Learning Numpy - Simple Tutorial For Beginners - NumPy - Data Types Part 3¶
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.
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
What Character Code Does The Data Type Have?¶
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)
| Data Types | Description | |||
|---|---|---|---|---|
| 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) |
Example 1¶
# using array-scalar type
import numpy as np
dt = np.dtype(np.int32)
print (dt)
Example 2¶
#int8, int16, int32, int64 can be replaced by equivalent string 'i1', 'i2','i4', etc.
import numpy as np
dt = np.dtype('i4')
print (dt)
Example 3¶
# using endian notation
import numpy as np
dt = np.dtype('>i4')
print (dt)
Example 4¶
# first create structured data type
import numpy as np
dt = np.dtype([('age',np.int8)])
print (dt)
Example 5¶
# 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)
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