Data Visualization in Python of b.tech student.pptx

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1. Data Visualization in Python Unlocking Insights with Visual Storytelling POTHUGANTI SIDDAIAH 235U1A0497 ECE-B

2. Importance of Data Visualization "Why Visualize Data ?” Understand complex data Identify patterns and trends Communicate insights effectively Drive business decisions

3. Python Libraries for Data Visualization "Popular Libraries“ Logos and names of libraries: - Matplotlib - Seaborn - Plotly - Bokeh - Altair

4. " Matplotlib : The Foundation" Features : - 2D and 3D plotting - Various chart types (line, scatter, bar, etc.) - Customization options - Code example: import matplotlib.pyplot as plt

5. " Seaborn : Visualization with Style" Features : - Built on top of Matplotlib - High-level interface for attractive visuals - Integrated themes and color palettes - Code example: import seaborn as sns

6. " Plotly : Interactive Visualizations" Features : - Interactive plots and dashboards - Support for various chart types and 3D plots - Web-based and offline capabilities - Code example: import plotly.graph_objects as go

7. " Bokeh : Interactive Visualizations with Ease" Features : - Interactive plots and dashboards - Support for various chart types and streaming data - Web-based and offline capabilities - Code example: from bokeh.plotting import figure

8. " Altair: Declarative Visualization" Features: - Declarative syntax for visualization - Support for various chart types and interactions - Integration with other libraries - Code example: import altair as alt

9. "Effective Data Visualization" - Know your audience and purpose - Choose appropriate chart types - Keep it simple and clear - Use color effectively - Label and annotate your visualizations

10. "Data Visualization in Action" Examples : Scientific research Business intelligence Healthcare Finance

11. CONCLUSION Summary: Python offers a range of libraries for data visualization Call to action: Start exploring and creating your own visualizations!