Python for Data Science - Assignments

Introduction

This repository contains assignments completed as part of the Python for Data Science course. The goal of this course is to develop proficiency in using Python for various data analysis and manipulation tasks, covering foundational programming concepts, data structures, and basic data processing techniques. Each assignment demonstrates practical applications of Python, with detailed explanations provided for clarity.

Topics Covered

• Basic Python syntax and operations
• Variables and data types
• Conditional statements and loops
• Functions and error handling
• Lists, dictionaries, and other data structures
• File handling and data manipulation
• Libraries for data science (NumPy, Pandas, etc.)
• Object-oriented programming
• Data visualization

Each section describes the assignment and its objectives, followed by an explanation of the solution implemented.

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Assignment 1: Introduction to Python

Objective

The first assignment focuses on building familiarity with basic Python syntax and foundational programming constructs. Key concepts such as printing messages, performing arithmetic operations, and working with variables, lists, and dictionaries are introduced.

Tasks and Solutions

1. Print a greeting message
   This task involves printing a simple greeting message using Python's print() function.

2. Arithmetic operations
   Students are required to perform basic arithmetic operations such as addition, subtraction, multiplication, and division using predefined variables.

3. Personalized greeting
   This task demonstrates how to define a variable and use it in a formatted string to print a personalized message.

4. List operations
   Create and manipulate a list containing several elements (e.g., university names) and demonstrate list indexing.

5. Dictionary operations
   Create a dictionary with student information and iterate through its key-value pairs to display them.

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Assignment 2: Intermediate Python Exercises

Objective

In the second assignment, students practice more advanced Python features such as function definition, list comprehension, and handling mixed data types.

Tasks and Solutions

1. FizzBuzz Function
   Write a function fizzbuzz(n) that prints "Fizz" for multiples of 3, "Buzz" for multiples of 5, and "FizzBuzz" for multiples of both. For other numbers, print the number itself.

2. Basic Data Filtering
   Create a list with mixed data types (integers, strings, floats) and filter only the integers using list comprehension.

3. Simple To-Do List
   Implement a simple to-do list with functions to add tasks and show the list of tasks.

4. Temperature Converter
   Write a function to convert temperatures from Celsius to Fahrenheit and print the converted values for given temperatures.

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Assignment 3: Object-Oriented Programming

Objective

This assignment introduces the principles of object-oriented programming (OOP) and demonstrates how to create classes, objects, and methods in Python.

Tasks and Solutions

1. Student Class Implementation
   Students create a Student class with attributes for name, student ID, and a list of courses. Each course contains information about its name, grade, and credits. The class includes two methods: one for calculating the GPA based on the grades and credits of the courses, and another for printing student information.

   • GPA Calculation: The GPA is calculated by summing the total points for all courses (where points are the product of the course grade and credits) and dividing by the total number of credits.
   • Student Info Display: The student’s name, ID, GPA, and list of courses (with grades) are printed in a formatted way.

   The implementation demonstrates how OOP principles like encapsulation and object management can be applied to handle student data effectively.

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Assignment 4: Organizing and Analyzing Annotation Files

Objective

This assignment focuses on processing and analyzing a collection of annotation files based on naming conventions. Key concepts include working with file operations, sorting, grouping, and performing aggregations using Python. The assignment also introduces modular and structured approaches to handle datasets effectively.

Tasks and Solutions

1. Identify incorrectly formatted files
   Process a directory of annotation files to identify and count files that do not conform to the expected naming convention. Extract correctly formatted files for further analysis.

2. Group annotations by month
   Extract the date component from the filenames, identify the month, and group files into separate folders based on their month. Use Python’s os and shutil modules to create directories and move files.

3. Count unique satellites
   Extract the satellite identifier from the filenames, determine the total number of unique satellites, and count the number of annotations for each satellite.

4. Find the most frequently annotated satellite
   Identify the satellite with the highest number of annotations by comparing counts, and display the results.

5. Count unique regions
   Extract and analyze the region information from filenames, identify unique regions, and determine their count.

6. Sort and display filenames
   Sort the filenames in reverse order and print them in their original format to ensure that the sorting process is working as expected.

7. Identify the most annotated month
   Analyze the grouped annotations to find the month with the highest number of files and display the corresponding results.

Learning Outcomes

• Gain experience with Python file operations using modules like os and shutil.
• Practice data extraction, processing, and analysis using lists, sets, and dictionaries.
• Develop skills in grouping and organizing files programmatically.
• Enhance problem-solving abilities by applying structured approaches to data analysis tasks.

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Assignment 5: Data Analysis and File Handling

Objective

In this assignment, students worked on data analysis tasks involving file handling, datetime manipulation, and dictionary-based storage. The focus was on understanding how to manage and process data efficiently, particularly in relation to file handling and analysis of timestamped data.

