Verify Data Integrity Before Celebrating KPIs

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

I asked an LLM a "basic" Pandas question today and got a senior engineer-insight. My question was simple: "If I extract a row from a DataFrame, is it a Series? And if so... how?" Because logically, if DataFrames are column-oriented, grabbing a row shouldn't be easy. Turns out, it's not. Pandas has to perform a "Structure Flip" on the fly: • Takes a horizontal slice • Turns it vertical • Forces a type reconciliation Here's the side-effect: If your DataFrame has mixed types (Age as int, Name as str), Pandas forces the whole new Series to become dtype: object. It prioritizes structure over data type. Sometimes the tools we use every day are doing magic we completely take for granted. ✨ #DataScience #Python #Pandas #TechInsights

Today I learned about the merge() function in Pandas 🐼 The merge() function is used to combine two #DataFrames based on a common column (key). It works very similar to SQL joins and is extremely useful when working with multiple datasets. Basic syntax: pd.merge(df1, df2, on="key", how="type") Types of joins in Pandas: 🔹 Inner Join 🔹 Outer Join 🔹 Left Join 🔹 Right Join Understanding these joins is crucial for real-world data analysis, where data often comes from different sources. Small concept, big impact on data manipulation 🚀 #Python #Pandas #DataScience #LearningInPublic #DataAnalysis #100DaysOfCode #CareerSwitch

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™

📊 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

One thing I’ve realized while working with data: SQL and Pandas are not competitors. They’re partners. When I first learned SQL, I focused on writing queries that worked. Later, when I started using Python Pandas, I had a small realization… The logic is the same. Filtering rows. Grouping data. Joining tables. Aggregating results. The syntax changes — the thinking doesn’t. That’s when it clicked for me: Strong data professionals don’t just memorize commands. They understand concepts. If you truly understand how data is structured, filtered, grouped, and joined — switching between SQL and Pandas becomes much easier. Tools evolve. Concepts stay. #SQL #Python #Pandas #DataAnalytics #DataScience #DataEngineering #TechCareers

Data isn’t powerful until it’s visualized. Python turns raw numbers into stories using libraries like Matplotlib, Seaborn, Plotly, and more. Learn to uncover trends, patterns, and insights that drive decisions. Discover more and start mastering Data Visualization in Python today https://lnkd.in/gCpvp8Fj #DataVisualization #PythonProgramming #DataScience #DataAnalytics #DataStorytelling #USDSI #PythonForData #Matplotlib #Seaborn #Bokeh #Plotly #BigData #AnalyticsTools #DataDriven #BusinessIntelligence

I just saved myself 90 hours this month with one line of code. I used to spend hours manually cleaning datasets. Then I discovered Python's pandas profiling. One line of code now gives me: ✓ Missing value patterns ✓ Distribution insights ✓ Correlation matrices ✓ Duplicate detection What used to take me 2-3 hours now takes 30 seconds. The best part? It's helped me catch data quality issues I would've missed with manual reviews. Last week alone, it flagged an encoding error that would've skewed our entire quarterly analysis. For anyone doing regular data analysis: automate the repetitive stuff. Your brain is better used on the insights, not the cleanup. What's one tool or technique that's saved you hours recently? Always looking to learn from this community. #DataAnalysis #Python #DataScience #BusinessIntelligence #Analytics

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

🧹 Data preprocessing matters more than we think. Before any model or insight, data needs work—a lot of it. Up to 80% of a data scientist’s time goes into cleaning messy data: missing values, duplicates, wrong formats, and inconsistencies . Tools like Python & Pandas make this easier with functions to detect, remove, and intelligently fill missing values—but the real skill is knowing what to fix and how. Better data = better decisions. Always. #DataScience #DataCleaning #Python #Pandas #MachineLearning #Analytics