Marius Ciobanu’s Post

🎉 Excited to share that my article has been published on GoPenAI — a curated publication on Medium! This is Part 1 of my Pandas for Data Science Series, covering the essentials of reading, sorting, and displaying data — one of the most foundational skills in Python data analysis. GoPenAI reached out to me about publishing it, and I'm thrilled it's now live on their platform for their 3.8K+ followers. Whether you're just getting started with data analysis or looking to sharpen your Pandas skills, I hope you find it useful. Check it out here 👇 https://lnkd.in/dg2ujnKC #Python #Pandas #DataScience #MachineLearning #Medium #Programming

🌟 My Data Science Journey: Python, Pandas & NumPy I’ve been diving into Python for data science with TsAcademy, and it’s been an eye-opening experience! One exciting part? Learning to handle large datasets efficiently — something that Excel can struggle with. With pandas and NumPy, I can now read CSV files, filter data, and calculate key metrics programmatically. Here’s a peek at what I’ve been exploring: import pandas as pd df = pd.read_csv("my_data.csv") active_patients = df[df['status'] == 'Active'] print("Total active patients:", active_patients.shape[0]) Even this simple snippet shows the power of Python: faster analysis, better insights, and scalable workflows. What I love most is how these tools connect data science with real-world healthcare work, turning raw data into meaningful insights. I’m curious: How have you used Python or pandas to make your data work easier? #LearningDataScience #Python #Pandas #NumPy #DataAnalytics #HealthcareData #TSAcademy

📊 Learning Data Analysis with Python using Pandas. I am currently practicing the Pandas library and exploring how data can be cleaned, filtered, and analyzed. In this video, I practiced: • Loading a dataset • Exploring data with head() and info() • Filtering rows and columns • Basic data manipulation using Pandas I am on a journey to improve my data analysis skills. I would really appreciate advice from experienced professionals. 👉 What should I focus on next to grow faster in Data Science? #Python #Pandas #DataAnalysis #LearningInPublic #DataScience

Use Python to clean, explore, and visualize data Want the best data science courses in 2026 → https://lnkd.in/dbmuZd97 PYTHON FOR DATA ANALYSIS Your essential toolkit Data Cleaning dropna() Remove missing rows fillna() Fill missing values astype() Convert column types nan_to_num() Replace NaN with numbers reshape() Change array shape unique() Get distinct values Exploratory Data Analysis describe() Summary statistics groupby() Aggregate by categories corr() Correlation matrix plot() Basic line charts hist() Distribution view scatter() Relationship between variables sns.boxplot() Box distribution view Data Visualization bar() Bar charts xlabel() and ylabel() Axis labels sns.barplot() Bar with estimation sns.violinplot() Distribution + density sns.lineplot() Trend with confidence intervals plotly.express.scatter() Interactive plots Workflow Load data Clean data Explore patterns Visualize insights If you can do these four steps You can handle most real datasets Practice with real projects Not just notebooks #Python #DataAnalysis #EDA #DataScience #ProgrammingValley

📅 Day 13/30 — Introduction to Pandas (Series & DataFrame) Continuing my 30-day journey into data science, today I started learning Pandas, one of the most important libraries for data analysis. What I learned today: 📊 Understanding Series — a one-dimensional labeled array (like a single column) 🧾 Creating Series with both default and custom indexes 📋 Understanding DataFrame — a two-dimensional table (like Excel or SQL tables) 🧩 Learning how a DataFrame is a collection of multiple Series 🏷️ Working with index and column labels ⚙️ Modifying row and column names using index and columns It was interesting to see how Pandas makes data more structured and easier to work with compared to basic Python. ➡️ Next step: exploring data selection, filtering, and basic operations in Pandas. #LearningInPublic #Python #DataScience #Anaconda #JupyterNotebook #Pandas #30DaysOfLearning #ProgrammingJourney

🚀 Day 10/70 – Introduction to NumPy (Entering Real Analytics) Today I started learning NumPy 📊 NumPy (Numerical Python) is a powerful library used for numerical computations in Python. It is faster and more efficient than normal Python lists for mathematical operations. 📌 Why NumPy is Important in Data Analytics? ✔ Handles large datasets efficiently ✔ Supports multi-dimensional arrays ✔ Performs fast mathematical operations ✔ Foundation for Pandas & Machine Learning 📌 Installing NumPy Python id="p4y2zn" pip install numpy 📌 Creating a NumPy Array Python id="k8s9d1" import numpy as np arr = np.array([10, 20, 30, 40]) print(arr) 📌 Basic Operations Python id="w2mx5v" print(arr + 5) # Add 5 to each element print(arr * 2) # Multiply each element print(np.mean(arr)) # Average 👉 NumPy automatically applies operations to all elements (vectorization). 📊 Why This Is Powerful? In normal Python: Python id="q1b9er" numbers = [10, 20, 30, 40] new_list = [] for num in numbers: new_list.append(num * 2) With NumPy: Python id="c7u3ks" arr = np.array([10, 20, 30, 40]) print(arr * 2) Cleaner + Faster 🔥 #Day10 #NumPy #Python #DataAnalytics #LearningInPublic #FutureDataAnalyst #70DaysChallenge

