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)
- The 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 a 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.
1. Constructing a data type (dtype) object: A data type object is an instance of the 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
# Python Program to create a data type objectimport numpy as np# np.int16 is converted into a data type object.print(np.dtype(np.int16)) |
Output:
int16
Python
# Python Program to create a data type object# containing a 32 bit big-endian integerimport numpy as np# i4 represents integer of size 4 byte# > represents big-endian byte ordering and < represents little-endian encoding.# dt is a dtype objectdt = 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 the 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
# Python program to differentiate# between type and dtype.import numpy as npa = np.array([1])print("type is: ",type(a))print("dtype is: ",a.dtype) |
Output:
type is: dtype is: int32
2. Data type Objects with Structured Arrays: Data type objects are useful for creating structured arrays. A structured array is one that 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 the case of structured arrays, the dtype object will also be structured.
Python
# Python program for demonstrating# the use of fieldsimport 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 gradesprint(dt['grades'])# Data type of object with field name print(dt['name']) |
Output:
('<f8', (2,))
Python
# Python program to demonstrate # the use of data type object with structured array.import numpy as npdt = 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']
References :
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