Data Preprocessing with Python: Handling Missing Values and Data Structure

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 26 | 50 𝐃𝐚𝐲𝐬 𝐨𝐟 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐰𝐢𝐭𝐡 𝐏𝐲𝐭𝐡𝐨𝐧 Today's task was a continuation of yesterday’s analysis, comparing profitability and costs across products and using visualizations to reveal patterns that aren’t obvious from tables alone. ✔️ Measured absolute profit differences to compare product performance objectively ✔️ Analyzed cost gaps between the most and least profitable items ✔️ Used .loc for targeted access to specific cost values ✔️ Ranked products by profitability and visualized sales, costs, and profits for the lowest performers using a stacked bar chart Key takeaway: direct comparisons and well-ordered visualizations make it much easier to see where performance gaps come from and which products need closer attention. 𝐎𝐬𝐭𝐢𝐧𝐚𝐭𝐨 𝐑𝐢𝐠𝐨𝐫𝐞 #Python #NumPy #DataAnalysis #DataScience #MachineLearning #ArtificialIntelligence #DataAnalytics #LearnInPublic #GitHub #Data #TechCommunity #DailyPractice #Consistency #DataDriven #50_days_of_data_analysis_with_python #ostinatorigore

Starting my journey into Pandas for data analysis. In my first lesson, I worked hands-on with a real dataset and explored: • Reading CSV files with Pandas • Understanding the DataFrame structure • Exploring columns and inspecting data • Getting familiar with a real-world survey dataset I documented the process and shared both the code and detailed notes: 📓 Notebook (code):  https://lnkd.in/eZzEX394 📝 Notes (explanations):  https://lnkd.in/efvh2ApQ I’ll continue this series and share each step as I progress. #Python #Pandas #DataAnalytics #DataScienceJourney #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

𝐒𝐩𝐨𝐫𝐭𝐬 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐃𝐚𝐲 44: 50 𝐃𝐚𝐲𝐬 𝐨𝐟 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐰𝐢𝐭𝐡 𝐏𝐲𝐭𝐡𝐨𝐧 Today’s analysis focused on inspecting a sports dataset, evaluating and optimizing memory usage by converting object columns to categorical types, renaming and querying specific fields, and exporting a cleaned subset for further use—highlighting how data type management directly impacts performance and efficiency. 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

📊Basic but fundamental functions when analyzing data. When we start working with a new dataset, we shouldn’t jump straight into creating plots or thinking about models. The first step is to understand the data in its simplest form. Functions like df.shape, df.head(), df.info(), and duplicate checking seem basic, but they support the entire analysis afterward. Seeing the size of the dataset gives us an idea of the scale of the problem. Observing the first few rows shows us how the information is actually organized—not in theory, but in practice. The function info() acts as an X-ray of the structure: variable types, presence of missing values, and possible inconsistencies that could compromise any future metrics. And checking for duplicate data is essential to ensure that averages, totals, and counts are not being distorted. None of this is sophisticated. But it is precisely this step that prevents silent errors and mistaken conclusions later on Before asking “what do the data show?”, we must first ask: did I really understand this data set? #DataAnalysis #DataScience #DataAnalytics #Python #Pandas

Making Head()s and Tail()s of Your Data 🐼📊 Ever feel overwhelmed when first looking at a massive dataset? You don't need to load the whole thing to get a feel for it. That's where two of my favorite functions in the pandas library come in! df.head(): This function quickly shows you the first 5 rows of your DataFrame by default, providing an initial glimpse into the structure and data types. df.tail(): Conversely, this one displays the last 5 rows, which is super helpful for checking out recently added data or final entries. It's a simple, yet powerful, trick every data professional uses to start their data exploration and analysis journey on the right foot. #DataScience #Python #Pandas #DataAnalytics #DataManipulation #SQL #MachineLearning #LearningJourney# Abhishek kumar # Harsh Chalisgaonkar # SkillCircle™

Day 09: Beyond the Surface—Mastering Precision Data Selection in Pandas 🐼🎯 Data is only as useful as your ability to find what you need within it. Today, I moved deep into Pandas Indexing, transitioning from simple attribute selection to advanced positional and label-based filtering on Kaggle. Key Technical Takeaways: -The Power of loc vs. iloc: I mastered the distinction between position-based selection (iloc) and label-based selection (loc). A key "gotcha" I learned: while iloc follows standard Python slicing (excluding the end), loc is inclusive. -Logical Slicing: Moving beyond rows and columns, I implemented conditional selection. I can now filter massive datasets using boolean logic. -Dynamic Indexing: I explored how to manipulate the DataFrame index using set_index(), transforming a simple numerical count into meaningful, searchable labels like project titles. -Built-in Selectors: I used isin() and notnull() to my arsenal, allowing for clean, efficient filtering of specific categories and missing values. The ability to "query" data directly in Python is a massive productivity boost! #DataScience #Pandas #Python #Kaggle #DataAnalytics #TechSkills

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 37 / 60 — Python for Data Science 📊 Today I focused on feature engineering and data scaling before running my regression model. Using StandardScaler, I balanced confirmed, suspected, and probable cases so no single variable would dominate the analysis. After retraining the model, the R² score remained around 0.80, showing consistent performance even after introducing a new feature (total cases). Key takeaway: R² shows how well the model performs overall, while coefficients explain how each variable contributes to predicting deaths. Continuous improvement. One step at a time. 🚀 #DiAnalyst #PythonForDataScience #DataAnalytics #HealthcareAnalytics #PublicHealth #MachineLearningBasics #LearningInPublic