Every ndarray has an associated data type (dtype) object. This data type object (dtype) informs us about the layout of the array. This means it gives us information about :
- Type of the data (integer, float, Python object etc.)
- Size of the data (number of bytes)
- Byte order of the data (little-endian or big-endian)
- If the data type is a sub-array, what is its shape and data type.
The values of an ndarray are stored in a buffer which can be thought of as a contiguous block of memory bytes. So how these bytes will be interpreted is given by the dtype object.
Constructing a data type (dtype) object : Data type object is an instance of numpy.dtype class and it can be created using numpy.dtype.
Parameters:
obj: Object to be converted to a data type object.
align : [bool, optional] Add padding to the fields to match what a C compiler would output for a similar C-struct.
copy : [bool, optional] Make a new copy of the data-type object. If False, the result may just be a reference to a built-in data-type object.
# Python Program to create a data type object
import numpy as np
# np.int16 is converted into a data type object.
print(np.dtype(np.int16))
Output:
int16
# Python Program to create a data type object
# containing a 32 bit big-endian integer
import numpy as np
# i4 represents integer of size 4 byte
# > represents big-endian byte ordering and
# < represents little-endian encoding.
# dt is a dtype object
dt = np.dtype('>i4')
print("Byte order is:",dt.byteorder)
print("Size is:", dt.itemsize)
print("Data type is:", dt.name)
Output:
Byte order is: >
Size is: 4
Name of data type is: int32
The type specifier (i4 in above case) can take different forms:
b1, i1, i2, i4, i8, u1, u2, u4, u8, f2, f4, f8, c8, c16, a (representing bytes, ints, unsigned ints, floats, complex and fixed length strings of specified byte lengths)
int8,…,uint8,…,float16, float32, float64, complex64, complex128 (this time with bit sizes)
Note : dtype is different from type.
# Python program to differentiate
# between type and dtype.
import numpy as np
a = np.array([1])
print("type is: ",type(a))
print("dtype is: ",a.dtype)
Output:
type is:
dtype is: int32
Data type Objects with Structured Arrays : Data type objects are useful for creating structured arrays. A structured array is the one which contains different types of data. Structured arrays can be accessed with the help of fields.
A field is like specifying a name to the object. In case of structured arrays, the dtype object will also be structured.
# Python program for demonstrating
# the use of fields
import numpy as np
# A structured data type containing a
# 16-character string (in field ‘name’)
# and a sub-array of two 64-bit floating
# -point number (in field ‘grades’)
dt = np.dtype([('name', np.unicode_, 16),
('grades', np.float64, (2,))])
# Data type of object with field grades
print(dt['grades'])
# Data type of object with field name
print(dt['name'])
Output:
('<f8', (2,))
# Python program to demonstrate
# the use of data type object
# with structured array.
import numpy as np
dt = np.dtype([('name', np.unicode_, 16),
('grades', np.float64, (2,))])
# x is a structured array with names
# and marks of students.
# Data type of name of the student is
# np.unicode_ and data type of marks is
# np.float(64)
x = np.array([('Sarah', (8.0, 7.0)),
('John', (6.0, 7.0))], dtype=dt)
print(x[1])
print("Grades of John are: ", x[1]['grades'])
print("Names are: ", x['name'])
Output:
('John', [ 6., 7.])
Grades of John are: [ 6. 7.]
Names are: ['Sarah' 'John']