![]() ![]() The following is a simple scatter plot created using Matplotlib library. X-axis represents an attribute namely sepal length and Y-axis represents the attribute namely sepal width. So there are several different types of charts or graphs you can make in matplotlib, including line plots, bar graphs, histograms, pie charts, scatter plots. The following represents a sample scatter plot representing three different classes / species for IRIS flower data set. The scatter plot would show how different types of food make people feel different levels of fullness, satisfaction, and energy. For example, a scatter plot could be used to visualize the relationship between different types of food and how they make people feel. scatter plots can also be used to visualize relationships between non-numerical data sets. The scatter plot would show how the weight and height of different people are related. Visualize the relationship between two variables For example, a scatter plot could be used to visualize the relationship between someone’s weight and their height. ![]() Outlier detection can be used to find errors in data, or to identify unusual data points that may require further investigation. Outliers are typically easy to spot on a scatter plot, as they will lie outside the general trend of the data. The scatter plot can then be analyzed to look for patterns and trends. ![]() To create a scatter plot, the data points are plotted on a coordinate grid, and then a line is drawn to connect the points. I think the most elegant way is that suggesyted by. E.g.: import matplotlib.pyplot ( 1,2,3, 4,5,6,color 'red','green','blue') When you have a list of lists and you want them colored per list. Detect outliers: Scatter plots are often used to detect outliers, or data points that lie outside the general trend. The normal way to plot plots with points in different colors in matplotlib is to pass a list of colors as a parameter.For example, scatter plots can be used to show the distribution of ages in a population, the distribution of heights in a population, or the distribution of grades in a classroom. Visualize the distribution of data: Scatter plots can be used to visualize any type of data, but they are particularly useful for data that is not evenly distributed.Scatter plots can be used for the following: The X-axis can be used to represent one of the independent variables, while the Y-axis can be used to represent the other independent variables or dependent variable. These plots are created by using a set of X and Y-axis values. Scatter plots are a type of graph that shows the scatter plot for data points. Scatter plots are used in data science and statistics to show the distribution of data points, and they can be used to identify trends and patterns. A scatter plot is a type of data visualization that is used to show the relationship between two variables. ![]()
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