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Line Plots in Matplotlib
Introduction to Line Plots
Line plots are a type of chart used to display information as a series of data points called 'markers' connected by straight line segments. They are often used to visualize trends over a period of time or a specific interval.
Importance in Data Visualization
Line plots are essential in data visualization because they provide a clear visual representation of patterns, trends, and changes over time. This makes them ideal for time series data analysis.
Simple Line Plot
Creating a Simple Line Plot
A simple line plot can be created using Matplotlib's plot() function. This function takes in x and y data points and connects them with a line.
import matplotlib.pyplot as plt
x = [1, 2, 3, 4, 5]
y = [2, 3, 5, 7, 11]
plt.plot(x, y)
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Simple Line Plot')
plt.show()
Explanation
In this example, the plot() function is used to create a line connecting the data points (x, y). The xlabel(), ylabel(), and title() functions are used to label the axes and the plot.
Multiple Line Plots
Plotting Multiple Lines
Matplotlib allows multiple lines to be plotted on the same graph by calling the plot() function multiple times before calling show().
import matplotlib.pyplot as plt
x = [1, 2, 3, 4, 5]
y1 = [2, 3, 5, 7, 11]
y2 = [1, 4, 6, 8, 10]
plt.plot(x, y1, label='Line 1')
plt.plot(x, y2, label='Line 2')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Multiple Line Plots')
plt.legend()
plt.show()
Explanation
In this example, two lines are plotted on the same graph. The legend() function is used to add a legend that differentiates between the two lines.
Customizing Line Styles
Changing Line Styles
Matplotlib provides options to customize line styles, colors, and markers using additional parameters in the plot() function.
import matplotlib.pyplot as plt
x = [1, 2, 3, 4, 5]
y = [2, 3, 5, 7, 11]
plt.plot(x, y, linestyle='--', color='r', marker='o')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Customized Line Style')
plt.show()
Explanation
In this example, the line is customized with a dashed style, red color, and circle markers. These customizations enhance the visual appeal and clarity of the plot.
Plotting with Annotations
Using Annotations in Plots
Annotations can be added to line plots to highlight specific data points or regions, making the plot more informative.
import matplotlib.pyplot as plt
x = [1, 2, 3, 4, 5]
y = [2, 3, 5, 7, 11]
plt.plot(x, y)
plt.annotate('Peak', xy=(3, 5), xytext=(4, 6),
arrowprops=dict(facecolor='black', shrink=0.05))
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Line Plot with Annotation')
plt.show()
Explanation
In this example, an annotation is added to the plot to highlight the peak point. The annotate() function is used to place text and an arrow at the specified location.
Subplots with Line Plots
Creating Subplots
Subplots allow multiple plots to be displayed in a single figure, enabling comparison and analysis of different datasets.
import matplotlib.pyplot as plt
x = [1, 2, 3, 4, 5]
y1 = [2, 3, 5, 7, 11]
y2 = [1, 4, 6, 8, 10]
plt.subplot(2, 1, 1)
plt.plot(x, y1, 'r')
plt.title('Subplot 1')
plt.subplot(2, 1, 2)
plt.plot(x, y2, 'g')
plt.title('Subplot 2')
plt.tight_layout()
plt.show()
Explanation
In this example, two subplots are created vertically within the same figure. The subplot() function specifies the number of rows, columns, and plot index.
