semvec/pypi-packages huggingface dataset:
easy-json2json 0.0.2
A package for easy json parse.
Requires Python: >=3.6

# Easy JSON2JSON

## Description
A python package for deep JSON translation.

You just need to specify you template to get all data from another JSON to avoid doing that manually in your code.

# Install 
    pip install easy-json2json

# Usage example 

## Case #1 Simple JSON
```python

from easyjson2json import Json2Json

source = {"name": "Test Name"} # source JSON

template = {"first_name": "name"} # this is the structure you want your new JSON be 

translator = Json2Json(template=template, source=source)

result = translator.get_result()       

print(result)

# {'first_name': 'Test Name'}

```

## Case #2 Different structure
```python

from easyjson2json import Json2Json

source = {"name": "Test Name", "address": {"name": "My Street", "number": 23}} # when is not a plain JSON

template = {"first_name": "name", "address_name": ["address", "name"]} # you don't want all that structure 

translator = Json2Json(template=template, source=source)

result = translator.get_result()       

print(result)

# {'first_name': 'Test Name', 'address_name': 'My Street'}

```

## Case #3 From ugly JSON
```python

from easyjson2json import Json2Json

source = {"name": "My Name", "ADDRESS": {"name": "My Street", "number": 23}} # ugly JSON happens 

template = {"name": "name", "address": {"_source": "ADDRESS", "name": "name", "number": "number"}} # you want beautiful JSON

translator = Json2Json(template=template, source=source)

result = translator.get_result()       

print(result)

# {'name': 'My Name', 'address': {'name': 'My Street', 'number': 23}}
```

## Case #4 From List in JSON
```python

from easyjson2json import Json2Json

source = {"name": "My Name", "ADDRESSES": [{"name": "My Street", "number": 23}]} # when you have a list

template = {"name": "name", "addresses": {"_source_": "ADDRESSES", "name": "name", "number": "number"}} # then you get that list beautifuly

translator = Json2Json(template=template, source=source)

result = translator.get_result()       

print(result)

# {'name': 'My Name', 'addresses': [{'name': 'My Street', 'number': 23}]}

```


## Source Code

[easyjson2json](https://github.com/brworkit/python-package-easy-json2json.git)

## Author

[**2020 brworkit**](https://github.com/brworkit).

## License
[MIT License.](https://opensource.org/licenses/MIT)
QCNN 1.0.3
Deep Neural Network module for classification/regression

# Description  

Quantcore's implementation of a deep neural network. Used for classification and regression.

#Usage

The class takes a few requirements, # input nodes,  # hidden nodes, # layers, # output nodes, # learning rate, # number of epochs, # output type (specified with r for regression, c for classification), # train test split = 0.8 (percentage you would like to train on), # test = True (turn it off if you don't want to test), split = True (turn it off if you don't want the data split)

The train function takes one input, which is a DATAFRAME with the inputs and the outputs on the right side, e.g.
```
inputs = dataframe[:-1]
outputs = dataframe[-1]
```
Do not separate inputs from outputs, just have the outputs as the righter most column in your df

The call to train the function is class_.train(input)

The call to test is class_.feed_forward(input)

#Installation

```
pip install QCNN
```

#Sample Code
```

from QCNN import NeuralNetwork
import pandas as pd


nn= NeuralNetwork(2,2,3,1,.1,100000, train_test_split =1, split=False)

data_dict = {0:{'input1':0,'input2':0,'output':0},
            1:{'input1':1,'input2':0,'output':1},
            2:{'input1':0,'input2':1,'output':1},
            3:{'input1':1,'input2':1,'output':0}
            }

df = pd.DataFrame.from_dict(data_dict, orient = 'index')



nn.train(df)

```