# Numpy | Data Type Objects

• Last Updated : 15 Nov, 2018

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()

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),

# Data type of object with field 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),

# 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)

```('John', [ 6.,  7.])