Understanding *args and **kwargs in Python Functions

Do you want to trust your results? Start by questioning your data. In contexts like the #UN, where data informs policy and programmes, ensuring data quality is essential: across analytics, challenges rarely come from complex models, they emerge much earlier. The analysis runs. The chart looks right. The conclusion may be wrong. ➡️ That’s why data quality isn’t a “cleanup step”-  it is the analysis. #UNSSC’s "Data Quality for Impact with Python" is a hands-on course designed to build the habit that separates reliable insights from risky ones: validating data before using it. Because strong data practices aren’t built later, they’re built early. Enroll now: https://lnkd.in/dSJYvtbX

🔷A simple train test split is not always enough. I learned this the hard way when my model looked great on paper and struggled on real data. 📌Here is what nobody tells you about splitting data properly. The basic split gives you two sets. Training and testing. That works for simple projects. But what if you need to tune your model? You test different settings, pick the best one, and evaluate on the test set. The problem is that you have now indirectly used the test set to make decisions. It is no longer a fair judge. This is where a three way split becomes important. 🔹X_train, X_temp, y_train, y_temp = train_test_split( X, y, test_size=0.3, random_state=42 ) 🔹X_val, X_test, y_val, y_test = train_test_split( X_temp, y_temp, test_size=0.5, random_state=42 ) Now you have three sets. Training set. The model learns here. 70 percent of your data. Validation set. You tune and compare models here. 15 percent. Test set. You evaluate the final model here. Once. Never again. 15 percent. The test set is sacred. You look at it exactly one time at the very end. One more thing that most people miss. Always stratify your split when your target column is imbalanced. 🔹train_test_split(X, y, stratify=y, test_size=0.2) stratify=y makes sure both sets have the same proportion of each class. Without it you might end up with a training set that barely sees the minority class and a model that has no idea it exists. The split is not a formality. It is a decision that shapes every result that follows. Get it right before you touch anything else. ❓What split ratio do you use for your projects and why? #DataScience #MachineLearning #Python

🚀 Project Update – Task 1 Completed https://lnkd.in/g5VBSXJz 📊 Customer Shopping Behaviour Analysis 🔧 Task 1: Data Cleaning & Transformation using Python In this phase, I focused on preparing the raw dataset and converting it into a well-structured, analysis-ready format. ✅ Key Activities: Loaded and explored the dataset using Python Performed data inspection and statistical summary analysis Identified and handled missing values using appropriate techniques Standardized column names using snake_case convention Applied data transformations using functions like map() and qcut() Cleaned and formatted the dataset for consistency and usability Ensured the dataset is structured and ready for further analysis. 💡 This step is crucial as high-quality data directly impacts the accuracy of insights and decision-making. 📌 Looking forward to diving into SQL-based analysis in the next phase! #DataAnalytics #Python #DataCleaning #DataTransformation #SQL #LearningJourney #ProjectUpdate

4 of my #100DaysOfCode Moving from simple variables into actual Data Structures using Python Lists. As I grow in data analytics, I know organizing and manipulating data is the core of the job, so getting comfortable with lists is a critical foundation. Here is what I tackled in day 4. Randomisation: Using the Mersenne Twister (import random) and randint() to generate unpredictable outcomes. Lists: Creating, altering, and managing data structures using brackets []. List Methods: How to use .append(), .extend(), .insert(), and .pop(). Indexing: Accessing specific data points (and successfully conquering negative indexing!). To put it all together, we built a fully functional Rock, Paper, Scissors game that plays against the user.

Recently, I was working on a small Pandas analysis project involving merging user and order datasets. What looked like a straightforward merge turned into an interesting learning moment. The code ran correctly, the output looked structured, and everything seemed fine initially until I noticed one metric wasn’t aligning with what I expected. That led me to explore how dataset relationships can impact analysis after merges, especially when working with transactional data. I wrote a short blog sharing the example, what I observed, and the approach I used to fix it. #Python #Pandas #DataAnalysis #DataScience #SQL Read here:

