Mohammedali Saiyed’s Post

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

📊 4 weeks. 100K+ Wikipedia edits. 1 key finding. I'm happy to share WikiPulse – my first end-to-end data analytics project. The question: Do Wikipedia edit spikes happen before or after real-world events? The finding: Most significant spikes occur 1–2 days before events, suggesting editors anticipate rather than just react. Strongest signal: Academy Awards (r = 0.977, p < 0.05) Tech stack: Python (pandas, NumPy, SciPy, statsmodels) Wikipedia API for data collection SQLite for local database storage Plotly for interactive visualizations Streamlit for dashboard & deployment Live demo: https://lnkd.in/g9bNc3jB GitHub: https://lnkd.in/ghTQfdng Open to feedback and suggestions. #DataAnalytics #Python #Streamlit #PortfolioProject

Data is everywhere. But real value comes from how well you can work with it. Relying on just one tool? That’s limiting your growth. 📊 Excel helps you explore and validate ideas quickly 🗄️ SQL lets you dig deep and pull the right data 🐍 Python takes you a step ahead with automation and scalability The real advantage isn’t mastering one— it’s knowing when and how to use each. That’s what turns a beginner into a problem-solver. Which tool do you find yourself using the most right now? 👇 #DataAnalytics #SQL #Python #Excel #Upskilling #CareerGrowth

🐍 Working with data? Save this. Honest truth — I keep coming back to these commands more than I'd like to admit. In most data projects, cleaning takes up more time than the actual analysis, and having the right commands at hand makes a real difference. This Python Data Cleaning cheat sheet covers the 5 essentials I rely on constantly: ✅ Handling nulls and duplicates ✅ Quickly inspecting your dataset ✅ Renaming, converting & cleaning columns ✅ Filtering and slicing rows efficiently ✅ Merging and grouping data If you work with pandas regularly, this should always be within reach. Which of these do you use the most? 👇 #Python #DataScience #DataCleaning #Pandas #DataAnalytics

If you are doing data analysis in Python, pandas pivot tables are one of the most powerful tools you can master. They let you go from raw, messy data to a clean, structured summary in just a few lines of code —grouping by multiple dimensions, applying aggregation functions, handling missing values, and adding totals automatically. Once you understand pivot tables, your data analysis workflow becomes significantly faster and more insightful. If you are still doing everything manually with loops and conditional logic, it is time to learn pivot tables. Read the full post here: https://lnkd.in/eCaBFSB5 #Python #Pandas #DataScience #DataAnalysis #DataEngineering #Analytics

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

🚀 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

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

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

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.