This project delves into stock price data to explore historical trends, volatility, and seasonal patterns using Python’s data visualization and analysis tools. By conducting a comprehensive exploratory data analysis (EDA), we aim to uncover key insights into stock price movements over time, with a particular focus on periods of heightened market activity. Notably, our analysis reveals that the highest levels of volatility occurred during the 2008 global financial crisis, highlighting the profound impact of economic downturns on market behavior. This project emphasizes skill-building in data visualization and the application of Pandas, and it is intended purely as an educational exercise rather than a basis for financial advice or decision-making.

Teste de mapa dinâmico usando plotly com dados das eleições municipais de 2024.

test for section 2