Data Processing with Python training


Duration: 4 Days

Course fee:$799.00 (₹56,877.05)


Data, one of the leading terminology being used in any of the technology for enhancing the performance of the application. With the invent of Python, large collection of datasets can be calculated with the least amount of time which in turn gets the most impressive response. Data Processing tools like Numpy, Pandas, Matplotlib and Scipy are among the best for performing such activities on a large scale platform. Data Processing with Python training helps the developers or learners to get the basic understanding on the trending technology.

1. Understanding the basics of Data Processing Tools
2. Implementation on Datas using Numpy, Pandas, Scipy
3. Working with Anaconda platform

1. Expertised knowledge on Data Processing tools
2. Complete Hands-on exercise on Tools
3. Basic to Advanced understanding on Data Processing

Additional information

Course Content






Basics of Python

– Installation of Python on Windows, Ubuntu, Mac
– Concept of Python Interpreter
– How to use Python
– Understanding Data Structure using Python

Introduction to Anaconda Navigator

– Why we use Anaconda?
– Working of Anaconda for Python Libraries
– Working with Jupyter Qt Console

Overview of Numpy Library in Python

– How to Install Numpy in Python
– Using NdArray
– Understanding the process of Creating an Array using Numpy
– List of Available Data Types in Numpy
– Using the dtype option

Performing Basic Operations using Numpy

– Arithmetic Operators
– Matrix Product
– Increment and Decrement Operators
– Universal Functions
– Aggregate Functions

Understanding the Concept of Indexing, Slicing and Iteration

– Basics of Indexing in Numpy
– Basic of Slicing in Numpy
– Process of Iterating an Array
– What are Conditions and Boolean Arrays
– Overview of Shape Manipulation

Understanding Array Manipulation

– Joining Arrays
– Splitting Arrays

Other Common Concepts using Numpy

– Copying or viewing Objects
– Performing Vectorization
– Using Broadcasting
– Working with Structured Arrays

Process of Reading and Writing Array Data on Files

Introduction to Pandas Library in Python

– Basic Installation of Pandas Library

Overview of Pandas Data Structure

– Introduction to Series
– Introduction to Dataframes


– Concept of Declaring a series
– How to select Internal Elements
– Process of Assigning Values to Elements
– Defining a series from Numpy Arrays and Other Series
– How to filter values
– Basic Operations and Mathematical Functions
– Concept of Evaluating Values
– Understanding the concept of NaN Values
– Using Series as Dictionaries


– How to define a Dataframe
– Understanding the process of Assigning Values
– Defining Membership of a Value
– How to delete a Column
– Filtering values
– Process of Creating Dataframes from Nested Dict
– Understanding Transposition of Dataframe

Introduction to Index Objects

– Understanding Methods on Index
– Using Index with Duplicate Labels
– Additional Functionalities on Indexes

Working with Different Operations between Data Structures

– Using Flexible Arithmetic Methods
– Operations between Dataframes and Series

Understanding the Process of Reading and Writing Data using Pandas

– Working with CSV and Textual Files

Concept of Reading and Writing HTML Files

Reading Data from XML

Working with JSON Data

Database Interaction using Pandas

– Using SQLite3 for Loading and Writing Data
– Using PostgreSQL for Loading and Writing Data

Performing Data Manipulation using Pandas

– Introduction to Data Preparation
– Introduction to Data Transformation
– Introduction to Data Aggregation

Understanding the concept of Data Preparation

– Loading
– Assembling
– Reshaping
– Removing

Understanding the concept of Data Transformation

– Concept of removing Duplicates
– Using Mapping Functionalities
– Performing Permutation
– Concept of String Manipulation in detail

Understanding the Concept of Data Aggregation

– Using Groupby
– Understanding Group Iteration

Basics of Data Visualization using Matplotlib Library

Installation of Matplotlib

Using IPython and IPython QtConsole

Understanding the Architecture of Matplotlib

Overview of PyLab and PyPlot

Understanding the Chart Typology

– Working with Line Charts using Pandas
– Working with Histograms
– Working with Bar Charts
– Working with Horizontal Bar Charts
– Working with Multiserial Bar Charts

Advanced Concept of Charts using matplotlib

– Using Contour Plots
– Using Polar Charts

Introducing the concept of mplot3d Toolkit

– Working with 3D Surface
– Using Scatter Plots in 3D
– Using Bar Charts in 3D

Basics of SciPy

Understandintg the Environment Setup for Scipy

Basic Functionality for Scipy

– Introduction to Cluster
– Working with Constants
– Understanding the concept of FFTpack
– Integrate function with Scipy
– Using Interpolate
– Dealing with Input and Output
– Introduction to Linalg
– UsingNdimage
– Concept of Optimization
– Working with Stats
– Overview of CSGraph
– Dealing with Spatial
– Using the concept of ODR
– More advanced concept on some special Package


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