Discovering Box Plot: A Simple yet Powerful Data Visualization Tool

🔄 Transform Your Data with Pandas melt! 🐼 Ever faced a wide dataset that’s hard to analyze? That’s where melt comes in! It turns wide data into long format, making it perfect for analysis and visualization. 📊 Example: import pandas as pd df = pd.DataFrame({ 'Name': ['Alice', 'Bob'], 'Math': [90, 85], 'Science': [95, 80] }) melted = pd.melt(df, id_vars=['Name'], var_name='Subject', value_name='Score') print(melted) Output: Name Subject Score 0 Alice Math 90 1 Bob Math 85 2 Alice Science 95 3 Bob Science 80 💡 Why use melt? It’s perfect for tidy data, plotting, and group analysis! #Python #Pandas #DataScience #DataAnalysis #MachineLearning #CodingTips #DataVisualization #PythonTips 🐍📊

Data without visualization is just numbers. We're now in the seventh week of our Data Science journey with Digital Skola. This week, we learned how to turn raw data into meaningful stories through: 📊 Data visualization with Python - using Matplotlib and seaborn 📈 Introduction to Google Data Studio - building an interactive dashboard 📊 Alchemist project - applying what we have learned into a project Read the highlights from this week's session below 👇 #DataScience #BootcampDataScience #DigitalSkola #LearningProgressReview

We are back with another Learn with Lantana! There are a lot of choices out there! So which open-source software do we recommend for most public health analytics? The right choice depends on the task at hand. Watch today’s #LearnWithLantana to hear our Director of Data Science, Mathew Sapiano, share his perspective. New to the series? Make sure to check out our kick-off blog to learn more: https://lnkd.in/eZGSgBPD #PublicHealth #DataScience #RStats #Python #HealthIT #DataModernization #Lantana #OpenSource #PublicHealthAnalytics

📈 Levelling Up My Data Analysis Skills with SciPy! Today, I explored a powerful tool in data analysis — the linregress function from scipy.stats. It helped me perform linear regression easily and understand trends in real-world datasets, especially while working on sea-level rise prediction. 🔍 What I learned: linregress provides: - Slope of the line - Intercept - Correlation coefficient (r-value) - p-value - Standard error - Using this, I created trend lines and predicted future sea levels based on historical data. - This experience improved my understanding of time series analysis, statistics, and data visualisation. 🛠 Tools Used: - Python - Pandas - Matplotlib - SciPy (from scipy.stats import linregress) 📂 Project Code on GitHub: 🔗 Your GitHub link here https://lnkd.in/g-FfvuCs Excited to keep learning and exploring more in data analytics! 🚀 #DataScience #Python #SciPy #DataVisualization #GitHub #DataAnalytics #LearningJourney

Handling Missing Values in a Dataset 4 Simple and Effective Techniques! Missing data is one of the most common issues in any dataset and how you handle it can make or break your model’s performance. In my latest notebook, I explored 4 of the easiest and most practical methods to deal with missing values: 1. Basic Statistics (Mean, Median, Mode): Quick and effective for numerical or categorical features. 2. Backfill (bfill): Fills missing data with the next valid observation. 3. Forward Fill (ffill): Uses the previous valid observation to fill missing spots. 4. Linear Interpolation: Estimates missing values by connecting the dots between known data points. Each method is demonstrated clearly with Python examples in the notebook. Check out the full notebook here: https://lnkd.in/gBKgfjZx #missing #github #data #datascience #notebook #statistics #backfill #forwardfill #interpolation

