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Python is an extensive programming language that is majorly used for Rapid Application Development (RAD) and prototyping. It is one of the most simple and straightforward programming languages to learn with an easy syntax. Empower SoftTech is  a Python training institute in Hyderabad which offers this course with the support of high level and experienced faculty, who have immense knowledge and command over this subject.

Python course training in Hyderabad is designed to cover various aspects like python programming environment which includes fundamental python programming techniques like lambdas, reading & manipulating .csv files, and numpy library. Techniques for data manipulation and cleaning are very popular. It uses python pandas data science library. The course also introduces the abstraction of the series & data frame for data analysis. This forms the central data structures for performing data analysis.

Why Us:

  • Designed by Working Professionals
  • One-on-One training with doubts clarification
  • Live project and case studies
  • Practical Hands-on training
best-python-online-and-classroom-training-hyderabad

Python Course Contents

Introduction to Python

  • What is Python?
  • WHY PYTHON?
  • History
  • Features – Dynamic, Interpreted, Object oriented, Embeddable, Extensible, Large standard libraries, Free and Open source
  • Why Python is General Language?
  • Limitations of Python
  • What is PSF?
  • Python implementations
  • Python applications
  • Python versions
  • PYTHON IN REALTIME INDUSTRY
  • Difference between Python 2.x and 3.x
  • Difference between Python 3.7 and 3.8
  • Software Development Architectures

Python Software’s

  • Python Distributions
  • Download &Python Installation Process in Windows, Unix, Linux and Mac
  • Online Python IDLE
  • Python Real-time IDEs like Spyder, Jupyter Note Book, PyCharm, Rodeo, Visual Studio Code, ATOM, PyDevetc

Python Language Fundamentals

  • Python Implementation Alternatives/Flavors
  • Keywords
  • Identifiers
  • Constants / Literals
  • Data types
  • Python VS JAVA
  • Python Syntax

Different Modes of Python

  • Interactive Mode
  • Scripting Mode
  • Programming Elements
  • Structure of Python program
  • First Python Application
  • Comments in Python
  • Python file extensions
  • Setting Path in Windows
  • Edit and Run python program without IDE
  • Edit and Run python program using IDEs
  • INSIDE PYTHON
  • Programmers View of Interpreter
  • Inside INTERPRETER
  • What is Byte Code in PYTHON?
  • Python Debugger

Python Variables

  • bytes Data Type
  • byte array
  • String Formatting in Python
  • Math, Random, Secrets Modules
  • Introduction
  • Initialization of variables
  • Local variables
  • Global variables
  • ‘global’ keyword
  • Input and Output operations
  • Data conversion functions – int(), float(), complex(), str(), chr(), ord()

Operators

  • Arithmetic Operators
  • Comparison Operators
  • Python Assignment Operators
  • Logical Operators
  • Bitwise Operators
  • Shift operators
  • Membership Operators
  • Identity Operators
  • Ternary Operator
  • Operator precedence
  • Difference between “is” vs “==”

Input & Output Operators

  • Print
  • Input
  • Command-line arguments

Control Statements

  • Conditional control statements
  • If
  • If-else
  • If-elif-else
  • Nested-if
  • Loop control statements
  • for
  • while
  • Nested loops
  • Branching statements
  • Break
  • Continue
  • Pass
  • Return
  • Case studies

Data Structures or Collections

  • Introduction
  • Importance of Data structures
  • Applications of Data structures
  • Types of Collections
  • Sequence
  • Strings, List, Tuple, range
  • Non sequence
  • Set, Frozen set, Dictionary
  • Strings
  • What is string
  • Representation of Strings
  • Processing elements using indexing
  • Processing elements using Iterators
  • Manipulation of String using Indexing and Slicing
  • String operators
  • Methods of String object
  • String Formatting
  • String functions
  • String Immutability
  • Case studies

List Collection

  • What is List
  • Need of List collection
  • Different ways of creating List
  • List comprehension
  • List indices
  • Processing elements of List through Indexing and Slicing
  • List object methods
  • List is Mutable
  • Mutable and Immutable elements of List
  • Nested Lists
  • List_of_lists
  • Hardcopy, shallowCopy and DeepCopy
  • zip() in Python
  • How to unzip?
  • Python Arrays:
  • Case studies

Tuple Collection

  • What is tuple?
  • Different ways of creating Tuple
  • Method of Tuple object
  • Tuple is Immutable
  • Mutable and Immutable elements of Tuple
  • Process tuple through Indexing and Slicing
  • List v/s Tuple
  • Case studies

