Python Científico
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The document discusses the Python scientific ecosystem, highlighting key libraries such as NumPy, SciPy, Matplotlib, SymPy, and Pandas, which are essential for mathematical, scientific, and engineering applications. It emphasizes the features of these libraries, including data analysis, numerical routines, and interactive computing. The document also mentions Scikit-learn for machine learning and provides resources for further information.
Python Científico
- 3. CUBO
- 15. Mas e o ?
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- 17. Liberdade
- 18. ; Em muitas linguagens: Fim de linha Em Python: Inútil No Matlab: Suprima saída para o stdout
- 20. () count(2) Count é uma função? Um vetor?
- 21. WAT
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- 26. SciPy (pronounced “Sigh Pie”) is a Python-based ecosystem of open-source software for mathematics, science, and engineering.
- 27. Peças deste Lego
- 29. NumPy is the fundamental package for scientific computing with Python. It contains among other things: • A powerful N-dimensional array object • Sophisticated (broadcasting) functions • Tools for integrating C/C++ and Fortran code • Useful linear algebra, Fourier transform, and random number capabilities
- 31. The SciPy library is one of the core packages that make up the SciPy stack. It provides many user-friendly and efficient numerical routines such as routines for numerical integration and optimization.
- 33. matplotlib is a python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. matplotlib can be used in python scripts, the python and ipython shell (ala MATLAB or Mathematica), web application servers, and six graphical user interface toolkits.
- 35. IPython provides a rich architecture for interactive computing with: • Powerful interactive shells (terminal and Qt-based). • A browser-based notebook with support for code, text, mathematical expressions, inline plots and other rich media. • Support for interactive data visualization and use of GUI toolkits. • Flexible, embeddable interpreters to load into your own projects. • Easy to use, high performance tools for parallel computing.
- 37. SymPy is a Python library for symbolic mathematics. It aims to become a full- featured computer algebra system (CAS) while keeping the code as simple as possible in order to be comprehensible and easily extensible.
- 39. pandas is an open source, BSD-licensed library providing high-performance, easy-to- use data structures and data analysis tools for the Python programming language.
- 41. scikit-learn Machine Learning in Python: • Simple and efficient tools for data mining and data analysis • Accessible to everybody, and reusable in various contexts • Built on NumPy, SciPy, and matplotlib • Open source, commercially usable - BSD license
- 43. #NumPy
- 44. #SymPy
- 45. #Scikit-learn
- 46. #Juntando várias
- 47. Dúvidas?