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Course Detail

Course Name Python Programming
Course Code 23DLS506
Semester 1
Credits 4


Unit I

Introduction: History of Python, Need of Python Programming, Applications Basics of Python Programming, Running Python Scripts, Installing Python on Your Computer,  Using the Terminal Command Prompt, IDLE, and Other IDEs, Variables, Assignment, Keywords, Input-Output, Indentation.Types, Operators and Expressions: Types – Integers, Strings, Booleans; Operators- Arithmetic Operators, Comparison (Relational) Operators, Assignment Operators, Logical Operators, Bitwise Operators, Membership Operators, Identity Operators, Expressions and order of evaluations Control Flow- if, if-elif-else, for, while, break, continue, pass .Case Study: An Investment Report and Approximating Square Roots.

Unit II

Data Structures: Lists – Operations, Slicing, Methods; Tuples, Sets, Dictionaries, Sequences Comprehensions. Case Study: Nondirective Psychotherapy.

Functions: Defining Functions, Calling Functions, Passing Arguments, Keyword Arguments, Default Arguments, Variable-length arguments, Anonymous Functions, Fruitful Functions (Function Returning Values), Scope of the Variables in a Function – Global and Local Variables.

Modules: Creating modules, import statement, from. Import statement, name spacing. Python packages: Introduction to PIP, Installing Packages via PIP, Using Python Packages. Text Files: Text Files and Their Format, Writing Text to a File , Writing Numbers to a File , Reading Text from a   File , Reading Numbers from a File, Accessing and Manipulating Files and Directories on Disk. Case Study: Gathering Information from a File System

Unit III

Data Gathering and Cleaning: Cleaning Data, Checking for Missing Values, Handling the Missing Values, Reading and Cleaning CSV Data, Merging and Integrating Data, Reading Data from the JSON Format, Reading Data from the HTML Format, and Reading Data from the XML Format.

Regular expressions: Character matching in regular expressions, Extracting data using regular expressions, Combining searching and extracting and Escape character. Case Study: Detecting the e-mail addresses in a text file.

Popular Libraries for Data Visualization in Python: Matplotlib, Seaborn, Plotly, Geoplotlib, and Pandas. Data Visualization: Direct Plotting, Line Plot, Bar Plot, Pie Chart, Box Plot, Histogram Plot, Scatter Plot, Seaborn Plotting System , Strip Plot , Box Plot, Swarm Plot, Joint Plot , Matplotlib Plot , Line Plot Bar Chart ,Histogram Plot ,Scatter Plot , Stack Plot and Pie Chart.

Coding Simple GUI-Based Programs:  Windows and Labels, Displaying Images, Command Buttons and Responding to Events, Viewing the Images of Playing Cards, Entry Fields for the Input and Output of Text, and Using Pop-up Dialog Boxes.  Case Study: A GUI-Based ATM

Text Books

  1. Chun, W. (2006) Core python programming. Prentice Hall Professional.
  2. Embarak, O. (2018). Data Analysis and Visualization Using Python: Analyze Data to Create
    Visualizations for BI Systems. Apress.
  3. Lambert, K. A. (2011). Fundamentals of Python: First Programs. Cengage Learning.
  4. Severance, C. (2013). Python for informatics: Exploring information. CreateSpace.

Reference Books

  2. Learning Python, Mark Lutz, Orielly
  3. Python Programming: A Modern Approach, Vamsi Kurama, Pearson
  4. VanderPlas, J. (2016). Python data science handbook: Essential tools for working with data. “ O’Reilly Media, Inc.”.

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