Matplotlib_Presentation jk jdjklskncncsjkk

Matplotlib_Presentation jk jdjklskncncsjkk

• 1.
  What is Matplotlib? •Definition: Matplotlib is a Python library used for creating static, animated, and interactive visualizations. • Developed by: John D. Hunter in 2003. • Applications: • - Data analysis and visualization. • - Scientific research and presentation. • - Machine learning and AI model insights.
• 2.
  Key Features ofMatplotlib • - Versatility: Supports line plots, bar charts, scatter plots, histograms, and more. • - Customization: Control over colors, labels, legends, and styles. • - Integration: Works seamlessly with NumPy, Pandas, and other libraries. • - Interactivity: Interactive plotting using tools like Jupyter Notebook.
• 3.
  Installation • Install Matplotlibusing pip: • ```bash • pip install matplotlib • ``` • - Ensure Python and pip are installed. • - Use a virtual environment for better dependency management.
• 4.
  Basic Components • 1.Figure: The entire canvas for plotting. • 2. Axes: The specific area within a figure where data is plotted. • 3. Plot Elements: Titles, labels, legends, grids, etc.
• 5.
  Creating a BasicPlot • Example Code: • import matplotlib.pyplot as plt • x = [1, 2, 3, 4] • y = [10, 20, 25, 30] • plt.plot(x, y) • plt.title('Basic Plot') • plt.xlabel('X-axis') • plt.ylabel('Y-axis') • plt.show() • ```
• 6.
  Types of Plots •- Line Plot: Trends over time. • - Bar Chart: Comparison between categories. • - Histogram: Frequency distribution. • - Scatter Plot: Relationship between two variables. • - Pie Chart: Proportional data representation.
• 7.
  Customization Options • -Colors: Change line or marker colors. • - Line Styles: Dashed, dotted, etc. • - Markers: Represent data points (circle, square, etc.). • - Grid: Enable or disable gridlines. • Example: • ```python • plt.plot(x, y, color='red', linestyle='--', marker='o') • plt.grid(True) • ```
• 8.
  Advanced Visualizations • -Subplots: Multiple plots in one figure. • ```python • plt.subplot(1, 2, 1) # Row 1, Column 2, Plot 1 • plt.plot(x, y) • plt.subplot(1, 2, 2) # Row 1, Column 2, Plot 2 • plt.bar(x, y) • plt.show() • ``` • - 3D Plots: Using `mpl_toolkits.mplot3d`.
• 9.
  Working with Pandasand NumPy • Example: • ```python • import pandas as pd • import numpy as np • import matplotlib.pyplot as plt • data = pd.DataFrame({ • 'A': np.random.rand(10), • 'B': np.random.rand(10) • }) • data.plot(kind='bar') • plt.show() • ```
• 10.
  Best Practices • -Always label axes and add a title. • - Use legends for clarity. • - Avoid overcrowding plots with too much data. • - Choose appropriate plot types for your data.
• 11.
  Resources • - [Matplotlib Documentation](https://matplotlib.org/stable /contents.html) •- [Python Data Science Handbook](https://jakevdp.github.io/PythonD ataScienceHandbook/) • - Online tutorials and YouTube channels.