📊 Exploring Data with Pandas – Kaggle Notebook Data analysis begins with understanding the dataset, and Pandas is one of the most powerful Python libraries for this purpose. In this notebook, I explored different Pandas operations such as data reading, indexing, filtering, and basic analysis to better understand how to work with structured datasets. 🔎 What this notebook demonstrates: • Working with Pandas DataFrames • Data selection and filtering • Basic data exploration techniques • Practical hands-on exercises with Python Pandas is widely used in data science because it provides flexible data structures like Series and DataFrames that make analyzing structured data easier. 📌 Kaggle Notebook: https://lnkd.in/dQQPqq4V I’m continuously learning and sharing my journey in data analytics and Python. #DataAnalytics #Python #Pandas #Kaggle #DataScience #LearningJourney

Python for Data Analysis — Everything a Beginner Needs to Know! Python is the #1 language for Data Analysis — here's why & how 👇 📦 THE 4 MUST-KNOW PYTHON LIBRARIES: 🐼 1. PANDAS → Load, clean & manipulate data → Think of it as Excel — but 100x powerful → Most used library in data analysis 🔢 2. NUMPY → Handle numbers & mathematical operations → Works with arrays & matrices → The backbone of all data libraries 📊 3. MATPLOTLIB → Create charts & visualizations → Bar, Line, Pie, Scatter — all from code → Your first visualization library 🎨 4. SEABORN → Beautiful statistical charts → Built on top of Matplotlib → Makes your visuals look professional 🚀 YOUR LEARNING PATH: Week 1 → Python basics (variables, loops, functions) Week 2 → Pandas & NumPy Week 3 → Matplotlib & Seaborn Week 4 → Real project on Kaggle dataset 💡 Best FREE resources: → Kaggle Learn (kaggle.com/learn) → W3Schools Python → YouTube: "Python for Data Analysis" 💬 Are you learning Python right now? Comment below 👇 📌 Follow for Episode 10 coming soon! #Python #Episode9 #DataAnalysis #LearningInPublic

Recently I’ve been diving deeper into NumPy, one of the most fundamental libraries for numerical computing in Python. Instead of just using it in code, I wanted to understand how it actually works and why it’s so powerful. Here are some key things I learned: • NumPy Arrays (ndarray) NumPy uses homogeneous arrays, meaning all elements share the same data type. This allows efficient memory usage and fast numerical computation. • Why NumPy is fast NumPy is largely implemented in C, which allows Python to perform vectorized operations much faster than traditional Python loops. • Array creation methods I practiced creating arrays using functions like: np.array(), np.arange(), np.ones(), np.zeros(), np.identity(), and np.random.random(). • Understanding array attributes Learning attributes like ndim, shape, size, itemsize, and dtype helped me better understand how data is stored internally. • Array operations and statistics NumPy makes it easy to perform vectorized operations and statistical computations like: mean, median, variance, standard deviation, and dot products. • Data manipulation I explored powerful tools like: Indexing and slicing Iterating arrays with np.nditer() Reshaping with reshape() Flattening arrays with ravel() Transposing arrays with .T • Combining and splitting arrays Using functions like np.hstack(), np.vstack(), np.hsplit(), and np.vsplit(). What I’m realizing is that NumPy is the foundation for most of the Python data ecosystem — including libraries like Pandas, SciPy, and many machine learning frameworks. Every concept I learn here is another step toward becoming better in data science and machine learning. Small progress every day compounds. #Python #NumPy #LearningInPublic #DataScienceJourney #MachineLearning 😊 🗒️

🐍 Day 15 of My 30-Day Python Learning Challenge Today I improved my mini project (Log File Analyzer) by adding a useful feature. 📌 New Feature: Find the top 3 most frequent words in a file. 📌 Code: with open("sample.txt", "r") as file: content = file.read().lower() words = content.split() word_count = {} for word in words: word_count[word] = word_count.get(word, 0) + 1 top_words = sorted(word_count.items(), key=lambda x: x[1], reverse=True)[:3] print(top_words) 📌 Output: Top 3 most frequent words with counts 💡 Why this matters? This kind of logic is used in: • Search engines • Text analytics • Data science projects 📊 Quick Question What does "lambda x: x[1]" represent here? A) First value B) Second value C) Key D) Error Answer tomorrow 👇 #Python #MiniProject #DataProcessing #LearningInPublic #SoftwareDeveloper