Matplotlib Tutorial - GeeksforGeeks

07 Jul, 2021
Matplotlib is easy to use and an amazing visualizing library in Python. It is built on NumPy arrays and designed to work with the broader SciPy stack and consists of several plots like line, bar, scatter, histogram, etc. 
In this article, we will learn about Python plotting with Matplotlib from basics to advance with the help of a huge dataset containing information about different types of plots and their customizations.
Table Of Content 
Getting Started
Pyplot
Figure class
Axes Class
Setting Limits and Tick labels
Multiple Plots
What is a Legend?
Creating Different Types of Plots Line GraphBar chartHistogramsScatter PlotPie Chart3D Plots
Line Graph
Bar chart
Histograms
Scatter Plot
Pie Chart
3D Plots
Working with Images
Customizing Plots in Matplotlib
More articles on Matplotlib
Exercises, Applications, and Projects
 
Recent Articles on Matplotlib !!!
 
Before we start learning about Matplotlib we first have to set up the environment and will also see how to use Matplotlib with Jupyter Notebook:
 
Environment Setup for Matplotlib
Using Matplotlib with Jupyter Notebook
After learning about the environment setup and how to use Matplotlib with Jupyter let’s create a simple plot. We will be plotting two lists containing the X, Y coordinates for the plot.
 
Example:
 
Python3
import matplotlib.pyplot as plt   # initializing the data   x = [10, 20, 30, 40]   y = [20, 30, 40, 50]   # plotting the data   plt.plot(x, y)   # Adding the title   plt.title("Simple Plot")   # Adding the labels   plt.ylabel("y-axis")   plt.xlabel("x-axis")   plt.show()
Output:
 
In the above example, the elements of X and Y provides the coordinates for the x axis and y axis and a straight line is plotted against those coordinates. For a detailed introduction to Matplotlib and to see how basic charts are plotted refer to the below article.
 
Introduction to Matplotlib
Simple Plot in Python using Matplotlib
In the above article, you might have seen Pyplot was imported in code and must have wondered what is Pyplot. Don’t worry we will discuss the Pyplot in the next section. 
 
Pyplot is a Matplotlib module that provides a MATLAB like interface. Pyplot provides functions that interact with the figure i.e. creates a figure, decorates the plot with labels, creates plotting area in a figure.
 
Syntax:
 
matplotlib.pyplot.plot(*args, scalex=True, scaley=True, data=None, **kwargs)
 
Example:
 
Python3
# Python program to show pyplot module   import matplotlib.pyplot as plt    plt.plot([1, 2, 3, 4], [1, 4, 9, 16])    plt.axis([0, 6, 0, 20])    plt.show()
Output:
Refer to the below articles to get detailed information about Pyplot and functions associated with this class.
Pyplot in Matplotlib
Matplotlib.pyplot.plot() function in Python
Matplotlib.pyplot.title() in Python
matplotlib.pyplot.imshow() in Python
Matplotlib.pyplot.legend() in Python
Matplotlib.pyplot.subplots() in Python
Matplotlib.pyplot.colors() in Python
Matplotlib.pyplot.grid() in Python
>>> More Functions on Pyplot class
Matplotlib take care of the creation of inbuilt defaults like Figure and Axes. Don’t worry about these terms we will study about them in detail in the below section but let’s take a brief about these terms.
Figure: This class is the top-level container for all the plots means it is the overall window or page on which everything is drawn. A figure object can be considered as a box-like container that can hold one or more axes.
Axes: This class is the most basic and flexible component for creating sub-plots. You might confuse axes as the plural of axis but it is an individual plot or graph. A given figure may contain many axes but a given axes can only be in one figure.
Figure class is the top-level container that contains one or more axes. It is the overall window or page on which everything is drawn.
Syntax:
class matplotlib.figure.Figure(figsize=None, dpi=None, facecolor=None, edgecolor=None, linewidth=0.0, frameon=None, subplotpars=None, tight_layout=None, constrained_layout=None)
Example 1:
Python3
# Python program to show pyplot module   import matplotlib.pyplot as plt    from matplotlib.figure import Figure    # Creating a new figure with width = 5 inches   # and height = 4 inches   fig = plt.figure(figsize =(5, 4))    # Creating a new axes for the figure   ax = fig.add_axes([1, 1, 1, 1])    # Adding the data to be plotted   ax.plot([2, 3, 4, 5, 5, 6, 6], [5, 7, 1, 3, 4, 6 ,8])   plt.show()
Output:
 
