**Before we start:** This Python tutorial is a part of our series of Python Package tutorials. You can find other Matplotlib related topics too!

** Pythonistas typically use the Matplotlib plotting library to display numeric data in plots, graphs and charts in Python. A wide range of functionality is provided by matplotlib’s two APIs (Application Programming Interfaces):**

- Pyplot API interface, which offers a hierarchy of code objects that make matplotlib work like MATLAB.
- OO (Object-Oriented) API interface, which offers a collection of objects that can be assembled with greater flexibility than pyplot. The OO API provides direct access to matplotlib’s backend layer.

**The pyplot interface is easier to implement than the OO version and is more commonly used. For information about pyplot functions and terminology, refer to: What is Pyplot in Matplotlib**

**Display a plot in Python: Pyplot Examples**

Matplotlib’s series of pyplot functions are used to visualize and decorate a plot.

**How to Create a Simple Plot with the Plot() Function**

The* matplotlib.pyplot.plot()* function provides a unified interface for creating different types of plots.

The simplest example uses the * plot()* function to plot values as

*coordinates in a data plot. In this case,*

**x,y***takes 2 parameters for specifying plot coordinates:*

**plot()**- Parameter for an array of
**X**coordinates.**axis** - Parameter for an array of
coordinates.**Y axis**

A 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 plt import numpy as np# X axis parameter:xaxis = np.array([2, 8])# Y axis parameter:yaxis = np.array([4, 9]) plt.plot(xaxis, yaxis) plt.show()

**Figure 1**. A simple plot created with the *plot()** function:*

**How to Customize Plot Appearance with Marker & Linestyle**

* marker* and

*are matplotlib keywords that can be used to customize the appearance of data in a plot without modifying data values.*

**linestyle**is an argument used to label each data value in a plot with a ‘**marker****marker**‘.is an argument used to customize the appearance of lines between data values, or else remove them altogether.**linestyle**

In this example, each data value is labeled with the letter ** “o”,** and given a dashed linestyle

*:*

**“–”**import matplotlib.pyplot as plt import numpy as np xaxis = np.array([2, 12, 3, 9])# Mark each data value and customize the linestyle:plt.plot(xcoords, marker = “o”, linestyle = “--”) plt.show()

A partial list of string characters that are acceptable options for * marker* and

*:*

**linestyle**“-” solid line style “--” dashed line style “ “ no line “o” letter marker

**Matplotlib Scatter Plot Example**

**Matplotlib also supports more advanced plots, such as scatter plots.** In this case, the * scatter()* function is used to display data values as a collection of

*coordinates represented by standalone dots.*

**x,y**In this example, 2 arrays of the same length (one array for X axis values and another array for Y axis values) are plotted. Each value is represented by a dot:

import matplotlib.pyplot as plt# X axis values:x = [2,3,7,29,8,5,13,11,22,33]# Y axis values:y = [4,7,55,43,2,4,11,22,33,44]# Create scatter plot:plt.scatter(x, y) plt.show()

**Matplotlib Example: Multiple Data Sets in One Plot**

**Matplotlib is highly flexible, and can accommodate multiple datasets in a single plot**. In this example, we’ll plot two separate data sets,* xdata1* and

**xdata2**:import matplotlib.pyplot as plt import numpy as np# Create random seed:np.random.seed(5484849901)# Create random data:xdata = np.random.random([2, 8])# Create two datasets from the random floats:xdata1 = xdata[0, :] xdata2 = xdata[1, :]# Sort the data in both datasets:xdata1.sort() xdata2.sort()# Create y data points:ydata1 = xdata1 ** 2 ydata2 = 1 - xdata2 ** 4# Plot the data:plt.plot(xdata1, ydata1) plt.plot(xdata2, ydata2)# Set x,y lower, upper limits:plt.xlim([0, 1]) plt.ylim([0, 1]) plt.title(“Multiple Datasets in One Plot") plt.show()

**Matplotlib Example: ****Subplots**

**You can also use matplotlib to create complex figures that contain more than one plot**. In this example, multiple axes are enclosed in one figure and displayed in subplots:

import matplotlib.pyplot as plt import numpy as np# Create a Figure with 2 rows and 2 columns of subplots:fig, ax = plt.subplots(2, 2) x = np.linspace(0, 5, 100)# Index 4 Axes arrays in 4 subplots within 1 Figure:ax[0, 0].plot(x, np.sin(x), 'g') #row=0, column=0 ax[1, 0].plot(range(100), 'b') #row=1, column=0 ax[0, 1].plot(x, np.cos(x), 'r') #row=0, column=1 ax[1, 1].plot(x, np.tan(x), 'k') #row=1, column=1 plt.show()

