Visual Analysis of Weather

Compare and visualize climate patterns across cities. Weather data helps us understand not only daily conditions but also long-term climate differences. In this project, you’ll conduct a Visual Analysis of Weather, working with datasets from two different cities. Using Pandas for data preparation and Seaborn for visualization, you’ll explore and compare temperature and weather patterns side by side. You’ll begin by loading datasets containing daily or monthly weather observations, including attributes such as temperature, humidity, and precipitation. With Pandas, you’ll clean and transform the data—handling missing values, aligning time periods between the two cities, and calculating useful metrics like average monthly temperatures or daily differences. Once the data is ready, you’ll use Seaborn to create clear and comparative visuals. Line plots will help track seasonal trends across the year, box plots will highlight variability, and scatter plots can reveal correlations such as temperature vs. humidity. You’ll practice building visuals that make it easy to see how two cities are similar or different in their climate behavior. By the end of this project, you will be able to: Prepare and clean weather datasets with Pandas. Align and compare data from multiple cities. Create Seaborn visualizations that highlight seasonal and daily patterns. Summarize similarities and differences in climate across locations. Build comparative stories using real-world environmental data. This project combines technical data handling with visual storytelling, showing how climate data can be made understandable and engaging. By analyzing two cities side by side, you’ll not only practice core data skills but also develop insights into how geography and environment shape weather patterns.