🚀 Day 38/70 – Sampling in Statistics Today I learned about Sampling in Statistics 📊 Sampling is the process of selecting a small subset of data from a large population for analysis. ⸻ 📌 Why Sampling is Used ✔ Saves time and cost ✔ Easy to analyze ✔ Useful when full data is too large ⸻ 📌 Types of Sampling 1️⃣ Random Sampling • Every item has equal chance 2️⃣ Systematic Sampling • Select every nth item 3️⃣ Stratified Sampling • Divide into groups and sample from each 4️⃣ Convenience Sampling • Easily available data ⸻ 📌 Python Example import numpy as np data = np.arange(1, 101) # Random sample of 10 values sample = np.random.choice(data, size=10) print(sample) ⸻ 📊 Why It’s Important ✔ Represents large data efficiently ✔ Used in surveys and research ✔ Helps in making predictions ✔ Important for machine learning ⸻ Today’s Learning: Sampling helps analyze big data with smaller, manageable data 🔥 Day 38 completed 💪 Almost 40 days of consistency — keep going strong! #Day38 #Statistics #DataAnalytics #Python #LearningInPublic #FutureDataAnalyst #70DaysChallenge

🚀 Day 2: Strengthening the Logic Behind the Data I’m officially on Day 2 of my Python revision journey for Data Analytics! 📊 Today was all about the "brain" of our scripts: Operators and Conditional Statements. While these concepts seem basic, they are the gatekeepers of data cleaning and analysis. Here’s a quick breakdown of what I revisited today: =>Relational Operators: The foundation of comparison (==, !=, >, etc.). Essential for filtering datasets—like identifying all customers with a lifetime value over a certain threshold. =>Logical Operators: Using and, or, and not to combine conditions. This is where complex segmenting happens (e.g., "Show me users who signed up in 2023 AND haven't made a purchase"). =>Conditional Statements: Mastering if-elif-else blocks. This is how we automate decision-making in code, such as categorizing data into buckets or handling missing values dynamically. The goal? To move past just "writing code" and start writing efficient, readable logic. Data isn't just numbers; it’s the stories we tell by asking the right questions through code. 💡 Onward to Day 3! 🐍 #FKM #Python #DataAnalytics #LearningInPublic #DataAnalytics #CodingJourney #NxtWave #ContinuousLearning

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

The loop that takes 47 seconds becomes 0.3 seconds. Day 11 of 30 -- Advanced Pandas Optimization No new hardware. No rewrite. Just one change. Replace iterrows() with a vectorized expression. Here is what most Pandas developers do not realize: A DataFrame is just a NumPy array -- contiguous C memory. When you write df.iterrows(), Python converts every row to a Python dict. You are running a Python for-loop over a C array. That is where the 47 seconds comes from. Write df['total'] = df['qty'] * df['price'] instead. That is a C loop on the raw array. 157x faster. Today's topic covers: Why Pandas can be slow -- the Python loop trap explained Speed hierarchy -- iterrows 47s vs apply 28s vs itertuples 5s vs vectorized 0.3s dtype optimization -- 6 dtype conversions that cut memory by 70% before writing a single query Auto dtype downcast function that optimizes an entire DataFrame in 10 lines pd.eval and query for complex expressions without intermediate arrays Chunked processing -- 50M rows on a laptop with 6GB RAM Real scenario -- retail analytics, 48GB to 6GB, 4 hours to 8 minutes 8 optimization techniques including the SettingWithCopyWarning trap 5 mistakes including growing DataFrames in loops and loading unused columns Key insight: Pandas is not slow. Writing Python loops over Pandas DataFrames is slow. #Python #Pandas #DataEngineering #Performance #SoftwareEngineering #100DaysOfCode #PythonDeveloper #TechContent #BuildInPublic #TechIndia #DataScience #Analytics #PythonProgramming #LinkedInCreator #LearnPython #PythonTutorial

Day 24/75 — This one Python function helped me understand my data better 👇 When I started analyzing datasets, I felt overwhelmed. Too many rows. Too much information. Then I discovered this: df.groupby('city')['price'].mean() 💡 What it does: 👉 Groups data by a category 👉 Calculates insights (like average, sum, count) Example: Instead of looking at thousands of rows… I can instantly see: 📊 Average price per city 🚨 Why this is powerful: • Turns raw data into insights • Helps you compare groups easily • Makes analysis faster and clearer 👨💻 Now I use it all the time to: • Compare categories • Find patterns • Simplify data Small function… But a big upgrade in how I analyze data. Have you used groupby() before? 👇 #DataScience #Python #Pandas #DataAnalysis #LearningInPublic