Gatwiri Gladys’ Post

👀 Data isn’t just numbers — it’s how we tell stories that matter. Found this interesting comparison of Excel vs SQL vs Python (Pandas) 👇 It’s amazing how each tool can do similar tasks but in its own unique way. 💡 Here’s what I noticed: -- Excel → Great for quick analysis and small datasets -- SQL → Best for handling and querying structured data -- Python (Pandas) → Ideal for automation and advanced analysis No matter which tool we use, the goal is always the same — to find insights that make an impact. #DataAnalytics #Excel #SQL #Python #Pandas #DataScience #AnalyticsCommunity #LearningJourney #KeepLearning #DataDriven #Upskilling

Excited to share a quick comparison of Excel, SQL, and Python (Pandas) for data tasks. Whether you're loading data, filtering rows, aggregating, or visualizing, each tool shines in its own way. • Excel: Great for quick tasks like sorting or Pivot Tables. • SQL: Perfect for querying databases and joins. • Python (Pandas): Ideal for advanced analysis and automation. #DataAnalytics #SQL #Python #Excel #Pandas #DataScience #AnalyticsTools #CareerDevelopment #DataDriven #LearningAnalytics

Hello Everyone, As part of my Data Cleaning and Visualization skills upskilling, I've created Product Analytics Dashboard. The dashboard shows "Descriptive Statistics" about 1000 products downloaded from Kaggle in form of CSV. Before preparing charts, I performed process of data cleaning. (It was a manual process because of only 1000 records but it can definitely be automated through Python scripts). The charts created are basic Bar, Column, Line and Pie types. I've used all best practices used in Visualizations. Please have a look at my dashboard and suggest improvements. #DataCleaning #DataVisualizations #CSV #Dashboard #Python #PivotTables #Excel #Analytics

A few months ago, I spent hours cleaning a messy dataset... Half the time I was in SQL, the other half in Python. At one point, I actually asked myself — “Which one’s better for cleaning data?” Here’s what I learned SQL is amazing for quick, large-scale cleaning. Filtering duplicates, handling NULLs, standardizing formats — it’s fast and clean. Python, on the other hand, is perfect for complex stuff. When I need custom logic, pattern fixing, or automation — Pandas just does the job. So which one’s better? Honestly, neither alone. The real power is when you 𝐮𝐬𝐞 𝐛𝐨𝐭𝐡. Start with SQL for structured prep. Then switch to Python for deeper transformations and automation. That combo saves hours — and gives you cleaner, more reliable insights. Clean data isn’t just a technical skill. It’s what separates good analysts from great ones. #DataAnalytics #Python #SQL #DataCleaning #CareerGrowth

SQL vs Pandas: Both are powerful for data analysis — just used differently 👇 🔹 SQL → Works best for querying large databases. 🔹 Pandas → Great for data manipulation in Python. Example: SQL: SELECT AVG(salary) FROM employees; Pandas: df['salary'].mean() Different tools, same goal — turning data into insights! 📊 #SQL #Pandas #Python #DataAnalytics #LearningEveryday

I’ve been exploring how Python can turn raw data into useful insights — here’s my third project putting that into action. For my third Python project, I created a Weather Data Analyzer that reads weather information from a CSV file and generates a summary report. This program processes data from “weather data.csv”, which includes daily readings of temperature and humidity. Using Python, it: 1. Reads and stores each record as a dictionary with the date, temperature, and humidity. 2. Calculates the average temperature across all days. 3. Identifies the highest and lowest temperatures and displays the day with the maximum reading. 4. Writes a clear summary report into “Weather Summary.txt” showing the average, highest, and lowest temperatures, along with total days analyzed. This project helped me understand how to handle numerical data, work with lists and dictionaries, and perform calculations efficiently. It also reinforced how Python can turn raw data into meaningful summaries with minimal code. 👉 Check out my GitHub Project: https://lnkd.in/euAvbMrv #Python #DataAnalysis #Learning #Programming #Coding #GitHub #Projects #FileHandling #WeatherData

 "Before you analyze data — you must understand it." This week, I explored Python data types — the building blocks of every data analysis project. Here’s what makes them powerful: Lists → Store multiple values in one variable. Tuples → Like lists, but unchangeable. Dictionaries → Perfect for labeled data. Strings & Numbers → The base for every operation. Understanding data types helps structure your analysis and avoid future errors. Follow me, Meraab Hanif, for more Python & Excel learning insights. Comment “Python” if you’d like me to share beginner practice exercises. 🌐 See my work & dashboards → meraabhanif.my.canva.site P.S. Which Python data type do you find most useful so far?

From SQL to Python, If you’ve ever switched between SQL and Python for data analysis, you know the pain of translating queries into pandas syntax. That’s why I love this quick reference guide It shows how common SQL operations map directly to Python pandas from filtering and grouping to joins and unions. Here are a few gems: 🔹 WHERE → df[df['column'] == 'value'] 🔹 ORDER BY → df.sort_values(by='column') 🔹 JOIN → pd.merge(table1, table2, on='key') 🔹 UNION ALL → pd.concat([table1, table2]) Simple. Powerful. Pythonic. Save this post for your next data project and make switching between SQL and Python effortless.

If you want to learn Python for data analysis prioritise: - NumPy (maths) - Pandas/Polars (data wrangling) - Matplotlib (Data visualisation) - Seaborn (built on top of matplotlib, has higher level interface capabilities) - OS (Operating System Interaction for working with files and folders) Master the above and you'll be able to defend yourself against any data requests that come your way. Good luck.