master yarn-cluster --num-executors 100 \
    --jars pyspark-xgboost-1.0-SNAPSHOT.jar  \
    --py-files pyspark-xgboost-1.0-SNAPSHOT.jar \
    --files test.py

简介
----

pyspark版本的xgboost

首先执行：
----------

pip install PyXGBoost

代码示例：
----------

.. code:: python

    from pyspark.sql import SparkSession
    from PyXGBoost import PyXGBoostClassifier, PyXGBoostClassificationModel
    spark = SparkSession \
        .builder \
        .appName("pyspark xgboost") \
        .getOrCreate()

    df=spark.read.csv("src/main/resources/iris.csv",schema="sepal_length double, sepal_width double, petal_length double,petal_width double,label int")
    df=df.fillna(0)
    #same as xgboost param map
    params0 = {
        "objective" :"binary:logistic"
        , "eta" : 0.01
        , "max_depth" : 6
        , "min_child_weight" : 50
        , "colsample_bytree" : 0.5
        , "silent" : 0
        , "seed" : 12345
    }

    xgb=PyXGBoostClassifier(params0)

    xgb.set_num_round(11) \
        .set_num_workers(11)
        
    feature_names=["sepal_length","sepal_width","petal_length","petal_width"]
    xgbModel=xgb.train(df,feature_names, "label")
    xgbModel.saveOverwrite("hdfs://xxxx")
    #xgbModel.write().overwrite().save("hdfs://xxxx")
    xgbModel=PyXGBoostClassificationModel.load("hdfs://xxxx")
    result_df=xgbModel.transform(df,feature_names)

提交
----

命令需要在两个地方带上jar包，示例：

.. code:: shell

    spark-submit --master yarn-cluster --num-executors 100 \
    --jars pyspark-xgboost-1.0-SNAPSHOT.jar  \
    --py-files pyspark-xgboost-1.0-SNAPSHOT.jar \
    --files test.py
geniuspy 0.0.2
A Python wrapper around the Genius API
Requires Python: >=3.6

# Geniuspy

A Python wrapper around the Genius API.

## Introduction

This package provides a pure Python interface for the [Genius API](https://docs.genius.com/).

Genius provides information about songs lyrics and music knowledge. The aim of this package is to make the use of the Genius API easier when coding.

## Installing

You can install geniuspy using:

    $ pip install geniuspy

## Getting the code

The code is hosted at https://github.com/Lletfrix/geniuspy.

Download the latest development version with:

    $ git clone git://github.com/lletfrix/geniuspy.git
    $ cd geniuspy