Python Cheat Sheet: Essential Commands and Concepts

📊 Data Analysis with Python — Speeding Up Your Analysis - post [17/20] Large #datasets can slow you down. Unless you know these tricks: # Vectorized operations (faster than loops) df["sales_tax"] = df["sales"] * 0.07 # Use .apply() carefully df["discounted"] = df["price"].apply(lambda x: x * 0.9) # Check memory usage df.info(memory_usage="deep") Avoid row-by-row loops whenever possible. #Pandas works best with vectorized operations. Write efficient code today to save hours tomorrow. Have you ever hit a “Python too slow” moment? How did you fix it? #PythonDataSeries #PythonTips #DataAnalysis

Python flies for product code-until a CPU-bound task slams into the GIL. My go-to fix: keep the FastAPI app in Python, move the hot loop to Rust via PyO3. Simple idea, senior impact: write a tiny Rust function, expose it as a Python module, and call it from your endpoint. PyO3 lets you release the GIL and run true parallel threads, with safe memory and minimal copying of bytes/NumPy buffers. Examples that feel advanced yet stay readable: 1. Text similarity: compute cosine similarity for 10k embeddings to re-rank search results or recommendations. 2. Image uploads: resize + quick blur/noise check before storing, so you catch bad images early. 3. Data crunching: fast JSON/CSV parsing with type coercion and basic validation for ETL jobs. 4. Security/telemetry: compress and hash log chunks before shipping to storage to cut bandwidth and cost. My playbook: prototype in Python, profile, isolate the pure-CPU part, design a tiny interface (in/out as bytes or arrays), then return clean Python types. #Python #Rust #PyO3 #FastAPI #Backend #Software #Development #Performance

From 55 Sheets to 1 Hour: How Python Transformed My Excel Workflow Traditional Excel automation methods — like formulas or VBA — work fine for small datasets. But when your file reaches 700MB with 55 sheets, performance becomes a real bottleneck. With Python’s data processing libraries (like pandas), I can consolidate and summarize the entire workbook in just one hour — with clean logic and reusable code. This isn’t just about being “faster.” It’s about redefining efficiency and quality in our workflow. Many people may think it’s not that important. But when the time saved allows your team to focus on more valuable, thoughtful work, that’s where the real impact of automation lies. #Python #ExcelAutomation #DataAnalysis #Automation #Efficiency #DigitalTransformation

𝗣𝘆𝘁𝗵𝗼𝗻 𝗶𝗻 𝗘𝘅𝗰𝗲𝗹 Python is not replacing Excel formulas — it simply makes some things easier or even possible. If you ask whether it’s worth using Python with Excel, I’d say definitely YES — but only when you really need it. For simple tasks like XLOOKUP() or SUMPRODUCT(), and when you’re working with a single file, 𝐄𝐱𝐜𝐞𝐥 is enough. But if you need to merge multiple files, 𝐏𝐲𝐭𝐡𝐨𝐧 can make the process much faster and more reliable. Now, about Python in Excel — here are some of my first insights: 👌 Great to explore and understand how Python code works directly inside Excel. 📈 Python plotting libraries offer many chart types and a lot of control — but the code can be quite complex to write.  𝄜 You can merge tables within the same workbook. ‼️ You cannot open or merge external Excel files, and you cannot write data to external files. In this case use Python outside. Have you tried Python in Excel yet? What’s been your experience so far🧐 #Python #DataAnalyst #BusinessAnalyst #Excel #ExcelTips #ExcelwithPython #MergeFiles #List #Python #PythonInExcel

Today’s learning session was all about strengthening my logic and memory in both SQL and Python. I’m making sure to build solid fundamentals before moving into complex projects. - SQL Practice Highlights: Created multiple stored procedures with IN, OUT, and INOUT parameters. Calculated total quantity, total revenue by category, and final revenue after discount. Built a procedure to show products by category dynamically — really helped me understand parameter handling in SQL. These small tasks reminded me how powerful stored procedures can be when optimizing repeated operations in real projects. - Python Practice Highlights: Strengthened my understanding of loops, string methods (strip, replace, lower), and password validation logic. Practiced with match–case, for loops, and simple logic-building exercises (like multiplication tables and star pyramids). Each small script helps me think like a problem-solver rather than just a coder. It’s not about doing something big every day — it’s about consistent small wins that build confidence and muscle memory over time. #SQL #Python #DataAnalytics #LearningJourney #100DaysOfCode #SelfLearning #ProblemSolving #CareerInData