Set Collection

  • What is set?
  • Different ways of creating set
  • Difference between list and set
  • Iteration Over Sets
  • Accessing elements of set
  • Python Set Methods
  • Python Set Operations
  • Union of sets
  • functions and methods of set
  • Python Frozen set
  • Difference between set and frozenset ?
  • Case study

Dictionary Collection

  • What is dictionary?
  • Difference between list, set and dictionary
  • How to create a dictionary?
  • PYTHON HASHING?
  • Accessing values of dictionary
  • Python Dictionary Methods
  • Copying dictionary
  • Updating Dictionary
  • Reading keys from Dictionary
  • Reading values from Dictionary
  • Reading items from Dictionary
  • Delete Keys from the dictionary
  • Sorting the Dictionary
  • Python Dictionary Functions and methods
  • Dictionary comprehension

Functions

  • What is Function?
  • Advantages of functions
  • Syntax and Writing function
  • Calling or Invoking function
  • Classification of Functions
  • No arguments and No return values
  • With arguments and No return values
  • With arguments and With return values
  • No arguments and With return values
  • Recursion
  • Python argument type functions :
    • Default argument functions
    • Required(Positional) arguments function
    • Keyword arguments function
    • Variable arguments functions
  • ‘pass’ keyword in functions
  • Lambda functions/Anonymous functions
    • map()
    • filter()
    • reduce()
  • Nested functions
  • Non local variables, global variables
  • Closures
  • Decorators
  • Generators
  • Iterators
  • Monkey patching

Advanced Python

Python Modules

  • Importance of modular programming
  • What is module
  • Types of Modules – Pre defined, User defined.
  • User defined modules creation
  • Functions based modules
  • Class based modules
  • Connecting modules
  • Import module
  • From … import
  • Module alias / Renaming module
  • Built In properties of module

Packages

  • Organizing python project into packages
  • Types of packages – pre defined, user defined.
  • Package v/s Folder
  • py file
  • Importing package
  • PIP
  • Introduction to PIP
  • Installing PIP
  • Installing Python packages
  • Un installing Python packages

OOPs

  • Procedural v/s Object oriented programming
  • Principles of OOP – Encapsulation , Abstraction (Data Hiding)
  • Classes and Objects
  • How to define class in python
  • Types of variables – instance variables, class variables.
  • Types of methods – instance methods, class method, static method
  •  
  • Object initialization
  • ‘self’ reference variable
  • ‘cls’ reference variable
  • Access modifiers – private(__) , protected(_), public
  • AT property class
  • Property() object
  • Creating object properties using setaltr, getaltr functions
  • Encapsulation(Data Binding)
  • What is polymorphism?
  • Overriding
  1. i) Method overriding
  2. ii) Constructor overriding
  • Overloading
  1. i) Method Overloading
  2. ii) Constructor Overloading

iii)  Operator Overloading

  • Class re-usability
  • Composition
  • Aggregation
  • Inheritance – single , multi level, multiple, hierarchical and hybrid inheritance and Diamond inheritance
  • Constructors in inheritance
  • Object class
  • super()
  • Runtime polymorphism
  • Method overriding
  • Method resolution order(MRO)
  • Method overriding in Multiple inheritance and Hybrid Inheritance
  • Duck typing
  • Concrete Methods in Abstract Base Classes
  • Difference between Abstraction & Encapsulation
  • Inner classes
  • Introduction
  • Writing inner class
  • Accessing class level members of inner class
  • Accessing object level members of inner class
  • Local inner classes
  • Complex inner classes
  • Case studies

Exception Handling & Types of Errors

  • What is Exception?
  • Why exception handling?
  • Syntax error v/s Runtime error
  • Exception codes – AttributeError, ValueError, IndexError, TypeError…
  • Handling exception – try except block
  • Try with multi except
  • Handling multiple exceptions with single except block
  • Finally block
    • Try-except-finally
    • Try with finally
    • Case study of finally block
  • Raise keyword
    • Custom exceptions / User defined exceptions
    • Need to Custom exceptions
  • Case studies

Regular expressions

  • Understanding regular expressions
  • String v/s Regular expression string
  • “re” module functions
  • Match()
  • Search()
  • Split()
  • Findall()
  • Compile()
  • Sub()
  • Subn()
  • Expressions using operators and symbols
  • Simple character matches
  • Special characters
  • Character classes
  • Mobile number extraction
  • Mail extraction
  • Different Mail ID patterns
  • Data extraction
  • Password extraction
  • URL extraction
  • Vehicle number extraction
  • Case study

