Pyplot API Documentation

Matplotlib is a powerful Python library for data visualization. The matplotlib.pyplot module offers a collection of command-style functions that emulate the functionality of MATLAB, making it intuitive for many users. Each Pyplot function facilitates the creation and customization of plots by managing plotting areas, adding various plot types, and decorating them with essential elements like labels, titles, and other annotations.

Introduction to Jupyter Notebooks

When working with Matplotlib, especially in an interactive environment, Jupyter Notebooks are commonly used. A new notebook created in Jupyter Notebook is saved with the .ipynb extension, which stands for IPython Notebook. This interface enables users to write and execute Python code interactively, making it ideal for exploration and iterative development of visualizations.

Types of Plots in Matplotlib Pyplot

Matplotlib's Pyplot API provides a wide array of plotting functions to cater to diverse data visualization needs. Below is a comprehensive list of key plotting functions:

Basic Plots

• Bar Plot (bar): Creates a vertical bar plot, suitable for representing categorical data and comparing values across different categories.

  import matplotlib.pyplot as plt
  plt.bar(['A', 'B', 'C'], [10, 20, 15])
  plt.show()

• Horizontal Bar Plot (barh): Generates a horizontal bar plot, useful when category labels are long or for a different visual emphasis.

  import matplotlib.pyplot as plt
  plt.barh(['X', 'Y', 'Z'], [5, 15, 10])
  plt.show()

• Line Plot (plot): Plots a series of data points connected by lines, commonly used for time-series data or showing trends.

  import matplotlib.pyplot as plt
  plt.plot([1, 2, 3, 4], [1, 4, 9, 16])
  plt.show()

• Scatter Plot (scatter): Displays individual data points, excellent for visualizing the relationship between two variables and identifying patterns or correlations.

  import matplotlib.pyplot as plt
  plt.scatter([1, 2, 3, 4], [2, 3, 5, 6])
  plt.show()

• Pie Chart (pie): Represents proportions of a whole as slices of a circular pie. Best used for a small number of categories.

  import matplotlib.pyplot as plt
  plt.pie([10, 20, 30, 40], labels=['Apples', 'Bananas', 'Cherries', 'Dates'])
  plt.show()

Distribution and Frequency Plots

• Histogram (hist): Plots a histogram to visualize the distribution of a single variable by dividing the data into bins and counting the frequency of values in each bin.

  import matplotlib.pyplot as plt
  import numpy as np
  data = np.random.randn(1000)
  plt.hist(data, bins=30)
  plt.show()

• 2D Histogram (hist2d): Creates a two-dimensional histogram for visualizing the joint distribution of two variables.

  import matplotlib.pyplot as plt
  import numpy as np
  x = np.random.rand(1000)
  y = np.random.rand(1000)
  plt.hist2d(x, y, bins=20)
  plt.show()

• Box Plot (boxplot): Displays a box-and-whisker plot, summarizing the distribution of a dataset through quartiles, median, and potential outliers.

  import matplotlib.pyplot as plt
  import numpy as np
  data1 = np.random.randn(100)
  data2 = np.random.randn(100) + 1
  plt.boxplot([data1, data2], labels=['Group A', 'Group B'])
  plt.show()

Specialized Plots

• Polar Plot (polar): Creates a plot in polar coordinates, where data is represented by an angle and a radius.

  import matplotlib.pyplot as plt
  import numpy as np
  theta = np.linspace(0, 2*np.pi, 100)
  r = np.sin(2*theta)
  plt.polar(theta, r)
  plt.show()

• Stacked Area Plot (stackplot): Draws a stacked area plot, useful for visualizing cumulative data over a period, where the area under each series is stacked on top of the previous ones.

  import matplotlib.pyplot as plt
  x = np.arange(5)
  y1 = np.array([1, 2, 3, 4, 5])
  y2 = np.array([2, 3, 4, 5, 6])
  y3 = np.array([3, 4, 5, 6, 7])
  plt.stackplot(x, y1, y2, y3, labels=['Series 1', 'Series 2', 'Series 3'])
  plt.legend()
  plt.show()

• Stem Plot (stem): Creates a stem plot, visualizing discrete data points as vertical lines (stems) extending from a baseline to the data value.

  import matplotlib.pyplot as plt
  import numpy as np
  x = np.arange(1, 6)
  y = np.array([1, 3, 2, 5, 4])
  plt.stem(x, y)
  plt.show()

• Step Plot (step): Generates a step plot, where the data is represented by horizontal and vertical lines, often used for time-series data or discrete signals.

  import matplotlib.pyplot as plt
  x = [1, 2, 3, 4, 5]
  y = [2, 4, 1, 5, 3]
  plt.step(x, y)
  plt.show()

• Quiver Plot (quiver): Plots a 2D field of arrows, used to represent vector fields, showing magnitude and direction at various points.

  import matplotlib.pyplot as plt
  x, y = np.mgrid[0:5, 0:5]
  u = np.cos(x)*y
  v = np.sin(y)*x
  plt.quiver(x, y, u, v)
  plt.show()

Image Functions in Matplotlib Pyplot

Matplotlib's Pyplot module also offers functionalities for handling image data:

• Read Image (imread): Reads an image file from a specified path and returns it as a NumPy array.