Here's how to override the default card preview of a Pandas DataFrame in Python in Excel. https://lnkd.in/du9_yWg7 The preview card shown is just one way of doing it - you can define it in any way you want - whatever is useful to your workflow, or your client's workflow. You create a function that defines the card layout, then register it with the excel.repr module. Place all of that in the editable Initialization pane in Python in Excel and your custom preview card will be used for every DataFrame in your workbook. It's all explained in the post. I hope it's useful! #data #analytics #python #excel

The Pandas Trick That Changed Everything 🐼 The moment I discovered .query() in Pandas, I stopped writing messy bracket filters forever 🤯 We've all been there: df[(df['age'] > 25) & (df['salary'] > 50000) & (df['department'] == 'Sales')] ❌ Hard to read ❌ Confusing everytime There's a better way: df.query("age > 25 and salary > 50000 and department == 'Sales'") ✅ Clean and readable What's your favorite underrated Pandas method? 👇 Let me know in the comments. #DataScience #Python #Pandas #DataAnalysis #Analytics #DataEngineering #MachineLearning #TechTips #Programming

🌟 New Blog Just Published! 🌟 📌 Python One-Liners to Speed Up CSV Processing 🚀 ✍️ Author: Hiren Dave 📖 When data moves between spreadsheets, databases, and analytics tools, CSV files are the lingua franca of interchange. Their simplicity is appealing, yet the very ubiquity of CSV introduces a hidden...... 🕒 Published: 2025-10-15 📂 Category: Tech 🔗 Read more: https://lnkd.in/dN_tvi4A 🚀✨ #python #csv #one-liners

𝑬𝒗𝒆𝒓 𝒐𝒑𝒆𝒏𝒆𝒅 𝒂𝒏 __𝒊𝒏𝒊𝒕__.𝒑𝒚 𝒇𝒊𝒍𝒆 𝒂𝒏𝒅 𝒕𝒉𝒐𝒖𝒈𝒉𝒕… “𝑰𝒕’𝒔 𝒃𝒍𝒂𝒏𝒌!?” 😅  That little blank file actually has a big role in Python projects. __init__.py tells Python that the folder it’s in should be treated as a package This seemingly "blank" file is the magic that transforms a regular directory into a Python package. Here's what it does: 𝑷𝒂𝒄𝒌𝒂𝒈𝒆 𝑹𝒆𝒄𝒐𝒈𝒏𝒊𝒕𝒊𝒐𝒏 : Tells Python "hey, this folder is a package, not just a random directory" 𝑰𝒎𝒑𝒐𝒓𝒕 𝑪𝒐𝒏𝒕𝒓𝒐𝒍 : Lets you define what gets exposed when someone imports your package 𝑰𝒏𝒊𝒕𝒊𝒂𝒍𝒊𝒛𝒂𝒕𝒊𝒐𝒏 𝑪𝒐𝒅𝒆 : Runs setup code when the package is first imported 𝑪𝒍𝒆𝒂𝒏𝒆𝒓 𝑰𝒎𝒑𝒐𝒓𝒕𝒔 : Instead of 𝒇𝒓𝒐𝒎 𝒎𝒚𝒑𝒂𝒄𝒌𝒂𝒈𝒆.𝒎𝒐𝒅𝒖𝒍𝒆.𝒔𝒖𝒃𝒎𝒐𝒅𝒖𝒍𝒆 𝒊𝒎𝒑𝒐𝒓𝒕 𝑴𝒚𝑪𝒍𝒂𝒔𝒔 you can enable 𝒇𝒓𝒐𝒎 𝒎𝒚𝒑𝒂𝒄𝒌𝒂𝒈𝒆 𝒊𝒎𝒑𝒐𝒓𝒕 𝑴𝒚𝑪𝒍𝒂𝒔𝒔 𝑷𝒓𝒐 𝒕𝒊𝒑 : While Python 3.3+ introduced namespace packages (packages without init.py), explicitly including this file is still 𝒃𝒆𝒔𝒕 𝒑𝒓𝒂𝒄𝒕𝒊𝒄𝒆 for clarity and compatibility. So yes , it may look blank, but it’s quietly doing important work behind the scenes #Python #Programming #SoftwareDevelopment #CodingLife

🚀 Essential Python/ Pandas 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