PyCharm: A popular integrated development environment (IDE) for Python, offering powerful coding assistance, debugging, and support for web development frameworks.
Jupyter Notebooks: Widely used for interactive data exploration, visualization, and sharing, Jupyter Notebooks provide an interactive computing environment.
Pandas: A powerful data manipulation and analysis library, Pandas is essential for handling structured data in Python, offering data structures like DataFrames.
NumPy: The fundamental package for scientific computing in Python, NumPy provides support for large, multi-dimensional arrays and matrices, along with mathematical functions to operate on them.
Matplotlib: A versatile plotting library for Python, Matplotlib enables the creation of static, animated, and interactive visualizations in various formats.
TensorFlow: An open-source machine learning framework, TensorFlow is widely used for building and training machine learning models, particularly in deep learning applications.
Scikit-learn: A simple and efficient tool for data analysis and modeling, Scikit-learn provides a range of machine learning algorithms and tools for tasks such as classification, regression, and clustering.
Requests: A popular HTTP library for making web requests in Python, Requests simplifies the process of sending HTTP requests and handling responses.
Django: A high-level web framework for building robust and scalable web applications, Django follows the “don’t repeat yourself” (DRY) principle and encourages rapid development.
Flask: A lightweight web framework that is easy to use and extensible, Flask is ideal for building web applications and APIs with simplicity and flexibility in mind.