 
Example 2: Creating multiple plots
 
Python3
# Python program to show pyplot module   import matplotlib.pyplot as plt    from matplotlib.figure import Figure    # Creating a new figure with width = 5 inches   # and height = 4 inches   fig = plt.figure(figsize =(5, 4))    # Creating first axes for the figure   ax1 = fig.add_axes([1, 1, 1, 1])    # Creating second axes for the figure   ax2 = fig.add_axes([1, 0.5, 0.5, 0.5])   # Adding the data to be plotted   ax1.plot([2, 3, 4, 5, 5, 6, 6], [5, 7, 1, 3, 4, 6 ,8])   ax2.plot([1, 2, 3, 4, 5], [2, 3, 4, 5, 6])   plt.show()
Output:
 
 
Refer to the below articles to get detailed information about the Figure class and functions associated with it.
 
Matplotlib.figure.Figure() in Python
Matplotlib.figure.Figure.add_axes() in Python
Matplotlib.figure.Figure.clear() in Python
Matplotlib.figure.Figure.colorbar() in Python
Matplotlib.figure.Figure.get_figwidth() in Python
Matplotlib.figure.Figure.get_figheight() in Python
Matplotlib.figure.Figure.subplots() in Python
 
>>> More Functions in Figure Class
 
Axes class is the most basic and flexible unit for creating sub-plots. A given figure may contain many axes, but a given axes can only be present in one figure. The axes() function creates the axes object. Let’s see the below example.
 
Syntax:
 
matplotlib.pyplot.axis(*args, emit=True, **kwargs)
Example 1:
 
Python3
# Python program to show pyplot module   import matplotlib.pyplot as plt    from matplotlib.figure import Figure    # Creating the axes object with argument as    # [left, bottom, width, height]   ax = plt.axes([1, 1, 1, 1])
Output:
 
 
Example 2: 
 
Python3
# Python program to show pyplot module   import matplotlib.pyplot as plt    from matplotlib.figure import Figure    fig = plt.figure(figsize = (5, 4))   # Adding the axes to the figure   ax = fig.add_axes([1, 1, 1, 1])   # plotting 1st dataset to the figure   ax1 = ax.plot([1, 2, 3, 4], [1, 2, 3, 4])   # plotting 2nd dataset to the figure   ax2 = ax.plot([1, 2, 3, 4], [2, 3, 4, 5])   plt.show()
Output:
 
Refer to the below articles to get detailed information about the axes class and functions associated with it.
 
Matplotlib – Axes Class
Matplotlib.axes.Axes.update() in Python
Matplotlib.axes.Axes.draw() in Python
Matplotlib.axes.Axes.get_figure() in Python
Matplotlib.axes.Axes.set_figure() in Python
Matplotlib.axes.Axes.properties() in Python
>>> More Functions on Axes Class
 
 
 
You might have seen that Matplotlib automatically sets the values and the markers(points) of the x and y axis, however, it is possible to set the limit and markers manually. set_xlim() and set_ylim() functions are used to set the limits of the x-axis and y-axis respectively. Similarly, set_xticklabels() and set_yticklabels() functions are used to set tick labels.
 
Example:
 
Python3
# Python program to show pyplot module   import matplotlib.pyplot as plt    from matplotlib.figure import Figure    x = [3, 1, 3]    y = [3, 2, 1]    # Creating a new figure with width = 5 inches   # and height = 4 inches   fig = plt.figure(figsize =(5, 4))    # Creating first axes for the figure   ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])    # Adding the data to be plotted   ax.plot(x, y)   ax.set_xlim(1, 2)   ax.set_xticklabels((   "one", "two", "three", "four", "five", "six"))    plt.show()
Output:
 
 
Till now you must have got a basic idea about Matplotlib and plotting some simple plots, now what if you want to plot multiple plots in the same figure. This can be done using multiple ways. One way was discussed above using the add_axes() method of the figure class. Let’s see various ways multiple plots can be added with the help of examples.
 
