JESUS FAJARDO’s Post

Working with messy data in Excel lately, and I came across a really useful add-in that makes data abstraction much easier. What stood out for me is the ability to write Python directly inside Excel — no need to switch to VS Code or any separate environment. It simplifies the workflow and makes handling complex data tasks much more efficient. Combining Excel with Python is a powerful way to level up data analysis and streamline the entire process. Definitely worth exploring if you deal with real-world messy data. 🚀 #Leaning #Python #Datacleaning #DataExtraction #Excelwithpython

Data scientists often switch between SQL and Pandas, which can make workflows unnecessarily complex. Tools like DuckDB now allow SQL queries directly on DataFrames combining the power of SQL with the flexibility of Python. The image shows a simple way to think about which tool works best for different tasks. For a quick explanation of this approach, read here: https://lnkd.in/eZPTyP2 #DataScience #Python #SQL #Analytics

Today I explored 3 key concepts: 🐍 Python — String Methods Practiced 10 built-in string functions: .upper(), .lower(), .strip(), .split(), .replace(), .find(), .count(), .startswith(), .endswith(), .join() 📊 Power BI — DAX Functions Took a test on DAX aggregation functions — SUM, AVERAGE, COUNT & CALCULATE. Revision is the best way to solidify concepts! 🧭 Aptitude — Logical Reasoning Worked on directional sense problems — N, S, E, W and left/right turns. Consistency is the key. #Python #PowerBI #DAX #Aptitude #LearningInPublic #DataAnalytics #100DaysOfLearning

✅Day 5 – Working with Strings in Python Today I practised "Strings in Python" — one of the most important data types in real-world datasets. Strings are simply text data. ✅Examples: * Customer Name * Email Address * Product Category * City Name ✅What I Learned Today: * How to create strings * String concatenation * Changing case (upper/lower) * Finding text inside a string In data analytics, most datasets contain a lot of text data. Cleaning and manipulating strings is essential before analysis. ✅Today’s lesson reminded me: Understanding text data is just as important as understanding numbers. Building step by step. #Python #DataAnalytics #LearningJourney #BusinessAnalytics #Consistency

✅Day 9 – For Loops in Python Today I learned about For Loops in Python. A for loop allows us to repeat a task multiple times automatically. ✅Example: numbers = [10, 20, 30] for num in numbers: print(num) This loop prints each value from the list one by one. ✅Why This Matters in Data Analytics -- In real-world data analysis, we often need to: -- Process large datasets -- Perform repeated calculations -- Apply the same operation to many values -- Loops help automate these repetitive tasks efficiently. ✅Today's takeaway: Automation is a key skill in data analytics, and loops make it possible. #Python #DataAnalytics #LearningJourney #BusinessAnalytics #Consistency

How to export and import files between Python and Excel? Stop manual work. Use these two snippets to automate your data workflow with pandas: Import: Read Excel files into Python for analysis. Export: Save results back to Excel (use index=False for a clean file). Simple, fast, and error-free. #Python #Excel #Pandas #Automation #DataAnalysis

Understanding data types is the first step to writing better Python programs 💡 In Python, everything is an object — and every object has a data type. 📌 Main Data Types in Python: 🔹 Numeric Types int → Whole numbers (10, -5, 100) float → Decimal numbers (10.5, -3.14) complex → Complex numbers (3+4j) 🔹 Sequence Types str → Text values ("Hello") list → Ordered, changeable collection [1, 2, 3] tuple → Ordered, unchangeable collection (1, 2, 3) 🔹 Set Types set → Unordered, no duplicate values {1, 2, 3} 🔹 Mapping Type dict → Key-value pairs {"name": "Vyshnavi", "age": 22} 🔹 Boolean Type bool → True or False #Python #PythonProgramming #LearnPython #Coding #DataTypes #ProgrammingBasics #DeveloperJourney

Machine Learning Data Visualization using plotnine #machinelearning #datascience #datavisualization #plotnine Plotnine is a data visualization package for Python based on the grammar of graphics, a coherent system for describing and building graphs. The syntax is similar to ggplot2, a widely successful R package. https://lnkd.in/giV_TKem

Return a #Python #Pandas data frame's index to a regular column with reset_index: df = df.reset_index() • 1-column index? It's now a regular column. • Multi-index? Its columns are all regular columns. reset_index returns a new data frame. It doesn't modify the original.

🚀 Day 29/100 – Python, Data Analytics & Machine Learning Journey 📊 Started SQL – The Backbone of Data Analytics Today I learned: 11. Constraints in SQL(PRIMARY KEY, FOREIGN KEY, UNIQUE,NOTNULL, CHECK,DEFAULT) 12. ORDER BY Clause 📌 Code & notes :- https://lnkd.in/dmFHqCrK #100DaysOfPython #MachineLearning #AIML #Python #LearningInPublic