Why in recent days there is a boom towards learning python? What is your idea regarding learning python and in which field we use python?
In programming paradigms like functional programming (FP), immutable data structures are preferred because they do not change once created. Instead of modifying existing data, FP encourages creating new data structures through functions. This aligns with FP principles of avoiding side effects and enRead more
In programming paradigms like functional programming (FP), immutable data structures are preferred because they do not change once created. Instead of modifying existing data, FP encourages creating new data structures through functions. This aligns with FP principles of avoiding side effects and ensuring predictable program behavior.
In contrast, object-oriented programming (OOP) often utilizes mutable data structures where object states can be modified directly. Objects encapsulate both data and methods that manipulate that data, allowing for dynamic changes to state over time.
Immutable data structures in FP promote safer concurrency and easier debugging by preventing unintended modifications to shared data. They also facilitate clearer reasoning about program behavior since data remains consistent.
Mutable data structures in OOP provide flexibility in modeling real-world objects where state changes are expected and managed within the object’s methods. However, they require careful handling to maintain consistency and avoid unexpected behavior, especially in concurrent environments.
Thus, while FP emphasizes immutability for simplicity and reliability, OOP uses mutable data structures to encapsulate behavior and state changes within objects, supporting dynamic and flexible programming models.
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People are increasingly learning Python programming due to its versatility and widespread application in data science, machine learning (ML), artificial intelligence (AI), and high-frequency trading (HFT). Data Science: Python's robust libraries, such as Pandas, NumPy, and Matplotlib, simplify dataRead more
People are increasingly learning Python programming due to its versatility and widespread application in data science, machine learning (ML), artificial intelligence (AI), and high-frequency trading (HFT).
Data Science:
Python’s robust libraries, such as Pandas, NumPy, and Matplotlib, simplify data manipulation, analysis, and visualization. Its ease of use and readability make it ideal for handling large datasets and performing complex computations.
Machine Learning (ML) and Artificial Intelligence (AI):
Python offers powerful libraries like TensorFlow, Keras, and PyTorch that streamline the development of ML and AI models. Its simplicity allows researchers and developers to focus on innovation rather than intricate syntax, accelerating the implementation of algorithms and models.
High-Frequency Trading (HFT):
Python’s efficient data processing capabilities and extensive libraries make it suitable for HFT, where speed and accuracy are critical. Libraries such as NumPy and Pandas enable quick data analysis and decision-making, essential for executing trades within milliseconds.
These are the reasons as well as the booming areas for a career in python
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