Method 1: Using the add_axes() method 
 
The add_axes() method figure module of matplotlib library is used to add an axes to the figure.
 
Syntax:
 
add_axes(self, *args, **kwargs)
 
Example:
 
Python3
# Python program to show pyplot module   import matplotlib.pyplot as plt    from matplotlib.figure import Figure    # Creating a new figure with width = 5 inches   # and height = 4 inches   fig = plt.figure(figsize =(5, 4))    # Creating first axes for the figure   ax1 = fig.add_axes([0.1, 0.1, 0.8, 0.8])    # Creating second axes for the figure   ax2 = fig.add_axes([0.5, 0.5, 0.3, 0.3])   # Adding the data to be plotted   ax1.plot([5, 4, 3, 2, 1], [2, 3, 4, 5, 6])   ax2.plot([1, 2, 3, 4, 5], [2, 3, 4, 5, 6])   plt.show()
Output:
 
The add_axes() method adds the plot in the same figure by creating another axes object.
 
Method 2: Using subplot() method. 
 
This method adds another plot to the current figure at the specified grid position.
 
Syntax:
 
subplot(nrows, ncols, index, **kwargs)
subplot(pos, **kwargs) 
subplot(ax)
Example:
 
Python3
import matplotlib.pyplot as plt    # data to display on plots    x = [3, 1, 3]    y = [3, 2, 1]    z = [1, 3, 1]    # Creating figure object    plt.figure()    # addind first subplot    plt.subplot(121)    plt.plot(x, y)    # addding second subplot    plt.subplot(122)    plt.plot(z, y)
Output:
Note: Subplot() function have the following disadvantages – 
It does not allow adding multiple subplots at the same time.
It deletes the preexisting plot of the figure.
Method 3: Using subplots() method
This function is used to create figure and multiple subplots at the same time.
Syntax:
matplotlib.pyplot.subplots(nrows=1, ncols=1, sharex=False, sharey=False, squeeze=True, subplot_kw=None, gridspec_kw=None, **fig_kw)
Example:
Python3
import matplotlib.pyplot as plt    # Creating the figure and subplots   # according the argument passed   fig, axes = plt.subplots(1, 2)   # plotting the data in the 1st subplot   axes[0].plot([1, 2, 3, 4], [1, 2, 3, 4])   # plotting the data in the 1st subplot only   axes[0].plot([1, 2, 3, 4], [4, 3, 2, 1])   # plotting the data in the 2nd subplot only   axes[1].plot([1, 2, 3, 4], [1, 1, 1, 1])
Output:
 
 
Method 4: Using subplot2grid() method
 
This function give additional flexibility in creating axes object at a specified location inside a grid. It also helps in spanning the axes object across multiple rows or columns. In simpler words, this function is used to create multiple charts within the same figure.
 
Syntax:
 
Plt.subplot2grid(shape, location, rowspan, colspan)
 
Example:
 
Python3
import matplotlib.pyplot as plt    # data to display on plots    x = [3, 1, 3]    y = [3, 2, 1]    z = [1, 3, 1]    # adding the subplots   axes1 = plt.subplot2grid (   (7, 1), (0, 0), rowspan = 2,  colspan = 1)    axes2 = plt.subplot2grid (   (7, 1), (2, 0), rowspan = 2, colspan = 1)    axes3 = plt.subplot2grid (   (7, 1), (4, 0), rowspan = 2, colspan = 1)    # plotting the data   axes1.plot(x, y)   axes2.plot(x, z)   axes3.plot(z, y)
Output:
 
 
Refer to the below articles to get detailed information about subplots
 
How to create multiple subplots in Matplotlib in Python?
How to Add Title to Subplots in Matplotlib?
How to Set a Single Main Title for All the Subplots in Matplotlib?
How to Turn Off the Axes for Subplots in Matplotlib?
How to Create Different Subplot Sizes in Matplotlib?
How to set the spacing between subplots in Matplotlib in Python?
Matplotlib Sub plotting using object oriented API
Make subplots span multiple grid rows and columns in Matplotlib
 
A legend is an area describing the elements of the graph. In simple terms, it reflects the data displayed in the graph’s Y-axis. It generally appears as the box containing a small sample of each color on the graph and a small description of what this data means.
 
