semvec/pypi-packages huggingface dataset:
jsontreeview 2.6
A simple python program for viewing JSON data more clearly
Requires Python: >=3.7, <4

# JsonTreeView
  A simple python program for viewing JSON data more clearly
  
 # How to use
  ```py
  from jsontreeview import treeView
  ...
  # Works for both dict and list types
  treeView(obj, output="out.txt") # optional output file argument, default is stdout
```

# Example
  Input:
  ```json
  {"glossary":{"title":"exampleglossary","GlossDiv":{"title":"S","GlossList":{"GlossEntry":{"ID":"SGML","SortAs":"SGML","GlossTerm":"StandardGeneralizedMarkupLanguage","Acronym":"SGML","Abbrev":"ISO8879:1986","GlossDef":{"para":"Ameta-markuplanguage,usedtocreatemarkuplanguagessuchasDocBook.","GlossSeeAlso":["GML","XML"]},"GlossSee":"markup"}}}}}
  ```
  Output:
  ```txt
  ["glossary"]:
   -["title"]: str
   -["GlossDiv"]:
    --["title"]: str
    --["GlossList"]:
     ---["GlossEntry"]:
      ----["ID"]: str
      ----["SortAs"]: str
      ----["GlossTerm"]: str
      ----["Acronym"]: str
      ----["Abbrev"]: str
      ----["GlossDef"]:
       -----["para"]: str
       -----["GlossSeeAlso"]:
        ----->[0]: str
        ----->[1]: str
      ----["GlossSee"]: str
  ```
desklank 0.1.4
Text-mode peer-to-peer encrypted communication utilizing LANK protocol and DeskApp framework

# desklank

Text-mode peer-to-peer encrypted communication utilizing LANK protocol and
DeskApp framework.

## Installation

It is easy to install desklank using pip:
```
pip install desklank
```

## Usage

Once installed, simply run the `desklank` command from any terminal.

## Author

Created by Shawn A. Wilson [lannocc@yahoo.com] aka LANNOCC.
pyspark-dbscan 1.0.6
An "Efficient" Implementation of DBSCAN on PySpark
Requires Python: >=3.6

# pyspark_dbscan
An Implementation of DBSCAN on PySpark

```python
import dbscan
from sklearn.datasets import make_blobs
from pyspark.sql import types as T, SparkSession
from scipy.spatial import distance

spark = SparkSession \
        .builder \
        .appName("DBSCAN") \
        .config("spark.jars.packages", "graphframes:graphframes:0.7.0-spark2.3-s_2.11") \
        .config('spark.driver.host', '127.0.0.1') \
        .getOrCreate()
X, labels_true = make_blobs(n_samples=750, centers=centers, cluster_std=0.4, random_state=5)
data = [(i, [float(item) for item in X[i]]) for i in range(X.shape[0])]
schema = T.StructType([T.StructField("id", T.IntegerType(), False),
                               T.StructField("value", T.ArrayType(T.FloatType()), False)])
#please repartition appropriately                            
df = spark.createDataFrame(data, schema=schema).repartition(10)
df_clusters = dbscan.process(spark, df, .2, 10, distance.euclidean, 2, "checkpoint")
```