semvec/pypi-packages huggingface dataset:
df2mysql 0.0.3
提供了一组方法，用于与 Microsoft SQL Server 数据库进行交互。允许执行各种操作，如连接到 SQL Server、创建和删除数据库、创建和删除表、添加和删除列、插入和检索数据等。
License: MIT
Requires Python: >=3.7
Keywords: df2mysql,Microsoft SQL Server,数据库交互,数据库连接,数据库操作,数据库管理

**df2mysql** 是一个用于连接和操作 Microsoft SQL Server 数据库的 Python 包。它提供了一个名为 **Database** 的类，可以使用该类的方法执行各种数据库操作，如创建和删除数据库、创建和删除表、插入和检索数据等。

使用方法：
以下是使用 **df2mysql** 包的一般步骤：

1. 安装包：通过运行 **pip install df2mysql** 安装包。

2. 导入包：在你的 Python 脚本或交互式会话中使用 **from df2mysql import Database** 导入 **Database**类。

3.创建 **Database** 实例：通过创建 **Database** 类的实例并提供 SQL Server 主机、用户名和密码来建立数据库连接。

```python
db = Database(host='your_host', user='your_username', password='your_password')
```
4.连接到数据库：使用 **connect_db(database)** 方法连接到指定的数据库。


```python
db.connect_db(database='your_database')
```
5.执行数据库操作：使用 **Database** 类提供的方法执行数据库操作，如创建表、插入数据、检索数据等。


```python
db.create_table(database='your_database', table_name='your_table', table_head=['column1', 'column2'])
db.insert_data(database='your_database', table_name='your_table', data=your_data)
result = db.select_data(database='your_database', table_name='your_table')
```
6.关闭连接：在完成所有数据库操作后，使用 close() 方法关闭数据库连接。

```python
db.close()
```

请确保已安装 pymssql 和其他所需的依赖项，并提供正确的数据库连接信息。这样，你就可以使用 df2mysql 包连接到 Microsoft SQL Server 数据库并执行相应的操作了。
tfields 0.5.0
Tensors, tensor fields, graphs, mesh manipulation, CAD and more on the basis of numpy.ndarrays. All objects keep track of their coordinate system. Symbolic math operations work for object manipulation.
License: MIT
Requires Python: >=3.8,<4.0
Keywords: tensors, tensor-fields, graphs, mesh, numpy, math

library documentation
=====================

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.. pypi
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    :target: https://pypi.python.org/pypi/tfields
.. ci
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    :target: https://gitlab.mpcdf.mpg.de/dboe/tfields/-/pipelines/latest

.. latest release
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    :target: https://gitlab.mpcdf.mpg.de/dboe/tfields/-/releases

.. coverage
.. image:: https://gitlab.mpcdf.mpg.de/dboe/tfields/badges/master/coverage.svg
    :target: https://gitlab.mpcdf.mpg.de/dboe/tfields/commits/master

.. docs
.. image:: https://img.shields.io/website-up-down-green-red/https/dboe.pages.mpcdf.de/tfields.svg?label=docs
    :target: https://dboe.pages.mpcdf.de/tfields

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   :target: https://github.com/pre-commit/pre-commit
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Overview
--------

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Tensors, tensor fields, graphs, mesh manipulation, CAD and more on the basis of numpy.ndarrays. All objects keep track of their coordinate system. Symbolic math operations work for object manipulation.

..
    [overview-end]

Licensed under the ``MIT License``

Resources
~~~~~~~~~

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* Source code: https://gitlab.mpcdf.mpg.de/dboe/tfields
* Documentation: https://dboe.pages.mpcdf.de/tfields
* Pypi: https://pypi.python.org/pypi/tfields


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    [resources-end]


Features
~~~~~~~~

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The following features should be highlighted:

* Tensors
* TensorFields
* TensorMaps with fields
* Mesh manipulation by graph theory
* TODO
  
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Review of alternative/supplementary APIs
----------------------------------------
* `sympy.diffgeom`: https://docs.sympy.org/latest/modules/diffgeom.html - Symbolic differential algebra implementation. It is not yet covering a broad variety of functionality and is not so well maintained.
* Units: You can find a comparison of modules handling units here: https://socialcompare.com/en/comparison/python-units-quantities-packages - astropy looks best to our understanding but sympy would integrated nicer without an additional package.
gmmvi 1.1
A library for learning Gaussian mixture models for variational inference
License: MIT
Requires Python: >=3.7
Keywords: variational inference,machine learning

GMMVI: Gaussian Mixture Model Variational Inference
===================================================

A framework for optimizing Gaussian mixture models for variational inference.
Please refer to the `online documentation <https://gmmvi.rtfd.io>`_ for details.