A Legend can be created using the legend() method. The attribute Loc in the legend() is used to specify the location of the legend. The default value of loc is loc=”best” (upper left). The strings ‘upper left’, ‘upper right’, ‘lower left’, ‘lower right’ place the legend at the corresponding corner of the axes/figure.
 
The attribute bbox_to_anchor=(x, y) of legend() function is used to specify the coordinates of the legend, and the attribute ncol represents the number of columns that the legend has. Its default value is 1.
 
Syntax:
 
matplotlib.pyplot.legend([“blue”, “green”], bbox_to_anchor=(0.75, 1.15), ncol=2)
 
Example:
 
Python3
import matplotlib.pyplot as plt    # data to display on plots    x = [3, 1, 3]    y = [3, 2, 1]    plt.plot(x, y)   plt.plot(y, x)   # Adding the legends   plt.legend(["blue", "orange"])   plt.show()
Output:
 
 
Refer to the below articles to get detailed information about the legend – 
 
Matplotlib.pyplot.legend() in Python
Matplotlib.axes.Axes.legend() in Python
Change the legend position in Matplotlib
How to Change Legend Font Size in Matplotlib?
How Change the vertical spacing between legend entries in Matplotlib?
Use multiple columns in a Matplotlib legend
How to Create a Single Legend for All Subplots in Matplotlib?
How to manually add a legend with a color box on a Matplotlib figure ?
How to Place Legend Outside of the Plot in Matplotlib?
How to Remove the Legend in Matplotlib?
Remove the legend border in Matplotlib
 
 
Till now you all must have seen that we are working with only the line charts as they are easy to plot and understand. Line Chart is used to represent a relationship between two data X and Y on a different axis. It is plotted using the pot() function. Let’s see the below example
 
Example:
 
Python3
import matplotlib.pyplot as plt    # data to display on plots    x = [3, 1, 3]    y = [3, 2, 1]    # This will plot a simple line chart   # with elements of x as x axis and y   # as y axis   plt.plot(x, y)   plt.title("Line Chart")   # Adding the legends   plt.legend(["Line"])   plt.show()
Output:
 
 
Refer to the below article to get detailed information about line chart.
 
Line chart in Matplotlib
Line plot styles in Matplotlib
Plot a Horizontal line in Matplotlib
Plot a Vertical line in Matplotlib
Plot Multiple lines in Matplotlib
Change the line opacity in Matplotlib
Increase the thickness of a line with Matplotlib
Plot line graph from NumPy array
How to Fill Between Multiple Lines in Matplotlib?
 
A bar plot or bar chart is a graph that represents the category of data with rectangular bars with lengths and heights that is proportional to the values which they represent. The bar plots can be plotted horizontally or vertically. A bar chart describes the comparisons between the discrete categories. It can be created using the bar() method.
 
Syntax:
 
plt.bar(x, height, width, bottom, align)
Example:
 
Python3
import matplotlib.pyplot as plt    # data to display on plots    x = [3, 1, 3, 12, 2, 4, 4]    y = [3, 2, 1, 4, 5, 6, 7]    # This will plot a simple bar chart   plt.bar(x, y)   # Title to the plot   plt.title("Bar Chart")   # Adding the legends   plt.legend(["bar"])   plt.show()
Output:
 
 
Refer to the below articles to get detailed information about Bar charts –
 
Bar Plot in Matplotlib
Draw a horizontal bar chart with Matplotlib
Create a stacked bar plot in Matplotlib
Stacked Percentage Bar Plot In MatPlotLib
Plotting back-to-back bar charts Matplotlib
How to display the value of each bar in a bar chart using Matplotlib?
How To Annotate Bars in Barplot with Matplotlib in Python?
How to Annotate Bars in Grouped Barplot in Python?
A histogram is basically used to represent data in the form of some groups. It is a type of bar plot where the X-axis represents the bin ranges while the Y-axis gives information about frequency. To create a histogram the first step is to create a bin of the ranges, then distribute the whole range of the values into a series of intervals, and count the values which fall into each of the intervals. Bins are clearly identified as consecutive, non-overlapping intervals of variables. The hist() function is used to compute and create histogram of x.
 
