semvec/pypi-packages huggingface dataset:
gmsdb 3.0
GMSDB Clusterer
Requires Python: >=3.6
Keywords: clusterization

# GMSDB Clusterer

# Use
pip install gmsdb

## Simple use (sklearn -like style), no speed improvement, recommended significance level alpha=0.1. 
Maximal number of gaussian components=50:

from gmsdb import GMSDB

clf=GMSDB(n_components=50)

clf.fit(X)

Y=clf.predict(X)

## Complex use (with speed improvement for stages 1 and 3):

clf=GMSDB(min_components=2,step_components=100,n_components=900,rand_search=1000,rand_level=0.5)

## Complex use (with custom significance level alpha=0.15):

clf=GMSDB(n_components=50,alpha_stage2=0.15,alpha_stage4=0.15)

## Verbose use (show debug information):

clf=GMSDB(n_components=50,verbose=True)


# Desciption:
https://arxiv.org/abs/2309.02623
dataframe-with-buttons 1.0.0
Display Dataframe with buttons
Requires Python: >=3.7

# Dataframe with button

Streamlit component that allows you to display Dataframe with a button

## Installation instructions

```sh
pip install dataframe_with_buttons
```

## Usage instructions

```python
import streamlit as st
import pandas as pd
from dataframe_with_button import static_dataframe
from dataframe_with_button import editable_dataframe
import json
df = pd.DataFrame({
    "BATCH_ID": ["item1", "item2", "item3"],
    "Name": ["Apple", "Banana", "Cherry"],
    "Price": [1.2, 0.8, 2.5],
    "IN_STOCK": [True, False, True],
    "EMAIL": ["abc@gmail.com", "cde@k.com", "abc@gmail.com"]
})

df["EMAIL"] = pd.Categorical(df["EMAIL"], categories=["abc@gmail.com", "cde@k.com"])
result = static_dataframe(df, clickable_column="BATCH_ID")
result2 = editable_dataframe(df, clickable_column="BATCH_ID")
```
<img width="790" alt="Screenshot 2025-01-03 at 10 35 48 PM" src="https://github.com/user-attachments/assets/6581bf69-04f7-4f39-aaed-412d11417cf5" />