Exploring Restaurant Tips with Python EDA

📊 Python Data Visualization Cheat Sheet Data tells a story — visualization is how we make it speak. This cheat sheet brings together the most-used plots from Matplotlib and Seaborn, all in one place for quick reference and daily practice. From line plots and bar charts to heatmaps and KDEs, these are the visuals every data analyst and data scientist should feel comfortable with. Simple concepts, strong foundations. 🚀 Save it, revisit it, and keep building clarity through visuals. #Python #DataVisualization #Matplotlib #Seaborn #DataScience #DataAnalytics #EDA #LearningInPublic #TechSkills #Consistency

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

Column transformation + groupby changed how I analyze data 📊 Raw data doesn’t give insights. Prepared data does. While working with Pandas, I realized how powerful simple column transformations are: • Cleaning percentage columns and converting them to numeric • Creating new logic-based columns (BONUS vs NO BONUS) • Adding derived columns instead of touching raw data Once the columns made sense, groupby unlocked the patterns. Grouping by department and aggregating values revealed insights that were invisible at the row level. Big lesson: ➡️ Clean columns first ➡️ Group second ➡️ Insights follow Question for data folks: Do you transform your columns before groupby — or learn this the hard way? 😅 #DataAnalytics #Python #Pandas #GroupBy #LearningInPublic

When KPIs suddenly look amazing, it’s tempting to celebrate 😅 Then my data reflex says: confirm the level of detail first. If our data is more detailed than we think, joins/merges and aggregations can quietly multiply rows and inflate metrics with zero errors. In PySpark/Python, I quickly check it by doing a groupBy(key).count() to spot duplicates, compare row counts before vs after the transformation, and sanity check a small sample end-to-end. Moral of the story : Celebrate after the checks, not before. #DataEngineering #PySpark #Python #DataQuality

THIS PART OF DATA ANALYSIS IS NEVER GLAMOROUS..... But it decides whether your insights are trusted or ignored. Dirty data is normal. Clean data is what makes you a real Data Analyst. I follow a simple Pandas cleaning workflow before every analysis: Missing values Duplicates Wrong data types Messy dates No theory. No shortcuts. Just real-life Pandas steps. 📄 I’ve shared this exact workflow in a short PDF. If you’re learning Pandas, start here. Clean first. Analyze later. #Pandas #DataAnalytics #Python #DataCleaning #LearningInPublic

🔗 GitHub Repo: https://lnkd.in/gXxpbdG3 Ecommerce_customer_dataset EDA using Python Performed Exploratory Data Analysis on a Kaggle dataset to understand patterns and customer insights through visualization and preprocessing Project Highlights: Data cleaning & preprocessing Exploratory analysis (univariate & bivariate) Visualizations to understand customer behaviour Insights derived from trends and relationships Tools Used: Python | Pandas | NumPy | Matplotlib | Seaborn #Python #EDA #DataAnalytics #AnalyticsPortfolio #DataVisualization #AL/ML

📈 From Raw Data to Meaningful Tables using Pandas! Every data analyst starts with raw, messy data… And Pandas is the tool that turns it into something powerful 🐼 Today I practiced creating my first DataFrame in Python: 🔹 This structured table can now be: • Sorted • Filtered • Analyzed • Visualized Step by step, I’m building my skills in Python, Pandas, Excel, SQL & Power BI and sharing my learning journey. 💡 If you’re starting your analytics journey too — let’s learn together! #Pandas #PythonForBeginners #DataAnalytics #LearningJourney #FutureDataAnalyst #LearningInPublic

🔮 Day 12: Predictive Insights — When Power BI Meets Python Today I experimented with forecasting patient inflow using Python inside Power BI. Even a simple time-series model revealed patterns like: • Consistent Monday peaks • Low mid-week visits • Unpredictable emergency spikes Imagine how useful this is for staffing, inventory, and scheduling! I love when two worlds meet — the clarity of Power BI and the intelligence of Python. #MachineLearning #Python #PowerBI #HealthcareForecasting

🐍 Data Aggregation Using pandas groupby() Once data is clean, the next step is summarizing it to uncover meaningful insights — and groupby() in pandas makes this powerful and simple. Here’s what I’m practicing with groupby() 👇 • Grouping data by categories (like region, product, or date) • Calculating totals, averages, and counts • Comparing performance across groups • Turning detailed data into summary-level insights groupby() helps transform large datasets into clear, business-ready information with just a few lines of code. #Python #Pandas #GroupBy #DataAggregation #DataAnalysis #EDA #LearningJourney #AspiringDataAnalyst #DataCommunity