🚀 Week 9 of Data Science from Sheryians Coding School This week, I dove deep into handling messy data using pandas . Here's what I explored and practiced: 🔹 Missing Values Counting missing values in a Data Frame Identifying missing entries Removing rows with missing values Filling missing data with mean/ appropriate values Imputing missing values for cleaner datasets 🔹 Outliers Detecting extreme values using IQR and Z-score Replacing or removing outliers to avoid skewed results 🔹 Categorical Data Encoding Converting categorical columns with Label Encoding Creating separate columns for categories using One-Hot Encoding Counting occurrences of each category 🔹 Feature Scaling & Transformation Converting all text to lowercase for consistency Scaling numeric columns with Min-Max normalization and standardization Subtracting min or mean, dividing by 100, and rounding values for better readability 🔹 Other Key Operations Finding and capping extreme values Sorting numeric columns to inspect extremes Converting gender columns to 0/1 Creating ranges and normalizing data #DataScience #Python #Pandas #LearningUpdate

Hey, #Datafam,just wrapped up an exciting hands-on exercise on Practical Visualization using the famous Iris dataset. This project is inspired by my mentor and instructor George Boma Smith at SmartHub Global. In this project, I: ✅ Loaded and organized the dataset using pandas and scikit-learn ✅ Visualized relationships between features such as petal and sepal dimensions with Matplotlib ✅ Customized figure size, labels, and fonts to enhance readability and presentation ✅ Explored the use of colormaps for clearer visual distinction among iris species This exercise reinforced how powerful visual analytics can be in uncovering data patterns and communicating insights effectively. Next up: experimenting with more advanced visualizations and interactive plots. #Python #Matplotlib #MachineLearning #IrisDataset #DataScience

DAY 5: The Detail I Almost Ignored (But Shouldn't Have) Final post in this NumPy series, and this one's about something I almost scrolled past: int64. When NumPy creates an integer array, it defaults to int64. I thought "cool, whatever" and moved on. Then I learned what that actually means: int32 can hold numbers up to ~2.1 billion int64 can hold numbers up to ~9.2 QUINTILLION Why does NumPy go bigger by default? Because when you're working with real data: Datasets can have millions of rows Financial calculations deal with huge numbers Scientific computing needs precision One overflow error can break everything It's one of those small decisions that shows NumPy was built by people who've dealt with real-world data problems. 5 days ago, NumPy was just "that array library." Now? I get why it's the foundation of everything in data science. It's not just about faster code—it's about thinking differently. Operations on entire arrays instead of looping through elements one by one. Still so much to learn (array slicing, broadcasting, vectorization...) but these fundamentals finally make sense. To everyone who's been liking and commenting this week—thank you! Your engagement kept me motivated to keep learning and sharing 🙏 What should I dive into next? Drop suggestions below 👇 #DataScience #Python #NumPy #WeekOfLearning #DataAnalytics

Step19..continue …towards Data Science and ML model creation ****** This is how to visualization of data from scratch to master**** How to start write code 1. Please follow my steps, which is very helpful when we start creating ML Model. 2. Guys use google colab for practices. Problem --: How to show data in a graphical mode.. Solution --: matplotlib is solution in python # Data visualization # This is simple line visualization with help of matplotlib # import required libraries import numpy as np import matplotlib.pyplot as plt # Create data x = np.arange(1,10,2) # We create a data point between 1-10 with 2 difference y = 3*x + 2 # create y axis plt.plot(x,y) # plot x and y axis plt.show()

📶 Experiment 6: Data Visualization using Matplotlib In this lab, I explored the power of data visualization — transforming raw data into meaningful insights using Matplotlib and Seaborn. 🔍 Key learning outcomes: • Creating various plot types — line, bar, histogram, box, and scatter plots • Customizing visuals with titles, labels, legends, and styles • Using Seaborn for advanced and aesthetic statistical plots This exercise strengthened my ability to communicate data insights effectively — an essential skill for every data science professional. 📁 Explore the repository here : 👉 https://lnkd.in/epWys7e7 #DataScience  #Python #DataVisualization  #Matplotlib  #Seaborn  #EDA  #MachineLearning  #Analytics  #DataAnalysis  #JupyterNotebook  #LearningJourney Ashish Sawant sir