Elevate Your Analysis with Matplotlib Visual Flair!

Matplotlib use case.


Matplotlib is an essential modern visualization library in Python for 2D plots of arrays and datasets.
It is a multi-platform data visualization tool built on NumPy arrays and ready to be used with the broader SciPy stack. Matplotlib produces publication-quality figures in a variety of predefined formats and interactive environments across platforms.

Matplotlib DS cheat sheet.
Matplotlib for Data Science meme.

Python Knowledge Base: Make coding great again.
- Updated: 2024-10-14 by Andrey BRATUS, Senior Data Analyst.




    The biggest power of visualization is that it allows us visual access to huge amounts of data in easily acceptable visual format. Matplotlib proposes to use several plots like lineplots, barcharts, scatterplots, histogram etc.


  1. Plot creation algorithm.


  2. The main steps for creating the plots with matplotlib are:

    1 Preparing the data
    2 Creating the plot
    3 Plotting
    4 Customizing the plot
    5 Saving the plot
    6 Showing the plot



    
    import matplotlib.pyplot as plt
    
    x = [2,3,4,5] # Step 1
    y = [10,20,25,30]
    fig = plt.figure() # Step 2
    ax = fig.add_subplot(111) # Step 3
    ax.plot(x, y, color='lightblue', linewidth=3) # Step 4
    ax.scatter([2,4,6],
    [12,15,25],
    color='darkgreen',
    marker='^')
    ax.set_xlim(1, 6.5)
    plt.savefig('figure.png') # Step 5
    plt.show() # Step 6
    
    Matplotlib simple example.

  3. Initial Data Preparation.


  4. 
    import numpy as np
    
    x = np.linspace(0, 10, 100)
    y = np.cos(x)
    z = np.sin(x)
    
    data = 2 * np.random.random((10, 10))
    data2 = 3 * np.random.random((10, 10))
    Y, X = np.mgrid[-3:3:100j, -3:3:100j]
    U = -1 - X**2 + Y
    V = 1 + X - Y**2
    

  5. Creating a Plot.


  6. 
    import matplotlib.pyplot as plt
    
    # adding figures
    fig = plt.figure()
    fig2 = plt.figure(figsize=plt.figaspect(2.0))
    
    # adding axes
    ax1 = fig.add_subplot(221) # row-col-num
    ax3 = fig.add_subplot(212)
    fig3, axes = plt.subplots(nrows=2,ncols=2)
    fig4, axes2 = plt.subplots(ncols=3)
    
  7. Plotting - 1D Data.


  8. 
    # 1D Data
    
    fig, ax = plt.subplots()
    lines = ax.plot(x,y) #Draw points with lines or markers connecting them
    ax.scatter(x,y) #Draw unconnected points, scaled or colored
    axes[0,0].bar([1,2,3],[3,4,5]) #Plot vertical rectangles (constant width)
    axes[1,0].barh([0.5,1,2.5],[0,1,2]) #Plot horiontal rectangles (constant height)
    axes[1,1].axhline(0.45) #Draw a horizontal line across axes
    axes[0,1].axvline(0.65) #Draw a vertical line across axes
    ax.fill(x,y,color='blue') #Draw filled polygons
    ax.fill_between(x,y,color='yellow') #Fill between y-values and 0
    
    Matplotlib 1d data example.

  9. Plotting - 2D Data.


  10. 
    # 2D Data
    
    axes2[0].pcolor(data2) #Pseudocolor plot of 2D array
    axes2[0].pcolormesh(data) #Pseudocolor plot of 2D array
    CS = plt.contour(Y,X,U) #Plot contours
    axes2[2].contourf(data) #Plot filled contours
    axes2[2]= ax.clabel(CS) #Label a contour plot
    
    Matplotlib 2d data graph.

  11. Plotting - Vectors.


  12. 
    # Vector fields
    
    axes[0,1].arrow(0,0,0.5,0.5) #Add an arrow to the axes
    axes[1,1].quiver(y,z) #Plot a 2D field of arrows
    axes[0,1].streamplot(X,Y,U,V) #Plot a 2D field of arrows
    
  13. Plotting - Data distributions.


  14. 
    # Data distributions
    
    ax1.hist(y) #Plot a histogram
    ax3.boxplot(y) #Make a box and whisker plot
    ax3.violinplot(z) #Make a violin plot
    
  15. Customizing a Plot - Markers.


  16. 
    fig, ax = plt.subplots()
    ax.scatter(x,y,marker=".")
    ax.plot(x,y,marker="o")
    
    Matplotlib markers customization.

  17. Customizing a Plot - Linestyles.


  18. 
    plt.plot(x,y,linewidth=4.0)
    plt.plot(x,y,ls='solid')
    plt.plot(x,y,ls='--')
    plt.plot(x,y,'--',x**2,y**2,'-.')
    plt.setp(lines,color='r',linewidth=4.0)
    
    Matplotlib kinestyle customization.

  19. Customizing a Plot - Text & Annotations.


  20. 
    ax.text(1, -2.1, 'Example Graph', style='italic')
    ax.annotate("Sine", xy=(8, 0), xycoords='data', xytext=(10.5, 0), textcoords='data', arrowprops=dict(arrowstyle="->", connectionstyle="arc3"),)
    
  21. Customizing a Plot - Mathtext.


  22. 
    plt.title(r'$sigma_i=15$', fontsize=20)
    
  23. Customizing a Plot - Limits, Legends & Layouts.


  24. 
    #Limits & Autoscaling
    ax.margins(x=0.0,y=0.1) #Add padding to a plot
    ax.axis('equal') #Set the aspect ratio of the plot to 1
    ax.set(xlim=[0,10.5],ylim=[-1.5,1.5]) #Set limits for x-and y-axis
    ax.set_xlim(0,10.5) #Set limits for x-axis
    
    #Legends
    ax.set(title='An Example Axes', #Set a title and x-and y-axis labels
    ylabel='Y-Axis',
    xlabel='X-Axis')
    ax.legend(loc='best') #No overlapping plot elements
    
    #Ticks
    ax.xaxis.set(ticks=range(1,5), #Manually set x-ticks
    ticklabels=[3,100,-12,"foo"])
    ax.tick_params(axis='y', #Make y-ticks longer and go in and out
    direction='inout',
    length=10)
    
    #Subplot Spacing
    fig3.subplots_adjust(wspace=0.5, #Adjust the spacing between subplots
    hspace=0.3,
    left=0.125,
    right=0.9,
    top=0.9,
    bottom=0.1)
    fig.tight_layout() #Fit subplot(s) in to the figure area
    
    #Axis Spines
    ax1.spines['top'].set_visible(False) #Make the top axis line for a plot invisible
    ax1.spines['bottom'].set_position(('outward',10)) #Move the bottom axis line outward
    
  25. Saving the Plot.


  26. 
    #Save figures
    plt.savefig('foo.png')
    
    #Save transparent figures
    plt.savefig('foo.png', transparent=True)
    
  27. Show Plot.


  28. 
    plt.show()
    
  29. Close & Clear.


  30. 
    plt.cla() #Clear an axis
    plt.clf() #Clear the entire figure
    plt.close() #Close a window
    



See also related topics: