![]() ![]() This parameter allows you to specify the desired location of the legend. To adjust the position of the legend in a Matplotlib plot, you can use the loc parameter in the plt.legend() method. ![]() By following this code, you can modify the legend labels in a scatter plot to match your specific requirements. This approach simplifies legend generation by automatically assigning labels based on unique categories in the ‘furnishingstatus’ column. In this article, we will explore the concept of legends in data visualization using different types of plots and datasets. Python, being a popular programming language for data visualization, offers powerful libraries such as Matplotlib that facilitate the creation of legends, thereby providing the visual representation of plot elements. Similarly, in a scatter plot, distinct markers or colors may represent different categories of data, and the legend helps to decode these markers or colors. For instance, in a line plot, each line represents a distinct data series, which can be appropriately labeled within the legend, thereby enabling the viewer to understand which line represents which data. Typically, a legend consists of labels or markers that correspond to distinct data categories or plot elements, along with a description or key that explains what each label or marker represents. It helps to identify and differentiate between different data series or plot elements in the graph. BASIC INTRODUCTION TO LEGENDSĪ legend serves as a crucial component within a graph, providing valuable graphical representation and information about the elements present in the plot. Legends are especially useful when multiple lines, markers, or colors are used in a plot, as they provide a visual guide to understand the meaning behind these graphical representations. The simplest way to create a legend is by using the plt.legend() command, which automatically generates a legend for labeled plot elements. Let’s delve into the details of how you can create, display, and perform various actions with legends. To further expand your understanding of Python programming, don’t hesitate to explore additional tutorials available at Python Programming Tutorials. In this tutorial, we will learn how to add a legend to a Matplotlib plot in Python and customization of legends, thereby enhancing the overall clarity and interpretability of your graphs. Particularly, when dealing with graphs that contain multiple lines of varying colors, legends play a crucial role in clarifying the meaning behind these color-coded lines. As a powerful 2D plotting library, it allows you to input arrays of numbers and plot the data in various graphical representations, including bar graphs, histograms, scatter plots, and line plots. Matplotlib is a widely used Python package for data visualization. legend_elements ( ** kw ), loc = "lower right", title = "Price" ) plt. cmap ( 0.7 ), fmt = "$ ", func = lambda s : np. ![]() kw = dict ( prop = "sizes", num = 5, color = scatter. Note how we target at 5 elements here, but obtain only 4 in the # created legend due to the automatic round prices that are chosen for us. The *fmt* ensures to show the price # in dollars. Because we want to show the prices # in dollars, we use the *func* argument to supply the inverse of the function # used to calculate the sizes from above. ![]() add_artist ( legend1 ) # Produce a legend for the price (sizes). legend_elements ( num = 5 ), loc = "upper left", title = "Ranking" ) ax. Even though there are 40 different # rankings, we only want to show 5 of them in the legend. scatter ( volume, amount, c = ranking, s = 0.3 * ( price * 3 ) ** 2, vmin =- 3, vmax = 3, cmap = "Spectral" ) # Produce a legend for the ranking (colors). subplots () # Because the price is much too small when being provided as size for ``s``, # we normalize it to some useful point sizes, s=0.3*(price*3)**2 scatter = ax. uniform ( 1, 10, size = 40 ) fig, ax = plt. ![]()
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