Automating CSV Merging with Python and Pandas

Most people try to learn everything in Python… and end up learning nothing. If someone asked me how to start Data Analytics with Python in 7 days, I’d focus on just 7 things. Nothing extra. No overwhelm. Just the essentials. Day 1 – Basics that matter Learn print(), variables, and lists. Do a small calculation with data so you understand how Python works. Day 2 – Explore data Use df.head() and df.describe() to open and understand any CSV file. Day 3 – Clean messy data Learn dropna() and fillna() to handle missing values. Day 4 – Real business analysis Use groupby() to answer questions like: “Which region generates the most sales?” Day 5 – Quick insights Use query() and nlargest() to filter data and find top results instantly. Day 6 – Build a mini project Complete workflow: Load → Clean → Analyze → Export insights. Day 7 – Show your work Upload the project to GitHub and share it on LinkedIn. That’s it. You now have a portfolio project, practical Python experience, and proof you can analyze real data. Simple > complicated. #DataAnalytics #Python #LearningInPublic #DataScience #SQL #CareerGrowth

Episode 9: What I Can Do With Python One common challenge in data cleaning is this: How do you quickly see all the unique values across every column in a dataset? Not just one column at a time…but the entire dataset in one view. If you’ve worked with real data, you know how important this is. It helps you spot inconsistencies, compare entries with a data dictionary, and decide what needs to be cleaned or standardised. This week, I attempted to do exactly that. My first instinct was Excel. I tried combining functions, nesting formulas, and exploring different approaches to get all unique entries across columns at once. It sounded like something that should be possible, but after spending quite some time on it, I couldn’t get exactly what I wanted. And VBA wasn’t something I wanted to rely on (you would know about this from my previous post 😩). So I switched to Python. I wrote a simple function (maybe not so simple) and brought it back into Excel using Python. I called it 'uniq_row_per_col()' . The function takes a dataset (as an Excel range) and returns the unique values for each column. It assumes the first row contains headers, handles duplicate column names automatically (similar to pandas), and keeps case sensitivity so inconsistencies can be clearly identified. In practice, this makes data cleaning much easier. Instead of checking columns one by one, I can now: — View all unique entries across the dataset at once — Compare them directly with a data dictionary Identify inconsistencies quickly (typos, casing differences, variations) — Decide what needs standardisation or removal Behind the scenes, pandas handles the data manipulation, while xlwings manages the interaction between Excel and Python. This is something I’m beginning to appreciate more, not just using tools as they are, but extending them to fit the workflow I need. I’ve attached a short demo video showing how it works. Would this be useful in your data cleaning process? See you in Episode 10 🚀 #WhatICanDoWithPython #Python #DataCleaning #Excel #DataAnalysis #Automation #BuildInPublic #xlwings

Excel users often ask: What is the difference between Python and pandas? Python is the general language which you can accessorize with bolt-on libraries designed for more specific purposes. The pandas module represents one such add-on library and allows you to replicate all-things-Excel in the Pythonverse. To the Excel user wanting to learn Python, pandas provides the perfect on-ramp because it converts your workbooks into “virtual spreadsheets” called dataframes. Dataframes look and act just like spreadsheets. It’s just columns and rows, columns and rows. The great thing about pandas is that you can replicate your favorite Excel formulas without all that copy/pasting. VLOOKUP, SUMIF, COUNTIF, you name it. You can import workbooks, export workbooks, even format them to replicate your favorite Excel styles. Python and pandas are easy to learn. I taught myself the basics in three months by leveraging my Excel expertise and converting what I have always done manually into its equivalent code. Good luck and good coding! #Excel, #Python,#pandas, #dataframes, #Productivity, #DataAnalysis, #Automation

What I Learned from My First Data Analysis Project Using Python One of the biggest turning points in my data journey was working on my first real project using Python. It wasn’t perfect but it taught me lessons I couldn’t get from just watching tutorials. Here are some key things I learned: 1️⃣ Data is Never Clean I quickly realized that most of my time wasn’t spent analyzing—but cleaning messy data. 2️⃣ Understanding the Problem Comes First Before writing any code, I had to clearly understand what I was trying to solve. 3️⃣ Python Makes Analysis Powerful Using libraries like Pandas and NumPy helped me manipulate and analyze data efficiently. 4️⃣ Small Errors Can Break Everything A missing bracket or wrong column name can stop your entire workflow—attention to detail is key. 5️⃣ Visualization Tells the Story Libraries like Matplotlib helped me present insights in a way that’s easy to understand. 6️⃣ Google is Your Best Friend I didn’t know everything and that’s okay. Learning how to search and solve problems is a skill on its own. 7️⃣ Practice Builds Confidence The more I worked on the project, the more confident I became using Python for data analysis. My biggest takeaway: You don’t truly learn data analysis until you start working on real projects. That first project wasn’t just about Python it was about thinking like a data analyst. What was your biggest lesson from your first project? #DataAnalytics #Python #LearningInPublic #DataScience #BeginnerJourney #TechGrowth