File &Directory handling

  • Introduction to files
  • Opening file
  • File modes
  • Reading data from file
  • Writing data into file
  • Appending data into file
  • Line count in File
  • CSV module
  • Creating CSV file
  • Reading from CSV file
  • Writing into CSV file
  • Object serialization – pickle module
  • XML parsing
  • JSON parsing

Python Logging

  • Logging Levels
  • implement Logging
  • Configure Log File in over writing Mode
  • Timestamp in the Log Messages
  • Python Program Exceptions to the Log File
  • Requirement of Our Own Customized Logger
  • Features of Customized Logger

Date & Time module

  • How to use Date & Date Time class
  • How to use Time Delta object
  • Formatting Date and Time
  • Calendar module
  • Text calendar
  • HTML calendar

OS module

  • Shell script commands
  • Various OS operations in Python
  • Python file system shell methods
  • Creating files and directories
  • Removing files and directories
  • Shutdown and Restart system
  • Renaming files and directories
  • Executing system commands

Multi-threading & Multi Processing

  • Introduction
  • Multi tasking v/s Multi threading
  • Threading module
  • Creating thread – inheriting Thread class , Using callable object
  • Life cycle of thread
  • Single threaded application
  • Multi threaded application
  • Can we call run() directly?
  • Need to start() method
  • Sleep()
  • Join()
  • Synchronization – Lock class – acquire(), release() functions
  • Case studies

Garbage collection

  • Introduction
  • Importance of Manual garbage collection
  • Self reference objects garbage collection
  • ‘gc’ module
  • Collect() method
  • Threshold function
  • Case studies

Python Data Base Communications(PDBC)

  • Introduction to DBMS applications
  • File system v/s DBMS
  • Communicating with MySQL
  • Python – MySQL connector
  • connector module
  • connect() method
  • Oracle Database
  • Install cx_Oracle
  • Cursor Object methods
  • execute() method
  • executeMany() method
  • fetchone()
  • fetchmany()
  • fetchall()
  • Static queries v/s Dynamic queries
  • Transaction management
  • Case studies

Python – Network Programming

  • What is Sockets?
  • What is Socket Programming?
  • The socket Module
  • Server Socket Methods
  • Connecting to a server
  • A simple server-client program
  • Server
  • Client

Tkinter & Turtle

  • Introduction to GUI programming
  • Tkinter module
  • Tk class
  • Components / Widgets
  • Label , Entry , Button , Combo, Radio
  • Types of Layouts
  • Handling events
  • Widgets properties
  • Case studies

Data analytics modules

  • Numpy
  • Introduction
  • Scipy
  • Introduction
  • Arrays
  • Datatypes
  • Matrices
  • N dimension arrays
  • Indexing and Slicing
  • Pandas
  • Introduction
  • Data Frames
  • Merge , Join, Concat
  • MatPlotLib introduction
  • Drawing plots
  • Introduction to Machine learning
  • Types of Machine Learning?
  • Introduction to Data science

DJANGO

  • Introduction to PYTHON Django
  • What is Web framework?
  • Why Frameworks?
  • Define MVT Design Pattern
  • Difference between MVC and MVT

PANDAS

Pandas – Introduction

Pandas – Environment Setup

Pandas – Introduction to Data Structures

  • Dimension & Description
  • Series
  • DataFrame
  • Data Type of Columns
  • Panel

Pandas — Series

  • Series
  • Create an Empty Series
  • Create a Series f
  • rom ndarray
  • rom dict
  • rom Scalar
  • Accessing Data from Series with Position
  • Retrieve Data Using Label (Index)

Pandas – DataFrame

  • DataFrame
  • Create DataFrame
  • Create an Empty DataFrame
  • Create a DataFrame from Lists
  • Create a DataFrame from Dict of ndarrays / Lists
  • Create a DataFrame from List of Dicts
  • Create a DataFrame from Dict of Series
  • Column Selection
  • Column Addition
  • Column Deletion
  • Row Selection, Addition, and Deletion

Pandas – Panel

  • Panel()
  • Create Panel
  • Selecting the Data from Panel

Pandas – Basic Functionality

  • DataFrame Basic Functionality

Pandas – Descriptive Statistics

  • Functions & Description
  • Summarizing Data

Pandas – Function Application

  • Table-wise Function Application
  • Row or Column Wise Function Application
  • Element Wise Function Application

Pandas – Reindexing

  • Reindex to Align with Other Objects
  • Filling while ReIndexing
  • Limits on Filling while Reindexing
  • Renaming

Pandas – Iteration

  • Iterating a DataFrame
  • iteritems()
  • iterrows()
  • itertuples()

