The document discusses the importance of data visualization in Python for understanding complex data, identifying trends, and driving business decisions. It highlights popular libraries such as Matplotlib, Seaborn, Plotly, Bokeh, and Altair, each with unique features and code examples. Effective visualization techniques and applications in various fields like research, business, and healthcare are also emphasized, encouraging readers to explore and create their own visualizations.

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!