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.