The x data is from a normal distribution where the mean is 2.0 and STD 1.0.Then let’s create a scatter plot from the randomized data: import numpy Let’s create two lists filled with 100 numbers picked from the normal distribution. Make sure to have NumPy installed on your system: pip install numpy This example uses NumPy to generate random data from a normal distribution. Here is the resulting scatter plot: Example-Randomly Distributed Data Call (x, y) for creating a scatter plot.įor example, let’s create a scatter plot with 100 random x and y values as the data points: import matplotlib.pyplot as plt.Specify a group of data points x and y.If you don’t have it yet, install it by running the following command in your command line: pip install matplotlib How to Create a Scatter Plot in Python ![]() ![]() To create a scatter plot, you need to have matplotlib module installed. To create scatter plots for visualizing these relationships in Python, first install matplotlib on your machine. ![]() These relationships can be linear, non-linear, positive, negative, strong, or weak. Generally, scatter plots are used to demonstrate the relationship between two variables. Given randomized x and y data, the scatter plot looks something like this: Scatter Plots in Python Where x and y are lists of numbers or the data points for the plot.įor example, let’s create a scatter plot where x and y are lists of random numbers between 1 and 100: import matplotlib.pyplot as plt You can create scatter plots in Python by using the matplotlib as follows: import matplotlib.pyplot as plt
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