# The first cell in the grid is the new Axes location. # ‘111’ is a MATlab convention used in Matplotlib # to create a grid with 1 row and 1 column.
from mpl_toolkits.mplot3d import Axes3DĪx = fig.add_subplot(111, projection='3d') We’ve already created a 2D scatter plot above, but in this example we’ll create a 3D scatter plot: Matplotlib can also handle 3D plots by allowing the use of a Z axis. A Phase Spectrum of two signals with different frequencies is plotted in one figure: Matplotlib Example: 3D Plot In this advanced example, we’ll plot a phase spectrum of two signals (represented as functions) that each have different frequencies: import matplotlib.pyplot as plt Plt.show() Matplotlib Example: Phase Spectrum PlotĪ phase spectrum plot lets us visualize the frequency characteristics of a signal. # Create a Figure and multiple subplots containing Axes:Īx.t_major_formatter(maticker.PercentFormatter(xmax=1.0, decimals=1)) The areas in the bar graph will be proportional to the frequency of a random variable, and the widths of each bar graph will be equal to the class interval: In this example, we’ll combine matplotlib’s histogram and subplot capabilities by creating a plot containing five bar graphs. M ultiple axe in subplots displayed in one figure:Ī histogram is used to display frequency distributions in a bar graph. # Create a Figure with 2 rows and 2 columns of subplots: In this example, multiple axes are enclosed in one figure and displayed in subplots: import matplotlib.pyplot as plt You can also use matplotlib to create complex figures that contain more than one plot. Plt.title(“Multiple Datasets in One Plot") # Create two datasets from the random floats: In this example, we’ll plot two separate data sets, xdata1 and xdata2: Matplotlib is highly flexible, and can accommodate multiple datasets in a single plot. Matplotlib Example: Multiple Data Sets in One Plot In this example, 2 arrays of the same length (one array for X axis values and another array for Y axis values) are plotted. In this case, the scatter() function is used to display data values as a collection of x,y coordinates represented by standalone dots. Matplotlib also supports more advanced plots, such as scatter plots. “o” letter marker Matplotlib Scatter Plot Example Plt.plot(xcoords, marker = “o”, linestyle = “-”)Ī partial list of string characters that are acceptable options for marker and linestyle: “-” solid line style # Mark each data value and customize the linestyle: In this example, each data value is labeled with the letter “o”, and given a dashed linestyle “–”: import matplotlib.pyplot as plt linestyle is an argument used to customize the appearance of lines between data values, or else remove them altogether.marker is an argument used to label each data value in a plot with a ‘ marker‘.Marker and linestyle are matplotlib keywords that can be used to customize the appearance of data in a plot without modifying data values. A simple plot created with the plot() function: How to Customize Plot Appearance with Marker & Linestyle Parameter for an array of Y axis coordinates.Ī line ranging from x=2, y=4 through x=8, y=9 is plotted by creating 2 arrays of (2,8) and (4,9): import matplotlib.pyplot as pltįigure 1.Parameter for an array of X axis coordinates.In this case, plot() takes 2 parameters for specifying plot coordinates: The simplest example uses the plot() function to plot values as x,y coordinates in a data plot. The () function provides a unified interface for creating different types of plots. How to Create a Simple Plot with the Plot() Function Matplotlib’s series of pyplot functions are used to visualize and decorate a plot. For information about pyplot functions and terminology, refer to: What is Pyplot in Matplotlib Display a plot in Python: Pyplot Examples The pyplot interface is easier to implement than the OO version and is more commonly used. The OO API provides direct access to matplotlib’s backend layer. OO (Object-Oriented) API interface, which offers a collection of objects that can be assembled with greater flexibility than pyplot.
A wide range of functionality is provided by matplotlib’s two APIs (Application Programming Interfaces): Pythonistas typically use the Matplotlib plotting library to display numeric data in plots, graphs and charts in Python.
