Hidden Gems in NumPy: 7 Functions Every Data Scientist Should Know

Seven useful NumPy functions are highlighted for data scientists to enhance their data analysis workflows. The content explains functions like `np.where` for vectorized conditional logic, `np.clip` for constraining values within a set range, and `np.ptp` for finding the peak-to-peak range of data. Using a simple temperature array as a running example, it demonstrates practical applications for each function. Other functions covered include `np.diff` for calculating differences between elements, `np.gradient` for estimating smooth trends, and `np.percentile` for analyzing data distribution and identifying thresholds.