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# Matplotlib ## What is Matplotlib? - Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. - Matplotlib makes easy things easy and hard things possible. - Create publication quality plots. - Make interactive figures that can zoom, pan, upda...

# Matplotlib ## What is Matplotlib? - Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. - Matplotlib makes easy things easy and hard things possible. - Create publication quality plots. - Make interactive figures that can zoom, pan, update. - Customize visual style and layout. - Export to many file formats. - Embed in JupyterLab and graphical user interfaces. - Use a rich array of third-party packages built on Matplotlib. ## Installation ```bash pip install matplotlib ``` ## Pyplot - `matplotlib.pyplot` is a collection of functions that make Matplotlib work like MATLAB. - Provides a convenient interface to the Matplotlib object-oriented plotting library. - Each pyplot function makes some change to a figure: e.g., creates a figure, creates a plotting area, plots some lines, decorates the plot with labels, etc. ### Example ```python import matplotlib.pyplot as plt import numpy as np # Prepare the data x = np.linspace(0, 10, 100) # Plot the data plt.plot(x, x, label='linear') # Add a legend plt.legend() # Show the plot plt.show() ``` ### Resulting Plot Description The plot displays a single line representing a linear relationship. The x-axis ranges from 0 to 10, and the y-axis mirrors this range, indicating a direct proportional relationship where y = x. The line is labeled "linear," as indicated by the legend in the upper left corner of the plot. The plot is a basic visualization created using Matplotlib, demonstrating a simple line graph. ## Plotting ### Line Plot ```python plt.plot(x, y, marker='o', linestyle='--') ``` This plots y versus x as lines and/or markers. ### Scatter Plot ```python plt.scatter(x, y, c=colors, s=sizes, alpha=0.5) ``` A scatter plot of y vs. x with varying marker size and/or color. ### Bar Plot ```python plt.bar(x, height, width=0.8, bottom=None, *, align='center', data=None, **kwargs) ``` Make a bar plot. ### Histogram ```python plt.hist(x, bins=None, range=None, density=False) ``` Compute and draw the histogram of x. ### Pie Chart ```python plt.pie(x, explode=None, labels=None, colors=None, autopct=None) ``` Plot a pie chart. ## Anatomy of a Plot The image illustrates the basic components of a Matplotlib plot. Here's a breakdown of the key elements: - **Figure**: The entire image or window that contains the plot, encompassing all the elements within it. - **Axes**: This is the region where the data is plotted. It includes: - **Data**: The actual points, lines, or shapes representing the information being visualized. - **Axis**: The lines that denote the scales, typically the x and y axes. - **Labels**: Text providing context, such as axis labels (e.g., "X-axis" and "Y-axis") and the plot title ("Title"). - **Ticks**: Markers indicating specific values along the axes. - **Tick Labels**: Text annotations specifying the values at each tick mark. - **Spines**: The lines connecting the axis tick marks and forming the boundary of the plot area. Additionally, the plot may include a legend to explain different plot elements, annotations to highlight specific data points, and a grid for easier data interpretation. The illustration serves as a comprehensive guide to understanding the nomenclature and structure of a Matplotlib plot. ## Plotting Function | Arguments | Description | |---|---| | `x, y` | The horizontal/vertical coordinates of the data points. `x` values are optional. | | `fmt` | A format string e.g. `'ro'` for red circles. | | `linewidth` | The width of the line. | | `linestyle` | The style of the line. | | `color` | The color of the line. | | `marker` | The marker style. | | `markersize` | The size of the marker. | | `label` | The label for this plot. | ## Example ```python import matplotlib.pyplot as plt import numpy as np x = np.linspace(0, 2, 100) # Sample data. # Note that even in the OO-style, we use `.pyplot.figure` to create the figure. fig, ax = plt.subplots() # Create a figure and an axes. ax.plot(x, x, label='linear') # Plot some data on the axes. ax.plot(x, x**2, label='quadratic') # Plot more data on the axes. ax.plot(x, x**3, label='cubic') # Plot more data on the axes. ax.set_xlabel('x label') # Add an x-axis label to the axes. ax.set_ylabel('y label') # Add a y-axis label to the axes. ax.set_title("Simple Plot") # Add a title to the axes. ax.legend() # Add a legend. ``` ### Resulting Plot Description The plot displays three curves representing different mathematical relationships as a function of 'x'. The ‘x’ axis ranges from 0 to 2, and the ‘y’ axis spans from 0 to approximately 8. The plot contains: - **Linear**: A straight line, indicating a direct proportional relationship where y=x. - **Quadratic**: A parabolic curve, representing a quadratic relationship where $y=x^2$. - **Cubic**: A curve that rises more steeply, indicating a cubic relationship where $y=x^3$ The plot includes labeled 'x' and 'y' axes and a title, "Simple Plot". A legend in the upper left corner identifies each curve. This visualization demonstrates how Matplotlib can be used to plot multiple functions on the same graph, enhancing data comparison and analysis.