*Figure 2. **M**ultiple axe in subplots displayed in one figure:*

**Matplotlib Example: Histogram Plot**

**A histogram is used to display frequency distributions in a bar graph.**

In this example, we’ll combine matplotlib’s histogram and subplot capabilities by creating a plot containing five bar graphs. 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:

import matplotlib.plot as plt import matplotlib.ticker as maticker import numpy as np# Create random variable:data = np.random.normal(0, 3, 800)# Create a Figure and multiple subplots containing Axes:fig, ax = plt.subplots() weights = np.ones_like(data) / len(data)# Create Histogram Axe:ax.hist(data, bins=5, weights=weights) ax.yaxis.set_major_formatter(maticker.PercentFormatter(xmax=1.0, decimals=1)) plt.title(“Histogram Plot”) plt.show()

**Matplotlib Example: Phase Spectrum Plot**

**A phase spectrum plot lets us visualize the frequency characteristics of a signal.**

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 import numpy as np# Generate pseudo-random numbers:np.random.seed(0)# Sampling interval:dt = 0.01 #Sampling Frequency:Fs = 1 / dt # ex[;aom Fs]# Generate noise:t = np.arange(0, 10, dt) res = np.random.randn(len(t)) r = np.exp(-t / 0.05)# Convolve 2 signals (functions):conv_res = np.convolve(res, r)*dt conv_res = conv_res[:len(t)] s = 0.5 * np.sin(1.5 * np.pi * t) + conv_res# Create the plot:fig, (ax) = plt.subplots() ax.plot(t, s)# Function plots phase spectrum:ax.phase_spectrum(s, Fs = Fs) plt.title(“Phase Spectrum Plot”) plt.show()

*Figure 3. ** A Phase Spectrum of two** signals with different frequencies is plotted in one figure:*

**Matplotlib Example: 3D Plot**

**Matplotlib can also handle 3D plots by allowing the use of a Z axis.** We’ve already created a 2D scatter plot above, but in this example we’ll create a 3D scatter plot:

from mpl_toolkits.mplot3d import Axes3D import matplotlib.pyplot as plt fig = plt.figure()# Create 1 3D subplot:ax = fig.add_subplot(111, projection='3d')# ‘111’ is a MATlab convention used in Matplotlib# to create a grid with 1 row and 1 column.# The first cell in the grid is the new Axes location.# Create x,y,z coordinates: x =[1,2,3,4,5,6,7,8,9,10] y =[11,4,2,5,13,4,14,2,4,8] z =[2,3,4,5,5,7,9,11,19,9]# Create a 3D scatter plot with x,y,z orthogonal axis, and red "o" markers:ax.scatter(x, y, z, c='red', marker="o")# Create x,y,z axis labels:ax.set_xlabel('X Axis') ax.set_ylabel('Y Axis') ax.set_zlabel('Z Axis') plt.show()

**How to Use a Matplotlib Backend**

**Matplotlib can target just about any output format you can think of.** Most commonly, data scientists display plots in their Jupyter notebook, but you can also display plots within an application.

In this example, matplotlib’s OO backend uses the Tkinter * TkAgg()* function to generate Agg (Anti-Grain Geometry) high-quality rendering, and the Tk

*function to display a plot:*

**mainloop()**from tkinter import * from tkinter.ttk import * import matplotlib matplotlib.use("TkAgg") from matplotlib.figure import Figure# OO backend (Tkinter) tkagg() function:from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg root = Tk() figure = Figure(figsize=(5, 4), dpi=100) plot = figure.add_subplot(1, 1, 1) x = [ 0.1, 0.2, 0.3, 0.4 ] y = [ -0.1, -0.2, -0.3, -0.4 ] plot.plot(x, y, color="red", marker="o", linestyle="--") canvas = FigureCanvasTkAgg(figure, root) canvas.get_tk_widget().grid(row=0, column=0) root.mainloop()

**Figure 4.** An OO backend plot displayed using Tkinter *tkagg()** function:*

**Final Tip**: matplotlib script execution creates a text output in the Python console (not part of the UI plot display) that may include warning messages or be otherwise visually unappealing. To fix this, you can add a semicolon **(;)** at the end of the last line of code before displaying the plot. For example:

# pyplot scatter() function: plt.scatter(x, y); plt.show()

**The following tutorials will provide you with step-by-step instructions on how to work with Matplotlib, including:**

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