semvec/pypi-packages huggingface dataset:
gitbaba 0.1.0
A lightweight CLI tool to simplify GitHub workflows. With GitBaba you can push local projects, create new repositories, delete repos, and manage authentication without typing long Git or GitHub commands. Perfect for developers who want a faster shortcut to manage GitHub projects.
License: MIT
Keywords: git, github, cli, automation, gitbaba

# 🚀 GitBaba – Simplify Your GitHub Workflow

GitBaba is a lightweight CLI tool that helps developers push, create, and manage GitHub repositories with ease.
No more typing long `git` commands or remembering repo URLs — just use `gitbaba` and get the job done faster.

---

## ✨ Features

* �\udkey **Login / Logout** with GitHub token (no password prompts every time)
* 📦 **Push projects** from any local directory to a new/existing GitHub repo
* 🌿 **Branch selection** (main, master, or custom)
* 🗝 **Delete repositories** directly from GitHub
* ⚡ **One-command workflow** — no need to manually `git init`, `commit`, or `push`

---

## 🛠️ Installation

You can install GitBaba from **PyPI**:

```bash
pip install gitbaba
```

Verify installation:

```bash
gitbaba --help
```

---

##🚦 Usage

### 💑 Login

First, authenticate using your **GitHub Personal Access Token (PAT)**:

```bash
gitbaba login
```

### 🚀 Push Project

From inside any project folder:

```bash
gitbaba push
```

* Enter repo name
* Choose branch (`main`, `master`, or custom)
* Your project will be uploaded instantly 🎉

### 🗝 Delete Repository

```bash
gitbaba delete
```

* Enter repo name
* Confirm deletion

### 👋 Logout

```bash
gitbaba logout
```

---

## 💼 Requirements

* Python 3.8+
* Git installed on your system
* Dependencies: `requests`, `colorama`

Install dependencies automatically with `pip install gitbaba`.

---

## 📷 Demo (Example Output)

```bash
$ gitbaba push

📦 Enter GitHub repo name: my-awesome-project
🌿 Choose branch: [1] main [2] master [3] custom → 1
🚀 Project pushed to GitHub repo 'my-awesome-project' on branch 'main'
```

---

## 🔒 Authentication

GitBaba uses your **GitHub Personal Access Token (PAT)**, which is stored locally in
`~/.gitbaba`. You can log out anytime with:

```bash
gitbaba logout
```

> Tip: Generate a PAT from your GitHub account with repo access enabled.

---

## 📜 License

Licensed under the [MIT License](LICENSE).
Free to use, modify, and distribute.

---

## 👨‍💼 Author

**Your Name**

* GitHub: [https://github.com/botolmehedi](https://github.com/botolmehedi)
* Email: [hello@mehedi.fun](mailto:hello@mehedi.fun)
boto3-dataclass-mq 1.40.0
Dataclass for boto3 mq.
License: MIT
Requires Python: <4.0,>=3.10

Welcome to ``boto3-dataclass-mq`` Documentation
========================================================================================================================
- `Homepage <https://github.com/MacHu-GWU/boto3_dataclass-project>`_
- `Documentation <https://boto3-dataclass.readthedocs.io/en/latest/>`_
- `Repository <https://github.com/MacHu-GWU/boto3_dataclass-project>`_
- `Issues <https://github.com/MacHu-GWU/boto3_dataclass-project/issues>`_
- `Install <https://pypi.org/pypi/boto3-dataclass-mq>`_
- `Download <https://pypi.org/pypi/boto3-dataclass-mq#files>`_
boto3-dataclass-mpa 1.40.0
Dataclass for boto3 mpa.
License: MIT
Requires Python: <4.0,>=3.10

Welcome to ``boto3-dataclass-mpa`` Documentation
========================================================================================================================
- `Homepage <https://github.com/MacHu-GWU/boto3_dataclass-project>`_
- `Documentation <https://boto3-dataclass.readthedocs.io/en/latest/>`_
- `Repository <https://github.com/MacHu-GWU/boto3_dataclass-project>`_
- `Issues <https://github.com/MacHu-GWU/boto3_dataclass-project/issues>`_
- `Install <https://pypi.org/pypi/boto3-dataclass-mpa>`_
- `Download <https://pypi.org/pypi/boto3-dataclass-mpa#files>`_
boto3-dataclass-migrationhubstrategy 1.40.0
Dataclass for boto3 migrationhubstrategy.
License: MIT
Requires Python: <4.0,>=3.10