Syntax:
 
matplotlib.pyplot.hist(x, bins=None, range=None, density=False, weights=None, cumulative=False, bottom=None, histtype=’bar’, align=’mid’, orientation=’vertical’, rwidth=None, log=False, color=None, label=None, stacked=False, \*, data=None, \*\*kwargs)
Example:
 
Python3
import matplotlib.pyplot as plt    # data to display on plots    x = [1, 2, 3, 4, 5, 6, 7, 4]    # This will plot a simple histogram   plt.hist(x, bins = [1, 2, 3, 4, 5, 6, 7])   # Title to the plot   plt.title("Histogram")   # Adding the legends   plt.legend(["bar"])   plt.show()
Output:
 
 
Refer to the below articles to get detailed information about histograms.
 
Plotting Histogram in Python using Matplotlib
Create a cumulative histogram in Matplotlib
How to plot two histograms together in Matplotlib?
Overlapping Histograms with Matplotlib in Python
Bin Size in Matplotlib Histogram
Compute the histogram of a set of data using NumPy in Python
Plot 2-D Histogram in Python using Matplotlib
Scatter plots are used to observe relationship between variables and uses dots to represent the relationship between them. The scatter() method in the matplotlib library is used to draw a scatter plot.
 
Syntax:
 
matplotlib.pyplot.scatter(x_axis_data, y_axis_data, s=None, c=None, marker=None, cmap=None, vmin=None, vmax=None, alpha=None, linewidths=None, edgecolors=None)
Example:
 
Python3
import matplotlib.pyplot as plt    # data to display on plots    x = [3, 1, 3, 12, 2, 4, 4]   y = [3, 2, 1, 4, 5, 6, 7]   # This will plot a simple scatter chart   plt.scatter(x, y)   # Adding legend to the plot   plt.legend("A")   # Title to the plot   plt.title("Scatter chart")   plt.show()
Output:
 
 
Refer to the below articles to get detailed information about the scatter plot.
 
matplotlib.pyplot.scatter() in Python
How to add a legend to a scatter plot in Matplotlib ?
How to Connect Scatterplot Points With Line in Matplotlib?
How to create a Scatter Plot with several colors in Matplotlib?
How to increase the size of scatter points in Matplotlib ?
 
A Pie Chart is a circular statistical plot that can display only one series of data. The area of the chart is the total percentage of the given data. The area of slices of the pie represents the percentage of the parts of the data. The slices of pie are called wedges. The area of the wedge is determined by the length of the arc of the wedge. It can be created using the pie() method.
 
Syntax:
 
matplotlib.pyplot.pie(data, explode=None, labels=None, colors=None, autopct=None, shadow=False)
Example:
 
Python3
import matplotlib.pyplot as plt    # data to display on plots    x = [1, 2, 3, 4]    # this will explode the 1st wedge   # i.e. will separate the 1st wedge   # from the chart   e  =(0.1, 0, 0, 0)   # This will plot a simple pie chart   plt.pie(x, explode = e)   # Title to the plot   plt.title("Pie chart")   plt.show()
Output:
 
 
Refer to the below articles to get detailed information about pie charts.
 
matplotlib.axes.Axes.pie() in Python
Plot a pie chart in Python using Matplotlib
How to set border for wedges in Matplotlib pie chart?
Radially displace pie chart wedge in Matplotlib
 
Matplotlib was introduced keeping in mind, only two-dimensional plotting. But at the time when the release of 1.0 occurred, the 3D utilities were developed upon the 2D and thus, we have a 3D implementation of data available today.
 
Example:
 
Python3
import matplotlib.pyplot as plt    # Creating the figure object   fig = plt.figure()   # keeping the projection = 3d   # ctreates the 3d plot   ax = plt.axes(projection = '3d')
Output:
 
 
The above code lets the creation of a 3D plot in Matplotlib. We can create different types of 3D plots like scatter plots, contour plots, surface plots, etc. Let’s create a simple 3D line plot.
 
