A guide to begin coding in Python

Familiarize learners with Python, its benefits, and the tools to get started.

• What is Python?

  • History and growth of Python.
  • Real-world applications.
  • Why Python is beginner-friendly.

• Setting Up Python

  • Installing Python on the local machine.
  • Introduction to IDEs like VS Code, Jupiter, and Google Colab.

• First Python Script

  • Writing a "Hello, World!" program.
  • Running Python scripts in IDEs and the terminal.
• Applications of Python
  • Web Development
  • Data Science
  • Automation, etc

 

https://www.projectguru.in/why-choose-python-as-your-first-programming-language/

Problem

• Problem Understanding
• Stages of Problem Solving
• Structuring Problems
• Search Problems
• Decision problems
• Brute force technique

Algorithm

• Design of Algorithms
• Role of Pseudocode
• algorithm validation
• program verification
• Divide and Conquer
• Greedy Algorithm
• Time complexity
• Space Complexity
• algorithmic gap
• Different types of sort
• online platforms, such as LeetCode, HackerRank, or Codeforces, to practice solving algorithmic problems

https://www.projectguru.in/a-comprehensive-guide-to-python-programming-using-google-colab/

Introduce Python syntax and help learners understand foundational programming rules. Build a program to learn how to handle input and output on the console. 

• Understanding Syntax

  • Indentation and its importance in Python.
  • Writing clean and readable code.

• Comments and Docstrings

  • Single-line and multi-line comments.
  • Using comments to explain code.

• Print 

  • Using the print() function.
  • Formatting output.
• Return
• Declaring a variable
  • Input()
  • Int()
  • Str()
  • Create a data type table
• Operators Table
  • Arithmatic
  • Assignment
• Data Types, Variable & structures
  • Numbers, Strings, Booleans
  • Declaring Variables
  • Type Conversion
  • Lists, Tuples, Dictionaries, and Sets.

https://www.projectguru.in/python-syntax-to-correctly-handle-string-data-type/

https://www.projectguru.in/using-string-operators-to-concatenate-objects-in-python/

https://www.projectguru.in/understanding-a-python-list-and-useful-list-functions/

https://www.projectguru.in/how-to-remove-items-from-a-python-list/

https://www.projectguru.in/manipulating-list-elements-in-python/

https://www.projectguru.in/working-with-lists-and-sets-in-python/

https://www.projectguru.in/basics-of-set-and-creating-a-set-in-python/

https://www.projectguru.in/mathematical-set-operations-in-python/

https://www.projectguru.in/useful-set-functions-in-python/

https://www.projectguru.in/python-set-functions-for-the-difference-between-sets/

https://www.projectguru.in/getting-familiar-in-working-with-a-python-tuple/

https://www.projectguru.in/helpful-python-tuple-operations/

Explain the Prerequisites required to perform the task. Explain with examples and use cases related to analysis and preprocessing. Use a single case to apply as many functions.

• append()
• eval()
• getattr()
• max()
• min()
• range()
• reduce()
• round()
• slice()
• sorted()
• strip()
• abs()
• complex()
• delattr()
• dict()
• divmod()
• enumerate()
• exec()
• filter()
• float()
• format()
• frozenset()
• getattr()
• hex()
• input()
• map()
• pow()
• str()

Questions

• What is file handling?
• Purpose of file handling in programming
• What is file position pointer?
• Integrity of a file
• Best practices
• File Management Principles
• File Naming Conventions
• File formats and purpose

Functions

• open(file,mode)
• write()
• CLOSE()
• remove()
• tell()

Questions

• What are conditions and when are they used?
• Role of conditions in a program?
• What are boolean values and how are they used?
• Difference between print() and return.

Expressions

• Equals: a == b
• Not Equals: a != b
• Less than: a < b
• Less than or equal to: a <= b
• Greater than: a > b
• Greater than or equal to: a >= b
• Mixing Operators
• Ternary Operators
• Short Hands

Match case

• Matching with patterns
• Matching by the length of an Iterable
• Guarding Match-Case Statements

Nested conditions

Questions

• What is the importance of control flow in programming?
• How is it used?
• How does Python execute a program?
• Sequential Control Flow
• Selection Control Flow
• Repetition Control Flow
• Role of flow charts
• What is a loop and why is it used?

