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Introduction to Matplotlib

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.

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()

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()

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()

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