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1. Why is Python still popular for data science in 2026?
Python is easy to learn, works fast, and offers many libraries for data analysis, machine learning, and real-world applications.
2. Which Python libraries are commonly used to clean and organize data?
NumPy handles numeric data, while Pandas manages tables, missing values, and structured data such as surveys and sales records.
3. How is Python used in machine learning projects today?
Python libraries help build models for predictions, recommendations, image recognition, and pattern detection across industries.
4. What role does data visualization play in Python-based projects?
Visualization tools turn data into clear charts and graphs, making trends and results easier to understand and explain.
5. Can Python models be used in real apps and websites?
Yes, Python frameworks connect trained models to websites and apps, allowing predictions and insights to run live.
