Python Data Analysis with Pandas

📅 Day 6/30 — Building a “Pages You Might Like” Feature Continuing my 30-day journey into data science, today I built a basic version of the “Pages You Might Like” feature using pure Python. What I worked on today: 📄 Understanding user interests and page data 🔍 Finding patterns based on user preferences ⚙️ Using loops, conditions, and dictionaries to process data 💡 Generating simple page recommendations It was interesting to see how recommendation features can be created using core Python logic without relying on external libraries. ➡️ Next step: exploring more ways to analyze datasets using Python. #LearningInPublic #Python #Anaconda #JupyterNotebook #DataScience #30DaysOfLearning #ProgrammingJourney

Initially, when we deal with the dataset the first step is to filter/clean the dataset for our requirements and the filtering can be done with the help of python libraries (Numpy and pandas). To understand this I have taken the dataset of yellow taxi drivers 2018 dataset of U.S.A's from kaggle. Firstly I tried with the Numpy library(Numpy excels at fast, numerical computation). It filters well with some functions and methods. But the problem is that the dataset will be in a single datatype(like sometimes int/ decimal can be in string). To filter it should be converted into the required datatype. Here comes the pandas library(cleaning, manipulation of dataset). It provides some tools which helps to work on dataset which has different datatypes for different columns. #dataAnalytics #python #Datascience

While learning data science tools, I created structured notes and code snippets for NumPy, Pandas, and Matplotlib. Instead of keeping them to myself, I’ve shared everything on GitHub so others can benefit too. If you're learning Python for data analysis, this might help you get started or revise faster. 🔗 Check it out here: https://lnkd.in/d4VTnZSJ Would love your feedback! #DataScience #Python #OpenSource #LearningJourney #GitHub

25 Python 🐍 libraries every data professional should know !!!!! I used to think I needed to learn all of these before I could call myself a Python developer. Turns out, the best way to learn a library is to have a problem that needs it. Start with NumPy + Pandas for data. Add Matplotlib when you need to see it. Reach for Scikit-learn when you want to predict something. The rest follow naturally. Save this for when you need it — and drop a comment with which library you're learning right now 👇 #Python #DataScience #Programming #MachineLearning #DataAnalytics #LearnPython #TechSkills #PythonLibraries #DataEngineering #ContinuousLearning #PythonDeveloper #AI #TechCommunity #UpSkill

🚀 Just went through a NumPy Crash Course — and one thing is clear: 👉 NumPy is the foundation of data analytics & data science in Python. From arrays to indexing, slicing, and functions like arange() — everything starts here. 💡 Master NumPy, and the rest becomes much easier. Still learning, still growing. #DataAnalytics #Python #NumPy #LearningJourney