NumPy Data Types & Memory Optimization (أنواع البيانات في نومباي) #8
Welcome back to our NumPy for Machine Learning series! Today, we explore NumPy Data Types (dtypes). Understanding how to choose the right data type is the key to building memory-efficient, high-performance machine learning models.
In this deep dive, you will learn:
✅ The 5 basic numerical types: Boolean, Integer, Unsigned Integer, Float, and Complex.
✅ Memory vs. Precision: Choosing between 8-bit, 32-bit, and 64-bit sizes.
✅ The dtype parameter: How to define and convert types during array creation.
✅ Handling Strings: Understanding Unicode (U) types and fixed-length string truncation.
✅ Best Practices: Why we use NumPy for numbers and when to switch to Pandas for complex strings.
🚀 What's Next? Now that we've mastered the data, we’ll start exploring Common NumPy Functions & Methods in the next video!
00:00 Channel Intro
00:14 Introduction to NumPy Data Types
00:19 Numerical Data Types: Boolean & Integers
00:46 Understanding Bits & Memory Size (8, 16, 32, 64)
01:04 Floating Point & Complex Numbers
01:32 Balance: Memory Saving vs. Precision
02:03 Coding: Defining dtypes (bool, int32)
03:31 Converting to Unsigned Integers (uint64)
04:01 Float & Complex Type Conversions 04:55 Working with String Data Types (Unicode)
05:51 Truncating Strings with Fixed Length (U5)
07:01 Conclusion & Next: Common Functions
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