Agile Python Programming Applied for Everyone
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Agile Python Programming Applied for Everyone

About the Book

This book provides a practical demonstration of python programming as an agile tool for data cleaning, integration, analysis, and visualization. It focuses on life-base scenarios approach as a means to facilitate programming structures and to develop related intellectual conceptual. This volume provides the reader with practical examples of basic language structure, control statements, iterations, functions and modules, file processing, data integration, analysis, and visualization. It focuses on learning by doing where the reader can practically employ python programming to resolve simple and complex problems in IT, business, bioinformatics, education, health, etc. using agile programming syntax. Different from the other scripts, this book supply readers with concise code-based examples of real-life scenarios. The book fits for academics, professionals, and researchers where they not only can explore numerous scenarios but also gain the benefits of the developed online cloud bases source code; which can be accessed using a barcode reader, for straightforward and agile practical implementation to resolve a research problem. 

Chapter 1, this chapter covers the basic syntax structure of python program, add comments and the use of quotations. Also, how to write python script in multiline form. Declare and initialize variables and its naming rules. Read data from the user and perform main operations arithmetical, logical, relational and assign operations. In addition, an exercise with model answers is given for practicing and showing real-life scenarios.

Chapter 2, this chapter covers selection statements syntax and forms i.e. if statement, if else and the nested if statement. Iteration statements are also illustrated with practical examples using for loop, while loop and nest loop statements. Exceptional handling using try and except statement are demonstrated with examples. In addition, an exercise with model answers is given for practicing and showing real-life scenarios.

Chapter 3, this chapter covers string processing including special string operators, slicing and concatenation, string conversions and formatting symbols, loop through strings. Python provides various string methods and functions which facilitate text processing and extraction, these methods and functions are discussed with examples. The in operator, parsing and extracting strings are also illustrated. In addition, an exercise with model answers is given for practicing and showing real-life scenarios.

Chapter 4, this chapter covers functions and modules, we illustrated built-in python functions, define and call functions, passing parameters and return statement. We demonstrated python conversion functions, as well as the random numbers generating functions. Moreover, we illustrated python math library and functions, anonymous python function, the scope of variables. Also, we covered creating modules, packages, importing, and its function calling. In addition, an exercise with model answers is given for practicing and showing real-life scenarios.

Chapter 5, this chapter covers python different data collection structures. A List is a sequence of values of any data type, we illustrated lists creation, accessing, updating and python basic operations that can be implemented on lists. In addition, we demonstrated lists indexing, slicing, matrices, built-in list functions, list methods, list sorting and traversing, parsing lists of strings, and lists aliasing. Dictionary is a key-value pair structure; we illustrated how to create dictionaries, updating and accessing values in the dictionary, delete dictionary elements, built-in dictionary functions and methods. A tuple is a sequence of immutable python objects; we demonstrate how to create tuples, updating tuples, accessing values in tuples, and cover the basic tuples operations. In addition, an exercise with model answers is given for practicing and showing real-life scenarios.

Chapter 6, this chapter covers python file I/O processing and regular expressions. We illustrated file I/O processing and regular expressions, screen in/out processing, opening and closing files, accessing file object attributes, reading and writing to files. Regular expressions, regular expression patterns, special character classes, repetition cases, alternatives, and anchors. In addition, an exercise with model answers is given for practicing and showing real-life scenarios.

Chapter 7, this chapter covers data gathering and cleaning, how to handle missing values. Read data from different sources (offline, online, and direct from the cloud), read different data format such as CSV, text, HTML, Jason, etc. Implement data integration and prepare collected data for further analysis. In addition, an exercise with model answers is given for practicing and showing real-life scenarios.

Chapter 8, this chapter covers data exploring and analysis. We illustrated the series data structure, create a series, accessing data from series with the position. DataFrame structure, create a DataFrame, updating and accessing data frames. Including columns and rows selection, addition, deletion. We demonstrated Panel data structure, create a panel, accessing data in a panel. Data analysis, statistical analysis, data grouping, iterating through groups, aggregations, transformations, filtration. In addition, an exercise with model answers is given for practicing and showing real-life scenarios.

Chapter 9, this chapter covers Python Data Visualization. Direct Plotting, Line Plotting, Bar Plotting, Pie Chart, Box Plotting, Histogram Plotting, A Scatterplot. Seaborn Plotting System, Strip Plotting, Boxplot, SWARMPLOT, JOINTPLOT. Matplotlib Plotting, Line Plotting, Bar Chart, Histogram Plotting, Scatter Plot, Stack Plots, And Pie Chart. In addition, an exercise with model answers is given for practicing and showing real-life scenarios.

Chapter 10, this chapter covers case study with model answers.

This book is a translation into English of Agile Python Programming Applied for Everyone which was originally written in English.