Pandas – Sorting

  • By Label
  • Sorting Algorithm

Pandas – Working with Text Data

Pandas – Options and Customization

  • get_option(param)
  • set_option(param,value)
  • reset_option(param)
  • describe_option(param)
  • option_context()

Pandas – Indexing and Selecting Data

  • .loc()
  • .iloc()
  • .ix()
  • Use of Notations

Pandas – Statistical Functions

  • Percent_change
  • Covariance
  • Correlation
  • Data Ranking

Pandas – Window Functions

  • .rolling() Function
  • .expanding() Function
  • .ewm() Function

Pandas – Aggregations

  • Applying Aggregations on DataFrame

Pandas – Missing Data

  • Cleaning / Filling Missing Data
  • Replace NaN with a Scalar Value
  • Fill NA Forward and Backward
  • Drop Missing Values
  • Replace Missing (or) Generic Values

Pandas – GroupBy

  • Split Data into Groups
  • View Groups
  • Iterating through Groups
  • Select a Group
  • Aggregations
  • Transformations
  • Filtration

Pandas – Merging/Joining

  • Merge Using ‘how’ Argument

Pandas – Concatenation

  • Concatenating Objects
  • Time Series

Pandas – Date Functionality

Pandas – Timedelta

Pandas – Categorical Data

  • Object Creation

Pandas – Visualization

  • Bar Plot
  • Histograms
  • Box Plots
  • Area Plot
  • Scatter Plot
  • Pie Chart

Pandas – IO Tools

  • csv

Pandas – Sparse Data

Pandas – Caveats & Gotchas

Pandas – Comparison with SQL

NUMPY

 NUMPY − INTRODUCTION

NUMPY − ENVIRONMENT

NUMPY − NDARRAY OBJECT

NUMPY − DATA TYPES

  • Data Type Objects (dtype)

NUMPY − ARRAY ATTRIBUTES

  • shape
  • ndim
  • itemsize
  • flags

NUMPY − ARRAY CREATION ROUTINES

  • empty
  • zeros
  • ones

NUMPY − ARRAY FROM EXISTING DATA

  • asarray
  • frombuffer
  • fromiter

NUMPY − ARRAY FROM NUMERICAL RANGES

  • arange
  • linspace
  • logspace

NUMPY − INDEXING & SLICING

NUMPY − ADVANCED INDEXING

  • Integer Indexing
  • Boolean Array Indexing

NUMPY − BROADCASTING

NUMPY − ITERATING OVER ARRAY

  • Iteration
  • Order
  • Modifying Array Values
  • External Loop
  • Broadcasting Iteration

NUMPY – ARRAY MANIPULATION

  • reshape
  • ndarray.flat
  • ndarray.flatten
  • ravel
  • transpose
  • ndarray.T
  • swapaxes
  • rollaxis
  • broadcast
  • broadcast_to
  • expand_dims
  • squeeze
  • concatenate
  • stack
  • hstack and numpy.vstack
  • split
  • hsplit and numpy.vsplit
  • resize
  • append
  • insert
  • delete
  • unique

NUMPY – BINARY OPERATORS

  • bitwise_and
  • bitwise_or
  • invert()
  • left_shift
  • right_shift

NUMPY − STRING FUNCTIONS

NUMPY − MATHEMATICAL FUNCTIONS

  • Trigonometric Functions
  • Functions for Rounding

NUMPY − ARITHMETIC OPERATIONS

  • reciprocal()
  • power()
  • mod()

NUMPY − STATISTICAL FUNCTIONS

  • amin() and numpy.amax()
  • ptp()
  • percentile()
  • median()
  • mean()
  • average()
  • Standard Deviation
  • Variance

NUMPY − SORT, SEARCH & COUNTING FUNCTIONS

  • sort()
  • argsort()
  • lexsort()
  • argmax() and numpy.argmin()
  • nonzero()
  • where()
  • extract()

NUMPY − BYTE SWAPPING

  • ndarray.byteswap()

NUMPY − COPIES & VIEWS

  • No Copy
  • View or Shallow Copy
  • Deep Copy

NUMPY − MATRIX LIBRARY

  • empty()
  • matlib.zeros()
  • matlib.ones()
  • matlib.eye()
  • matlib.identity()
  • matlib.rand()

NUMPY − LINEAR ALGEBRA

  • dot()
  • vdot()
  • inner()
  • matmul()
  • Determinant
  • linalg.solve()

NUMPY − MATPLOTLIB

  • Sine Wave Plot
  • subplot()
  • bar()

NUMPY – HISTOGRAM USING MATPLOTLIB

  • histogram()
  • plt()

NUMPY − I/O WITH NUMPY

  • save()
  • savetxt()

 

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