Sonia Martin Gutierrez’s Post

🚀 Day-53 of #100DaysOfCode 📊 NumPy Practice – Conditional Array Modification Today I practiced conditional filtering using NumPy. 🔹 Concepts Practiced: ✔ Boolean indexing ✔ Conditional replacement ✔ Vectorized operations ✔ Efficient array manipulation 🔹 Key Learning: Using boolean indexing (a[a < 0] = 0) allows fast and clean data transformation without loops — one of NumPy’s biggest advantages. Slowly building strong fundamentals in NumPy & Data Handling 💡🔥 #Python #NumPy #DataScience #ArrayManipulation #100DaysOfCode #LearnPython #CodingPractice #PythonDeveloper

🚀 Day-56 of #100DaysOfCode 📊 NumPy Practice – Finding Unique Values & Frequency Today I practiced identifying unique elements and counting their occurrences using NumPy. 🔹 Concepts Practiced: ✔ np.unique() ✔ Frequency counting ✔ Handling duplicate values ✔ Efficient array analysis 🔹 Key Learning: Using return_counts=True makes frequency analysis simple and efficient without loops — very useful in data preprocessing. Slowly stepping into data analysis concepts using NumPy 💡🔥 #Python #NumPy #DataAnalysis #ArrayOperations #100DaysOfCode #LearnPython #CodingPractice #PythonDeveloper

Day 89 – Data Analytics Project (Continuation) 📊 Continued my project by analyzing screen time distribution across age groups using a box plot in Jupyter Notebook. 🔎 Key insight: Screen time varies significantly between age groups, and the distribution (median, quartiles, outliers) tells a deeper story than just averages. Learning to move from plotting graphs → to extracting meaningful insights. #Day89 #DataAnalytics #Python #DataVisualization #LearningJourney

Day 2/100: Mastering Data Types and Logic Today was all about how Python handles information. I dived deep into the mechanics of data and mathematical operations. What I tackled today: Data Types: Understanding Integers, Floats, and Booleans. Subscripting: Pulling specific characters out of a string (like a pro!). The len() Function: Measuring the length of data. Type Conversion: Converting data (e.g., String to Integer) to make calculations possible. Math in Python: Using mathematical operators, the round() function, and assignment operators. Daily Project: Tip/Bill Calculator I created a program that calculates how much each person should pay when splitting a bill, including the tip. It’s a real-world tool built with just a few lines of code! Step by step, I'm getting more comfortable with the logic. 🚀 #Python #100DaysOfCode #DataTypes #CodingCommunity #SoftwareDevelopment

🚀 Solved the “Two Sum” Problem | Data Structures & Algorithms Practice Today I solved the classic Two Sum problem—a fundamental question in data structures & algorithms. 🔹 Problem: 1 Given an array of integers and a target value, return the indices of two numbers such that they add up to the target. ⏱️ Complexity: Time Complexity: O(n) Space Complexity: O(n) 🔗 GitHub Repository (more DSA problems inside): https://lnkd.in/gdrbnQDF #DSA #ProblemSolving #Python #CodingJourney #SoftwareEngineering #LeetCode

🎉 Just crushed my Data Structures and Algorithms course in Python! 🔥 Started with the fundamentals, then tackled linear powerhouses like Stacks, Queues, and Lists—mastering inserts, updates, deletes, and beyond. Now unlocking the magic of non-linear structures for smarter, faster solutions. This has supercharged my problem-solving for data analytics! What's your go-to data structure for real-world projects? Stack or Queue fan? Drop your tips below—I'd love to hear! 👇 #DataStructures #Algorithms #Python #Coding #DataAnalytics #TechTips

Titanic Survival Analysis using Python As part of my learning journey in data science, I worked on analyzing the Titanic passenger dataset to understand survival patterns. What this task involved: Cleaning real-world messy data (handling missing Age values) Analyzing survival rates based on gender and passenger class Creating age groups to study survival trends Visualizing insights using Matplotlib and Seaborn Documenting the complete analysis in a Jupyter Notebook Key learnings: How to handle missing values effectively How to extract insights using groupby analysis How data visualization helps in storytelling GitHub Repository: https://lnkd.in/g5HcFGUw This task helped me strengthen my data analysis and visualization skills and understand how data scientists work with real-world datasets. #MainCrafts #DataScience #Python #DataAnalysis #Visualization

🚀 Day-54 of #100DaysOfCode 📊 NumPy Practice – Filtering Even Numbers Today I practiced generating random arrays and filtering values using NumPy. 🔹 Concepts Practiced: ✔ np.random.randint() ✔ Boolean indexing ✔ Modulo operation ✔ Vectorized filtering 🔹 Key Learning: NumPy allows powerful filtering operations without using loops, making code cleaner and computationally efficient. Step by step moving deeper into NumPy & Data Analysis fundamentals 💡🔥 #Python #NumPy #DataScience #ArrayFiltering #100DaysOfCode #LearnPython #CodingPractice #PythonDeveloper

Most people think randomness means “everything is equal”. It doesn’t. These charts are built from real lottery draw data (2020–2025), analyzed with Python on a mobile environment (Pydroid3). 📊 Distributions are structured ⚖️ Balance converges around specific ranges 🔗 Even true RNG creates repeating pairs and triples This is not prediction. This is understanding how randomness behaves. Data always leaves fingerprints.

Why do companies still use Excel? Python can do in 5 lines what Excel does in 5 hours. But Ali from finance spent 15 years mastering it, and nobody’s telling Ali that.