shivjot-core 1.0.5

High-performance Python utilities for AI/ML and Image Processing.

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.

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Installation

Install from PyPI:

pip install shivjot-core

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Requirements

• Python 3.8+
• pandas
• numpy

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Importing the Library

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.

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Pandas Accessor API

The library extends pandas.DataFrame with a custom accessor:

df.shivjot

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1. analyze()

Signature

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

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

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

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2. normalize()

Signature

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

normalized_df = df.shivjot.normalize()

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Standalone Utility Function

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convert_to_frequency()

Signature

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

import numpy as np

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

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Example Workflow

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)

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License

MIT License