🚀 Day 23 of 365: NumPy Operations and Functions 🚀
Ajinkya Deokate
Data Scientist | Researcher | Author | Public Speaking Expert @PlanetSpark | Freelancer
Hey there, data scientists!
Welcome to Day 23 of our #365DaysOfDataScience journey! 🎉
Yesterday we got cozy with arrays, and today we’re diving deeper into NumPy’s powerful mathematical functions and some handy techniques like indexing and slicing. Ready to crunch some numbers? Let’s go!
🔑 What We’ll Be Doing Today:
NumPy Mathematical Functions
- Learn how to calculate mean, median, standard deviation, and more using NumPy’s built-in functions.
Array Indexing, Slicing, and Conditional Selection
- Grab specific elements, rows, or columns using indexing.
- Use slicing to manipulate parts of arrays.
- Perform conditional selections on arrays to filter values based on conditions (e.g., selecting only elements greater than a certain number).
📚 Learning Resources:
Read:
Recommended by LinkedIn
- Check out the NumPy functions documentation for a deep dive into its mathematical capabilities.
Practice:
- Head to Kaggle or HackerRank for some fun NumPy challenges on arrays and functions. These are great for reinforcing today’s concepts.
✏️ Today’s Task:
It’s time to put your new skills into practice! Write a Python script that:
- Creates a NumPy array.
- Uses NumPy functions to calculate the mean, sum, variance, and other stats of the array.
- Experiment with array indexing, slicing, and conditional selections to extract and manipulate different parts of your array.
Tip: Don’t be afraid to play around with the data! The more you experiment, the more comfortable you’ll get with these operations.
Let’s continue this NumPy journey together! Feel free to share what stats you’ve calculated or any cool slicing tricks you’ve discovered! 📈👩💻👨💻
Happy Learning and See You Soon!
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