Here’s an example of using plt.legend() to add a legend to a plot: The legend displays labels for each line or marker in the plot, making it easier to understand the meaning of the plot. Plt.legend() is a Matplotlib command used to add a legend to a plot. Finally, we add a title to the figure using the fig.suptitle() function, and we display the plot using the plt.show() command. We use axs to access the top subplot and axs to access the bottom subplot. We then create a plot in each of the subplots using the axs object returned by the plt.subplots() function. The figsize parameter is used to set the size of the figure. We then use plt.subplots(nrows=2, ncols=1, figsize=(6, 8)) to create a figure object with a grid of two subplots in a single column.
In this example, we first create two NumPy arrays x, y1, and y2, each containing values for a sinusoidal function. Here’s an example of using plt.subplots(nrows, ncols) to create a grid of subplots:įig, axs = plt.subplots(nrows=2, ncols=1, figsize=(6, 8))Īxs.plot(x, y1, color=’blue’, label=’sin(x)’)Īxs.plot(x, y2, color=’green’, label=’cos(x)’) The nrows parameter specifies the number of rows of subplots to create, and the ncols parameter specifies the number of columns of subplots to create. Plt.subplots(nrows, ncols) is a Matplotlib command used to create a figure object and one or more subplot axes in a grid format. Finally, we add labels and a title to the plot using the plt.xlabel(), plt.ylabel(), and plt.title() functions, and we display the plot using the plt.show() command. We then use plt.hist(x, bins=30) to create a histogram plot of the data with 30 bins. In this example, we first create a NumPy array x containing 1000 random values drawn from a normal distribution. Here’s an example of using plt.hist(x, bins) to create a histogram plot: The x array contains the values for the histogram, and the bins parameter specifies the number of bins to use in the histogram. Plt.hist(x, bins) is a Matplotlib command used to create a histogram plot using the data provided in the x array and the number of bins specified in the bins parameter. We then use plt.scatter(x, y) to create a scatter plot of the data. In this example, we first create two NumPy arrays x and y, each containing 100 random values between 0 and 1. Here’s an example of using plt.scatter(x, y) to create a scatter plot: The arrays can be of any length, as long as they have the same length.
The x array contains the values for the x-axis, and the y array contains the values for the y-axis. Plt.scatter(x, y) is a Matplotlib command used to create a scatter plot using the data provided in the x and y arrays. Matplotlib provides many more features and options for creating a wide variety of visualizations. These are just a few basic examples of what you can do with Matplotlib. In this example, we have customized the line style using ‘o-‘ (which creates circles for each data point connected by a line), changed the color to blue, increased the line width to 2, and increased the size of the markers to 10. Plt.plot(x, y, ‘o-‘, color=’blue’, linewidth=2, markersize=10) You can customize various aspects of your plot, such as the line style, color, and marker using various options in the plot() function. You can add labels and titles to your plot using the xlabel(), ylabel(), and title() functions, respectively. To create a basic plot using Matplotlib, you can use the plot() function. Here, we are importing the pyplot module of Matplotlib and giving it an alias plt to make it easier to use in our code. You can do this using the following command: To use Matplotlib, you first need to import it into your Python environment. Here is a brief introduction to using Matplotlib in Python: It provides a wide range of tools for creating plots, graphs, charts, and other visualizations for scientific computing, data analysis, and data visualization. Matplotlib is a Python library used for creating static, interactive, and animated visualizations in Python.
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