Welcome to ``boto3-dataclass-migrationhubstrategy`` Documentation
========================================================================================================================
- `Homepage <https://github.com/MacHu-GWU/boto3_dataclass-project>`_
- `Documentation <https://boto3-dataclass.readthedocs.io/en/latest/>`_
- `Repository <https://github.com/MacHu-GWU/boto3_dataclass-project>`_
- `Issues <https://github.com/MacHu-GWU/boto3_dataclass-project/issues>`_
- `Install <https://pypi.org/pypi/boto3-dataclass-migrationhubstrategy>`_
- `Download <https://pypi.org/pypi/boto3-dataclass-migrationhubstrategy#files>`_
boto3-dataclass-migrationhuborchestrator 1.40.0
Dataclass for boto3 migrationhuborchestrator.
License: MIT
Requires Python: <4.0,>=3.10

Welcome to ``boto3-dataclass-migrationhuborchestrator`` Documentation
========================================================================================================================
- `Homepage <https://github.com/MacHu-GWU/boto3_dataclass-project>`_
- `Documentation <https://boto3-dataclass.readthedocs.io/en/latest/>`_
- `Repository <https://github.com/MacHu-GWU/boto3_dataclass-project>`_
- `Issues <https://github.com/MacHu-GWU/boto3_dataclass-project/issues>`_
- `Install <https://pypi.org/pypi/boto3-dataclass-migrationhuborchestrator>`_
- `Download <https://pypi.org/pypi/boto3-dataclass-migrationhuborchestrator#files>`_
boto3-dataclass-migrationhub-config 1.40.0
Dataclass for boto3 migrationhub_config.
License: MIT
Requires Python: <4.0,>=3.10

Welcome to ``boto3-dataclass-migrationhub-config`` Documentation
========================================================================================================================
- `Homepage <https://github.com/MacHu-GWU/boto3_dataclass-project>`_
- `Documentation <https://boto3-dataclass.readthedocs.io/en/latest/>`_
- `Repository <https://github.com/MacHu-GWU/boto3_dataclass-project>`_
- `Issues <https://github.com/MacHu-GWU/boto3_dataclass-project/issues>`_
- `Install <https://pypi.org/pypi/boto3-dataclass-migrationhub-config>`_
- `Download <https://pypi.org/pypi/boto3-dataclass-migrationhub-config#files>`_
boto3-dataclass-migration-hub-refactor-spaces 1.40.0
Dataclass for boto3 migration_hub_refactor_spaces.
License: MIT
Requires Python: <4.0,>=3.10

Welcome to ``boto3-dataclass-migration-hub-refactor-spaces`` Documentation
========================================================================================================================
- `Homepage <https://github.com/MacHu-GWU/boto3_dataclass-project>`_
- `Documentation <https://boto3-dataclass.readthedocs.io/en/latest/>`_
- `Repository <https://github.com/MacHu-GWU/boto3_dataclass-project>`_
- `Issues <https://github.com/MacHu-GWU/boto3_dataclass-project/issues>`_
- `Install <https://pypi.org/pypi/boto3-dataclass-migration-hub-refactor-spaces>`_
- `Download <https://pypi.org/pypi/boto3-dataclass-migration-hub-refactor-spaces#files>`_
boto3-dataclass-mgn 1.40.0
Dataclass for boto3 mgn.
License: MIT
Requires Python: <4.0,>=3.10

Welcome to ``boto3-dataclass-mgn`` Documentation
========================================================================================================================
- `Homepage <https://github.com/MacHu-GWU/boto3_dataclass-project>`_
- `Documentation <https://boto3-dataclass.readthedocs.io/en/latest/>`_
- `Repository <https://github.com/MacHu-GWU/boto3_dataclass-project>`_
- `Issues <https://github.com/MacHu-GWU/boto3_dataclass-project/issues>`_
- `Install <https://pypi.org/pypi/boto3-dataclass-mgn>`_
- `Download <https://pypi.org/pypi/boto3-dataclass-mgn#files>`_
boto3-dataclass-mgh 1.40.0
Dataclass for boto3 mgh.
License: MIT
Requires Python: <4.0,>=3.10

