Pandas Cheat Sheet: Essential Commands for Data Analysis

🚀 Master SQL – The Language Every Data Analyst Speaks! 💻 Whether you're analyzing millions of rows or just starting your data journey — these 5 SQL commands are the foundation of every powerful query: 🔹 SELECT – Choose the data you want 🔹 FROM – Pick the table that holds it 🔹 WHERE – Filter what truly matters 🔹 GROUP BY – Summarize and find insights 🔹 ORDER BY – Organize your results like a pro 💡 Master these, and you’ll unlock 80% of what you need to analyze data effectively! 📊 Start simple. Think analytically. Query smarter. #SQL #DataAnalytics #DataScience #Learning #CareerGrowth #DataAnalyst #PowerBI #Python #Upskill #TheShanchalDataLab

📊 Pandas Merge vs Merge_Ordered — What’s the Difference? If you’ve worked with pandas, you’ve probably used merge() — but have you explored merge_ordered()? 🤔 Here’s a quick breakdown 👇 🔹 merge() Used for combining any two DataFrames based on common columns or indexes. ➡️ Works just like SQL joins (inner, left, right, outer) ➡️ Does not care about order — it just matches keys. pd.merge(df1, df2, on='id', how='inner') 🔹 merge_ordered() Used when order matters — ideal for time-series or sequential data. ➡️ Performs an ordered merge (keeps data sorted). ➡️ Has fill_method to handle missing values (like forward fill). pd.merge_ordered(df1, df2, on='date', fill_method='ffill') 💡 In short: Use merge() → when combining data by key (like SQL joins). Use merge_ordered() → when merging chronological or ordered data while preserving sequence. #DataScience #Python #Pandas #DataAnalytics #LearningEveryday

Rolling Averages: SQL vs Pandas — same goal, different context. Both SQL and pandas can compute moving averages, but the best choice depends on where your data lives and how you work. 📊 SQL: AVG(value) OVER ( ORDER BY date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW ) - Efficient window function, great for large datasets, production pipelines, and dashboards. 🐍 pandas: df['rolling_7d'] = df['value'].rolling('7D').mean() - Perfect for time-series analysis, experimentation, and ML feature engineering. Key difference: SQL windows are row-based (some engines support time-based). pandas windows can be row-based or time-based — flexible but memory-bound. Choose SQL for scale. Choose pandas for flexibility. Understand both for mastery. 💡 #DataEngineering #Analytics #Python #SQL #Pandas

Day 61 of My Data Analytics Journey Today, I dived deeper into one of the most powerful tools in data analytics — the Pandas DataFrame. Think of a DataFrame as a smart Excel sheet in Python but faster, more flexible, and perfect for handling real-world data. From rows and columns to indexing, slicing, and exploring data — it’s amazing how much you can do with just a few lines of code! Learning this feels like unlocking a new superpower in data analysis. #Pandas #DataFrame #PythonForData #DataAnalytics #LearningJourney #EntriElevate

Every single Data Science journey starts in a spreadsheet. 🚀 Stop viewing Excel or Google Sheets as "basic." They contain the conceptual core of advanced data analysis. If you can't summarize raw data efficiently here, you'll struggle with SQL and Python. The biggest secret in data work is: ➡️ A Pivot Table is just a simple way to perform a GROUP BY and AGGREGATE operation (the heart of reporting). ➡️ Filtering is your first step in Exploratory Data Analysis (EDA). Before you touch complex code, you must master the art of data inspection and summarization. This is the bedrock of all data-driven decision-making. Dive into Day 12 and master Spreadsheets as your Data Science foundation: 🔗 https://lnkd.in/d9MP_XiR #DataScienceFoundation #PivotTables #Spreadsheets #DataAnalytics #DataWrangling #SholaAjayi

📊 Day 3: Building the Vendor Summary – Turning Raw Data into Business Insights After exploring the data, it was time to bring everything together. Using Python, SQL, and Pandas, I created a Vendor Summary Table that merges sales, purchases, and freight data into one comprehensive view. ⚙️ Here’s what this script does: Merges multiple database tables using SQL joins and common keys. Cleans and standardizes columns for consistency. Creates powerful KPIs like: 🔹 Gross Profit 🔹 Profit Margin (%) 🔹 Stock Turnover Ratio 🔹 Sales-to-Purchase Ratio 💡 This transformed table became the backbone for my next phase — performance analysis and visualization. Next up 👉 Day 4: Visualizing Vendor Performance and Deriving Insights #Python #SQL #Pandas #DataTransformation #BusinessAnalytics #VendorPerformance #DataEngineering #DataScience

Data Science has never been easier. Thanks to Snowflake AISQL, anyone with basic SQL skills can now perform data science tasks that used to be challenging to master. And your data stays put. No moving it around to a Python environment or making external calls to an LLM. What's even easier than using Cortex AISQL is watching your money disappear when you don't think about pricing implications, model selection, etc. 💸 If you're ready to learn how easy it is to use AISQL AND have a good understanding of the pricing structure and what models to use, check out my article on SELECT's blog which walks you through everything you need to know. https://lnkd.in/eJ-5QAXt

LLM functions in SQL In #Snowflake we can use LLM functions in SQL, like AI_COMPLETE or the one Jeff showed below: AI_CLASSIFY. They are called AISQL. You can read about it here: https://lnkd.in/eby4tnrs That is a game changer. You don't need to do complicated things to do LLM. If you have the data in your table, you can do prompting and LLM to it. It is that easy! Keep learning! My Linkedin articles: https://lnkd.in/eRTNN6GP. My blog: https://lnkd.in/eDdTNzzW #AI #SQL Snowflake

🧹 Day 1 of #DataAnalystErrorSeries — Don’t Skip the Data Cleaning Stage! Every great insight begins with clean, trustworthy data. But here’s the truth — most analytical mistakes happen before analysis even begins. Common data prep errors include: 1️⃣ Using incomplete or inconsistent data sources. 2️⃣ Ignoring missing values and duplicates. 3️⃣ Not understanding what each column truly represents. 4️⃣ Forgetting to validate data after cleaning. 5️⃣ Using biased samples that don’t represent reality. Remember, if your input is wrong, your output can’t be right. A clean dataset isn’t just neat — it’s powerful. 🧠 What’s one data cleaning challenge you’ve faced recently? #DataCleaning #DataPreparation #DataQuality #Analytics #DataAnalytics #PowerBI #Tableau #SQL #Python #DataIntegrity #BusinessIntelligence

🔁 Day 25–28: Recall and Practice Sessions These days were focused on revision and hands-on practice of everything learned so far — from Python basics to Power BI dashboards. 🔹 Revised OOPs, NumPy, Pandas, and Matplotlib concepts. 🔹 Practiced data preprocessing, visualization, and DAX calculations in Power BI. 🔹 Worked on improving dashboard design and report presentation. 🔹 Cleared doubts and strengthened my overall understanding through practical exercises. 🧠 These recall sessions helped reinforce key concepts and boosted my confidence in applying data analytics tools effectively. #Python #Pandas #Matplotlib #PowerBI #DAX #DataAnalytics #LearningJourney #HandsOnExperience #Revision