Fixing Date Sorting in Excel with Python

Day 21/75 — A small pattern I noticed in my data 👇 While analyzing a dataset, I plotted a simple distribution chart. And something interesting showed up: 👉 Most values were clustered in a small range 👉 But a few values were extremely high 📊 That’s when I realized: My data was **skewed**. Here’s the simple code I used: df['price'].hist() 💡 Why this matters: If I only looked at the average… I would get a misleading picture. Because: 👉 A few high values were pulling the average up 🚨 Lesson: Before trusting any number: • Always visualize your data • Check for skewness • Look for outliers 👨💻 Since then, I always: 👉 Plot first, analyze later Small step… But it changes how you understand data. Do you usually visualize your data before analysis? 👇 #DataScience #Python #Pandas #DataAnalysis #LearningInPublic

As part of building out my data science portfolio, I’ve put together a small project exploring how SQL-style analysis translates into pandas workflows. The goal was to take a typical analytical task — working with transactional data — and implement it end-to-end in Python, while keeping the structure and clarity of SQL queries. The notebook covers: - filtering and aggregating data - ranking and comparing groups - analysing how revenue is distributed across customers This sits alongside my other projects in machine learning, A/B testing, and time series, and helps round out the core toolkit: moving from data extraction → analysis → insight. https://lnkd.in/eN77xEw2

Ever had your Pandas integers mysteriously turn into floats? 🧐 It’s a common headache: you have a column of IDs or counts, one missing value (NaN) appears, and suddenly your 1 becomes 1.0. The secret is in the capitalization: int64 vs Int64. 🔹 int64 (numpy-backed): The default. High performance, but cannot handle nulls. If a NaN sneaks in, Pandas "upcasts" the whole column to floats to accommodate it. 🔹 Int64 (pandas-nullable): The "modern" way. It uses a mask to support pd.NA. Your integers stay as integers even with missing data. No more 1.0 where you expected a 1! Pro-tip: Use .astype('Int64') during your data cleaning phase to keep your schemas clean and predictable. #Python #Pandas #DataScience #DataEngineering #CodingTips #Dataanalyst

Just learned something interesting today 👇 In Data Analytics, cleaning data takes up almost 70–80% of the total work—not analysis. That means the real skill isn’t just knowing tools like Excel or Python… It’s knowing how to handle messy, real-world data. Small lesson, big perspective shift. What’s something surprising you’ve learned recently? #DataAnalytics #LearningInPublic #DataScience #GrowthMindset

I started using Pandas last week. After a month of Python and NumPy, I thought I was ready. First impression: it feels like Excel. But smarter. In code. NumPy gave me arrays—rows of numbers I could analyze mathematically. Pandas gives me DataFrames—full tables with column names, mixed data types, and the ability to ask real questions of real data. The difference hit me immediately: With NumPy I was working with arrays I created myself. With Pandas I loaded an actual CSV file. Real column names. Real messy data. Real supply chain numbers. And in 3 lines of code: pd.read_csv() df.head() df.info() I could already see which suppliers had missing data, what their delivery rates looked like, and which columns needed cleaning. That's not practice anymore. That's actual analysis. This is where Python stops being theoretical and starts being useful. #Python #Pandas #LearningInPublic #SupplyChain #DataAnalytics

Combining data from multiple sources is one of the most common tasks in data analysis and data engineering and in pandas, pd.concat() is the primary tool for getting it done. But there is more to it than just passing two DataFrames and getting one back. Understanding when to use axis=0 vs axis=1, how join handles mismatched columns, why concatenating inside a loop is a performance trap, and when to use concat vs merge. These are the details that separate clean, efficient data pipelines from slow, buggy ones. Get comfortable with pd.concat() and combining data from multiple sources becomes one of the fastest steps in your workflow. Read the full post here: https://lnkd.in/es7KJ7Y9 #Python #Pandas #DataScience #DataEngineering #Analytics #ETL

After working with NumPy, one question came to my mind 👇 “If NumPy is so powerful… why do we need Pandas?” Here’s what I understood: NumPy is great for: - numerical operations - fast array computations But when working with real-world data, things are not that clean. We deal with: - missing values - column names - mixed data (numbers + text) That’s where Pandas comes in. 👉 Built on top of NumPy 👉 Designed for structured data (tables) Think of it like this: NumPy → handles raw numbers efficiently Pandas → makes data easier to read, clean, and analyze This helped me connect the dots: It’s not about choosing one… It’s about using the right tool at the right stage Now exploring Pandas to work with real datasets more effectively. What do you find easier to work with — NumPy or Pandas? #NumPy #Pandas #Python #DataEngineering #DataScience #CodingJourney #TechLearning

🐼 Turn Your Pandas Skills into Data Wizardry Every data analyst reaches a point where basic Pandas just isn’t enough. You know how to load data. You know how to filter. You know how to group. But the real magic? ✨ It happens when you start using Pandas efficiently. That’s exactly why I put together this Pandas cheat sheet. Not to teach the basics—but to help you: 🔹 Work faster with large datasets 🔹 Write cleaner, more readable code 🔹 Unlock powerful one-liners 🔹 Avoid common performance pitfalls Because in data analysis, it’s not just about getting results—it’s about getting them smartly. If you want to go from “someone who uses Pandas” to “someone who masters it”… this is for you. #Python #Pandas #DataAnalytics #DataScience #Productivity #LearnPython

Charts make everything better, am I right? Over the last little while, I learned data processing and visualization at @TripleTen. I used Python and Matplotlib to turn rows of numbers into charts that actually make sense and are easy to understand. Magic, right? 📊✨ I may or may not try to visualize everything in my life. Starting with my caffeine intake, things are getting kinda real between myself and the coffeemaker. #DataArtistry #MatplotlibMagic #VisualThinker #TripleTen

Pandas Basics Every Data Beginner Should Know 🐼 If you're starting your journey in Python for Data Analysis, understanding Pandas is a must. This simple visual guide breaks down the core concepts: ✔️ What Pandas is and why it’s used ✔️ Difference between Series and DataFrame ✔️ Reading, exploring, and selecting data ✔️ Handling missing values and applying functions ✔️ Basic data aggregation and sorting