Numpy Data Types: A Comprehensive Analysis of dtype and astype

The homogeneity of NumPy arrays enables efficient data processing, and the data type (dtype) is crucial as it determines element storage, memory usage, and operation rules. A reasonable choice of dtype can optimize performance and avoid waste. A dtype is an object describing the array's type, viewable via `arr.dtype`, and can be explicitly specified during creation (e.g., `np.int32`). Common types include int (8/16/32/64-bit), uint (unsigned integers), float (32/64-bit), bool, and object. The `astype` method is used for type conversion, returning a new array without modifying the original. Examples include converting integers to floats (`arr.astype(np.float64)`), floats to integers (truncating decimals, e.g., `2.9` to `2`), and boolean-integer conversions (`True`→`1`, non-zero→`True`). It should be noted that converting to a smaller type may cause overflow (e.g., `int64` to `int32`), and floating-point to integer conversion does not round. Mastering dtype and `astype` allows flexible data handling, avoiding memory waste and calculation errors, thus laying a foundation for subsequent analysis.