This repository contains a collection of the Python Piscine for Data Science divided into five modules, each focusing on different aspects of programming and problem-solving.
This module serves as an introduction to Python programming, covering fundamental concepts such as data types, string manipulation, time formatting, and basic algorithms. It includes exercises that involve changing data types, formatting time, finding the true type of objects, identifying null and false values, and more.
| Exercise | Description |
|---|---|
| Hello.py | Change data types such as arrays, dictionaries, and tuples. |
| format_ft_time.py | Format time. |
| find_ft_type.py | Find the true type of a built-in object (e.g., list, dictionary). |
| NULL_not_found.py | Identify null and false values in Python (e.g., NaN, 0, False). |
| whatis.py | Identify odd and even numbers. |
| building.py | Analyze a string and return information such as spaces, special characters, uppercase letters, etc. |
| filterstring.py and ft_filter.py | Recode the original filter function and create a script to filter words in a sentence based on a given criteria. |
| sos.py | Translate text to Morse code. |
| Loading.py | Recode tqdm. |
In this module, the focus is on array manipulation and image processing. It covers topics such as array slicing, image loading, zooming, rotation, and applying filters to images. These exercises provide hands-on experience with working with arrays and processing image data.
| Exercise | Description |
|---|---|
| give_bmi.py | Calculate the body mass index and return a list of indexes above a given limit. |
| array2D.py | Understand array manipulation with a chosen library (e.g., NumPy) and slice matrices. |
| load_image.py | Load images using a chosen library (e.g., PIL) and display the image array in RGB format. |
| zoom.py | Manipulate image data by cropping the image matrix and converting it to grayscale. |
| rotate.py | Rotate an image by manipulating the matrix. |
| pimp_image.py | Apply filters such as red, green, blue, grayscale, and inverse to an image. |
The DataTable module revolves around handling data tables and CSV files. It introduces concepts such as data loading, visualization, and analysis. Exercises include loading CSV files, plotting life expectancy and population data, and creating correlation plots between different variables.
| Exercise | Description |
|---|---|
| load_csv.py | Use a library to handle CSV files and data tables. Load a CSV file and display its content. |
| aff_life.py | Plot life expectancy of a country using data from a CSV file (e.g., using Matplotlib). |
| aff_pop.py | Compare a set of countries in a plot based on their population over a range of years. |
| projection_life.py | Plot income vs life expectancy for all countries for a given year and find correlation. |
This module delves into Object-Oriented Programming (OOP) concepts in Python. It covers topics such as inheritance, class composition, and object instantiation. Exercises include implementing inheritance, avoiding common pitfalls like the diamond problem, and creating class-based calculators.
| Exercise | Description |
|---|---|
| S1E9.py | Inheritance. |
| S1E7.py | Inheritance without going through the base class. |
| DiamondTrap.py | Inherit from a class that doesn't pass through the base class and avoid the diamond trap. |
| ft_calculator.py | Create a class calculator with lists of numbers. |
| ft_calculator.py | Create a static class calculator with 2 lists of numbers. |
The Dod module focuses on more advanced topics such as statistics, function decorators, and data classes. Exercises include calculating statistics for a list, implementing function decorators for limiting function calls, and working with data classes to create structured objects.
| Exercise | Description |
|---|---|
| statistics.py | Calculate statistics such as mean, median, quartile, standard deviation, and variance for a list. |
| in_out.py | Learn about nested functions and nonlocal. |
| callLimit.py | Implement decorators and create a decorator that limits a function after N calls. |
| new_student.py | Learn about data classes and create struct-like objects. |
