semvec/pypi-packages huggingface dataset:
export-to-telegraph 0.0.151
Library for export webpage to Telegraph.
Requires Python: >=3.0

# export_to_telegraph

Library for export webpage to Telegraph.

## usage

```
import export_to_telegraph
export_to_telegraph.TOKEN = YOUR_TELEGRAPH_TOKEN
telegraph_url = export_to_telegraph.export(webpage_url)
```

If export failed, `telegraph_url` will be None.

## how to generate token

Token is not needed if you don't plan to edit the generated telegraph.

Example code:

```
from html_telegraph_poster import TelegraphPoster
poster = TelegraphPoster()
api_response = poster.create_api_token(shortname, longname)
token = api_response['access_token']
```

## how to install

`pip3 install export_to_telegraph`
pyroexalt 1.0.0
Fork of pyrogram. Elegant, modern and asynchronous Telegram MTProto API framework in Python for users and bots
Requires Python: ~=3.8
Keywords: telegram chat messenger mtproto api client library python

<p align="center">
    <a href="https://github.com/Mayuri-Chan/pyrofok">
        <img src="https://docs.pyrogram.org/_static/pyrogram.png" alt="Pyrofork" width="128">
    </a>
    <br>
    <b>Telegram MTProto API Framework for Python</b>
    <br>
    <a href="https://github.com/Mayuri-Chan">
        Homepage
    </a>
    •
    <a href="https://github.com/Mayuri-Chan/pyrofork/issues">
        Issues
    </a>
    •
    <a href="https://t.me/FZXParadox">
        Support Channel
    </a>
</p>

## PyroExalt 

> Elegant, modern and asynchronous Telegram MTProto API framework in Python for users and bots
> Made for Private Use, Not for Production, No Copyright Intentioned 

``` python
from pyrogram import Client, filters

app = Client("my_account")


@app.on_message(filters.private)
async def hello(client, message):
    await message.reply("Hello from Pyrofork!")


app.run()
```
chart_me 0.2.0
Charting that keeps you in the EDA flow
Requires Python: >=3.10

# _Chart Me_ Charting that keeps you in the EDA flow

---

Chart Me is a high-level charting library designed to expedite the Exploratory Data Analysis Process(eda). There are a few automated eda tools like [sweet viz](https://pypi.org/project/sweetviz/) that give a great initial set of visualizations. [lux](https://github.com/lux-org/lux) is a great tool to leverage - which gives a feel of AI assistance - but not always on the mark. The other alternative is hand-writing Altair code, which takes me out of the EDA flow looking up syntax or building functions..._Chart Me_ serves to keep you in the data analytics flow of discovery by keeping visualization commands to **one function** to remember.
[![v0.1.5](https://img.shields.io/pypi/v/chart_me.svg)](https://pypi.org/project/chart_me/)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![Checked with mypy](http://www.mypy-lang.org/static/mypy_badge.svg)](http://mypy-lang.org/)
[![Imports: isort](https://img.shields.io/badge/%20imports-isort-%231674b1?style=flat&labelColor=ef8336)](https://pycqa.github.io/isort/)
[![Interrogate](https://raw.githubusercontent.com/lgarzia/chart_me/master/interrogate_badge.svg)](https://interrogate.readthedocs.io/)

## Chart Me Documentation

See [Read the Docs](https://chart-me.readthedocs.io/en/latest/index.html) for more details

## **Usage Warnings**

Currently a proof-of-concept mode at this time. Limited to univariate & bivariate charts and doesn't support geographical data and is lightly tested.

## Installation

```bash
$ pip install chart-me
```

## Simple Usage

`chart_me` to quickly generate visualizations during eda process

```python
import chart_me as ce
ce.chart_me(df, 'col_1', 'col_2') #<-- reads as c-e-chart_me

```

![example](https://github.com/lgarzia/chart_me/blob/master/docs/source/_static/Example_Screenshot.png?raw=true)
spark-column-analyzer 0.2.3
A package for analyzing Spark DataFrame columns


# Spark Column Analyzer

## Overview

Spark Column Analyzer is a Python package that provides functions for analyzing columns in PySpark DataFrames. It calculates various statistics such as null count, null percentage, distinct count, distinct percentage, min_value, max_value, avg_value and historams
for each column. It also create a plot and saves it to the directory you are running the code from.

## Installation

You can install Spark Column Analyzer using pip:

```sh
pip install spark-column-analyzer


## Usage

### Analyzing Columns

To analyze columns in a PySpark DataFrame, you can use the `analyze_column` function provided by the package. Here's an example:

```python
from pyspark.sql import SparkSession
from spark_column_analyzer.column_analyzer import analyze_column

# Initialize SparkSession
spark = SparkSession.builder \
    .appName("ColumnAnalyzer") \
    .getOrCreate()

# Create a sample DataFrame
df = spark.createDataFrame([
    (1, "A", None),
    (2, "B", 100),
    (3, "C", 200),
    (4, "D", 300),
    (5, "E", None)
], ["ID", "Letter", "Value"])

# Analyze a specific column
result = analyze_column(df, "Value")
print(result)
Running Tests
To run tests for Spark Column Analyzer, follow these steps:

Clone the repository:
git clone https://github.com/michTalebzadeh/spark_column_analyzer.git
Navigate to the project directory:
cd spark_column_analyzer
Install the dependencies:
pip install -r requirements.txt
Run the tests:
python -m unittest discover tests
Contributing
If you'd like to contribute to Spark Column Analyzer, please open an issue or submit a pull request on GitHub.

License
This project is licensed under the MIT License - see the LICENSE file for details.

vbnet

Feel free to customize this template according to your project's specific details and require

Example

Doing analysis for column Postcode

Json formatted output

{
    "PricePaid": {
        "exists": true,
        "num_rows": 1819,
        "data_type": "int",
        "null_count": 0,
        "null_percentage": 0.0,
        "distinct_count": 1133,
        "distinct_percentage": 62.29,
        "min_value": 10000001,
        "max_value": 448500000,
        "avg_value": 32620476.65,
        "histogram": {
            "bins": [
                10000001.0,
                14216347.144230768,
               448500000.0,

            ],
            "counts": [
                468,
                286,
                258,

            ]
        }
    }
}