• Save Image (imsave): Saves a NumPy array as an image file to a specified location.

• Display Image (imshow): Displays an image on the current axes. This function is fundamental for visualizing image data directly within a plot.

  import matplotlib.pyplot as plt
  from PIL import Image
  # Assuming 'sample.png' is in the same directory
  img = plt.imread('sample.png')
  plt.imshow(img)
  plt.axis('off') # Hide axes for image display
  plt.show()

Axis Functions in Matplotlib Pyplot

Axis functions are crucial for customizing the appearance and limits of plot axes, ensuring clarity and informative data presentation:

• Add Axes (axes): Adds an axes object to the current figure, allowing for more complex subplot arrangements or independent control over specific plot areas.

• Add Text (text): Inserts text at a specified location within the current axes, useful for annotations or labeling specific data points.

  import matplotlib.pyplot as plt
  plt.plot([1, 2, 3], [4, 5, 6])
  plt.text(2, 5, 'Important Point')
  plt.show()

• Set Title (title): Defines the main title for the current axes.

  import matplotlib.pyplot as plt
  plt.plot([1, 2], [3, 4])
  plt.title('My First Plot')
  plt.show()

• Set X-Axis Label (xlabel): Assigns a descriptive label to the x-axis.

  import matplotlib.pyplot as plt
  plt.plot([1, 2], [3, 4])
  plt.xlabel('X Values')
  plt.show()

• Set X-Axis Limits (xlim): Gets or sets the lower and upper bounds for the x-axis.

  import matplotlib.pyplot as plt
  plt.plot([1, 2, 3, 4], [1, 4, 9, 16])
  plt.xlim(0, 5)
  plt.show()

• Set X-Axis Scale (xscale): Adjusts the scaling of the x-axis (e.g., linear, log, symlog).

  import matplotlib.pyplot as plt
  import numpy as np
  x = np.linspace(1, 10, 100)
  y = x**2
  plt.plot(x, y)
  plt.xscale('log')
  plt.show()

• Set X-Axis Ticks (xticks): Gets or sets the locations and labels of the tick marks on the x-axis.

  import matplotlib.pyplot as plt
  plt.plot([1, 2, 3, 4], [1, 4, 9, 16])
  plt.xticks([1, 2, 3, 4], ['One', 'Two', 'Three', 'Four'])
  plt.show()

• Set Y-Axis Label (ylabel): Assigns a descriptive label to the y-axis.

  import matplotlib.pyplot as plt
  plt.plot([1, 2], [3, 4])
  plt.ylabel('Y Values')
  plt.show()

• Set Y-Axis Limits (ylim): Gets or sets the lower and upper bounds for the y-axis.

  import matplotlib.pyplot as plt
  plt.plot([1, 2, 3, 4], [1, 4, 9, 16])
  plt.ylim(0, 20)
  plt.show()

• Set Y-Axis Scale (yscale): Adjusts the scaling of the y-axis (e.g., linear, log, symlog).

  import matplotlib.pyplot as plt
  import numpy as np
  x = np.linspace(1, 10, 100)
  y = x**2
  plt.plot(x, y)
  plt.yscale('log')
  plt.show()

• Set Y-Axis Ticks (yticks): Gets or sets the locations and labels of the tick marks on the y-axis.

  import matplotlib.pyplot as plt
  plt.plot([1, 4, 9, 16], [1, 2, 3, 4])
  plt.yticks([1, 2, 3, 4], ['A', 'B', 'C', 'D'])
  plt.show()

Figure Functions in Matplotlib Pyplot

Figure functions provide control over the overall figure container and its display properties:

• Add Text to Figure (figtext): Inserts text at a specific location relative to the figure, not the axes.

  import matplotlib.pyplot as plt
  plt.plot([1, 2], [3, 4])
  plt.figtext(0.5, 0.9, 'Figure Title', ha='center')
  plt.show()

• Create a New Figure (figure): Generates a new figure window or canvas for plotting. It's good practice to call this before creating a new plot to ensure it appears in its own figure.

  import matplotlib.pyplot as plt
  plt.figure(figsize=(8, 6)) # Creates a new figure with specified size
  plt.plot([1, 2, 3], [1, 2, 1])
  plt.show()

• Display Figure (show): Renders and displays the current figure or all open figures. This is typically the last command in a plotting script.

• Save Figure (savefig): Saves the current figure to a file (e.g., PNG, JPG, PDF).

  import matplotlib.pyplot as plt
  plt.plot([1, 2, 3], [1, 4, 9])
  plt.savefig('my_plot.png')
  plt.show()

• Close Figure (close): Closes a specified figure window or all open figure windows. This is useful for freeing up memory, especially when generating many plots in a loop.

  import matplotlib.pyplot as plt
  plt.plot([1, 2], [3, 4])
  plt.show()
  plt.close() # Closes the current figure

Conclusion

Matplotlib's Pyplot API is a comprehensive and versatile toolkit for creating, customizing, and displaying a vast range of plots. From simple line and scatter plots to complex statistical visualizations and image handling, Pyplot offers the flexibility to effectively communicate data insights through compelling visual representations. Its command-style interface, inspired by MATLAB, makes it accessible and efficient for both novice and experienced Python users engaged in data analysis and visualization.