Welcome to Part 7 of Data Analyst Roadmap series, Python Foundations — Start "Thinking in Data" The biggest mistake beginners make when starting Python for data analysis? Rushing straight into Pandas, fancy charts, and machine learning models without understanding the bedrock fundamentals. If you don't understand how Python thinks about data under the hood, your analysis will break. Today’s visual is Stage 1: The core building blocks before the cool stuff. Why does this matter so much? Look at the top left of the image. 🔹 In Python, 100 + 20 = 120. (Math!) 🔹 But "100" + "20" = "10020". (Text concatenation!) If you don't understand Data Types, 30% of your beginner errors will come from simple mistakes just like that. You must control Python, not let it guess what you want. Here is your checklist for "Thinking in Data": Containers: Are you storing ordered data (use a List, like a to-do list) or do you need fast, labelled lookups (use a Dictionary, like a contact book)? Repetition: Are you manually doing the same thing 10 times? Use a Loop. Reusable Logic: Are you copy-pasting the same analysis code? Wrap it in a Function so you can use it anywhere. Mastering these basics means you understand memory and control logic. Which of these fundamental concepts took you the longest to truly grasp when starting out? Loops always trip people up at first! Let me know below. #PythonForDataScience #DataAnalytics #LearningToCode #Pandas #TechSkills #DataCareers #CodingNewbie #Roadmap

Excel or Python? Which one is better? 👇 Lately, I’ve been navigating the "Great Divide" between Excel and Python while handling large-scale datasets (90,000+ rows). Here’s what my recent experience has taught me: 📉 The Excel Reality Check: Excel remains the undisputed king for quick analysis, ad-hoc reporting, and day-to-day business tasks. It’s intuitive, fast, and accessible. However, once complex operations meet massive row counts, the "spinning wheel" starts to appear or even crash. 🐍 The Python Advantage: This is where Python truly shines. For scalability, automation, and handling heavy data lifting smoothly, Python is a game-changer. It transforms a potential crash into a seamless, repeatable workflow. The Verdict? They aren't rivals; they’re complementary. I’ve found the most success using: 1️⃣Excel for speed, simplicity, and stakeholder-ready reporting. 2️⃣Python for deep analysis, data cleaning, and long-term scalability. The most important thing is to choose the right tool for the job! 🛠️ #DataAnalytics #Python #Excel #Learning #Data #TechTips

Python is great for data science. But using it to clean data is overkill. A popular YouTube tutorial shows how to clean SurveyMonkey data using Python and Pandas, it took the developer 1 hour. The same transformation in Power Query? 5 minutes. Most data analysts don't realize Excel can do this. They assume Python is the only serious option for data cleaning. But Power Query has been built into Excel since 2010, and it handles transformations like unpivoting, merging, grouping, and calculated columns without writing a single line of code. In this video, I walk through the exact same dataset and show you how to clean it 12x faster using Power Query. If you've been putting off learning Python just to clean data, you don't need to. Watch the video and download the practice file: https://lnkd.in/d7E3TiDU ❓Do you use Python or Power Query for data cleaning? #Excel #Python #DataCleaning

🚀 Day 3/60 – Variables & Data Types (The Foundation of Python) If you don’t understand this, Python will feel confusing. If you master this, everything becomes easy. Let’s break it down 👇 🧠 What is a Variable? A variable is like a container that stores data. Example 👇 name = "Adeel" age = 25 Here: • name stores text • age stores a number 🔢 Python Data Types (Most Important Ones) 1️⃣ String (Text) name = "Adeel" 2️⃣ Integer (Whole Number) age = 25 3️⃣ Float (Decimal Number) price = 99.99 4️⃣ Boolean (True/False) is_active = True 🧪 Let’s Print Them print(name) print(age) print(price) print(is_active) ⚡ Pro Tip (Very Important) Python is dynamically typed 👉 You don’t need to declare data types x = 10 x = "Now I am text" Same variable. Different type. ❌ Common Beginner Mistake name = Adeel # ❌ Error Correct way: name = "Adeel" # ✅ Always use quotes for text. 🔥 Challenge for today Create 4 variables: • Your name • Your age • Your favorite number • Are you learning Python? (True/False) Then print all of them. Comment “DONE” when finished ✅ Follow Adeel Sajjad if you’re serious about learning Python in 60 days 🚀 #Python #LearnPython #PythonProgramming #Coding #Programming

🚀 Day 8/70 – Functions in Python Today I learned about Functions in Python 🐍 A function is a reusable block of code that performs a specific task. In Data Analytics, functions help us: ✔ Avoid repeating code ✔ Organize logic clearly ✔ Build reusable analysis steps ✔ Improve code readability 📌 Basic Function Syntax def greet(): print("Hello, Data World!") greet() 📌 Function with Parameters def add_numbers(a, b): return a + b result = add_numbers(10, 5) print(result) 👉 Output: 15 📊 Data Analytics Example def calculate_average(marks): total = sum(marks) return total / len(marks) marks = [70, 80, 90, 60] average = calculate_average(marks) print("Average:", average) Using functions makes analysis clean, structured, and reusable 🔥 💡 Why Functions Matter in Real Projects? ✔ Modular coding ✔ Easier debugging ✔ Better scalability ✔ Essential for automation & data pipelines Consistency builds confidence 💪 8 Days Done. Improving every single day. #Day8 #Python #DataAnalytics #LearningInPublic #FutureDataAnalyst #70DaysChallenge