Python List Comprehension for Efficient Data Analysis

Worked on Python dictionaries, focusing on key–value data storage, access patterns, and safe manipulation techniques. Practiced retrieving values, adding and updating entries, removing key–value pairs, and iterating through dictionaries using different built-in methods. Also reinforced the importance of using .get() for safer access when key availability is uncertain. Key takeaways: Accessing dictionary values using keys Adding and updating key–value pairs dynamically Removing entries using del and pop Using .get() to avoid runtime errors when keys are missing Iterating through keys, values, and key–value pairs with .items() Structuring dictionaries for clean and predictable data handling #Python #Dictionaries #DataStructures #ProgrammingFundamentals #SoftwareDevelopment #CleanCode

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

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

Day 08 / 100 – Sets in Python Today I learned about sets in Python, a powerful data structure used to store unique elements. A set is: Unordered Mutable Does not allow duplicate values This makes sets very useful when working with data that may contain repetitions. Key operations with sets include: Adding and removing elements Finding common elements (intersection) Combining sets (union) Comparing datasets Sets are commonly used in: Removing duplicate values from data Comparing datasets Membership testing Data preprocessing and analysis #100DaysOfDataScience #Day08 #PythonLearning #SetsInPython #PythonBasics #DataStructures #LearningInPublic #DataScienceJourney #CodingLife

𝗣𝘆𝘁𝗵𝗼𝗻 𝗗𝗮𝗶𝗹𝘆 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 | 𝗛𝗮𝗰𝗸𝗲𝗿𝗥𝗮𝗻𝗸 – 𝗡𝗲𝘀𝘁𝗲𝗱 𝗟𝗶𝘀𝘁𝘀 | 𝗗𝗮𝘆 𝟭𝟬 This Python problem exposes weak data-handling instantly. Day 10 of my Python Daily Challenge 🚀 Today’s task wasn’t about lists. It was about thinking in layers. 👉 Store names + scores 👉 Find the second lowest score 👉 Print names in alphabetical order Where most people slip 👇 • Forgetting duplicate scores exist • Mixing sorting logic with filtering • Ignoring output order requirements 💡 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗽𝗮𝘁𝘁𝗲𝗿𝗻 𝗳𝗿𝗼𝗺 𝗗𝗮𝘆 𝟭𝟬: Before writing code, separate the steps: collect → filter → sort → print Clear steps = clean solutions. That’s why I’m focusing on Python patterns, not shortcuts — one problem a day, stronger logic every time. Which part confuses you more — filtering or sorting? 👇 #Python #HackerRank #DailyCoding #ProblemSolving #InterviewPrep #LearnInPublic #Consistency

🐍 Day 8 — Lists in Python Day 8 of #python365ai 📋 A list is an ordered collection of items in Python. It allows you to store multiple values in a single variable. Example: fruits = ["apple", "banana", "orange"] Lists are: Ordered Changeable Able to hold mixed data types 📌 Why this matters: Lists are everywhere — from storing datasets to handling user inputs and model outputs. 📘 Practice task: Create a list of your favourite three foods and print it. #python365ai #PythonLists #PythonBasics #LearnPython #DataScience #Coding

𝐏𝐨𝐥𝐲𝐦𝐨𝐫𝐩𝐡𝐢𝐬𝐦 𝐢𝐧 𝐏𝐲𝐭𝐡𝐨𝐧 (Same method name, different behaviour depending on the object.) 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐏𝐨𝐥𝐲𝐦𝐨𝐫𝐩𝐡𝐢𝐬𝐦? Polymorphism means “many forms”. In Python, it allows the same function, method, or operator to behave differently depending on the object. It makes code flexible, reusable, and closer to real‑world modelling. 𝐖𝐡𝐲 𝐔𝐬𝐞 𝐏𝐨𝐥𝐲𝐦𝐨𝐫𝐩𝐡𝐢𝐬𝐦? --> Write cleaner, reusable code -->Handle different objects with a unified interface --> Model real‑world scenarios (e.g., animals speak differently) --> Extend or override behaviour easily #Python #OOP #Polymorphism #ObjectOrientedProgramming #CodeReusability 

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

🚀 Python Learning – Day 17 Today I explored more NumPy concepts related to data structure and layout: Array shape Reshaping arrays Understanding axis These are important when working with real datasets. 🔹 Shape of an Array import numpy as np arr = np.array([[1, 2, 3], [4, 5, 6]]) print(arr.shape) 🔹 Reshaping an Array new_arr = arr.reshape(3, 2) print(new_arr) 🔹 Using Axis print(arr.sum(axis=0)) # column-wise print(arr.sum(axis=1)) # row-wise Understanding shape and axis helps avoid mistakes in data analysis. Moving forward with NumPy basics. 🔥 #Python #NumPy #DataAnalytics #LearningJourney #DailyLearning

Exploratory Data Analysis doesn’t have to be time-consuming. In this video, I demonstrate how YData Profiling can generate a comprehensive data report in minutes using Python. Have you used automated EDA tools before? I’d love to hear your experience. #DataScience #Python #YDataProfiling #EDA #ProfessionalGrowth