Welcome to ``boto3-dataclass-mgh`` Documentation
========================================================================================================================
- `Homepage <https://github.com/MacHu-GWU/boto3_dataclass-project>`_
- `Documentation <https://boto3-dataclass.readthedocs.io/en/latest/>`_
- `Repository <https://github.com/MacHu-GWU/boto3_dataclass-project>`_
- `Issues <https://github.com/MacHu-GWU/boto3_dataclass-project/issues>`_
- `Install <https://pypi.org/pypi/boto3-dataclass-mgh>`_
- `Download <https://pypi.org/pypi/boto3-dataclass-mgh#files>`_
boto3-dataclass-meteringmarketplace 1.40.0
Dataclass for boto3 meteringmarketplace.
License: MIT
Requires Python: <4.0,>=3.10

Welcome to ``boto3-dataclass-meteringmarketplace`` Documentation
========================================================================================================================
- `Homepage <https://github.com/MacHu-GWU/boto3_dataclass-project>`_
- `Documentation <https://boto3-dataclass.readthedocs.io/en/latest/>`_
- `Repository <https://github.com/MacHu-GWU/boto3_dataclass-project>`_
- `Issues <https://github.com/MacHu-GWU/boto3_dataclass-project/issues>`_
- `Install <https://pypi.org/pypi/boto3-dataclass-meteringmarketplace>`_
- `Download <https://pypi.org/pypi/boto3-dataclass-meteringmarketplace#files>`_
boto3-dataclass-memorydb 1.40.0
Dataclass for boto3 memorydb.
License: MIT
Requires Python: <4.0,>=3.10

Welcome to ``boto3-dataclass-memorydb`` Documentation
========================================================================================================================
- `Homepage <https://github.com/MacHu-GWU/boto3_dataclass-project>`_
- `Documentation <https://boto3-dataclass.readthedocs.io/en/latest/>`_
- `Repository <https://github.com/MacHu-GWU/boto3_dataclass-project>`_
- `Issues <https://github.com/MacHu-GWU/boto3_dataclass-project/issues>`_
- `Install <https://pypi.org/pypi/boto3-dataclass-memorydb>`_
- `Download <https://pypi.org/pypi/boto3-dataclass-memorydb#files>`_
boto3-dataclass-medical-imaging 1.40.0
Dataclass for boto3 medical_imaging.
License: MIT
Requires Python: <4.0,>=3.10

Welcome to ``boto3-dataclass-medical-imaging`` Documentation
========================================================================================================================
- `Homepage <https://github.com/MacHu-GWU/boto3_dataclass-project>`_
- `Documentation <https://boto3-dataclass.readthedocs.io/en/latest/>`_
- `Repository <https://github.com/MacHu-GWU/boto3_dataclass-project>`_
- `Issues <https://github.com/MacHu-GWU/boto3_dataclass-project/issues>`_
- `Install <https://pypi.org/pypi/boto3-dataclass-medical-imaging>`_
- `Download <https://pypi.org/pypi/boto3-dataclass-medical-imaging#files>`_
boto3-dataclass-mediatailor 1.40.0
Dataclass for boto3 mediatailor.
License: MIT
Requires Python: <4.0,>=3.10

Welcome to ``boto3-dataclass-mediatailor`` Documentation
========================================================================================================================
- `Homepage <https://github.com/MacHu-GWU/boto3_dataclass-project>`_
- `Documentation <https://boto3-dataclass.readthedocs.io/en/latest/>`_
- `Repository <https://github.com/MacHu-GWU/boto3_dataclass-project>`_
- `Issues <https://github.com/MacHu-GWU/boto3_dataclass-project/issues>`_
- `Install <https://pypi.org/pypi/boto3-dataclass-mediatailor>`_
- `Download <https://pypi.org/pypi/boto3-dataclass-mediatailor#files>`_
boto3-dataclass-mediastore-data 1.40.0
Dataclass for boto3 mediastore_data.
License: MIT
Requires Python: <4.0,>=3.10

