Enlightening Paths: Python's Vision into the Electric Vehicle Market

This article goes deep into understanding the size of the electric vehicle (EV) market using Python programming. We use tools like pandas, matplotlib, and seaborn to dig into the data and see how EV registrations have been changing over time. We look at different angles like how electric ranges are spread out, what the average range looks like for each model year, and who the big players are in terms of market share for manufacturers and models. One of our primary aims is to forecast the future market size using the data at our disposal, utilising methods like Compound Annual Growth Rate (CAGR) calculations. Additionally, we rely on visual tools like histograms, scatter plots, and bar charts to guide us in visualising the trajectory of the EV market's growth and to gain insights into its ongoing evolution. This study not only shows us how quickly the EV sector is growing but also demonstrates just how powerful Python can be for doing in-depth market size analyses.

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Publication Details

Journal
Journal of Communication Engineering and VLSI Design
Published
2024-12-13
DOI
https://doi.org/10.48001/jocevd.2024.2228-35
Primary Topic
Computational Physics and Python Applications
Type
article
Field-Weighted Citation Impact
0.00
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article

Enlightening Paths: Python's Vision into the Electric Vehicle Market

Mohammed Abid Abrar, Mohammad Enayet Hussain, T. Aditya Sai Srinivas, Md Shafiul Abrar et al.
Journal of Communication Engineering and VLSI Design
Computational Physics and Python Applications
article

Enlightening Paths: Python's Vision into the Electric Vehicle Market

Mohammed Abid Abrar, Mohammad Enayet Hussain, T. Aditya Sai Srinivas, Md Shafiul Abrar, MD Imaduddin Aiman
article en

Abstract

This article goes deep into understanding the size of the electric vehicle (EV) market using Python programming. We use tools like pandas, matplotlib, and seaborn to dig into the data and see how EV registrations have been changing over time. We look at different angles like how electric ranges are spread out, what the average range looks like for each model year, and who the big players are in terms of market share for manufacturers and models. One of our primary aims is to forecast the future market size using the data at our disposal, utilising methods like Compound Annual Growth Rate (CAGR) calculations. Additionally, we rely on visual tools like histograms, scatter plots, and bar charts to guide us in visualising the trajectory of the EV market's growth and to gain insights into its ongoing evolution. This study not only shows us how quickly the EV sector is growing but also demonstrates just how powerful Python can be for doing in-depth market size analyses.

Journal of Communication Engineering and VLSI DesignVol. 2(2)
Narayan Medical College and Hospital (IN)
Openalex Percentile: Top 77%
Computational Physics and Python Applications
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