semvec/pypi-packages huggingface dataset:
satility 0.0.3
Utility to download and process SPOT-VGT and PROBA-V scenes via Terracatalogue
Requires Python: <4.0,>=3.8

# satility

[![Release](https://img.shields.io/github/v/release/JulioContrerasH/satility)](https://img.shields.io/github/v/release/JulioContrerasH/satility)
[![Build status](https://img.shields.io/github/actions/workflow/status/JulioContrerasH/satility/main.yml?branch=main)](https://github.com/JulioContrerasH/satility/actions/workflows/main.yml?query=branch%3Amain)
[![codecov](https://codecov.io/gh/JulioContrerasH/satility/branch/main/graph/badge.svg)](https://codecov.io/gh/JulioContrerasH/satility)
[![Commit activity](https://img.shields.io/github/commit-activity/m/JulioContrerasH/satility)](https://img.shields.io/github/commit-activity/m/JulioContrerasH/satility)
[![License](https://img.shields.io/github/license/JulioContrerasH/satility)](https://img.shields.io/github/license/JulioContrerasH/satility)

This is a template repository for Python projects that use Poetry for their dependency management.

- **Github repository**: <https://github.com/JulioContrerasH/satility/>
- **Documentation** <https://JulioContrerasH.github.io/satility/>

## Getting started with your project

First, create a repository on GitHub with the same name as this project, and then run the following commands:

```bash
git init -b main
git add .
git commit -m "init commit"
git remote add origin git@github.com:JulioContrerasH/satility.git
git push -u origin main
```

Finally, install the environment and the pre-commit hooks with

```bash
make install
```

You are now ready to start development on your project!
The CI/CD pipeline will be triggered when you open a pull request, merge to main, or when you create a new release.

To finalize the set-up for publishing to PyPI or Artifactory, see [here](https://fpgmaas.github.io/cookiecutter-poetry/features/publishing/#set-up-for-pypi).
For activating the automatic documentation with MkDocs, see [here](https://fpgmaas.github.io/cookiecutter-poetry/features/mkdocs/#enabling-the-documentation-on-github).
To enable the code coverage reports, see [here](https://fpgmaas.github.io/cookiecutter-poetry/features/codecov/).

## Releasing a new version

- Create an API Token on [PyPI](https://pypi.org/).
- Add the API Token to your projects secrets with the name `PYPI_TOKEN` by visiting [this page](https://github.com/JulioContrerasH/satility/settings/secrets/actions/new).
- Create a [new release](https://github.com/JulioContrerasH/satility/releases/new) on Github.
- Create a new tag in the form `*.*.*`.
- For more details, see [here](https://fpgmaas.github.io/cookiecutter-poetry/features/cicd/#how-to-trigger-a-release).

---

Repository initiated with [fpgmaas/cookiecutter-poetry](https://github.com/fpgmaas/cookiecutter-poetry).
ifeval 0.0.1
Evaluate all if-statement predicates in a Python file
Requires Python: >=3.8

# ifeval

**Evaluate all `if`-statement predicates in a Python file and simplify the code by removing unreachable branches.**

This tool parses a Python source file, evaluates constant `if` conditions (`if f(x)`, `if x is None`, etc.), and rewrites the file to include only the branches that would execute.

---

## Installation

Clone the repository and install with pip:

```bash
git clone https://gitlab.com/knvvv/ifeval.git
cd ifeval
pip install .
```

## Usage

### Dry run (default)

To preview changes without modifying the file:

```bash
ifeval path/to/your_script.py
```

It will print the diff showing what would be changed.

### Apply changes

To actually rewrite the file in-place, add the --no-dry flag:

```bash
ifeval path/to/your_script.py --no-dry
```

## Example
Given the following code:

```python
print("Hello")
if True:
    print("Keep this")
else:
    print("Remove this")
```

`ifeval` simplifies it to:

```python
print("Hello")
print("Keep this")
```

## ⚠️ **A word of caution** ⚠️

This package executes all the code from files being analyzed. **Do not run it on untrusted or potentially malicious code**, as it may lead to arbitrary code execution.
pylibswathi 0.1
A Python library for data analysis and visualization using pandas, numpy, matplotlib, and seaborn.
Requires Python: >=3.6

# pylibswathi 📊

pylibswathi is a lightweight and easy-to-use Python library built to accelerate Exploratory Data Analysis (EDA) and preliminary dataset inspection for data science projects. It wraps essential Pandas, NumPy, Seaborn, and Matplotlib utilities to help you quickly understand the structure, quality, and relationships within your dataset.

---

## ✨ Features

- ✅ Quick DataFrame summary: shape, datatypes, missing values, and statistics  
- ✅ Visualize correlation matrix with heatmaps  
- ✅ Detect outliers using the IQR method  
- ✅ Plot numeric feature distributions  
- ✅ Report missing values as proportions  
- ✅ Get value counts for all categorical columns  

---

## 📦 Installation


bash
pip install pylibswathi


Since this is a custom library, clone or copy the source code into your project directory or package it using setuptools if needed.

bash
# Clone this repository or include the file directly
git clone https://github.com/yourusername/pylibswathi.git




In your Python project:




from pylibswathi import df_summary, missing_report, correlation_matrix, outlier_summary, plot_distributions, value_counts_all




🧪 How to Use


1. df_summary(df)
Provides an overall summary of the DataFrame including shape, data types, missing values, and descriptive statistics.



import pandas as pd
from pylibswathi import df_summary

df = pd.read_csv("your_data.csv")
df_summary(df)


2. missing_report(df)
Returns a Series with the proportion of missing values in each column (sorted descending).


from pylibswathi import missing_report

missing_report(df)


3. correlation_matrix(df)
Plots a heatmap of correlation coefficients for all numeric features in the DataFrame.


from pylibswathi import correlation_matrix

correlation_matrix(df)



4. outlier_summary(df)
Returns a count of outliers per numeric column using the IQR method.

from pylibswathi import outlier_summary

outlier_summary(df)


5. plot_distributions(df)
Displays histograms of all numeric columns in the DataFrame.


from pylibswathi import plot_distributions

plot_distributions(df)


6. value_counts_all(df)
Returns a dictionary with value counts for all object (categorical) columns.


from pylibswathi import value_counts_all

value_counts_all(df)



🧑‍💻 Developer
Swathi Dattha Lakshmi Pasupuleti
Information Technology | Web Dev Enthusiast

📜 License
This project is licensed under the MIT License. Feel free to use and modify!

🙌 Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you'd like to change.

🧠 Future Features
Advanced outlier detection (Z-score, Isolation Forest)

Interactive plots using Plotly

Automatic report generation using pandas-profiling or sweetviz


Let me know if you'd like help setting this up as a proper Python package