50 Days of Data Analysis with Python: Validating Product Data

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 40 of my Data Engineering journey 🚀 Today I went deeper into data filtering, sorting, and aggregation using Pandas. 📘 What I learned today (Pandas Filtering & Aggregation): • Filtering rows using conditions • Combining multiple conditions • Sorting values with sort_values() • Selecting specific columns • Grouping data using groupby() • Applying aggregate functions (sum, mean, count) • Understanding how Pandas handles missing values • Writing cleaner transformation logic Pandas feels like SQL inside Python but more flexible. Instead of just querying data, I’m now transforming it programmatically. This is real data manipulation. Why I’m learning in public: • To stay consistent • To build accountability • To improve daily Day 40 done ✅ Next up: data cleaning & handling missing values in Pandas 💪 #DataEngineering #Python #Pandas #LearningInPublic #BigData #CareerGrowth #Consistency

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

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

🚨 Most data issues aren’t caught until it’s too late 🚨 By the time dashboards break or stakeholders notice, it’s already expensive to fix 😬 Early validation is the secret to reliable data pipelines: ✅ Define clear expectations for your datasets ✅ Implement them with code ✅ Catch failures before they reach dashboards Catching issues early saves time, trust, and effort — and keeps your data pipelines running smoothly. #DataEngineering #DataQuality #DataPipelines #Analytics #EngineeringBestPractices #Python #GreatExpectations #DataOps

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

🚀 Stop reading entire CSVs into memory! Generators are your best friend for massive datasets. Before (messy): def process_large_file_bad(filepath): import pandas as pd df = pd.read_csv(filepath) results = [] for index, row in df.iterrows(): if row['value'] > 100: results.append(row['id']) return results After (clean): def process_large_file_good(filepath): import pandas as pd results = [] for chunk in pd.read_csv(filepath, chunksize=10000): filtered = chunk[chunk['value'] > 100] results.extend(filtered['id'].tolist()) return results Why this matters for data engineers: Chunking with generators avoids MemoryError on multi-gigabyte files, keeping our ETL pipelines stable. What's your favorite memory-saving trick in PySpark or Pandas? #DataEngineering #Python #PandasTips #ETL #Generators

🚀 𝗟𝗲𝘃𝗲𝗹 𝗨𝗽 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗦𝘁𝗼𝗿𝘆𝘁𝗲𝗹𝗹𝗶𝗻𝗴: 𝗧𝗵𝗲 𝗣𝗶𝗲 𝗣𝗹𝗼𝘁 In the world of Data Science, choosing the right visualization is just as important as the analysis itself. The 𝗣𝗶𝗲 𝗣𝗹𝗼𝘁 remains a fundamental tool for conveying "parts-of-a-whole" relationships at a glance. As shown in the guide below, these charts are highly effective when you need to: 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁 𝗣𝗿𝗼𝗽𝗼𝗿𝘁𝗶𝗼𝗻𝘀: Instantly identify dominant categories (like Python’s 40% share). 𝗦𝗵𝗼𝘄 𝗖𝗼𝗺𝗽𝗼𝘀𝗶𝘁𝗶𝗼𝗻: Perfect for showing how a single entity is broken down into segments. Simplify Complexity: Great for non-technical stakeholders to grasp relative sizes quickly. 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗧𝗶𝗽: For those using 𝗠𝗮𝘁𝗽𝗹𝗼𝘁𝗹𝗶𝗯, the plt.pie() function is incredibly versatile. 𝗕𝗼𝗻𝘂𝘀 𝗧𝗶𝗽: Always ensure your percentages sum to 𝟭𝟬𝟬% to maintain data integrity! Whether you’re a student or a seasoned developer, mastering these basics is the foundation of great data communication. 📊 How do you prefer to visualize categorical data? Are you Team Pie Chart or Team Bar Chart? Let’s discuss below! 👇 #DataScience #Python #Matplotlib #DataVisualization #Coding #Analytics #TechCommunity

📊 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