semvec/pypi-packages huggingface dataset:
shivjot-core 1.0.5
High-performance Python utilities for AI/ML and Image Processing.
License: MIT
Requires Python: >=3.8

# shivjot-core

shivjot-core is a lightweight Python utility library that extends Pandas DataFrames with additional data analysis and preprocessing capabilities, along with a standalone image frequency transformation utility.

---

## Installation

Install from PyPI:

```bash
pip install shivjot-core
```

---

## Requirements

- Python 3.8+
- pandas
- numpy

---

## Importing the Library

```python
import pandas as pd
import shivjot_core  # Enables the .shivjot accessor
from shivjot_core import convert_to_frequency
```

Importing `shivjot_core` automatically registers the `.shivjot` accessor on all Pandas DataFrames.

---

# Pandas Accessor API

The library extends `pandas.DataFrame` with a custom accessor:

```
df.shivjot
```

---

## 1. analyze()

### Signature

```python
df.shivjot.analyze()
```

### Description

Performs descriptive statistical analysis on all numeric columns in the DataFrame.

Automatically:
- Detects numeric columns
- Computes summary statistics

### Parameters

None

### Returns

A DataFrame containing descriptive statistics, including:

- count  
- mean  
- standard deviation  
- minimum  
- maximum  

### Example

```python
df = pd.read_csv("data.csv")

summary = df.shivjot.analyze()
print(summary)
```

---

## 2. normalize()

### Signature

```python
df.shivjot.normalize()
```

### Description

Applies Min-Max normalization to all numeric columns in the DataFrame.

The transformation formula used:

```
(x - min) / (max - min)
```

Each numeric column is scaled between 0 and 1.

### Parameters

None

### Returns

A new Pandas DataFrame with normalized numeric columns.

### Example

```python
normalized_df = df.shivjot.normalize()
```

---

# Standalone Utility Function

---

## convert_to_frequency()

### Signature

```python
convert_to_frequency(image_matrix)
```

### Description

Transforms image data from the spatial domain to the frequency domain using Fast Fourier Transform (FFT).

Useful for:
- Image analysis
- Noise filtering
- Frequency-based image processing

### Parameters

| Name | Type | Description |
|------|------|-------------|
| image_matrix | numpy.ndarray | A 2D (grayscale) or 3D (RGB) image array |

### Returns

numpy.ndarray  
Frequency-domain representation of the image.

### Example

```python
import numpy as np

# image_array is a NumPy array containing pixel values
frequency_data = convert_to_frequency(image_array)
```

---

# Example Workflow

```python
import pandas as pd
import shivjot_core
from shivjot_core import convert_to_frequency

# Load dataset
df = pd.read_csv("data.csv")

# Analyze dataset
print(df.shivjot.analyze())

# Normalize dataset
df_scaled = df.shivjot.normalize()

# Convert image to frequency domain
freq = convert_to_frequency(image_array)
```

---

# License

MIT License