What is Python Data Science?
Python is the most popular language for data science, and there are a number of libraries and frameworks that you can use to work with data in Python. Some popular options include:
- NumPy: NumPy is a library for scientific computing in Python that provides support for large, multi-dimensional arrays and matrices of numerical data, as well as a large collection of mathematical functions to operate on these arrays.
- Pandas: Panda is a library for data manipulation and analysis in Python. It provides tools for reading and writing data in various formats, as well as tools for manipulating and summarizing data.
- Matplotlib: Matplotlib is a library or collection for data visualization in Python. It provides a range of functions for creating static, animated, and interactive visualizations of data.
- Scikit-learn: Scikit-learn is a library for machine learning in Python which provides a wide range of algorithms for tasks such as classification, regression, clustering, and dimensionality reduction.
To start with data science in Python, you will need to install one or more of these libraries, as well as a Python development environment. You can then follow tutorials and examples to learn how to use them to work with data in Python. There are also many resources available online, such as online courses and books, that can help you learn more about data science with Python.
What cannot be used for it would be a better question. Here are some prominent locations where Python may be seen:
Web development – Python is used by programmers, engineers, and data scientists to scrape websites or model apps.
Automating Reports – Analysts or product managers can use Python to help build reports and save time if they must produce an identical Excel report every single week.
Business and finance – Used for academic research, forecasting models, and reporting.