Welcome to ``boto3-dataclass-mediastore-data`` Documentation
========================================================================================================================
- `Homepage <https://github.com/MacHu-GWU/boto3_dataclass-project>`_
- `Documentation <https://boto3-dataclass.readthedocs.io/en/latest/>`_
- `Repository <https://github.com/MacHu-GWU/boto3_dataclass-project>`_
- `Issues <https://github.com/MacHu-GWU/boto3_dataclass-project/issues>`_
- `Install <https://pypi.org/pypi/boto3-dataclass-mediastore-data>`_
- `Download <https://pypi.org/pypi/boto3-dataclass-mediastore-data#files>`_
boto3-dataclass-mediastore 1.40.0
Dataclass for boto3 mediastore.
License: MIT
Requires Python: <4.0,>=3.10

Welcome to ``boto3-dataclass-mediastore`` Documentation
========================================================================================================================
- `Homepage <https://github.com/MacHu-GWU/boto3_dataclass-project>`_
- `Documentation <https://boto3-dataclass.readthedocs.io/en/latest/>`_
- `Repository <https://github.com/MacHu-GWU/boto3_dataclass-project>`_
- `Issues <https://github.com/MacHu-GWU/boto3_dataclass-project/issues>`_
- `Install <https://pypi.org/pypi/boto3-dataclass-mediastore>`_
- `Download <https://pypi.org/pypi/boto3-dataclass-mediastore#files>`_
boto3-dataclass-mediapackagev2 1.40.0
Dataclass for boto3 mediapackagev2.
License: MIT
Requires Python: <4.0,>=3.10

Welcome to ``boto3-dataclass-mediapackagev2`` Documentation
========================================================================================================================
- `Homepage <https://github.com/MacHu-GWU/boto3_dataclass-project>`_
- `Documentation <https://boto3-dataclass.readthedocs.io/en/latest/>`_
- `Repository <https://github.com/MacHu-GWU/boto3_dataclass-project>`_
- `Issues <https://github.com/MacHu-GWU/boto3_dataclass-project/issues>`_
- `Install <https://pypi.org/pypi/boto3-dataclass-mediapackagev2>`_
- `Download <https://pypi.org/pypi/boto3-dataclass-mediapackagev2#files>`_
boto3-dataclass-mediapackage-vod 1.40.0
Dataclass for boto3 mediapackage_vod.
License: MIT
Requires Python: <4.0,>=3.10

Welcome to ``boto3-dataclass-mediapackage-vod`` Documentation
========================================================================================================================
- `Homepage <https://github.com/MacHu-GWU/boto3_dataclass-project>`_
- `Documentation <https://boto3-dataclass.readthedocs.io/en/latest/>`_
- `Repository <https://github.com/MacHu-GWU/boto3_dataclass-project>`_
- `Issues <https://github.com/MacHu-GWU/boto3_dataclass-project/issues>`_
- `Install <https://pypi.org/pypi/boto3-dataclass-mediapackage-vod>`_
- `Download <https://pypi.org/pypi/boto3-dataclass-mediapackage-vod#files>`_
boto3-dataclass-mediapackage 1.40.0
Dataclass for boto3 mediapackage.
License: MIT
Requires Python: <4.0,>=3.10

Welcome to ``boto3-dataclass-mediapackage`` Documentation
========================================================================================================================
- `Homepage <https://github.com/MacHu-GWU/boto3_dataclass-project>`_
- `Documentation <https://boto3-dataclass.readthedocs.io/en/latest/>`_
- `Repository <https://github.com/MacHu-GWU/boto3_dataclass-project>`_
- `Issues <https://github.com/MacHu-GWU/boto3_dataclass-project/issues>`_
- `Install <https://pypi.org/pypi/boto3-dataclass-mediapackage>`_
- `Download <https://pypi.org/pypi/boto3-dataclass-mediapackage#files>`_
boto3-dataclass-medialive 1.40.0
Dataclass for boto3 medialive.
License: MIT
Requires Python: <4.0,>=3.10

