Matheus de Almeida Cantarutti’s Post

Some days being a Data Analyst feels like 20% SQL, Python, and Excel , and 80% squinting at the screen because one click just broke everything 👀 You chase the problem, question everything, and of course the breakthrough hits at 4:59pm💡now you’re stuck on the throne 👑 The real reward? That feeling when the numbers finally make sense… until the next click. 😅 #DataAnalytics #DataHumor

What Is Macro ? Think of an Excel Macro as a "Record" button for your keyboard and mouse. Instead of doing the same boring, repetitive work every day, you teach Excel how to do it for you. #DataAnalytics #DataAnalysis #DataAnalyst #PowerBI #SQL #Python #JobSearch #BigData

5 Pandas functions I use almost every day. If you come from SQL, these will feel familiar right away. 1. query() Filter rows the same way you would use a WHERE clause. 2. groupby() Aggregate your data by category. The Python equivalent of GROUP BY. 3. merge() Combine two DataFrames together. Works just like a JOIN. 4. value_counts() Count how often each value appears in a column. Great for a quick data quality check. 5. fillna() Replace missing values with a default. One line instead of a whole if-else block. The full code is in the image. Which one do you use the most? #Python #Pandas #DataScience #SQL #LearningInPublic

If you’re working with data in Python, pandas is your best friend—and the DataFrame is its heart. 🚀 🔍 What is a DataFrame? Think of it as a smart spreadsheet or SQL table living inside your code. It’s a 2-dimensional structure where:   • Rows are your individual records.   • Columns are your variables (Product, Price, Stock).   • Index is the unique ID for every row. 💡 Why use them?   • Speed: Process millions of rows instantly without clunky loops.   • Simplicity: Clean, filter, and aggregate data with single commands like .groupby() or .dropna().   • Flexibility: Easily handle different data types (numbers, text, dates) in one place.   • Power: Seamlessly feeds into visualization and Machine Learning models.

Excited to share my recent mini project – a Mini Expense Tracker built using Python. Designed to record and manage daily expenses using a simple file-based approach, providing basic insights into spending patterns. Key Features: • Add, view, and delete expense records. • Calculate total expenditure. • Store and retrieve data using file handling. Key Learnings: • Python fundamentals • File handling • Lists, strings, and basic data processing • Exception handling This is a small step towards my journey in Data Analytics and Data Engineering. #Python #DataAnalytics #BeginnerProject #Learning #SoftwareDevelopment

🫡 Just finished building a Financial Statement Analyzer using Python, pandas, and matplotlib. The project loads financial statement data from CSV, calculates key ratios like gross margin, current ratio, return on assets, and debt-to-equity, then compares year-over-year performance and generates plain-English financial summaries. I also added visualizations for revenue and net income trends to make the analysis more interpretable. (the data I entered in the csv is fictional) This project was a great way to combine software engineering with financial analysis and think more deeply about how financial reporting can be translated into code. Code: https://lnkd.in/gX-U9UBf #python #pandas #matplotlib #finance #financialanalysis #dataanalysis #github

Day 6/10 🚀 This is where your data starts to take shape. Collections — the backbone of every Python program. Without the right one? Slower code, messy logic. With the right one? Faster lookups, cleaner design. 📋 What I covered today: 01 → Lists — slicing & comprehensions 02 → Tuples — immutability & unpacking 03 → Dictionaries — CRUD & O(1) lookup 04 → Sets — unique values & operations 05 → Frozenset 06 → Advanced — defaultdict, Counter, namedtuple 07 → Iterators — iter() & next() 08 → Mini Project — Inventory Management System Built a simple system using dictionaries to manage stock & pricing — a real-world pattern used in inventory and data pipelines. Day 1 ✅ Day 2 ✅ Day 3 ✅ Day 4 ✅ Day 5 ✅ Day 6 ✅ 4 more to go. Drop a 🐍 if you’ve ever used a list when a set would’ve been better 😄 #Python #Collections #DataEngineering #LearningInPublic #CleanCode #10DaysOfPython #DataStructures

I recently redesigned my portfolio website to better reflect how I approach data and analytics work. https://sharmahemang.com The goal was to make it clearer and more aligned with real-world problem solving, focusing on how data is turned into structured analysis, reliable metrics, and decision-ready insight. #DataAnalytics #DataScience #MachineLearning #SQL #Python #AnalyticsEngineering #Sydney

The Backfill That Changed History 🐍 The analysis looked clean. The trends made sense. The story was clear. A week later — the numbers changed. Not because the logic was wrong. Because the data wasn't final. Backfills, late-arriving records, corrected entries — they quietly rewrite history. In real-world data systems — "final" is often just temporary. 👇 See the visual below — how it breaks your analysis and 4 checks to protect against it. #DataAnalytics #Python #AnalyticsThinking #LearningInPublic

🚀 Essential Python snippets to explore data:   1.   .head() - Review top rows 2.   .tail() - Review bottom rows 3.   .info() - Summary of DataFrame 4.   .shape - Shape of DataFrame 5.   .describe() - Descriptive stats 6.   .isnull().sum() - Check missing values 7.   .dtypes - Data types of columns 8.   .unique() - Unique values in a column 9.   .nunique() - Count unique values 10.   .value_counts() - Value counts in a column 11.   .corr() - Correlation matrix