Example:
 
Python3
import matplotlib.pyplot as plt    x = [1, 2, 3, 4, 5]   y = [1, 4, 9, 16, 25]   z = [1, 8, 27, 64, 125]   # Creating the figure object   fig = plt.figure()   # keeping the projection = 3d   # ctreates the 3d plot   ax = plt.axes(projection = '3d')   ax.plot3D(z, y, x)
Output:
 
 
Refer to the below articles to get detailed information about 3D plots.
 
Three-dimensional Plotting in Python using Matplotlib
3D Scatter Plotting in Python using Matplotlib
3D Surface plotting in Python using Matplotlib
3D Wireframe plotting in Python using Matplotlib
3D Contour Plotting in Python using Matplotlib
Tri-Surface Plot in Python using Matplotlib
Surface plots and Contour plots in Python
How to change angle of 3D plot in Python?
How to animate 3D Graph using Matplotlib?
Draw contours on an unstructured triangular grid in Python using Matplotlib
 
The image module in matplotlib library is used for working with images in Python. The image module also includes two useful methods which are imread which is used to read images and imshow which is used to display the image.
 
Example:
 
Python3
# importing required libraries    import matplotlib.pyplot as plt    import matplotlib.image as img    # reading the image    testImage = img.imread('g4g.png')    # displaying the image    plt.imshow(testImage) 
Output:
Refer to the below articles to get detailed information about working with images using Matplotlib.
Working with Images in Python using Matplotlib
Working with PNG Images using Matplotlib
How to Display an Image in Grayscale in Matplotlib?
Plot a Point or a Line on an Image with Matplotlib
How to Draw Rectangle on Image in Matplotlib?
How to Display an OpenCV image in Python with Matplotlib?
Calculate the area of an image using Matplotlib
Style Plots using Matplotlib
Change plot size in Matplotlib – Python
How to Change the Transparency of a Graph Plot in Matplotlib with Python?
How to Change the Color of a Graph Plot in Matplotlib with Python?
How to Change Fonts in matplotlib?
How to change the font size of the Title in a Matplotlib figure ?
How to Set Tick Labels Font Size in Matplotlib?
How to Set Plot Background Color in Matplotlib?
How to generate a random color for a Matplotlib plot in Python?
Add Text Inside the Plot in Matplotlib
How to add text to Matplotlib?
How to change Matplotlib color bar size in Python?
How to manually add a legend with a color box on a Matplotlib figure ?
How to change the size of axis labels in Matplotlib?
How to Hide Axis Text Ticks or Tick Labels in Matplotlib?
How To Adjust Position of Axis Labels in Matplotlib?
Hide Axis, Borders and White Spaces in Matplotlib
How to Create an Empty Figure with Matplotlib in Python?
Change the x or y interval of a Matplotlib figure
How to add a grid on a figure in Matplotlib?
How to change the size of figures drawn with matplotlib?
Place plots side by side in Matplotlib
How to Reverse Axes in Matplotlib?
How to remove the frame from a Matplotlib figure in Python?
Use different y-axes on the left and right of a Matplotlib plot
How to Add a Y-Axis Label to the Secondary Y-Axis in Matplotlib?
How to plot a simple vector field in Matplotlib ?
Difference Between cla(), clf() and close() Methods in Matplotlib
Make filled polygons between two horizontal curves in Python using Matplotlib
How to Save a Plot to a File Using Matplotlib?
How to Plot Logarithmic Axes in Matplotlib?
How to put the y-axis in logarithmic scale with Matplotlib ?
How to draw 2D Heatmap using Matplotlib in python?
Plotting Correlation Matrix using Python
Plot Candlestick Chart using mplfinance module in Python
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Stem and Leaf Plots in Python
Python | Basic Gantt chart using Matplotlib
How to Plot List of X, Y Coordinates in Matplotlib?
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How to Draw a Circle Using Matplotlib in Python?
How to Plot Mean and Standard Deviation in Pandas?
How to plot a complex number in Python using Matplotlib ?
3D Sine Wave Using Matplotlib – Python
Plotting A Square Wave Using Matplotlib, Numpy And Scipy
How to Make a Square Plot With Equal Axes in Matplotlib?
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Normal Distribution Plot using Numpy and Matplotlib
How to Create a Poisson Probability Mass Function Plot in Python?
Find all peaks amplitude lies above 0 Using Scipy
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