Data Types
| Sr.No. | Data Types & Description |
|---|---|
| 1 | bool_ - Boolean (True or False) stored as a byte |
| 2 | int_ - Default integer type (same as C long; normally either int64 or int32) |
| 3 | intc - Identical to C int (normally int32 or int64) |
| 4 | intp - Integer used for indexing (same as C ssize_t; normally either int32 or int64) |
| 5 | int8 - Byte (-128 to 127) |
| 6 | int16 - Integer (-32768 to 32767) |
| 7 | int32 - Integer (-2147483648 to 2147483647) |
| 8 | int64 - Integer (-9223372036854775808 to 9223372036854775807) |
| 9 | uint8 - Unsigned integer (0 to 255) |
| 10 | uint16 - Unsigned integer (0 to 65535) |
| 11 | uint32 - Unsigned integer (0 to 4294967295) |
| 12 | uint64 - Unsigned integer (0 to 18446744073709551615) |
| 13 | float_ - Shorthand for float64 |
| 14 | float16 - Half precision float: sign bit, 5 bits exponent, 10 bits mantissa |
| 15 | float32 - Single precision float: sign bit, 8 bits exponent, 23 bits mantissa |
| 16 | float64 - Double precision float: sign bit, 11 bits exponent, 52 bits mantissa |
| 17 | complex_ - Shorthand for complex128 |
| 18 | complex64 - Complex number, represented by two 32-bit floats (real and imaginary components) |
| 19 | complex128 - Complex number, represented by two 64-bit floats (real and imaginary components) |
| 20 | object - python object type |
| 21 | np.string_ - fixed-length string type |
| 22 | np.unicode_ - fixed-length unicode type |
NumPy numerical types are instances of dtype (data-type) objects
Data Type Objects (dtype)
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
Example
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)
[('abc', 21, 50.0), ('xyz', 18, 75.0)]
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)