Pandas Series Analysis with Python

𝐓𝐢𝐦𝐞 𝐒𝐞𝐫𝐢𝐞𝐬 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐃𝐚𝐲 43: 50 𝐃𝐚𝐲𝐬 𝐨𝐟 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐰𝐢𝐭𝐡 𝐏𝐲𝐭𝐡𝐨𝐧 Today’s work focused on exploring time series data by resampling, visualizing trends with line and bar plots, and applying a rolling average to smooth short-term fluctuations and highlight longer-term patterns. 𝐎𝐬𝐭𝐢𝐧𝐚𝐭𝐨 𝐑𝐢𝐠𝐨𝐫𝐞 #Python #NumPy #DataAnalysis #DataScience #MachineLearning #ArtificialIntelligence #DataAnalytics #LearnInPublic #GitHub #Data #TechCommunity #DailyPractice #Consistency #DataDriven #50_days_of_data_analysis_with_python #SQL #Learning #ostinatorigore

𝐃𝐚𝐲 20 | 50 𝐃𝐚𝐲𝐬 𝐨𝐟 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐰𝐢𝐭𝐡 𝐏𝐲𝐭𝐡𝐨𝐧 Today’s focus was on exploring data and visualizing insights using Pandas and Matplotlib. ✔️ Created a DataFrame to organize product data ✔️ Identified the most profitable product and visualized it with a bar plot ✔️ Determined the least profitable product and calculated the profit difference ✔️ Plotted costs and profits across all products using a line chart ✔️ Calculated average cost and average profit per product Insight: using Pandas for both analysis and quick visualizations, alongside Matplotlib for more detailed plots, makes it easier to interpret data and communicate insights effectively. Day 20 complete. 𝐎𝐬𝐭𝐢𝐧𝐚𝐭𝐨 𝐑𝐢𝐠𝐨𝐫𝐞 #Python #NumPy #DataAnalysis #DataScience #MachineLearning #ArtificialIntelligence #DataAnalytics #LearnInPublic #GitHub #Data #TechCommunity #DailyPractice #Consistency #DataDriven #50_days_of_data_analysis_with_python #ostinatorigore

🚀 New Video on Pandas Advanced – Part 2 I’ve just published the next episode in the Pandas Advanced series where we focus on practical performance optimization and large data handling — exactly how data analysts work with real datasets in the wild. In this video you’ll learn how to: • Measure memory usage • Optimize data types • Process large CSV files in chunks • Use vectorized operations • Speed up groupby & filtering 🎥 Watch here 👉 https://lnkd.in/gg7p262m 📂 Code on GitHub: https://lnkd.in/gNFk2iPa If you’re serious about Python data analytics, this advanced workflow will level up your skills. #Pandas #Python #DataAnalytics #DataScience #PyAIHub #ProfessionalDevelopment

Today I learned the difference between .loc and .iloc in Pandas 🐼 These two methods are used to select data from a DataFrame, but they work in different ways. 🔹 .loc (Label-based indexing) Used when you want to access rows or columns using labels or index names. 🔹 .iloc (Integer-location based indexing) Used when you want to access data using integer positions (row and column numbers). Understanding this difference is very important while filtering, slicing, and manipulating data in real-world datasets. Small concept, but a common source of confusion for beginners (including me 😄). Learning one Pandas concept at a time 🚀 #Python #Pandas #DataScience #LearningInPublic #DataAnalysis #100DaysOfCode #CareerSwitch

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

🐼 pandas 3.0 is LIVE! The Python data library just got a MAJOR upgrade. Here's what you need to know: ✨ Native String Type - PyArrow-backed by default for faster ops 🔒 Copy-on-Write - Now the only mode (goodbye warnings!) 💻 pd.col() Syntax - Cleaner, more readable code ⚡ Performance Boost - Faster across the board ⚠️ Breaking changes ahead! Upgrade to 2.3 first, fix warnings, then move to 3.0. Swipe to see what's new 👉 Have you tried pandas 3.0? Drop your thoughts below! 💭 #Python #Pandas #DataScience #MachineLearning #DataAnalysis #Python3 #DataEngineering #Analytics #OpenSource #TechNews

Project Objective : Analyzed pizza order data using Python to predict pizza prices based on size, diameter, toppings, and extra options. The project focuses on understanding price-driving factors and building a prediction model. What this project covers : • Data cleaning and preprocessing • Exploratory Data Analysis (EDA) • Feature engineering • Price prediction using machine learning • Model evaluation Tools used : Python | Pandas | NumPy | Matplotlib | Jupyter Notebook GitHub : https://lnkd.in/gKr_JRWB #Python #MachineLearning #DataScience #EDA #StudentProject #LearningInPublic #GitHub

I recently updated a script for a rock-paper-scissors game to log every game outcome to a CSV file and conducted a small exploratory analysis using pandas. Here’s what I examined: - Total rounds played - Win counts and win percentages - User choice distribution This process reflects a fundamental data workflow: data generation → storage → analysis. Although it's a small dataset, the emphasis was on developing the habit of transforming raw events into structured, analyzable data. You can find the updated repository on GitHub: https://lnkd.in/ea3fxBbi #DataScience #DataAnalytics #Python #Pandas #LearningInPublic #GitHub