Scientific Python Training Overview
Accelebrate’s Scientific Python for Experienced Developers course teaches Python programmers how to use Python for data manipulation, statistics, graphing, and other operations.
Location and Pricing
Accelebrate offers instructor-led enterprise training for groups of 3 or more online or at your site. Most Accelebrate classes can be flexibly scheduled for your group, including delivery in half-day segments across a week or set of weeks. To receive a customized proposal and price quote for private corporate training on-site or online, please contact us.
In addition, some courses are available as live, instructor-led training from one of our partners.
Objectives
- Use benchmarks and profiling to speed up programs
- Process XML and JSON
- Manipulate arrays with NumPy
- Discover the diversity of SciPy subpackages and how to use them in your applications
- Use Jupyter notebooks for ad hoc calculations, plots, and what-if scenarios
- Import and analyze data with pandas
- Create a wide variety of data plots with matplotlab
- Manipulate images with PIL
- Solve equations with SymPy
Prerequisites
Students should be comfortable writing basic Python tasks and programming concepts, including file input/output and creating classes.
Outline
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Introduction
Python Refresher
- Data types
- Sequences
- Mapping types
- Program structure
- Files and console I/O
- Conditionals
- Loops
- Builtins
- Classes
Pythonic Idioms
- Small Pythonisms
- Lambda functions
- Sorting
- Packing and unpacking sequences
- List Comprehensions
- Generator expressions
XML and JSON
- Using ElementTree
- Creating a new XML document
- Parsing XML
- Finding by tags and XPath
- Parsing JSON into Python
- Parsing Python into JSON
Jupyter
- Jupyter basics
- Terminal and GUI shells
- Creating and using notebooks
- Saving and loading notebooks
- Ad hoc data visualization
Developer Tools
- Debugging applications
- Benchmarking code
- Profiling applications
NumPy
- NumPy basics
- Creating arrays
- Indexing and slicing
- Large number sets
- Transforming data
- Advanced tricks
SciPy
- The Python scientific stack
- What can SciPy do?
- Getting help
- Where to find things
- What is available?
A Tour of SciPy Subpackages
- Clustering
- Physical and mathematical constants
- FFTs
- Integral and differential solvers
- Interpolation and smoothing
- Input and output
- Linear algebra
- Image processing
- Distance regression
- Root-finding
- Signal Processing
- Sparse matrices
- Spatial data and algorithms
- Statistical distributions and functions
- C/C++ Integration
Pandas
- Pandas overview
- Dataframes
- Reading and writing data
- Data alignment and reshaping
- Fancy indexing and slicing
- Merging and joining data sets
Matplotlib
- Creating a basic plot
- Commonly used plots
- Ad hoc data visualization
- Advanced usage
- Exporting images
The Python Imaging Library (PIL)
- PIL overview
- Core image library
- Image processing
- Displaying images
SymPy
- What is SymPy?
- What can it do for you?
- Creating variables
- Defining equations
- Solving equations
Conclusion
Training Materials
All attendees receive comprehensive courseware covering all topics in the course.
Software Requirements
- Any Windows, Linux, or macOS operating system
- Python language
- Additional Python libraries including NumPy, SciPy, matplotlib, PIL, Jupyter, SymPy (we recommend Anaconda, a cross-platform Python bundle that already includes the necessary modules)
- An IDE with Python support (PyCharm Community Edition is an excellent free option, but there are several other good ones)