Iteration

• For
• While
• For Else
• If-elif-else Ladder
• Range
• Break
• Continue
• Pass

Error handling

• Understanding errors
• Types of errors
• Try, catch, 
• Raising Exceptions
• Handling Exceptions
• Exception chaining
• Cleanup action

Functions

• What is a function?
• Defining a function.
• Role of return in a function.
• Arguments
• Arbitrary Argument Lists
• Unpacking Argument Lists
• Scope and Lifetime: Local vs. Global Variables

 

Develop a program to manage student grades. The program will allow users to:

 

• Add student grades.
• View all stored grades.
• Calculate the average grade.
• Save grades to a file.
• Load grades from a file.

This task will require the use of conditional statements, functions, loops, file handling, error handling, and basic Python built-in functions.

Step 1: Menu System

• The program should repeatedly show a menu with the following options:
  1. Add a student grade.
  2. View all grades.
  3. Calculate the average grade.
  4. Save grades to a file.
  5. Load grades from a file.
  6. Exit the program.

Step 2: Add a Student Grade

• Prompt the user for the student’s name and grade.
• Validate the grade (must be between 0 and 100).
• Store the student’s name and grade in a dictionary.

Step 3: View All Grades

• Display all stored student grades in a tabular format (e.g., "Name: Grade").

Step 4: Calculate the Average Grade

• Compute and display the average of all grades.
• Handle the case where no grades are available.

Step 5: Save Grades to a File

• Save the student grades dictionary to a file in a readable format.
• Handle errors (e.g., if the file cannot be opened).

Step 6: Load Grades from a File

• Read grades from a file and load them into the program.
• Handle errors such as missing or corrupted files.

Step 7: Exit the Program

• Exit the program gracefully.

Implementation

1. Menu System:
   Use a while loop to repeatedly display the menu and process user input.

2. Functions:

   • Create separate functions for each option (e.g., add_grade(), view_grades(), etc.).
   • Use parameters and return values where appropriate.

3. Data Storage:

   • Use a dictionary to store student grades (grades = {}).

4. Error Handling:

   • Use try and except for file operations and input validation.

5. File Handling:

   • Use the open() function with modes ('w', 'r').

6. Validation:

   • Check if grades are between 0 and 100.
   • Ensure user inputs are valid before processing.

Bonus Features

• Allow sorting of grades (e.g., alphabetically by name or numerically by grade).
• Add a feature to delete a student’s grade.
• Implement an option to search for a specific student’s grade.

 

Questions

• What is it?
• Advantages?
• Which design patterns (e.g., Factory, Singleton, Observer, Decorator) must apply to a case?
• Responsibilities of each class
• When to use inheritance versus composition to structure my classes?
• How do we model interactions between classes to ensure loose coupling?
• Which data should be private, protected, or public, and why?

Key concepts

• Class
• Attributes
• Object
• Syntax
• Encapsulation
• Polymorphism
• Inheritance
• Abstraction
• Coupling
• Cohesion
• Association
• Aggregation
• Composition
• Modularity
• Constructors and methods

• Introduction to Pandas
• Series and DataFrames (Pandas’ primary data structures).
• Reading CSV, Excel, and text files into Pandas.
• Exploring the dataset using head(), tail(), and info().
• Accessing rows, columns, and subsets of data.
• Indexing and slicing data.
• Cleaning and Preprocessing Data:
  • Handling missing values with dropna() and fillna().
  • Renaming columns and removing unnecessary data.
• Task
  • Create a data frame
  • Print data info using head(), tail(), info()
  • Rename columns
  • Replace unnecessary values

 

• Descriptive Statistics:

  • Calculate mean, median, mode, standard deviation, and correlation.
  • Use describe() to summarize numeric data.

• Grouping and Aggregation:

  • Group data using groupby() for categorical variables.
  • Use agg() for custom aggregations.

• Filtering and Sorting Data:

  • Filtering data based on conditions (loc[] and query()).
  • Sorting values using sort_values().

• Working with Qualitative Data:

  • Analyze survey responses or text-based answers.
  • Count occurrences of categorical responses using value_counts().
• Introduction to Matplotlib for Data Visualization

  • Basics of Matplotlib:

    • Understanding pyplot and its components.
    • Creating basic plots: line plots, bar charts, scatter plots, and histograms.

  • Customizing Plots:

    • Adding titles, labels, legends, and gridlines.
    • Adjusting colors, styles, and markers.

