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Problem solving with Python programming focuses on applying computational thinking to real-world challenges by using Python’s simple and powerful syntax. The process begins with understanding the problem, breaking it into smaller logical steps, and then designing algorithms that can be translated into Python code. Python provides versatile data structures such as lists, dictionaries, and sets that help organize and manipulate data efficiently. Control structures like loops and conditionals guide the program’s flow, while functions promote modularity and reusability. Python’s extensive libraries, such as NumPy, Pandas, and Matplotlib, enhance problem-solving by enabling mathematical operations, data analysis, and visualization. Debugging and testing ensure that solutions are accurate and reliable. The approach encourages systematic thinking: analyzing inputs, processing data, and generating meaningful outputs. By combining logical reasoning with Python’s ease of learning and versatility, learners can solve problems ranging from simple tasks to advanced applications in data science, AI, and automation.