Скачать или смотреть Everything You Need to Know About NumPy dtypes (With Examples)

In this video, we explore NumPy Data Types (dtypes) and how data is stored in arrays:

✅ int, unsigned int
✅ float & floating-point precision
✅ str, byte-string
✅ bool, complex, object dtype
✅ U(Unicode string) and |S (byte string)
✅ Type conversion using astype()
✅ Integer & float limits with iinfo() and finfo()
✅ Comparing floats with isclose()
✅ Converting arrays to raw bytes using tobytes()

00:00:00 Introduction to NumPy data types
00:00:25 Creating arrays and checking data types
00:00:51 Specifying data types during array creation
00:01:15 Creating unsigned integer and boolean arrays
00:01:43 Including zero in boolean arrays and its effect
00:02:08 Converting data types using the `astype` method
00:02:35 Converting arrays to string and byte string types
00:03:23 Explanation of using `.astype()` for changing data types
00:03:49 Integer overflow and data type range limits
00:04:20 Understanding integer and floating-point data ranges
00:05:12 Floating-point precision issues
00:05:42 Using `np.isclose()` to handle floating-point comparisons
00:06:09 Introduction to strings and bytes in NumPy arrays
00:07:00 Effect of string length limits on array contents
00:07:23 Creating and inspecting byte string arrays
00:07:49 Controlling byte string length and truncation
00:08:13 Converting strings to bytes and padding with null bytes
00:08:39 Impact of padding and truncation on byte strings
00:09:06 Using complex data types in NumPy arrays
00:09:32 Handling mixed data types with object arrays
00:10:00 Conclusion and call to action

This video gives you a clear understanding of how NumPy handles data internally — very useful for Data Science, Machine Learning & high-performance Python.

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