Python Sets: Unordered, Mutable, and Duplicate-Free

Day 09 / 100 – Dictionaries in Python Today I learned about dictionaries in Python, one of the most powerful and widely used data structures. A dictionary stores data in key–value pairs, where: Each key is unique Values can be modified (mutable) Data is accessed using keys instead of indexes Dictionaries are extremely useful because they allow: Fast data lookup Structured data storage Easy mapping of related information Common use cases of dictionaries: Representing JSON-like data Storing configurations and settings Label-based datasets in data science Efficient data retrieval and aggregation #100DaysOfDataScience #Day09 #PythonLearning #DictionariesInPython #PythonBasics #DataStructures #LearningInPublic #DataScienceJourney #CodingLife

Day 10 / 100 – Strings in Python Today I learned about strings in Python, which are used to represent and manipulate text data. A string is a sequence of characters and is one of the most commonly used data types in Python. Strings are immutable, meaning their values cannot be changed directly. Common string operations include: Finding the length of text Converting text to upper or lower case Replacing parts of a string Splitting and joining text Strings are widely used in: Text processing and formatting Reading and writing files Handling user input Data cleaning and preprocessing in data science #100DaysOfDataScience #Day10 #PythonLearning #StringsInPython #PythonBasics #DataScienceJourney #LearningInPublic #CodingLife

List comprehension is a clean and efficient way to work with lists in Python. Instead of writing multiple lines using loops and conditions, you can filter or transform data in a single readable line. In this example, we extract only high sales values from a list. This approach is widely used in data analysis because it makes the code shorter, faster to read, and easier to maintain once your datasets grow. #PythonProgramming #LearnPython #ListComprehension #PythonTips #DataAnalytics #CleanCode #AnalyticsByAdnan

An exercise to help build the right mental model for Python data. The “Solution” link uses memory_graph to visualize execution and reveals what’s actually happening: - Solution: https://lnkd.in/exyGcDkE - Explanation: https://lnkd.in/ebPVvnhx - More exercises: https://lnkd.in/eQSdJdaW

Worked on data transformation and filtering in Python, focusing on multiple ways to process collections efficiently 🧠🐍. Explored transforming lists using both list comprehensions and map() with lambda functions, followed by filtering data using filter() and conditional comprehensions. Also practiced mapping list data into dictionaries and performing basic aggregations like sum, count, and average to extract meaningful insights. Key takeaways: -Transforming data using list comprehensions and map() -Filtering data and conditional list comprehensions -Converting list data into dictionaries using comprehensions -Applying aggregation operations such as sum, length, and average -Choosing readable and efficient approaches for data processing #Python #DataTransformation #FunctionalProgramming #ProgrammingFundamentals #SoftwareDevelopment

Python collections are data structures that store multiple values, making it easier to work with and manipulate data. The main collections are: ➡️Lists: ordered, mutable (changeable) ➡️Tuples: ordered, immutable ➡️Dictionaries: key-value pairs, mutable (tho not included in the exercise below) ➡️Sets: unordered, unique elements, mutable These collections help you organize and process data efficiently. #python #programminglanguage #coding

An exercise to help build the right mental model for Python data. The “Solution” link uses memory_graph to visualize execution and reveals what’s actually happening: - Solution: https://lnkd.in/eCwvs44e - Explanation: https://lnkd.in/ebPVvnhx - More exercises: https://lnkd.in/eQSdJdaW

Python Data Types: Your Complete Roadmap! 🗺️🐍 Just dropped a comprehensive guide covering ALL Python data types with practical examples! From numbers to dictionaries, I've broken down: • Integers, Floats, Strings • Lists, Tuples, Dictionaries   • Sets, Booleans • Plus real-world usage examples! Save this for when you need to choose the right data type! 💾 Like if you find it useful! ❤️ Follow for more Python deep dives! 🔄 #Python #Programming #Coding #LearnPython #Developer #PythonGuide  #Tech #Coding #Programming #LearnPython  #CodingTips #PythonTips #Developer #CodeNewbie #ProgrammingTips  #PythonProgramming #SoftwareDevelopment  #Tech #ComputerScience #DataScience #Day14

Day 11 / 100 – File Handling in Python Today I learned about file handling in Python, which allows us to read, write, and manage data stored in files. File handling is an essential concept because real-world applications often need to work with external data such as text files, logs, and datasets. Key concepts I learned: Opening a file using open() Reading data from files Writing and appending data to files Closing files properly to free resources Common file modes in Python: r → Read mode w → Write mode (creates/overwrites file) a → Append mode b → Binary mode File handling is widely used in: Data storage and retrieval Data preprocessing pipelines Logging and reporting Real-world data science applications #100DaysOfDataScience #Day11 #PythonLearning #FileHandling #PythonBasics #DataScienceJourney #LearningInPublic #CodingLife

Working with real data today. Read a CSV file, explored its structure, and extracted meaningful insights using Python & Pandas. Data inspection with info() Business insight using idxmax() Summary metrics with mean() Small steps, consistent progress. 🚀 #Python #Pandas #DataAnalysis #LearningByDoing #Consistency