🚀 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

📊 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

🎯 Learning Update: Data Visualization with Matplotlib 🎯 Matplotlib is a Python library used to create graphs and charts. 📊 It helps visualize data in a clear and simple way. You can use it to draw lines, bars, pie charts, and more easily. Today, I explored the core plotting functions in Matplotlib — one of the most powerful Python libraries for data visualization. 📊 Here’s what I learned: ✅ Plotting basics: plt.plot() to create visual graphs ✅ Labels & Titles: plt.xlabel(), plt.ylabel(), plt.title() for clear insights ✅ Grid & Axis control: plt.grid(), plt.xlim(), plt.ylim(), plt.xticks(), plt.yticks() for better chart structure ✅ Legend & Display: plt.legend(), plt.show() for a professional finish This hands-on learning gave me a deeper understanding of how data can be presented visually and effectively. Excited to keep building more visual stories with Python! 🚀 #Matplotlib #DataVisualization #Python #LearningJourney #DataScience

🐍 Python Command Cheat Sheet — Your Quick Reference → Basic Commands • print() – Display output • input() – Take user input • len() – Get data length → Variables & Data Types • int, float, bool, list, dict, tuple, set, str → Control Structures • if, elif, else • for, while • break, continue, pass → Functions • def, return, lambda → Modules & Packages • import • from module import function → Exception Handling • try, except, finally, raise → File Handling • open(), read(), write(), close() → List Comprehensions • [x for x in list if condition] → Decorators & Generators • @decorator_name • yield 🎓 Learn Python step-by-step → https://lnkd.in/dNB4GthH Credit: Gina Acosta Gutiérrez (@ginacostag) #Python #Coding #CheatSheet #ProgrammingValley #DataScience #PythonDeveloper #FreeCourses

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

➡️ Python Workflow for Data Analysis Every data project I work on follows a similar process — and Python fits in perfectly at each stage! Here’s a simple breakdown of how Python streamlines the data analysis journey 👇 1️⃣ Import data (CSV) 2️⃣ Clean & preprocess (Pandas) 3️⃣ Analyze patterns (NumPy / Pandas) 4️⃣ Visualize results (Matplotlib / Seaborn) 5️⃣ Export insights (Power BI / Excel) Which step is your favorite part of the process? #Python #DataAnalytics #DataScience #LearningJourney #PowerBI #Pandas

Day 1 of documenting my data analysis journey. 📝 After getting comfortable with Excel, I moved on to Python and started learning about arrays. When working with data in Python, especially with libraries like NumPy and Pandas, arrays form the foundation of how data is stored and processed. They let you slice, filter and transform data in a clean and efficient way. Arrays are important because they make computations faster and more structured. NumPy arrays, for example, are much quicker than Python lists since they’re stored in a continuous block of memory. One key concept I focused on today was array indexing. It’s simply how you access specific elements, rows or columns from an array, similar to how you’d select parts of a table. That’s it for today’s progress.🤸 Next, I’ll be exploring array transposition and shape manipulation. I’m taking it one step at a time and enjoying the process of understanding how data really works. Excited to see how this builds up over time.😊 #DataAnalysis #DataforHealth #Data #Datajourney #Documentation

Yes, data visualization tools with Python are powerful. There is no doubt about that. However, if you are blind, they leave you behind because the data visualizations are images. There’s no descriptions or anything like that for the visualizations that you generate. With tools like the monarch it helps with charts, but only if they’re in the right format, which these charts for things like pandas are not.

🚀 A Beginner’s Journey into Pandas! If you’re starting with Python for data analysis, pandas is an essential library to master. In my latest blog, I cover: - Creating Series and understanding indexing - Accessing data with .loc[] and .iloc[] - Boolean filtering for conditional selection - Reading CSV files in multiple ways - Exploring DataFrames using .info(), .describe(), .shape, and .columns Whether you’re a beginner or brushing up your pandas skills, this blog is packed with step-by-step examples and easy-to-understand explanations. 📌 Check it out here: https://lnkd.in/g9SuDZEK #Python #DataScience #Pandas #DataAnalysis #LearningPython #DataSkills

📘 SQL ➡️ Python Quickstart Guide A perfect cheat sheet for anyone shifting from database queries to data analysis. Learn how every SQL command translates into a Python pandas operation — all in one glance! 🚀 Save this for your next data project. #SQL #Python #DataAnalytics