Translations

About the Author

Dr. Ossama Hashim Khamees Embarak
Dr. Ossama Hashim Khamees Embarak

Dr. Ossama Embarak is an assistant professor of CIS and currently working as a program coordinator of CIS Division at HCT. He has a doctorate degree in Mathematical and Computer Sciences, Heriot-Watt University, Scotland, Edinburgh, UK. He has awarded two research grants interdisciplinary grant (199000 AED), and industrial grant (500000 AED), also he participated in many research projects in the field of Artificial intelligence, big data analysis, and data and knowledge mining. He delivered various training sessions for faculty members system wide in R Programming, python programming, and big data analysis. Ossama has over 18+ years of academic teaching, research and supervising projects in CIS emerging technologies. Recently, he published a book in recommendation systems structure. He has many publications, workshops, seminars in the areas of data mining, machine learning, Privacy, mobile applications, big data analysis and medical applications. He is a reviewer for few journals in the field of computer and information sciences, artificial intelligence, mobile and web technologies.as well, he is a co-chair for The Technical Program Committee (TPC) for various regional and international conference.

Table of Contents

Contents

Python Programming

For Data Science

History and Overview of Python

Basic features of Python

Free Software

Download Resources

Limitations of Python

Benefits Resources

Chapter 1 Getting Start with Python

1.1 basic Syntax

Lines and Indentation

Multi-Line Statements

Quotation in Python

Multiple Statements on a Single Line

Read data from user

1.2 Declaring Variable and Assigning Values

Multiple assign

Variable names and keywords

Statements and Expressions

1.3 Basic Operators in python

Arithmetic Operators

Relational Operators

Assign Operators

Logical Operators

1.4 Python Comments

1.5 Formatted strings

Conversion types

The replacement field {}

1.6 The Date and time module

Time Module Methods

Python Calendar module

1.7 Exercises and answers

Chapter 2

Control Statements

2.1 If statement

2.2 Iterations

2.2.1 Loop Control Statements

2.3 Try and except

2.4 Exercises and answers

Chapter 3

String Processing

3.1 String Special Operators

3.2 string slicing and concatenation

string conversions and formatting symbols

3.3 Loop through string

3.4 Python String Functions and Methods

The in operator

3.5 parsing and extracting string

3.6 Exercises and answers

Chapter 4

Functions & Module

4.1 user defined functions

Defining a Function

Invoking a Python Function

Python Function return Statement

Passing Parameters

4. built-in functions

Type conversion functions

4.3 Random numbers function

4.4 Math functions

4.5 Python Anonymous Function

Scope of Variable:

4.6 Create Python Modules

Create and Importing a Module:

A. Using import statement:

B. Use the From ….. import statement:

C. Use Built in Modules in Python:

D. Import and use Python Packages

4.7 Exercises and answers

Chapter 5

Data Collections Structure

5.1 Lists

Create lists

Accessing Values in Lists

add and Update Lists

Delete List Elements

Basic List Operations

Indexing, Slicing, and matrices

Built-in List Functions & Methods

list methods

List sorting and Traversing

Lists and strings

Parsing lines

Aliasing

5.2 Dictionaries

create Dictionaries

Updating and Accessing Values in Dictionary

Delete Dictionary Elements

Built-in Dictionary Functions & Methods

5.3 Tuples

Create tuples

Updating Tuples

Accessing Values in Tuples

Basic Tuples Operations

5.4 Exercises and answers

Chapter 6

File I/O processing & Regular expressions

6.1 File I/O processing

6.1.1 Screen in/out processing

6.1.2 Opening and Closing Files

6.1.3 The file Object Attributes

6.1.4 Reading and Writing Files

6.1.5 Directories in Python

6.2 Regular expressions

6.2.1 Regular Expression Patterns

6.2.2 Special Character Classes

6.2.3 Repetition Cases

Alternatives

Anchors

6.3 Exercises and answers

Chapter 7

Data Gathering and Cleaning

7.1 Data cleaning

Check missing values

Handle the missing values

7.2 Read and clean CSV File

7.3 Data Integration

7.4 Read the JSON File

7.5 Reading the HTML file

7.6 Exercises and answers

Chapter 8

Data Exploring and Analysis

8.1 Series data structure

8.1.1 Create a Series

8.1.2 Accessing Data from Series with Position

8.2 DataFrame data structure

8.2.1 Create a DataFrame.

8.2.2 Updating and Accessing DataFrame.

Column Selection

Column Addition

Column Deletion

Row Selection

Row Addition

Row Deletion

8.3 Panel data structure

8.3.1 Create a Panel

8.3.2 Accessing Data from Panel with Position

8.4 data analysis

8.4.1 statistical analysis

8.4.2 data grouping

Iterating through Groups

Aggregations

Transformations

Filtration

8.5Exercises and answers

Chapter 9

Data Visualization

9.1 direct plotting

Line plotting

Bar plotting

Pie Chart

Box Plotting

Histogram Plotting

A scatterplot

9.2 Seaborn Plotting system

strip plotting

boxplot

swarmplot

jointplot

9.3 Matplotlib plotting

Line plotting

Bar chart

histogram plotting

Scatter plot

Stack Plots

pie chart

9.4Exercises

Chapter 10 Case Study

Reference

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