NumPy is a powerful library for numerical computing in Python. It provides a high-performance array object, which can be used for storing and manipulating large arrays of numerical data. NumPy provides a wide range of functions for performing mathematical operations on arrays, including linear algebra, Fourier transforms, and more. NumPy also provides functions for reading and writing data in various formats, including binary and text files.
import numpy as np heights = np.array([172, 165, 188, 180, 163, 175]) weights = np.array([70, 68, 90, 82, 59, 74]) # Now we can use NumPy to calculate some basic statistics. mean_height = np.mean(heights) mean_weight = np.mean(weights) stddev_height = np.std(heights) stddev_weight = np.std(weights) # NumPy also provides a convenient way to calculate the correlation coefficient between two arrays corr = np.corrcoef(heights, weights)[0,1] # Finally, we can use NumPy to perform some basic filtering. For example, we can filter out individuals whose weight is above a certain threshold: threshold = 80 filtered_heights = heights[weights < threshold] filtered_weights = weights[weights < threshold] # This will create two new arrays containing only the heights and weights of individuals whose weight is less than 80.plotlib: Matplotlib is a data visualization library that is used for creating charts, graphs, and other visualizations. It provides a wide range of functions for creating scatter plots, line charts, bar charts, and more.