  • Combining Pandas with Matplotlib:

    • Plotting data directly from Pandas DataFrames.
    • Visualizing relationships between variables.
• Pie charts for proportions.
• Pivot tables and reshaping data
• Visualizing grouped data

 

Questions

• What is a design pattern?
• Importance and purpose
• Characteristics of Design Patterns
• Importance of re-usable codes

Types of programming paradigms

• Functional programming
• Procedural
• Declarative programming
• Concurrent

Types of patterns

• Creational pattern
  • Abstract Factory
  • Factory Method
  • Builder
• Structural patterns
  • Adapter
  • Bridge
  • Facad
• Behavioral
  • Command
  • Iterator
  • Observer

 

 

Questions

• What is a library?
• Advantage
• What is a module?
• What is a package?
• What is the difference between a package and a module?
• What is a framework?

Introduce

• Numpy
• SciKit
• Tensorflow
• Pandas
• Matplotlib
• Keras
• NLTK
• PyTorch

Questions

• How to measure performance?
• What is a bottleneck and what ways to identify it?
• How to set performance standards?

Discuss

• Request Throughput
• Logging module
• APM tools
• Debugging
• Interpreting Profiling Data

Optimization

• Memory Management
• Choosing the Right Data Structures
• Algorithmic Complexity and Optimization
• Multi-threading and Multi-processing
• Caching and Memoization

 

Questions

• What is it?
• Advantages?
• What is contextlib?

Utilities

• With statements
• Try, yield, finally
• Asynchronous Programming
• Nested context managers

Develop an object-oriented program that:

• Generates a sample dataset using Python libraries (e.g., numpy or pandas).
• Saves the dataset to a file (e.g., CSV).
• Performs basic statistical regression analysis.
• Visualizes the analysis with graphs (using matplotlib or seaborn).

This task will help reinforce OOP concepts while introducing basic data handling and visualization techniques.

Steps

1. Dataset Generation

• Create a dataset with at least two variables (e.g., X and Y).
• Use random data generation techniques, with Y as a linear function of X plus some noise.
• Save the dataset to a CSV file.

2. Statistical Analysis

• Calculate key statistics:
  • Mean, median, and standard deviation for X and Y.
  • Correlation coefficient between X and Y.
• Perform linear regression to determine the relationship between X and Y.

3. Visualization

• Generate plots:
  • Scatter plot of the dataset.
  • Regression line overlaying the scatter plot.

4. Object-Oriented Design

• Use OOP to organize the program into classes:
  • Dataset: Handles dataset generation and saving.
  • Statistics: Performs statistical calculations.
  • Regression: Performs linear regression.
  • Visualizer: Generates graphs.

Structure the program with each class in its file. Organize the project into a modular format.

statistical_analysis/
├── dataset.py # Contains the Dataset class
├── statistics.py # Contains the Statistics class
├── regression.py # Contains the Regression class
├── visualizer.py # Contains the Visualizer class
├── loader.py # Entry point to load and run the program
└── dataset.csv # Dataset file (generated dynamically)

Questions

• why data cleaning, research questions, and visualization are essential?
• How visualization helps in exploratory data analysis?
• How do missing values affect accuracy?
• Why is date conversion important for time-series analysis?
• When should we drop vs. fill missing values?
• What would happen if missing values are ignored?
• WHat is a categorical data?
• how to interpret the visualisation with outliers?
• If two variables are highly correlated, what does that mean for decision-making?
• If two expected correlations are missing, what does that suggest about the dataset?
• How do we identify unexpected values?
• How do outliers affect interpretation?
• Are outliers errors, extreme but valid, or indicators of hidden trends?
• Do extreme values push the mean in one direction?
• When would extreme values still be important?

Discussion

• Data analysis pipeline: Raw Data → Cleaned Data → Research Questions → Visualizations → Interpretation → Actionable Insights
• Loading & Cleaning a Dataset
• Creating Research Questions
• Visualizing Data to Answer Research Questions
• Trend over time
• Correlation heatmap
• Bar chart for categorical insights
• Relationship Analysis with scatter plot
• Peaks, dips and seasonalities or patterns.
• Detecting & Interpreting Outliers
  • Boxplot shows quartiles (Q1, Q2, Q3) and whiskers
  • Dots outside whiskers = potential outliers
• Detecting Outliers Using the IQR Method
• Detecting Outliers Using Z-Score (Standard Deviation)
• normal, skewed, or multimodal distribution

Explain

• What is Numpy?
• Its integrations in other modules like SciKit.
• Why numpy is preferred in statistical operations?
• What numerical operations does it help with?
• Why not use Pandas?
• In which cases Numpy is preferred?