Converting Data Types with NumPy's astype() Method

Exploratory Data Analysis (EDA) with Pandas - Cheat Sheet If you work with data in Python, this Pandas EDA cheat sheet is a handy reference 📊🐍 It covers:  • Data loading & inspection  • Cleaning & transformation  • Visualization basics Perfect for quick lookups while exploring datasets or revising core Pandas workflows. Feel free to save, share, or use it as a daily reference 🚀 #DataScience #Python #Pandas #EDA #MachineLearning #Analytics #DataAnalysis #LearningInPublic

Exploring data before modeling is more important than I thought. Through Exploratory Data Analysis (EDA) using Python, I learned how to understand data structure, handle missing values, detect outliers, and uncover patterns using visualizations. Working on a real-world dataset helped me realize how EDA builds the foundation for accurate analysis and better decision-making. Step by step, I’m getting more comfortable turning raw data into meaningful insights. #AnalyticsCareerConnect #EDA #Python #DataAnalysis #LearningJourney #DataAnalytics

#Day51 — Visualizing Data with Plotly 🚀 Exploring how Python powers data visualization 📈🐍 Plotly enables interactive 3D plots with minimal code. Depth adds clarity by revealing patterns beyond 2D views. Python makes this process efficient and flexible. Learning how visuals drive better decisions A strong reason Python leads in data analytics 🚀 #Python #DataAnalytics #DataVisualization #Plotly #3DVisualization #Analytics #DataScience #LearningInPublic #Upskilling

Today's Learning on Melting in Python: While working with data, sometimes we need to convert data from wide format to long format. That’s where the melt() function in pandas becomes extremely useful. 🔹 It helps in unpivoting DataFrames 🔹 Converts columns into rows 🔹 Makes data suitable for analysis & visualization 💡 Data reshaping is a key skill in data analytics! #Python #Pandas #DataAnalysis #Learning #DataScience

Today I started working with Pandas, one of the most powerful libraries for data analysis in Python. 📌 Practiced: • Creating DataFrames using NumPy data • Working with rows & columns • Selecting specific columns • Understanding how structured data is handled Seeing how raw data turns into a structured table format was exciting. This is where real data analysis begins 📊 Step by step building skills for: ➡ Data Analysis ➡ Data Science ➡ Machine Learning Consistency + daily practice = growth 🚀 #Python #Pandas #DataScienceJourney #DataAnalysis #CodingPractice #StudentDeveloper #MachineLearning #LearnInPublic

Boost your data analysis skills with these 5 essential Pandas commands every beginner and aspiring data scientist must know. Learn how to explore, clean, and summarize data efficiently using Python and Pandas. #Pandas #Python #DataAnalysis #DataScience #MachineLearning #Analytics #BigData #Coding #Programming #PythonForBeginners #DataAnalyst #EDA #LearnPython #TechSkills #AI #100DaysOfCode #datasciencewithrg #datasciencelovers

Day 30/50 — Understanding data distribution 📊 Today I analyzed frequency and percentage contribution of categories using Pandas. Instead of just reading rows, I learned how to identify dominant values in a dataset — an important step before decision making. Learning how data behaves before analyzing it. #50DaysChallenge #Python #Pandas #DataAnalytics #LearningInPublic

Basic pandas functions can already generate valuable insights, especially when working with sustainability data. You do not need complex models to start extracting meaning. With a clean dataset, a few core functions help you understand patterns, trends, and potential issues. #DataScience #DataAnalysis #Sustainability #Python #Pandas

Python alone is not enough for Data Analytics. These libraries do the real work 📊🐍 Pandas helps handle data, NumPy works with numbers, Matplotlib and Seaborn help visualize insights. This is Day 2 of my Python + Data Analytics series. Learning step by step, one day at a time. If you’re learning too, let’s grow together 🚀 #Python #DataAnalytics #LearningInPublic #PythonLibraries #Upskilling

Struggling with grouped data in pandas? When aggregation is too destructive in pandas, use groupby + transform. Same groups, same statistics — row-level integrity preserved. #pandas #python #datascience #analytics #machinelearning #dataengineering