Matplotlib is a widely used Python library for creating static, animated, and interactive visualizations.
- It provides a variety of tools to generate plots, graphs, and charts, making it a powerful tool for data visualization and analysis.
- It allows developers to create professional-quality visualizations using a simple and flexible API.
- The primary module in Matplotlib is
pyplot
, which provides a MATLAB-like interface for plotting.
1.) Marker in Matplotlib:
Marker refers to a symbol or shape used to represent data points on a plot.
- Markers are used to emphasize individual data points in a plot.
- You can customize the shape, size, and color of markers.
Common Marker Styles in Matplotlib:
- ‘o‘: Circle
- ‘.‘: Point
- ‘,‘: Pixel
- ‘x‘: Cross
- ‘+‘: Plus
- ‘^‘: Triangle Up
- ‘v’: Triangle Down
Syntax for Using Markers in Matplotlib:
- Markers are typically set using the marker parameter in the plotting function. The general syntax is:
plt.plot(x, y, marker='o') # 'o' for circle marker
Example of Using Markers in Matplotlib:
import matplotlib.pyplot as plt
# Data
x = [1, 2, 3, 4, 5]
y = [5, 7, 9, 11, 13]
# Line plot with markers
plt.plot(x, y, marker='o', color='blue', markersize=10, label='Line with Circles')
# Scatter plot with different markers
plt.scatter(x, y, marker='x', color='red', s=100, label='Scatter with X marker')
# Adding title and labels
plt.title('Using Markers in Matplotlib')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
# Adding legend
plt.legend()
# Show the plot
plt.show()
2.) Line in Matplotlib:
Line refers to a continuous curve drawn between a series of points in a plot.
- The line can be customized in terms of color, style, width, and markers at each data point.
- Lines are primarily used in line plots to represent data trends, especially over a continuous range of values, such as time or distance.
Line Properties in Matplotlib:
Matplotlib allows you to customize various aspects of lines in your plots:
- Color: Set the color of the line using standard color names or RGB values.
- Line Style: Choose the style of the line (e.g., solid, dashed, dotted).
- Line Width: Adjust the thickness of the line.
- Marker: Add markers at each data point along the line.
- Alpha: Set the transparency of the line.
Line Styles in Matplotlib:
- ‘-‘: Solid line (default)
- ‘–‘: Dashed line
- ‘-.’: Dash-dot line
- ‘:’: Dotted line
Syntax for Using Lines in Matplotlib:
plt.plot(x, y, color='blue', linestyle='-', linewidth=2, marker='o', markersize=5)
Example of Using Lines in Matplotlib:
import matplotlib.pyplot as plt
# Data points
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]
# Create a line plot with custom line style, color, and width
plt.plot(x, y, color='green', linestyle='-', linewidth=2, marker='o', markersize=8, label='Line 1')
# Add title and labels
plt.title('Line Plot Example')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
# Add grid lines for better readability
plt.grid(True)
# Add a legend
plt.legend()
# Show the plot
plt.show()
3.) Color in Matplotlib:
Colors can be applied to markers, lines, or areas in the plot.
Use named colors, hex codes, or RGB tuples.
plt.plot([1, 2, 3, 4], [10, 20, 25, 30], color='green')
plt.title("Plot with Color")
plt.show()
Label in Matplotlib:
Labels are added to axes and legend to describe the data.
Use xlabel, ylabel, and legend.
x = [1, 2, 3, 4]
y = [1, 4, 9, 16]
plt.plot(x, y, label='y = x^2')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title("Plot with Labels")
plt.legend()
plt.show()