semvec/pypi-packages huggingface dataset:
pandas-eda 1.2.0
Exploratory Data Analysis app to display table and frequent values for each column
Requires Python: >=3.6

## pandas EDA

Big Data? Machine Learning?

You work with your data -  
Manipulating it, merging, pivot and more.

But what happened in between? Did you get a lot of nans? Maybe duplicated values?  
You constantly need to check your data status,  
But you cannot do values_counts, isna and all those stuffs every second...

**pandas_eda** is an **Exploratory Data Analysis** tool that will show you status and frequent values for each column!  
You will be focused on what you have on the spot.

**Demo** is the best way to understand:  
[![demo](https://github.com/lisrael1/pandas_eda/blob/master/screenshots/front.jpg?raw=True)](https://youtu.be/OjBFoJIWdw0)

**install:**

```shell
pip install pandas_eda
```

**usage:**
```python
import pandas as pd
import pandas_eda
from time import sleep


# dummy data
df = pd._testing.makeMixedDataFrame()
# or load your data with pd.read_excel('whatever.xlsx')


# show original data. will pop up a web application
df.eda()  # can use this at debug mode too!

# that's it. but you can do more...


# manipulation #1
df.A += 10
process = df.eda(title='take 2')  # yes, you can open multiple EDA windows!
# process.kill()


# manipulation #2
df.loc[df.B==1, 'A'] -= 30
df.eda()


# no need at jupyter or debug mode...
# ending the script will end the eda too, so delaying the exit. 
sleep(600)  
```

**headless mode**
```python
import pandas as pd
import pandas_eda

# disabling the wrapping of a long table at the print...
pd.options.display.expand_frame_repr = False
pd.options.display.max_columns = 0
pd.options.display.min_rows = 20

df = pandas_eda._testing.generate_fake_table(samples=1_000)
eda = df.eda(cli_mode=True)

print('\n\n *** column statistics *** ')
print(eda.get_columns_statistics())
print('\n\n *** frequent values *** ')
print(eda.get_frequent_values())
```
**testing with**
```bash
python -m pandas_eda --test
```
**note:**  
If you're running on remote machine, the eda will be opened on the remote...

**alternatives:**  
After starting this tool I've found 2 cool alternatives:
* [sweetviz](https://pypi.org/project/sweetviz)
    <ul>Has a nice interactive report.</ul>
* [mito](https://www.trymito.io/) 
    <ul>Great for new table that needs also cleaning.<br>
    Works only at jupyter.</ul>
rcee 0.0.4
Remote code execution endpoint.
Requires Python: >=3.10
Keywords: rce,endpoint

# Remote code execution endpoint

## Some nice to haves left to implement.

- Monospace font on the command output.
- Error handling.
- Tests.

## Deploying a remote code execution endpoint

Drop into a shell and sing the magic incantation `pip install rcee` and `rcee`.
This will leave you haunted with an endpoint lingering on port `7999` by default.
From this point onward you're on your own and can perform any request you want.

> It's also possible to use pipx for the installation.
PickleHandler 1.0.11
A utility for saving and loading data using pickle with logging functionality.
Keywords: python,pickle,save,load,logging


# PickleHandler

PickleHandler is a Python package that provides a convenient way to save and load data using the pickle module. It also includes logging functionality to track data loading and saving operations.

## Installation

To install PickleHandler, you can use pip:

```bash
pip install PickleHandler
```

## Usage
Here's a simple example demonstrating how to use PickleHandler:

<a href="https://colab.research.google.com/drive/1l_2Xo7o26cUt6nU698z8KUCmirFD7S4P" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>

```bash
from PickleHandler import PickleHandler

# Initialize PickleHandler
handler = PickleHandler(folder_path="data", file_name="example.pkl")

# Save data
data = {"key": "value"}
handler.save(data, comment="Data saved 1")

# Load data
loaded_data = handler.load()
print(loaded_data)
```

## Contributing
Contributions are welcome! If you find a bug or have an idea for an improvement, please open an issue or submit a pull request on [GitHub](https://github.com/Prbn/PickleHandler).

