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NumPy supports many more data types than Python's built-in types, and they can basically correspond to C language data types, with some types corresponding to Python's built-in types. The following table lists common NumPy basic types. | Name | Description | | --- | --- | | bool_ | Boolean data type (True or False) | | int_ | Default integer type (similar to C's long, int32 or int64) | | intc | Same as C's int type, typically int32 or int64 | | intp | Integer type used for indexing (similar to C's ssize_t, generally still int32 or int64) | | int8 | Byte (-128 to 127) | | int16 | Integer (-32768 to 32767) | | int32 | Integer (-2147483648 to 2147483647) | | int64 | Integer (-9223372036854775808 to 9223372036854775807) | | uint8 | Unsigned integer (0 to 255) | | uint16 | Unsigned integer (0 to 65535) | | uint32 | Unsigned integer (0 to 4294967295) | | uint64 | Unsigned integer (0 to 18446744073709551615) | | float_ | Shorthand for float64 | | float16 | Half-precision float, includes: 1 sign bit, 5 exponent bits, 10 mantissa bits | | float32 | Single-precision float, includes: 1 sign bit, 8 exponent bits, 23 mantissa bits | | float64 | Double-precision float, includes: 1 sign bit, 11 exponent bits, 52 mantissa bits | | complex_ | Shorthand for complex128, i.e., 128-bit complex number | | complex64 | Complex number, representing two 32-bit floats (real and imaginary parts) | | complex128 | Complex number, representing two 64-bit floats (real and imaginary parts) | NumPy's numerical types are actually instances of dtype objects and correspond to unique character codes, including np.bool_, np.int32, np.float32, etc. * * * ## Data Type Object (dtype) A data type object (an instance of the numpy.dtype class) describes how the memory block corresponding to an array is used. It describes several aspects of the data: * The type of data (integer, float, or Python object) * The size of the data (e.g., how many bytes are used to store an integer) * The byte order of the data (little-endian or big-endian) * In the case of structured types, the names of the fields, the data type of each field, and the part of the memory block each field occupies * If the data type is a sub-array, what its shape and data type are. Byte order is determined by pre-setting in the data type. means big-endian (the most significant byte is stored at the smallest address, i.e., the high-order group is placed first). The dtype object is constructed using the following syntax: numpy.dtype(object, align, copy) * object - The data type object to convert to * align - If true, adds padding to make it similar to a C struct. * copy - Copies the dtype object; if false, it is a reference to the built-in data type object ### Examples Next, we can understand through examples. ## Example 1 import numpy as np dt = np.dtype(np.int32)print(dt) Output: int32 ## Example 2 import numpy as np dt = np.dtype('i4')print(dt) Output: int32 ## Example 3 import numpy as np dt = np.dtype('<i4')print(dt) Output: int32 The following example demonstrates the use of structured data types, where type fields and corresponding actual types will be created. ## Example 4 import numpy as np dt = np.dtype([('age',np.int8)])print(dt) Output: [('age', 'i1')] ## Example 5 import numpy as np dt = np.dtype([('age',np.int8)])a = np.array([(10,),(20,),(30,)], dtype = dt)print(a) Output: [(10,) (20,) (30,)] ## Example 6 import numpy as np dt = np.dtype([('age',np.int8)])a = np.array([(10,),(20,),(30,)], dtype = dt)print(a['age']) Output: The following example defines a structured data type `student`, containing a string field `name`, an integer field `age`, and a float field `marks`, and applies this dtype to an ndarray object. ## Example 7 import numpy as np student = np.dtype([('name','S20'), ('age', 'i1'), ('marks', 'f4')])print(student) Output: [('name', 'S20'), ('age', 'i1'), ('marks', 'f4')] ## Example 8 import numpy as np student = np.dtype([('name','S20'), ('age', 'i1'), ('marks', 'f4')])a = np.array([('abc', 21, 50),('xyz', 18, 75)], dtype = student)print(a) Output: [(b'abc', 21, 50.) (b'xyz', 18, 75.)] Each built-in type has a unique character code that defines it, as follows: | Character | Corresponding Type | | --- | --- | | b | Boolean | | i | (Signed) Integer | | u | Unsigned integer | | f | Floating-point | | c | Complex floating-point | | m | timedelta (time interval) | | M | datetime (date and time) | | O | (Python) Object | | S, a | (Byte-)string | | U | Unicode | | V | Raw data (void) |