semvec/pypi-packages huggingface dataset:
python-jsend-response 0.0.3
python-jsend-response is a simply python implementation of JSend.
Requires Python: >=3.5

# Description

python-jsend-response is a simple implementation of [JSend](https://github.com/omniti-labs/jsend).

JSend is a specification that lays down some rules for how JSON responses from web servers should be formatted. JSend focuses on application-level (as opposed to protocol- or transport-level) messaging which makes it ideal for use in REST-style applications and APIs.:

You'll have all kinds of different types of calls and responses. JSend separates responses into some basic types, and defines required and optional keys for each type:

<table>
<tr><th>Type</td><th>Description</th><th>Required Keys</th></tr>
<tr><td>success</td><td>All went well, and (usually) some data was returned.</td><td>status, data</td></tr>
<tr><td>failure</td><td>There was a problem with the data submitted, or some pre-condition of the API call wasn't satisfied</td><td>status, data</td></tr>
<tr><td>error</td><td>An error occurred in processing the request, i.e. an exception was thrown</td><td>status, message</td></tr>
</table>
demopp 0.0.2
Simple demo project

ï»¿Documents
=========

This is a basic demo program only for test purpose.


#### <i class="icon-file"></i> Lucky Number
    from demopp import *
    p = demo('à¦¹à¦“à¦¯à¦¼à¦¾à¦°')
    print(p)
#### <i class="icon-file"></i> Sum
    from demopp import *
    s = sum(3,2)
    print(s)
demop 0.0.9
Simple demo project

ï»¿Documents
-------------

This is a basic demo program only for test purpose.


#### <i class="icon-file"></i> Lucky Number
    from demop import *
    p = demo('à¦¹à¦“à¦¯à¦¼à¦¾à¦°')
    print(p)
#### <i class="icon-file"></i> Sum
    from demop import *
    s = sum(3,2)
    print(s)
gaussian-binomial-probability-distribution 0.1
Gaussian and Binomial distribution

This package contain Gaussian distribution and Binomial distribution

## Gaussian Distribution

Attributes: 

1. mean

2. standard deviation 

Methods: 

1. read_data_file(file_name) - to read in data from a txt file

2. calculate_mean() - to calculate mean of data set

3. calculate_stdev() - to calculate standard deviation of data set

4. plot_histogram() - to output a histogram of the instance variable data

5. pdf() - to calculate probability density function

6. plot_histogram_pdf() - to plot the normalized histogram of the data and a plot of the probability density function


## Binomial Distribution

Attributes: 

1. probability

2. size of trial

3. mean

4. standard deviation 

Methods: 

1. read_data_file(file_name) - to read in data from a txt file

2. calculate_mean() - to calculate mean from p and n

3. calculate_stdev() - to calculate standard deviation from p and n

4. replace_stats_with_data() - to calculate p and n from the data set

5. plot_bar() - to output a histogram of the instance variable data

6. pdf() - to calculate probability density function

7. plot_bar_pdf() - to plot the pdf of the binomial distribution

## Installation

```
pip install gaussian-binomial-probability-distributions
```

## How to use distribution package

Open python environment and import the package
```
python
from distributions import Gaussian
from distributions import Binomial
```