Advanced Python 3.x
Short course
Inhouse
Description
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Type
Short course
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Level
Advanced
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Methodology
Inhouse
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Duration
2 Days
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Start date
Different dates available
In this Python training course, students already familiar with Python programming will learn advanced Python techniques such as IPython Notebook, the Collections module, mapping and filtering, lamba functions, advanced sorting, writing object-oriented code, testing and debugging, NumPy, pandas,matplotlib, regular expressions, Unicode, text encoding and working with databases, CSV files, JSONand XML. This advanced Python course is taught using Python 3, however, differences between Python 2 and Python 3 are noted.
Facilities
Location
Start date
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About this course
Students wanting to further their knowledge of Python Programming.
Basic Python programming experience. In particular, you should be very comfortablewith: working with strings; working with lists, tuples and dictionaries; loops and conditionals; and writing your own functions. Experience in the following areas would be beneficial: some exposure to HTML, XML, JSON, and SQL.
This course contains numerous hands on exercises to build your Advanced skillset.
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Subjects
- XML training
- Testing
- XML
- Big Data
- Data Analytics
- Data science
- Advanced Python
- Python 3.x
- Python for scientists
- Raspberry pie
Teachers and trainers (1)
Bright Solutions
Trainer
Course programme
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IPython Notebook
Getting Started with IPython Notebook
Creating Your First IPython Notebook
IPython Notebook Modes
Useful Shortcut Keys
Markdown
Magic Commands
Getting Help
AdvancedPython Concepts
Advanced List Comprehensions
Collections Module
Mapping and Filtering
Lambda Functions
Advanced Sorting
Unpacking Sequences in Function Calls
Modules and Packages
Workingwith Data
Databases
CSV
Getting Data from the Web
HTML
XML
JSON
Classes and Objects
Creating Classes
Attributes, Methods and Properties
Extending Classes
Documenting Classes
Static, Class, Abstract Methods
Decorator
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Testing and Debugging
CreatingSimulations
Testing for Performance
The unittest Module
NumPy
One-dimensional Arrays
Multi-dimensional Arrays
Getting Basic Information about an Array
NumPy Arrays Compared to Python Lists
Universal Functions
Modifying Parts of an Array
Adding a Row Vector to All Rows
Random Sampling
pandas
Series and DataFrames
Accessing Elements from a Series
Series Alignment
Comparing One Series with Another
Element-wise Operations
Creating a DataFrame from NumPy Array
Creating a DataFrame from Series
Creating a DataFrame from a CSVl
Getting Columns and Rows
Cleaning Data
Combining Row and Column Selection
Scalar Data: at[] and iat[]
Boolean Selection
Plotting with matplotlib
RegularExpressions
Regular Expression Syntax
Python’s Handling of Regular Expressions
Unicodeand Encoding
Encoding and Decoding Files in Python
Converting a File from cp1252 to UTF-8
Advanced Python 3.x