NumPy supports a much greater variety of numerical types than Python does. The following table shows different scalar data types defined in NumPy.
NumPy numerical types are instances of dtype (data-type) objects, each having unique characteristics. The dtypes are available as np.bool_, np.float32, etc.
A data type object describes interpretation of fixed block of memory corresponding to an array, depending on the following aspects −
Type of data (integer, float or Python object)
Size of data
Byte order (little-endian or big-endian)
In case of structured type, the names of fields, data type of each field and part of the memory block taken by each field.
If data type is a subarray, its shape and data type
The byte order is decided by prefixing '<' or '>' to data type. '<' means that encoding is little-endian (least significant is stored in smallest address). '>' means that encoding is big-endian (most significant byte is stored in smallest address).
A dtype object is constructed using the following syntax −
numpy.dtype(object, align, copy)
The parameters are −
Object − To be converted to data type object
Align − If true, adds padding to the field to make it similar to C-struct
Copy − Makes a new copy of dtype object. If false, the result is reference to builtin data type object
Each built-in data type has a character code that uniquely identifies it.
'b' − boolean
'i' − (signed) integer
'u' − unsigned integer
'f' − floating-point
'c' − complex-floating point
'm' − timedelta
'M' − datetime
'O' − (Python) objects
'S', 'a' − (byte-)string
'U' − Unicode
'V' − raw data (void)
|bool_||Boolean (True or False) stored as a byte|
|int_||Default integer type (same as C long; normally either int64 or int32)|
|intc||Identical to C int (normally int32 or int64)|
|intp||Integer used for indexing (same as C ssize_t; normally either 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: sign bit, 5 bits exponent, 10 bits mantissa|
|float32||Single precision float: sign bit, 8 bits exponent, 23 bits mantissa|
|float64||Double precision float: sign bit, 11 bits exponent, 52 bits mantissa|
|complex_||Shorthand for complex128|
|complex64||Complex number, represented by two 32-bit floats (real and imaginary components)|
|complex128||Complex number, represented by two 64-bit floats (real and imaginary components)|
# using array-scalar type import numpy as np dt = np.dtype(np.int32) print (dt)
#int8, int16, int32, int64 can be replaced by equivalent string 'i1', 'i2','i4', etc. import numpy as np dt = np.dtype('i4') print (dt)
# using endian notation import numpy as np dt = np.dtype('>i4') print (dt)
# first create structured data type import numpy as np dt = np.dtype([('age',np.int8)]) print (dt)
# now apply it to ndarray object import numpy as np dt = np.dtype([('age',np.int8)]) a = np.array([(10,),(20,),(30,)], dtype = dt) print (a)
[(10,) (20,) (30,)]
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