Welcome to ``boto3-dataclass-medialive`` Documentation
========================================================================================================================
- `Homepage <https://github.com/MacHu-GWU/boto3_dataclass-project>`_
- `Documentation <https://boto3-dataclass.readthedocs.io/en/latest/>`_
- `Repository <https://github.com/MacHu-GWU/boto3_dataclass-project>`_
- `Issues <https://github.com/MacHu-GWU/boto3_dataclass-project/issues>`_
- `Install <https://pypi.org/pypi/boto3-dataclass-medialive>`_
- `Download <https://pypi.org/pypi/boto3-dataclass-medialive#files>`_
boto3-dataclass-mediaconvert 1.40.0
Dataclass for boto3 mediaconvert.
License: MIT
Requires Python: <4.0,>=3.10

Welcome to ``boto3-dataclass-mediaconvert`` Documentation
========================================================================================================================
- `Homepage <https://github.com/MacHu-GWU/boto3_dataclass-project>`_
- `Documentation <https://boto3-dataclass.readthedocs.io/en/latest/>`_
- `Repository <https://github.com/MacHu-GWU/boto3_dataclass-project>`_
- `Issues <https://github.com/MacHu-GWU/boto3_dataclass-project/issues>`_
- `Install <https://pypi.org/pypi/boto3-dataclass-mediaconvert>`_
- `Download <https://pypi.org/pypi/boto3-dataclass-mediaconvert#files>`_
boto3-dataclass-mediaconnect 1.40.0
Dataclass for boto3 mediaconnect.
License: MIT
Requires Python: <4.0,>=3.10

Welcome to ``boto3-dataclass-mediaconnect`` Documentation
========================================================================================================================
- `Homepage <https://github.com/MacHu-GWU/boto3_dataclass-project>`_
- `Documentation <https://boto3-dataclass.readthedocs.io/en/latest/>`_
- `Repository <https://github.com/MacHu-GWU/boto3_dataclass-project>`_
- `Issues <https://github.com/MacHu-GWU/boto3_dataclass-project/issues>`_
- `Install <https://pypi.org/pypi/boto3-dataclass-mediaconnect>`_
- `Download <https://pypi.org/pypi/boto3-dataclass-mediaconnect#files>`_
graphcart 0.1.4
A modular, one-stop data visualization package
License: MIT
Requires Python: >=3.8

# graphcart

**graphcart** is a modular, one‑stop Python package for dataset visualization.
It ships with a single high-level function, `visualize`, plus a set of focused
helpers grouped by domain (basic, statistical, correlation, 3D, time‑series, interactive).

## Installation
```bash
pip install graphcart
```

## Quick start
```python
import pandas as pd
import seaborn as sns
from visuals.core import visualize

df = sns.load_dataset("iris")
visualize(df, "scatter", x="sepal_length", y="sepal_width", hue="species")
visualize(df, "heatmap")
visualize(df, "scatter3d", x="sepal_length", y="sepal_width", z="petal_length")
```

## Plot types
- Basic: bar, line, scatter, hist, box, pie, area
- Statistical: violin, swarm, strip, kde
- Correlation: heatmap, pair
- Advanced: hexbin, bubble, radar, waterfall
- 3D: scatter3d, surface3d
- Time series: ts (line), rolling mean
- Interactive: scatter_int, map

## Contributing
PRs welcome! Please run tests with `pytest`.

## License
MIT
dpaste-magic 0.3.0
Magic command to dpaste / get line & cell content
License: MIT
Keywords: pastebin dpaste magic command IPython Jupyter Jupyterlab Colab

# dpaste_magic
Magic function that pushes or pulls code snippets out of pastebins.
dpaste.org is currently supported.

### Pushing code & cells
<pre>
  %dpaste answer = 42
  <i>https://dpaste.org/WXYZ</i>
</pre>
pushes line content & print the dpaste url.
<pre>
  %%dpaste
  answer = 42
  <i>https://dpaste.org/WXYZ</i>
</pre>
pushes cell content & print the dpaste url.
<pre>
  %%dpaste {-1x -1h -1d -1w}
  answer = 42
  <i>https://dpaste.org/WXYZ</i>
</pre>
returns a dpaste url with expires duration:
* -1x: just for one read
* -1h: one hour (default)
* -1d: one day
* -1w: one week

These are the supported expiration time supported by dpaste.org.

