![]() import matplotlib.pyplot as plt import numpy as np x np.linspace(0.1, 2 np.pi, 41) y np.exp(np.sin(x)) plt.stem(x, y) plt.show() The position of the baseline can be adapted using bottom. The first of those - adding a narrow boxplot to the margin - gives you any benefits to be gained from either display. stem plots vertical lines from a baseline to the y-coordinate and places a marker at the tip. Or you could add information to a histogram: If more information is better, there are many better choices than the histogram a stem and leaf plot, for example, or an ecdf / quantile plot. However, if you're comparing many dozens of distributions, having all the details of each may be more information than is easily compared - you may want to reduce the information to a smaller number of things to compare. I agree that boxplots are not as effective as a description of the distribution of a single sample, since they reduce it to a few points and that doesn't tell you a lot. If I do the same with a boxplot you have it immediately if that's what you're interested in, boxplots obviously win. and then you'll only get an approximation to it. Histograms use bars to show frequencies, while stem-and-leaf plots display individual data points.If I show you a histogram and ask you where the median is, you might be quite some time figuring it out. In summary, histograms and stem-and-leaf plots are fantastic tools for understanding data distributions. This would give you an idea of how fast most people completed the race and whether there were any outliers. A stem-and-leaf plot could show the distribution of finishing times, with stems representing the minutes and leaves representing the seconds. Now, imagine you’re reading a blog post about the running times of people in a local 5k race. Can any patterns, trends, or outliers be identified in the data?. ![]()
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