Github: https://github.com/Prbn/PickleHandler

## License
This project is licensed under [Creative Commons Attribution-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-sa/4.0/) - see the [LICENSE file for details](https://github.com/Prbn/PickleHandler/blob/main/License).
noticeboard 0.0.1
Python code for a smart noticeboard

# noticeboard
Programs for a Raspberry Pi embedded in a corkboard for my to-do list etc

The visible software on this is based on Emacs' org-mode, showing my
"things to do" list.

The noticeboard also has a keyboard on a motorized sliding shelf, a
PIR detector, a couple of LED halogen-replacement bulbs, and a Picam.

The 12V PSU for the screen and the keyboard motor can be switched on
and off by a solid-state relay controlled from the Pi.

The program can take commands on its standard input, and is intended
to be commanded by Emacs.  However, it must have access to the GPIO
pins.
sphinx-alice-theme 0.3.0
Provides the Alice theme for Sphinx projects.
Requires Python: >=3.9

# Alice Theme for Sphinx

[![pip Version](https://img.shields.io/pypi/v/sphinx-alice-theme.svg)][PyPi]
[![Build](https://github.com/jdknight/sphinx-alice-theme/actions/workflows/build.yml/badge.svg)][Build]
[![Documentation](https://github.com/jdknight/sphinx-alice-theme/actions/workflows/docs.yml/badge.svg)][Documentation]

Provides the Alice theme for [Sphinx][Sphinx] projects.

<div align="center">
    <img src="https://sphinx-alice-theme.jdknight.me/overview-01.png" width="30%"></img>
    <img src="https://sphinx-alice-theme.jdknight.me/overview-02.png" width="30%"></img>
    <img src="https://sphinx-alice-theme.jdknight.me/overview-03.png" width="30%"></img>
    <img src="https://sphinx-alice-theme.jdknight.me/overview-04.png" width="30%"></img>
    <img src="https://sphinx-alice-theme.jdknight.me/overview-05.png" width="30%"></img>
    <img src="https://sphinx-alice-theme.jdknight.me/overview-06.png" width="30%"></img>
    <img src="https://sphinx-alice-theme.jdknight.me/overview-07.png" width="30%"></img>
    <img src="https://sphinx-alice-theme.jdknight.me/overview-08.png" width="30%"></img>
    <img src="https://sphinx-alice-theme.jdknight.me/overview-09.png" width="30%"></img>
</div>

## Installation

This theme can be installed using [pip][pip]:

```shell
pip install -U sphinx-alice-theme
    (or)
python -m pip install -U sphinx-alice-theme
```

## Usage

For an existing Sphinx documentation set, the Alice theme can be applied by
configuring the [`html_theme`][html_theme] value to `sphinx_alice_theme`.
For example:

```python
html_theme = 'sphinx_alice_theme'
```

The following theme options are available:

```python
html_theme_options = {
    #'canonical_url': 'https://www.example.com',
    #'includehidden': False,
    #'navigation_depth': 4,
    #'prev_next_buttons_location': 'both', # top, bottom, none
    #'titles_only': False,
}
```

## Dependencies

- [Gumshoe][Gumshoe]


[Build]: https://github.com/jdknight/sphinx-alice-theme/actions/workflows/build.yml
[Documentation]: https://sphinx-alice-theme.jdknight.me/
[Gumshoe]: https://github.com/cferdinandi/gumshoe
[PyPi]: https://pypi.org/project/sphinx-alice-theme/
[Sphinx]: https://www.sphinx-doc.org/
[html_theme]: https://www.sphinx-doc.org/en/master/usage/configuration.html#confval-html_theme
[pip]: https://pip.pypa.io/
sweetener 0.2.1
A base library for writing compilers and interpreters
Requires Python: >=3.12, <4
Keywords: productivity,algorithms,data-structures,development,programming

Sweetener
=========

[![Python package](https://github.com/samvv/sweetener/actions/workflows/python-package.yml/badge.svg)](https://github.com/samvv/sweetener/actions/workflows/python-package.yml) [![Coverage Status](https://coveralls.io/repos/github/samvv/sweetener/badge.svg?branch=main)](https://coveralls.io/github/samvv/sweetener?branch=main) [![PyPI version](https://badge.fury.io/py/sweetener.svg)](https://pypi.org/project/sweetener)