Options:
* -o: return URL as value (for storage in a variable)
* -s: silent mode (URL not printed)

`my_url = %dpaste -o answer = 42`
push code on line, prints & stores url in `my_url` variable.

`my_url = %dpaste -o -s answer = 42`
push code on line & stores url in `my_url` variable (silent mode, no print).

### Getting back the paste
<pre>
  %getdpaste WXYZ
</pre>
retrieves snippet from WXYZ dpaste hash or URL and changes cell to:
<pre>
  answer = 42
</pre>

<pre>
  %getdpaste -u WXYZ
</pre>
add retrieved URL as a Python comment in first line:
<pre>
  #https://dpaste.org/WXYZ/raw

  answer = 42
</pre>

With `%dpaste -g` get option:
<pre>
  %dpaste -g WXYZ
</pre>
retrieves snippet from WXYZ dpaste hash or URL and changes cell to:
<pre>
  answer = 42
</pre>


## Installation

Use:
`pip install dpaste-magic`
to install the magic command.

First load the magic in a cell:

`%load_ext dpaste_magic`

and then use the function in your cell to dpaste its content.

`%%dpaste`, `%dpaste` or `%getdpaste`.


## TO DO

* increase tests range
* code unload function
better-regressions 0.9.0
Advanced regression methods with sklearn-like interface
License: MIT
Requires Python: >=3.10

# Better Regressions

Advanced regression methods with an sklearn-like interface.

## Current Features

- `Linear`:
  - Configurable regularization: Ridge with given `alpha` / BayesianRidge / ARD
  - "Better bias" option to properly regularize the intercept term
- `AdaptiveLinear`: Ridge regression with automatic shrinkage of features (like `ARDRegression`, but works in a different way and works better with correlated features)
- `Scaler`:
  - Configurable preprocessing: Standard scaling (by second moment) / Quantile transformation with uniform/normal output / Power transformation
  - `AutoScaler` to automatically select the best scaling method based on validation split
- `Smooth`: Boosting-based regression using smooth functions for features
  - `SuperSmoother`: Adaptive-span smoother for arbitrary complex functions.
  - `Angle`: Bagging of piecewise-linear functions, it's less flexible but because of that it's more robust to overfitting.
- `Soft`: Mixture of regressors based on quantile classification
- `Stabilize`: Robust scaling & clipping transformation for features/targets
- `AutoClassifier`: Classification with automatic model selection (LogisticRegression or XGBoost, with auto depth selection)
- `BinnedRegression`: Bins features and target, then trains a classifier. This way it can learn non-linear relationships and it also models the target distribution (not only its mean).
- `EDA`: Exploratory Data Analysis utilities
  - `plot_distribution`: Visualize sample distributions with fitted t-distribution parameters
  - `plot_trend`: Automatically detect and visualize relationships between variables + Pearson/Spearman correlation
    - For discrete features: Shows violin plots with distribution at each value
    - For continuous features: Fits trend lines with variance estimation and confidence intervals

## Installation

```bash
pip install better-regressions
```

## Basic Usage

```python
from better_regressions import auto_angle, auto_linear, Linear, Scaler, AutoClassifier
from better_regressions.eda import plot_distribution, plot_trend
from sklearn.datasets import make_regression, make_moons
import numpy as np

X, y = make_regression(n_samples=100, n_features=5, noise=0.1)
model = auto_angle(n_breakpoints=2)
model.fit(X, y)
y_pred = model.predict(X)
print(repr(model))

# Classification example
dataset = make_moons(n_samples=200, noise=0.3)
Xc, yc = dataset
clf = AutoClassifier(depth="auto")
clf.fit(Xc, yc)
yc_pred = clf.predict(Xc)

# EDA example
plot_distribution(y, name="Target Distribution")
plot_trend(X[:, 0], y, name="Feature 0 vs Target")
```

## Building new verison
1. Update `__version__` in `better_regressions/__init__.py` and `pyproject.toml`
2. `python -m build`
3. `python -m twine upload dist/*`