Visão Geral

O Curso Python Programming for Scientists, ensina cientistas, matemáticos, estatísticos e engenheiros a usar Python para computação científica e matemática. Os participantes aprendem o básico, bem como os módulos Python mais importantes para trabalhar com dados, desde matrizes a estatísticas e plotagem de resultados. O material é voltado para cientistas e engenheiros.

Objetivo

Após realizar este Curso Python Programming for Scientists você será capaz de:

• Crie e execute programas básicos
• Projetar e codificar módulos e classes
• Implementar e executar testes unitários
• Use benchmarks e perfis para acelerar programas
• Processar XML e JSON
• Manipule matrizes com NumPy
• Entenda a diversidade de subpacotes que compõem o SciPy
• Use notebooks Jupyter para cálculos ad hoc, gráficos e hipóteses?

Pre-Requisitos

• Embora não haja pré-requisitos de programação, a experiência em programação é útil. Os alunos devem ter uma sólida formação matemática e sentir-se confortáveis ​​ao trabalhar com arquivos e pastas, além de estar familiarizados com a linha de comando no Linux, Windows ou Mac OS.

Materiais

Conteúdo Programatico

The Python Environment

1. About Python
2. Starting Python
3. Using the interpreter
4. Running a Python script
5. Python scripts on Unix/Windows
6. Using the Spyder editor

Getting Started

1. Using variables
2. Builtin functions
3. Strings
4. Numbers
5. Converting among types
6. Writing to the screen
7. String formatting
8. Command line parameters

Flow Control

1. About flow control
2. White space
3. Conditional expressions (if,else)
4. Relational and Boolean operators
5. While loops
6. Alternate loop exits

Sequences

1. About sequences
2. Lists and tuples
3. Indexing and slicing
4. Iterating through a sequence
5. Sequence functions, keywords, and operators
6. List comprehensions
7. Generator expressions
8. Nested sequences

Working with Files

1. File overview
2. Opening a text file
3. Reading a text file
4. Writing to a text file
5. Raw (binary) data

Dictionaries and Sets

1. Creating dictionaries
2. Iterating through a dictionary
3. Creating sets
4. Working with sets

Functions

1. Defining functions
2. Parameters
3. Variable scope
4. Returning values
5. Lambda functions

Errors and Exception Handling

1. Syntax errors
2. Exceptions
3. Using try/catch/else/finally
4. Handling multiple exceptions
5. Ignoring exceptions

OS Services

1. The os module
2. Environment variables
3. Launching external commands
4. Walking directory trees
5. Paths, directories, and filenames
6. Working with file systems
7. Dates and times

Modules and Packages

1. Initialization code
2. Namespaces
3. Executing modules as scripts
4. Documentation
5. Packages and name resolution
6. Naming conventions
7. Using imports

Classes

1. Defining classes
2. Constructors
3. Instance methods and data
4. Attributes
5. Inheritance
6. Multiple inheritance

Programmer Tools

1. Analyzing programs with pylint
2. Creating and running unit tests
3. Debugging applications
4. Benchmarking code
5. Profiling applications

Excel Spreadsheets

1. The openpyxl module
2. Reading an existing spreadsheet
3. Creating a spreadsheet from scratch
4. Modifying an existing spreadsheet

Serializing Data

1. Creating XML Files
2. Parsing XML
3. Finding by tags and XPath
4. Reading JSON files
5. Writing JSON

iPython and Jupyter

1. About iPython and Jupyter
2. iPython basics
3. Magic commands
4. About Jupyter
5. Documentation cells

NumPy

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

Pandas

1. Pandas overview
2. Series and Dataframes
3. Reading and writing data
4. Advanced indexing and slicing
5. Merging and joining data sets

SciPy

1. What is SciPy
2. What you do with SciPy?
3. Tour of SciPy packages
4. Simple SciPy examples

Matplotlib

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