Sweetener is a Python library for quickly prototyping compilers and
interpreters. Extra care has been taken to make the algorithms as flexible as
possible to fit as many use cases. If you're still missing something, do not
hesitate to file an [issue][1]! In particular, this library contains the
following goodies:

 - **A typed record type** that allows you to build PODs very quickly.
 - **Base classes for an AST/CST**, including reflection procedures.
 - **A means for writing diagnostics** that can print part of your source code
   alongside your error messages.
 - **Generic procedures** like `clone`, `equal` and `lte` that work on most
   types in the standard library and you can specialize for your own types.

[1]: https://github.com/samvv/sweetener/issues

## Examples

Here's an example of using the `Record` type to define a record that holds two
fields and visualizes tham using GraphViz:

```py
from sweetener import Record, equal, visualize

class MySimpleRecord(Record):
    field_1: int
    field_2: str

r1 = MySimpleRecord('foo', 42)
r2 = MySimpleRecod(field_1='foo', field_2=42)

assert(equal(r1, r2))

visualize(r1)
```

Running this program will open a new window with the following content:

<img src="https://raw.githubusercontent.com/samvv/sweetener/main/sample-record.png" />

```py
from sweetener import BaseNode, visualize

class CalcNode(BaseNode):
    pass

class Expr(CalcNode):
    pass

class Add(Expr):
    left: Expr
    right: Expr

class Sub(Expr):
    left: Expr
    right: Expr

class Var(Expr):
    name: str

class Lit(Expr):
    value: int

def eval(node: Expr, vars = {}) -> int:
    if isinstance(node, Add):
        return eval(node.left, vars) + eval(node.right, vars)
    if isinstance(node, Sub):
        return eval(node.left, vars) - eval(node.right, vars)
    if isinstance(node, Lit):
        return node.value
    if isinstance(node, Var):
        return vars[node.name]
    raise RuntimeError('Could not evaluate a node: unrecognised node type')

prog = Sub(
    Add(
        Lit(1),
        Lit(2)
    ),
    Var('x')
)

visualize(prog, format='png')

assert(eval(prog, { 'x': 3 }) == 0)
```

Running this example will open a new window with the following content:

<img src="https://raw.githubusercontent.com/samvv/sweetener/main/sample-tree.png" />

## License

Sweetener is generously licensed under the MIT license, in the hope that it
inspires people to build new and cool software.
lvldb 0.1.2
Python bindings for LevelDB. Python 2-3 compatible
Keywords: Python,LevelDB

# ctypes bindings for LevelDB 

The existing three [LevelDB](http://code.google.com/p/leveldb/) interfaces ([py-leveldb](http://code.google.com/p/py-leveldb/), [cpy-leveldb](https://github.com/forhappy/cpy-leveldb), [plyvel](https://github.com/wbolster/plyvel/)) use the Python C API and compile Python modules that work with LevelDB's C++ interface. This Python module simply uses the ctypes library to LevelDB's C interface - making it more portable across Python implementations and easier to install and distribute. 

lvldb:
  * Python 2-3 compatible
  * supports get/put/delete (with standard read/write options)
  * supports bloom filters
  * supports leveldb LRU cache
  * allows for manual or automatic database closing (compare with py-leveldb)
  * provides write batches
  * provides iterators (full control of leveldb iteration, with additional idiomatic python iterator support)
  * provides prefix-based iteration (returns iterators that work as if all keys with a shared prefix had the prefix stripped and were dumped into their own database)
  * provides scoped sub-databases (presents a new database wrapper backed by an existing database with all keys prefixed by some prefix)
  * provides range iterators (for idioms like give me all keys between start and end)
  * provides an in-memory db implementation (for faster unit tests)
  * supports snapshots
  * fits in one file
  * requires no compilation
  
## Sample Usage 

```
# Python3
import lvldb

db = lvldb.DB("/path/to/db", create_if_missing=True)
db.put("key1", "value1")
db.put("key2", "value2")
print(f"key1 => {db.get('key1')} ")

for key, value in db:
    print(f"{key} => {value}")
```