semvec/pypi-packages huggingface dataset:
graphql-sqlalchemy 0.6.1
Generate GraphQL Schemas from your SQLAlchemy models
License: MIT
Requires Python: >=3.6.0,<4
Keywords: graphql,sqlalchemy

# graphql-sqlalchemy

[![PyPI version](https://badge.fury.io/py/graphql-sqlalchemy.svg)](https://badge.fury.io/py/graphql-sqlalchemy)
[![Build Status](https://travis-ci.com/gzzo/graphql-sqlalchemy.svg?branch=master)](https://travis-ci.com/gzzo/graphql-sqlalchemy)
[![codecov](https://codecov.io/gh/gzzo/graphql-sqlalchemy/branch/master/graph/badge.svg)](https://codecov.io/gh/gzzo/graphql-sqlalchemy)
[![Code Style](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black)

Generate GraphQL Schemas from your SQLAlchemy models

# Install
```
pip install graphql-sqlalchemy
```

# Usage

```python
    from ariadne.asgi import GraphQL
    from fastapi import FastAPI
    from sqlalchemy import create_engine
    from sqlalchemy.ext.declarative import declarative_base
    from sqlalchemy.orm import sessionmaker
    from graphql_sqlalchemy import build_schema

    engine = create_engine('sqlite:///config.db')
    Base = declarative_base()
    Session = sessionmaker(bind=engine)

    app = FastAPI()
    session = Session()

    schema = build_schema(Base)

    app.mount("/graphql", GraphQL(schema, context_value=dict(session=session)))
```

# Query

```graphql
query {
    user(
        where: {
            _or: [
                { id: { _gte: 5 } },
                { name: { _like: "%bob%" } },
            ]
        }
    ) {
        id
        name
    }
    user_by_pk(id: 5) {
        createtime
    }
}
```
linear-imputation 1.0.1
Fill-in missing values using data mean and correlation
License: MIT

# Linear Data Imputation

This allows you to fill-in missing values from your data. It uses a distribution estimated from the mean and covariance of your data.

## Installation

```
pip install linear-imputation
```

## How to use

You might have some data which is missing some values:

```
>>> import pandas as pd
>>> import numpy as np
>>> from linear_imputation import impute, Imputer
>>> 
>>> input_data = pd.DataFrame({'age': [10,20,30], 'pets':[100,200,None]})
>>> input_data                                                                                        
   age   pets
0   10  100.0
1   20  200.0
2   30    NaN
```

To fill-in the missing values of your data, you only have to call the `impute` function:

```
>>> impute(input_data) 
    age   pets
0  10.0  100.0
1  20.0  200.0
2  30.0  187.5
```

The filled-in values are considered the most likely, given the distribution of your data.

Sometimes it is useful to build a model from some training data to later apply this model to some other data.
Building a model is easy:

```
>>> model = Imputer(input_data)
```

You can then use it to fill-in missing values of other data you have:

```
>>> marty = {'name': "Marty", 'age': None, 'pets': 150} 
>>> model.impute(marty) 
{'name': 'Marty', 'age': 20.0, 'pets': 150}
```

The data to be completed can also be a pandas.DataFrame

```
>>> df = pd.DataFrame([marty, {'name': 'Tom', 'age': 35}]) 
>>> model.impute(df) 
    name   age    pets
0  Marty  20.0  150.00
1    Tom  35.0  206.25
```

You can also use a numpy.ndarray

```
>>> matrix = np.array([[10,100], [20, 200], [30, None]]) 
>>> impute(matrix)
array([[10, 100],
       [20, 200],
       [30, 187.5]])
```
django-no-floc 0.1.0
Middleware to opt out of Google's FLoC cohort tracking.
License: MIT
Keywords: django-no-floc

==============
django-no-floc
==============

|image|

|image1|

|image2|

Middleware to opt out of Google's FLoC cohort tracking.

Quickstart
==========

Install django-no-floc:

::

   pip install django-no-floc

Add it to your \`INSTALLED_APPS`:

.. code:: python

   MIDDLEWARE = (
       ...
       'no_floc.middleware.NoFLoCMiddleware',
       ...
   )

Running Tests
=============

Does the code actually work?

::

   source <YOURVIRTUALENV>/bin/activate
   (myenv) $ pip install tox
   (myenv) $ tox

Development commands
====================

::

   pip install -r requirements_dev.txt
   invoke -l

Credits
=======

Tools used in rendering this package:

-  `Cookiecutter <https://github.com/audreyr/cookiecutter>`__
-  `cookiecutter-djangopackage <https://github.com/pydanny/cookiecutter-djangopackage>`__

.. |image| image:: https://badge.fury.io/py/django-no-floc.svg
   :target: https://badge.fury.io/py/django-no-floc
.. |image1| image:: https://travis-ci.org/ph00lt0/django-no-floc.svg?branch=master
   :target: https://travis-ci.org/ph00lt0/django-no-floc
.. |image2| image:: https://codecov.io/gh/ph00lt0/django-no-floc/branch/master/graph/badge.svg
   :target: https://codecov.io/gh/ph00lt0/django-no-floc




History
-------

0.1.0 (2021-04-09)
++++++++++++++++++

* First release on PyPI.