Online Advanced Python training with our expert master

About The Event

Course Outline

Python:

Python Introduction

• * What is Python?
• * Why Python now?
• * How is the Job Market for Python Developers
• * Installation and documentation

Python Basics

• * Keywords and Identifier
• * Statement, Indentation and Comments
• * Variables and Datatypes
• * Native Datatypes
• * Input, Output and Import
• * Relational / Logical Operators

Python Functions

• * Running Python as a calculator
• * Python Functions
• * String Functions
• * Date Functions
• * Numeric Functions
• * Loading commands from the library
• * User Defined functions

Python commands

• * Numbers and other data type function
• * Strings
• * Lists
• * Length of a list; empty list
• * Sub lists (slicing)
• * Joining two lists
• * List methods
• * Range function
• * Boolean values
• * Expressions
• * Variables and assignment
• * Decisions
• * Loops
• * For loop
• * While loop
• * Else in loops
• * Break, continue, and pass

Data structures

• * More on lists
• * The del statement
• * Tuples and sequences
• * Sets
• * Dictionaries
• * Modules
• * Standard modules
• * The dir() Function
• * Packages

Files and Input and Output Operations on Files

• * Fancier Output Formatting
• * Reading and Writing Files
• * File Operations
• * Appending
• * Sorting
• * Merging
• * Python Directory and Files Management

Exceptions and Error Handling

• * Built-in Exceptions
• * Exception Handling - Try, Except and Finally
• * User-Defined Exception
• * Error Handling scenarios

Python Object & Class

• * Namespace and Scope
• * Inheritance
• * Multiple Inheritance
• * Operator Overloading

Additional Topics

• * Iterators
• * Generators
• * Closures
• * Decorators

Numpy

• * Introduction
• * Fundamental package for scientific computing with Python
• * N-dimensional array object
• * Linear algebra, Fourier transform, random number capabilities
• * Numerical Operations on Numpy Arrays
• * Concatenating, Flattening and Adding Dimensions
• * Python, Numpy and Probability
• * Synthetical Test Data With Python
• * Numpy: Boolean Indexing
• * Matrix Arithmetics under Numpy and Python

Introduction into Pandas

• * Defining Data Frames
• * Operations using Data Frames
• * Adding/deleting columns - Index operations
• * Stack/Unstack/Transpose functions
• * GroupBy function
• * Converting between different kinds of formats

Web Scraping with Python

• * Reading data from public websites
• * Identifying websites DOM structures
• * Using BeautifulSoup
• * Writing web data to local files
• * Handling errors from web data reads

Project outline

In this project you will learn how to read data from any public website and then store data into CSV file format. We will create a programmatic flow to ensure that the data appended to CSV file and then we create various Data Frames to generate analytical results. In this entire process we will use all the python features including classes, modules and libraries including (CSV, OS, GLOB, BS4, BeautifulSoup, Pandas, Numpy and many other modules). We will implement several filter conditions and data cleansing functions to ensure the quality. We will implement several String, Date functions.

About the trainer: 19 years of programming experience settled in australia

Online access details will be sent to the registered participants

For more details reach us 9515895141