Tasks and Solutions

1. Analyze Annotations by Date
   The task required processing annotation files stored in a specific directory. The objective was to extract the date and time from each file's name, aggregate the annotations by year and month, and identify the month with the most annotations.

   • The solution involved reading filenames, using regex to parse the dates, and using Pandas to group and visualize the data.
   • A bar plot was generated to visually show the number of annotations per month and year, and the month with the most annotations was identified.

2. Create and Save Data as JSON and Pickle
   Students were tasked with creating a dictionary where each key represented a month, and the corresponding value was a list of annotation names occurring in that month. They were then asked to save the data both in JSON and Pickle formats and later reload and verify the data.

   • The solution involved iterating over the annotation files, extracting relevant metadata (including date), and organizing the data by month.
   • The data was saved in JSON format, and then loaded back to verify its integrity. Additionally, the data was stored using Python's Pickle module for serialization.

3. Sort Annotations for the Second Half of 2024
   The third task involved filtering annotation files from the second half of 2024 (from July to December), sorting them by date from oldest to newest, and printing the sorted list.

   • The solution involved parsing the datetime from the filenames, filtering for files from July to December 2024, and sorting them by date.
   • The final list of annotations was printed in chronological order.

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Assignment 6: Titanic Dataset Analysis

Objective

In this assignment, students worked with the Titanic dataset to perform common data analysis tasks such as handling missing values, feature engineering, and filtering data. The goal was to practice data cleaning and transformation techniques that are often encountered in real-world data science projects.

Tasks and Solutions

1. Handle Missing Values
   Students were required to analyze the dataset for missing values in each column and implement strategies to fill in or handle the missing data. For example, they filled missing values in numerical columns with statistical measures like the mean and filled categorical columns with the most frequent value.

2. Feature Engineering
   A new feature (column) was created to provide additional insights into the dataset. In this case, students added a column to indicate the family size of each passenger. This was done by summing the number of siblings/spouses (SibSp) and parents/children (Parch) on board.

3. Data Filtering
   The task involved filtering passengers based on specific criteria, such as those who paid a fare above the average fare. This demonstrated the ability to analyze data subsets based on calculated or given thresholds.

4. Basic Data Exploration
   Several basic data exploration steps were carried out, such as counting missing values, identifying common trends, and understanding the structure of the dataset. This helped in the overall data cleaning process and set the foundation for more advanced analysis.

Libraries Used

• Pandas
• NumPy

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Assignment 7: Data Manipulation with Pandas

Objective

In this assignment, we practiced various data manipulation techniques using Pandas. The focus was on operations like extracting initials from names, merging DataFrames, and creating new columns based on existing data.

Tasks and Solutions

1. Extract Professor Initials

   • The task was to create a new column containing the initials of each professor from the professor column.
   • We used the apply() function with a helper function to split the name and extract the initials.

2. Merge DataFrames on a Common Column

   • We merged two DataFrames: one containing professor details and another containing courses they teach.
   • The merge was performed on the professor column to combine the data.

3. Extract Last Name

   • A new column with the professors' last names was created by splitting the professor column and extracting the second part (last name).

Learning Outcomes

• Gained experience in Pandas operations like apply() and merge().
• Learned how to create new columns based on existing data.
• Practiced basic data transformations and merging datasets.

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Assignment 8: Data Visualization with Seaborn

Objective

In this assignment, the focus was on exploring and visualizing data using Seaborn to gain insights into relationships between different variables. The tasks involved creating different types of visualizations to analyze the data's distribution, patterns, and correlations.

Tasks and Solutions

1. Visualizing Study Time by Student Name
   The task was to create a line plot to visualize how the study time varied across different students. This helped understand individual differences in study time and identify students with the most or least study time.

2. Exploring Grade Distribution
   You were required to plot a histogram to examine the distribution of grades among the students. The goal was to determine the most common grade ranges and identify any skewness or patterns in the data.

3. Analyzing Grades with an ECDF
   The task involved creating an ECDF plot for the grades to understand the cumulative distribution. This helped determine what percentage of students scored below a certain threshold and allowed for comparison between different groups of students.

4. Comparing Grades Across Courses
   You used a stripplot to show the grades for each course, allowing you to assess the spread of grades within each course. This helped identify which courses had the widest variations in grades.

5. Study Time Analysis by Gender
   The task required creating a swarmplot to visualize the relationship between study time and gender. This helped compare how males and females spent their time studying, identifying any significant differences.

6. Average Grade Comparison Across Courses
   You were asked to create a pointplot showing the average grade for each course. This plot helped compare the performance of students in different courses and identify which courses had the highest average grades.

Learning Outcomes

• You gained experience in using Seaborn for visual exploration of data and identified patterns in relationships between different variables.
• You learned to create and interpret different types of visualizations such as line plots, histograms, ECDFs, and categorical scatter plots to analyze both individual data points and overall distributions.
• The assignment helped improve your ability to present data visually and draw conclusions based on observed trends.