semvec/pypi-packages huggingface dataset:
fdet-offline-mobilenet-weights 1.0.1
Weights for the models in fdet-offline.
Requires Python: >=3.5
Keywords: face recognition detection biometry

# fdet-offline-mobilenet-weights

A PyPi-package can't be larger than 100MB, therefore the weights for [fdet-offline](https://github.com/vikrosj/fdet-offline) must be their own package.

fdet-offline-mobilenet-weights returns a partial torch.load-instance.
num-thai-cnv 1.0.1
Thai localization package
Requires Python: >=3.6
Keywords: num,thai

# num-thai-cnv
num-thai-cnv is python library, primarily focused on converting number into Thai text and vice versa.

# Installation
```sh
pip install num-thai-cnv
```
# Features 
    Convert number to Thai text and vice versa, convert Arabic to Thai number.
    
# Initialize the object
    from num_thai_cnv import ThaiNumberConverter
    thainum = ThaiNumberConverter()
    
# ThaiNumberConverter Methods
| Method name | Description |  Command  | Result   |
| ------ | ------ | -------------------- |  ----------- |
| ToText | Convert Arabic number to Thai text |  thainum.ToText(123) or thainum.ToText('123') | 'หนึ่งร้อยยี่สิบสาม'
| ToNum  | Convert Thai text to Arabic number | thainum.ToNum('หนึ่งร้อยยี่สิบสาม') | '123'
| ToThaiNumber | Convert Arabic number to Thai number | thainum(123) or thainum('123') |'๑๒๓'

### ThaiNumberConverter Notes

- ToText and ToThaiNumber method can support unlimited digit.
- ToNum method does whitelist the following words, violation will lead to error 'Check if text contains excluded word'. 

    | Words |Words  |Words   |
    | ------ | ---- | ------|
    |เอ็ด|หก| ร้อย
    |หนึ่ง|เจ็ด|พัน
    |สอง|แปด  |  หมื่น
    |สาม| เก้า |แสน
    |สี่|สิบ|ล้าน
    |ห้า|ยี่|
pandas-pipeline-graphviz 0.1.5
Pandas pipeline in graphviz
Requires Python: >=3.6

# Pandas pipeline in graphviz

Python package to build a nice explanative schema of a data processing pipeline in pandas.

It's heavily inspired by [dask's `.visualize` method](https://docs.dask.org/en/latest/graphviz.html), but improved with 2 useful features:
- visualize columns names in data nodes
- highlight created columns at each task

Here is an example from the [examples folder](examples):
![](examples/03_apply_pandas_pipeline_decorator.png)


## Installation

### Pip

Install with pip:
```bash
$ pip install pandas-pipeline-graphviz
```

### Manual installation

Install manually:
- git clone
- use `python setup.py`


## Usage

### Disclaimer

#### ⚠️ WARNING — it's a hack!

There are no reliable methods in python to get variables names, either as input or as output. The methods used in this package are quite _hacky_, as discussed in this [stackoverflow thread](https://stackoverflow.com/questions/2749796/how-to-get-the-original-variable-name-of-variable-passed-to-a-function).

To build the graph, this package makes use of:
- `globals()` **to get the names of input dataframes**, doing a comparison between the input dataframes and all the variables available in the global variables.
- `inspect.stack()` **to get the name of the output dataframe**, gathering the code lines calling the function and parsing it to find the output. Currently it supports only single-output transformations.

Both methods should be considered as experimental and the behavior of the decorator is expected to break easily if it's not used as presented in the [examples](examples).

#### Conditions for use

- do not use several decorators on your function, only this decorator, otherwise it will break the output dataframe name detection through `inspect.stack()`
- use only single output transformation functions, i.e. functions which return only 1 dataframe.

### Examples

See [examples folder](examples) in the repository.
fdet-offline-mtcnn-weights 1.0.0
Weights for the models in fdet-offline.
Requires Python: >=3.5
Keywords: face recognition detection biometry

# fdet-offline-mtcnn-weights

A PyPi-package can't be larger than 100MB, therefore the weights for [fdet-offline](https://github.com/vikrosj/fdet-offline) must be their own package.

fdet-offline-mtcnn-weights returns a partial torch.load-instance.
filterlib 0.1.3
A filter library for python
Requires Python: >=3.6

# filterlib

A filter library for python by Fabian Becker.

## What for?

With a `Filter` object you can easily check for attributes of an object to equal a given value.

## Usage

### How to create a filter

#### Create a filter using __init__()

Both attributes are optional.

`operator: str = "AND"` (`"AND"` or `"OR"`) sets the logical operator for the filter (checks all or any attributes). 

`allow_missing_attributes: bool = False` sets the tolerance for missing attributes. If set to true it will skip if the attribute is missing in the object to check.

#### Create a filter from its representation

The return value of a filters `__repr__` attribute can be used to store and later on recreate a filter using `filter_from_repr`

```python
from filterlib import Filter, filter_from_repr

f = Filter()

print(f == filter_from_repr(repr(f)))
# f and filter_from_repr(f.__repr__()) is the same
```

#### Additional attributes

The attribute a filter checks for are variable
```python
from filterlib import Filter
# Use the following systax:
f = Filter(<Optional[attibute name]><attributes magic method>=<value>)

# For example:
f = Filter(a__eq__=1)

# You can also use multiple attributes:
f = Filter(b__lte__=5,
           c__gt__=2)

# Or check for objects themselves
Filter(__eq__="Hello World!") == "Hello World!"
```

### Use a filter

Make sure you use the filters `__eq__` attribute.

```python
# Make sure you use
Filter() == x
# instead of
x == Filter()
```

## Example
```python
from filterlib import Filter
from typing import Optional


class Person:
    def __init__(self, 
                 name: str,
                 age: int,
                 best_friend: Optional[Person] = None):
        self.name = name
        self.age = age
        self.best_fiend = best_friend


f = Filter(name__eq__="John")
p = Person(name="John", age=35)
print(f == p)
# True

f = Filter(name__ne__="John")
p = Person(name="John", age=35)
print(f == p)
# False

f = Filter(name__eq__="John", age__lte=40)
p = Person(name="John", age=35)
print(f == p)
# True

f = Filter(name__eq__="John", age__lte=20)
p = Person(name="John", age=35)
print(f == p)
# False

f = Filter(operator="OR",
           name__eq__="John", 
           age__lte=20)
p = Person(name="John", age=35)
print(f == p)
# True

p = Person(name="John",
           age=35,
           best_friend=Person(name="Thomas",
                              age=36))
f = Filter(best_friend__name__eq__="Thomas", 
           age__lte=40)
print(f == p)
# True
```

## SQL compatability
You can use filters for sql statements
```python
from filterlib import Filter
import sqlite3

db = sqlite3.connect("myDB.sqlite")
db.execute("CREATE TABLE users(id, name)")
f = Filter(id__eq__=1)
db.execute(f"SELECT name FROM users WHERE {f}")
```