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

🐼 Pandas 3.0 is officially out — and it’s a big one for DataFrame users in Python 🚀 If you work with Pandas daily, this release is about cleaner semantics, safer code, and modern defaults. What’s new & why it matters: ✅ Proper string dtype by default → no more object surprises, better performance & clarity ✅ Copy-on-Write behavior → predictable DataFrame operations, goodbye SettingWithCopyWarning ✅ Cleaner column expressions with pd.col() → more readable transformations ✅ Stronger, clearer deprecation policy → easier upgrades for production code This is a major version upgrade, so some breaking changes are expected — but the payoff is a more robust and future-proof Pandas ecosystem. If Pandas is part of your data stack, 3.0 is worth paying attention to. #Pandas #Python #DataEngineering #DataScience #Analytics

Stop Googling "How do I do this SQL Group By in Pandas?" 🛑 SQL and Python are the twin pillars of data, but switching contexts kills productivity. I created this side-by-side cheat sheet to stop the syntax struggle. Inside: ✅ Select & Filter: The basics, translated. ✅ Joins: Inner, Outer, Left, Right made simple. ✅ Aggregations: Grouping logic for both. ✅ Null Handling: COALESCE vs .fillna() Fluency in both is a data superpower. 🦸♂️ ♻️ Repost to help a connection stop tab-switching today! #DataScience #SQL #Python #Coding #CheatSheet

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

Day 1: Starting Pandas — Built on Top of NumPy Today I learned why pandas is such a powerful library in Python, especially for data analysis. NumPy is great, but it has a few limitations: 🔹 A NumPy array can’t store mixed data types. If you combine integers, floats, and strings, it converts everything into strings for uniformity. 🔹 NumPy doesn’t offer strong support for labeled rows and columns. It works, but not as smoothly as pandas. Pandas solves these problems with two core data structures: Series (1D) and DataFrame (2D) — both built on NumPy, so NumPy functions still work on them. 🧪 Today’s Focus: The Pandas Series A Series is a one-dimensional labeled array. Example: mass_series = pd.Series([1.01, 4.00, 6.94, 9.01, 10.81], index=['H','He','Li','Be','B']) Output: H 1.01 He 4.00 Li 6.94 Be 9.01 B 10.81 Accessing Data You can access values in multiple ways: mass_series[2] — by index mass_series['He'] — by label mass_series.iloc[2] — always integer-based indexing, even with custom labels Excited to continue exploring pandas and move deeper into data analysis! 📊✨ #Python #Pandas #DataAnalysis #LearningJourney

𝐃𝐚𝐲 11 | 50 𝐃𝐚𝐲𝐬 𝐨𝐟 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐰𝐢𝐭𝐡 𝐏𝐲𝐭𝐡𝐨𝐧 Today focused on preprocessing mixed data types and preparing data for analysis and visualization. ✔️ Created NumPy arrays from mixed-type lists ✔️ Identified and separated numeric vs non-numeric values ✔️ Performed numerical operations after proper preprocessing ✔️ Generated squared values from cleaned numeric data ✔️ Structured multi-row arrays for analysis ✔️ Visualized relationships between variables using a scatter plot ✔️ Identified outliers through visual inspection Key takeaway: cleaning and structuring data correctly is a prerequisite for meaningful analysis and visualization. Day 11 complete. Building discipline through consistency. 𝐎𝐬𝐭𝐢𝐧𝐚𝐭𝐨 𝐑𝐢𝐠𝐨𝐫𝐞 #Python #NumPy #DataAnalysis #DataScience #MachineLearning #ArtificialIntelligence #DataAnalytics #LearnInPublic #GitHub #Data #TechCommunity #DailyPractice #Consistency #DataDriven #50_days_of_data_analysis_with_python #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

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

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 16 | 50 𝐃𝐚𝐲𝐬 𝐨𝐟 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐰𝐢𝐭𝐡 𝐏𝐲𝐭𝐡𝐨𝐧 Today’s focus was on Pandas Series and basic DataFrame creation, exploring how Series can simplify analysis and preparation of data. ✔️ Created a Pandas Series and counted the occurrences of each item ✔️ Checked for the presence of specific values in the Series ✔️ Extracted all unique values from the Series ✔️ Updated the Series by inserting new items at specific indices ✔️ Converted the Series into a DataFrame and inspected its shape and dimensions Key takeaway: Pandas Series provide a flexible structure for handling labeled data, and converting them to DataFrames allows for more advanced analysis. Day 16 complete. Building fluency with Pandas step by step. 𝐎𝐬𝐭𝐢𝐧𝐚𝐭𝐨 𝐑𝐢𝐠𝐨𝐫𝐞 #Python #NumPy #DataAnalysis #DataScience #MachineLearning #ArtificialIntelligence #DataAnalytics #LearnInPublic #GitHub #Data #TechCommunity #DailyPractice #Consistency #DataDriven #50_days_of_data_analysis_with_python #ostinatorigore