Temperature Analysis with NumPy

Compare and analyze city temperatures with NumPy. Weather data is everywhere, and analyzing it is a perfect way to practice data skills. In this project, you will use NumPy arrays to store, analyze, and compare temperature data from multiple cities. By working with arrays instead of raw lists, you’ll quickly calculate statistics, make comparisons, and extract meaningful insights. You’ll begin by creating arrays that represent daily or monthly temperatures for different cities. With NumPy’s aggregation functions, you’ll compute mean, median, min, max, and standard deviation, building a profile of each city’s climate. You’ll then compare datasets—for example, identifying which city is warmest on average, which has the widest temperature variation, or how two cities differ season by season. The project also includes working with vectorized operations to calculate differences between cities, detect anomalies, or highlight unusually hot or cold days. By experimenting with slicing and indexing, you’ll learn how to focus on specific ranges of data (such as summer vs. winter months). By the end of this project, you will be able to: Store and organize temperature data in NumPy arrays. Calculate descriptive statistics across multiple datasets. Compare cities based on averages and variability. Use vectorized operations for efficient analysis. Extract patterns and insights from real-world style data. This project is hands-on and practical, showing how NumPy can transform raw numbers into clear, comparative insights. Beyond just coding, you’ll gain a sense of how data analysis connects to real-world decisions—like understanding climate patterns or planning travel.