Visão Geral

O Curso Scientific Python for Experienced Developers, ensina aos programadores Python como usar Python para manipulação de dados, estatísticas, gráficos e outras operações.

Objetivo

Após realizar este Curso Scientific Python for Experienced Developers você será capaz de:

• Use benchmarks e perfis para acelerar programas
• Processar XML e JSON
• Manipule matrizes com NumPy
• Descubra a diversidade dos subpacotes SciPy e como utilizá-los em suas aplicações
• Use notebooks Jupyter para cálculos ad hoc, gráficos e cenários hipotéticos
• Importe e analise dados com pandas
• Crie uma ampla variedade de gráficos de dados com matplotlab
• Manipular imagens com PIL
• Resolva equações com SymPy

Pre-Requisitos

• Os alunos devem se sentir confortáveis ​​escrevendo tarefas básicas em Python e conceitos de programação, incluindo entrada/saída de arquivos e criação de classes. 

Materiais

Conteúdo Programatico

Introduction

Python Refresher

1. Data types
2. Sequences
3. Mapping types
4. Program structure
5. Files and console I/O
6. Conditionals
7. Loops
8. Builtins
9. Classes

Pythonic Idioms

1. Small Pythonisms
2. Lambda functions
3. Sorting
4. Packing and unpacking sequences
5. List Comprehensions
6. Generator expressions

XML and JSON

1. Using ElementTree
2. Creating a new XML document
3. Parsing XML
4. Finding by tags and XPath
5. Parsing JSON into Python
6. Parsing Python into JSON

Jupyter

1. Jupyter basics
2. Terminal and GUI shells
3. Creating and using notebooks
4. Saving and loading notebooks
5. Ad hoc data visualization

Developer Tools

1. Debugging applications
2. Benchmarking code
3. Profiling applications

NumPy

1. NumPy basics
2. Creating arrays
3. Indexing and slicing
4. Large number sets
5. Transforming data
6. Advanced tricks

SciPy

1. The Python scientific stack
2. What can SciPy do?
3. Getting help
4. Where to find things
5. What is available?

A Tour of SciPy Subpackages

1. Clustering
2. Physical and mathematical constants
3. FFTs
4. Integral and differential solvers
5. Interpolation and smoothing
6. Input and output
7. Linear algebra
8. Image processing
9. Distance regression
10. Root-finding
11. Signal Processing
12. Sparse matrices
13. Spatial data and algorithms
14. Statistical distributions and functions
15. C/C++ Integration

Pandas

1. Pandas overview
2. Dataframes
3. Reading and writing data
4. Data alignment and reshaping
5. Fancy indexing and slicing
6. Merging and joining data sets

Matplotlib

1. Creating a basic plot
2. Commonly used plots
3. Ad hoc data visualization
4. Advanced usage
5. Exporting images

The Python Imaging Library (PIL)

1. PIL overview
2. Core image library
3. Image processing
4. Displaying images

SymPy

1. What is SymPy?
2. What can it do for you?
3. Creating variables
4. Defining equations
5. Solving equations