Diana Baquero’s Post

Started learning Pandas — and now data actually makes sense After working with NumPy, I realized something: Handling real-world data (like CSV files) still felt a bit messy. That’s where Pandas comes in. It’s a Python library designed to make working with structured data simple and efficient. 📊 What’s happening here: • read_csv() loads data into a table-like structure • head() shows the first few rows • info() gives a summary of the dataset 💡 What I understood today: – Pandas organizes data in a structured format (DataFrame) – It makes reading and exploring data very easy – This is exactly how real datasets are handled in Data Science This feels like a big step from writing basic programs to actually understanding data. Next: Selecting specific columns and filtering data in Pandas #Python #Pandas #DataAnalysis #MachineLearning #LearningInPublic #DataScience Here is the code:

Data cleaning shouldn't be a headache. 🐍💻 Most of a Data Analyst's time isn't spent building models—it’s spent cleaning the mess. I’ve put together a minimalist Data Cleaning in Python Cheat sheet covering the essential steps to get your datasets "analysis-ready" in minutes. What’s inside: ✅ Standardizing formats & strings ✅ Handling duplicates & missing values ✅ Filtering outliers with the IQR method ✅ Quick data exploration commands Whether you're using Pandas for the first time or just need a quick syntax refresher, keep this one bookmarked.  #DataScience #DataAnalytics #Python #Pandas #DataCleaning #CodingTips #MachineLearning

Most beginners try to learn Pandas by memorizing everything. That’s the wrong approach. Good analysts: 1. Focus on a few core functions 2. Apply them to messy real data and 3. Build intuition over time These 8 functions handle most real-world cleaning: dropna, drop_duplicates, fillna, astype, groupby, merge, apply, rename Master these → you’re already ahead. #Python #Pandas #DataAnalytics #SQL #DataScience #Analytics #LearnPython #DataCleaning

Really Excited to work with cleaning data is one of the most important steps in data analysis. In Pandas, handling missing values becomes much easier with methods like: • dropna() – remove missing values • fillna() – replace missing values • ffill() – forward fill using previous values • bfill() – backward fill using next values • thresh= – keep rows/columns based on minimum non-null values #Python #Pandas #DataCleaning #DataAnalysis #DataScience

Knowing Python isn't enough... You need to know how to work with real data. That's where Pandas comes in. Day 5 of my 30-day Data Science challenge Here's what I simplified into this cheat sheet 👇 Data Loading → read_csv, read_excel, read_json Data Inspection → head(), info(), describe() Data Cleaning → dropna(), fillna(), rename() Data Selection → loc, iloc, df['col'] Data Manipulation → groupby(), merge(), sort_values() Filtering → df[df['col'] > value], query() This is something I keep coming back to every single day. Save this — you'll need it Which Pandas function do you use the most? 👇 #Pandas #Python #DataScience #LearningInPublic #DataScienceFresher

🚀 Data Cleaning = Reliable Insights Jumping into analysis without cleaning your data leads to costly mistakes. This Data Cleaning Cheat Sheet (Python – Pandas) highlights the essentials: Handle missing values & duplicates Convert data types correctly Clean and standardize text Detect outliers (IQR method) Apply effective filtering Structure and rename datasets 💡 Rule: Understand your data before analyzing it — start with .info() and .describe(). Clean data isn’t a step — it’s the standard. #DataAnalytics #Python #Pandas #DataCleaning #DataQuality

Day 24/75 — This one Python function helped me understand my data better 👇 When I started analyzing datasets, I felt overwhelmed. Too many rows. Too much information. Then I discovered this: df.groupby('city')['price'].mean() 💡 What it does: 👉 Groups data by a category 👉 Calculates insights (like average, sum, count) Example: Instead of looking at thousands of rows… I can instantly see: 📊 Average price per city 🚨 Why this is powerful: • Turns raw data into insights • Helps you compare groups easily • Makes analysis faster and clearer 👨💻 Now I use it all the time to: • Compare categories • Find patterns • Simplify data Small function… But a big upgrade in how I analyze data. Have you used groupby() before? 👇 #DataScience #Python #Pandas #DataAnalysis #LearningInPublic

In large organizations, transitioning repetitive reporting tasks from Excel to Python isn’t just a technical upgrade, it’s a scalability decision. As data volume and complexity grow, automation, version control, and reproducibility become critical. Excel remains powerful for quick insights, but Python ensures consistency, auditability, and long-term efficiency across teams.

Stop the Excel vs. Python war. Here is the actual answer: Use Excel when: ✅ Your audience only knows Excel ✅ The dataset fits in rows you can see ✅ Speed of delivery beats reproducibility Use Python when: ✅ The same report runs every week ✅ Data has 100k+ rows ✅ You need auditability and version control Use BOTH when: ✅ You want a job in 2025 The best analysts do not pick sides. They pick the right tool. Tool tribalism is the enemy of good analysis. Master both. Charge more. Ship faster. Which tool do YOU default to — and why? Let's debate 👇 #Excel #Python #DataAnalysis #DataScience #Analytics

I spent 2 hours cleaning data in Excel. My colleague did the same in 8 seconds. The difference? Python. Just 3 simple commands — One to load the file. One to remove duplicate rows. One to drop rows where key columns are empty. That's it. No formulas. No manual scrolling. No "find and replace" nightmares. Here's what most analysts don't realise → 60% of your time in Excel is spent on work Python can automate completely. That 60% is time you could spend on actual analysis. On insights. On decisions. On things that actually get you noticed. The 3 Pandas functions every analyst should learn first: → read_csv — loads your entire dataset in milliseconds → drop_duplicates — kills every duplicate row instantly → dropna — cleans empty rows in one shot Python isn't hard to learn. The hardest part is deciding to start. Are you already using Python in your workflow, or is Excel still your go-to? #Python #DataAnalytics #DataAnalyst #PandasPython #DataScience #ExcelVsPython #Analytics #CareerGrowth #TechSkills #Bengaluru