Analyzing Profitability with Python: Day 26

𝐃𝐚𝐲 30 | 50 𝐃𝐚𝐲𝐬 𝐨𝐟 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐰𝐢𝐭𝐡 𝐏𝐲𝐭𝐡𝐨𝐧 Today’s focus was on validating product-level data, identifying duplicates, and analyzing how pricing, costs, and revenue interact. ✔️ Checked data quality by identifying duplicates and understanding how often each product appears ✔️ Used pivot tables to clearly expose repeated products ✔️ Explored the relationship between price and total revenue with a regression plot ✔️ Compared revenue differences between selected products ✔️ Engineered new features by calculating total cost and profit margins directly in the DataFrame ✔️ Identified the product with the weakest profit margin Key takeaway: solid analysis starts with clean data, but real insight comes from combining validation, feature creation, and visualization to understand what’s actually driving performance. 𝐎𝐬𝐭𝐢𝐧𝐚𝐭𝐨 𝐑𝐢𝐠𝐨𝐫𝐞 #Python #NumPy #DataAnalysis #DataScience #MachineLearning #ArtificialIntelligence #DataAnalytics #LearnInPublic #GitHub #Data #TechCommunity #DailyPractice #Consistency #DataDriven #50_days_of_data_analysis_with_python #ostinatorigore

𝐑𝐮𝐧𝐧𝐞𝐫𝐬 𝐀𝐧𝐝 𝐈𝐧𝐜𝐨𝐦𝐞 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐃𝐚𝐲 35: 50 𝐃𝐚𝐲𝐬 𝐨𝐟 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐰𝐢𝐭𝐡 𝐏𝐲𝐭𝐡𝐨𝐧 Today’s work focused on cleaning and analyzing a combined runners and income dataset using pandas and NumPy. ✔️ Inspected dataset structure, shape, and missing values ✔️ Handled NaNs by dropping empty rows and imputing remaining values ✔️ Used describe() to summarize data and extract key statistics ✔️ Calculated total miles run using NumPy operations ✔️ Filtered individuals based on income thresholds ✔️ Created and exported a clean subset of the data for reuse This session reinforced the importance of data inspection, basic preprocessing, and targeted filtering before moving into deeper analysis. 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 of 150: Data Visualization with Matplotlib Today’s focus shifted from data collection to data storytelling. Raw data is powerful, but visualizing patterns is what makes that data actionable in a professional environment. Technical Focus: • Matplotlib Fundamentals: Implementing the pyplot module to transform structured datasets into visual representations. • Graphing Logic: Creating line graphs and bar charts to identify trends, specifically focusing on axis labeling, legends, and title formatting. • Data Integration: Bridging previous projects by visualizing data stored in CSV and JSON formats to track changes over time. • Customization: Experimenting with figure sizes, colors, and markers to improve the readability and professional quality of the output. Visualizing data is the final bridge between backend processing and meaningful insights. 130 days to go. #Python #DataVisualization #DataScience #Matplotlib #150DaysOfCode #DataAnalytics

𝐓𝐨𝐲𝐬 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬  𝐃𝐚𝐲 42: 50 𝐃𝐚𝐲𝐬 𝐨𝐟 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐰𝐢𝐭𝐡 𝐏𝐲𝐭𝐡𝐨𝐧 Today’s analysis involved validating the toy sales dataset, examining its structure and data types, computing key descriptive statistics, identifying high-value items across categories, and visualizing sales distributions through bar, pie, and multi-plot charts to understand revenue concentration patterns. 8 More days to go 😁 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 9 — 60 Days Data Analytics Challenge | Outlier Detection & Data Distribution Today I explored how data analysts identify outliers and understand data distribution using visualization techniques. 🔎 What I Practiced: • Visualizing distribution with histograms • Detecting outliers using boxplots • Comparing mean vs median to analyze data behavior • Understanding the impact of extreme values on analysis 📈 This practice helped me see how important it is to validate data before drawing conclusions. 💡 Key Learning: Accurate insights begin with understanding data distribution. #60DaysDataAnalyticsChallenge #EDA #DataAnalytics #Python #LearningInPublic

Stop using Lists for everything! 🚫🐍 In Data Science, efficiency is everything. Using the wrong data structure can slow down your data processing or lead to accidental bugs. I’ve found that understanding mutability (can it be changed?) vs. order is a game-changer when cleaning large datasets. For example, using a Set to find unique IDs is significantly faster than looping through a List. This "Cheat Sheet" simplifies the core differences: ✅ List: Ordered & Mutable ✅ Tuple: Ordered & Immutable ✅ Set: Unordered & Unique ✅ Dictionary: Mapping via Key-Value pairs Save this post for your next coding session! 📌 #Python #DataScience #DataEngineering #CleanCode #ProgrammingLife #TechTips

𝐖𝐞𝐛𝐬𝐢𝐭𝐞 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐃𝐚𝐲 33: 50 𝐃𝐚𝐲𝐬 𝐨𝐟 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐰𝐢𝐭𝐡 𝐏𝐲𝐭𝐡𝐨𝐧 Today focused on understanding website performance through data manipulation and visualization using pandas, Matplotlib, and Seaborn. ✔️ Calculated average visits per website and visits per unique visitor ✔️ Visualized top-performing websites with a descending bar plot ✔️ Identified the day with the highest average bounce rate ✔️ Tracked unique visitor trends over time with line plots ✔️ Analyzed visits and revenue by day of the week and referral source ✔️ Created a pie chart to see which referral source drove the most revenue This session reinforced how combining aggregation, grouping, and visualization helps uncover patterns and insights that aren’t obvious from raw data. 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

📊 Exploratory Data Analysis (EDA) with a Fruits Dataset 🍎🍊🍌 Recently explored a fruits dataset to understand how EDA helps turn raw data into meaningful insights. EDA turns raw data into meaningful insights. It helps analysts and organizations move from “guessing” to “knowing.” In today’s data-driven world, strong EDA skills are not optional — they are essential. Good Data → Good EDA → Better Decisions 🚀 #EDA #DataAnalytics #DataScience #SQL #Python #LearningByDoing #DataVisualization #CareerGrowth #Analytics

Day 54/100 of Data Analytics Journey 🚀 Advanced Seaborn today - visualizing multiple dimensions simultaneously. What I'm building: 📊 Correlation heatmaps (find hidden relationships) 🔍 Pair plots (compare all variables at once) 📈 Facet grids (multi-dimensional analysis) 🎯 Joint plots (bivariate distributions) The power: → 5 variables analyzed in 1 chart → Hidden patterns revealed instantly → Complex relationships simplified From simple charts to multi-dimensional insights. Progress: SQL ✅ | Power BI ✅ | Python 🔥 #DataAnalytics #Seaborn #Python #DataVisualization #100DaysOfCode