Forem: Boddu Sripavan

Delhi Climate Visualization using KitikiPlot

Boddu Sripavan — Sat, 29 Nov 2025 12:35:51 +0000

Introduction

Climate analysis systematically examines historical weather data over a specified period, focusing on key factors such as temperature, humidity, atmospheric pressure, etc. These parameters exhibit variations over time, necessitating effective methods for visualization and analysis. Consider the mean temperature trends in Delhi between 2013-01-01 and 2013-04-10 from [DELHI Weather dataset] https://www.kaggle.com/datasets/devitachi/delhi-weather-dataset).

Analysis

While temperature is inherently a continuous variable, discretizing it into categorical bins facilitates better interpretability and pattern identification. In this case, temperature values are grouped into five categories: temperatures below 10°C were classified as "Freezing", values within [10, 15) as "Chilly", within [15, 20) as "Mild", within [20, 25) as "Warm", within [25, 30) as "Hot", and values exceeding or equal to 30°C as "Scorching". Each category is associated with a unique color to enhance visual differentiation.

Using KitikiPlot, sequential temperature patterns are plotted. It reveals that abrupt changes exceeding five temperature units are absent. Instead, transitions predominantly occurred between adjacent categories, such as from "Chilly" to "Mild" or "Warm" to "Hot" without skipping intermediate classes. This visualization underscores the consistency and gradual nature of temperature variation in Delhi during the analyzed period. KitikiPlot thus emerges as a valuable tool for climatologists and meteorologists, offering clear, interpretable visualizations that aid in identifying trends and anomalies, ultimately providing actionable insights into climate dynamics.

Installation
!pip install kitikiplot

Source Code (Notebook):
https://github.com/BodduSriPavan-111/kitikiplot/blob/main/examples/Delhi_Weather.ipynb

KitikiPlot: Your New Go-To for Time-Series Data Visualization

Boddu Sripavan — Thu, 23 Oct 2025 10:28:56 +0000

KitikiPlot is a specialized Python library for visualizing sequential and time-series categorical sliding window data.

Installation
pip install kitikiplot

Use-Cases

Genomics: Visualize gene sequences and mutational patterns over sliding windows to identify significant genetic variations.
Weather Analysis: Monitor climate trends across time intervals to understand seasonal changes and extreme weather events.
Air Quality Monitoring: Track categorical pollutant levels and environmental events to assess air quality trends and inform policy decisions.

GitHub: https://github.com/BodduSriPavan-111/kitikiplot
Docs: https://kitikiplot-documentation.vercel.app/

Sliding Window Visualization using “KitikiPlot”, a novel Python Library

Boddu Sripavan — Thu, 18 Sep 2025 16:55:32 +0000

KitikiPlot is a specialized Python library for visualizing sequential and time-series categorical sliding window data.

Use-Cases

Genomics: Visualize gene sequences and mutational patterns over sliding windows to identify significant genetic variations.
Weather Analysis: Monitor climate trends across time intervals to understand seasonal changes and extreme weather events.
Air Quality Monitoring: Track categorical pollutant levels and environmental events to assess air quality trends and inform policy decisions.

GitHub: https://github.com/BodduSriPavan-111/kitikiplot

python #opensource #data #ai #datavisualization #matplotlib

Multidimensional Embedding Comparison with “diemsim”

Boddu Sripavan — Mon, 01 Sep 2025 16:15:47 +0000

Distance metrics are essential tools in data analysis and machine learning, helping to measure the similarity or difference between data points. Choosing the right metric impacts the accuracy and interpretation of results, especially in high-dimensional spaces. Our Python library, "diemsim" implements Dimension Insensitive Euclidean Metric, which surpasses Cosine similarity for Multidimensional Comparisons.

Getting Started:
pip install diemsim

GitHub: https://github.com/BodduSriPavan-111/diemsim