# Object data type (dtype) in NumPy Python

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Each ndarray has an associated data type object (dtype). This data type object (dtype) informs us about the layout of the array. This means it gives us information about:

• Data type (integer, floating point, Python object, etc.)
• Data size (number of bytes )
• Data byte order (big or big endian)
• If the data type is a subarray, what is its shape and data type.

The ndarray values ​​are stored in a buffer, which can be thought of as a contiguous block of bytes of memory. How these bytes are interpreted is determined by the dtype object.

1. Data type object (dtype) construction. A datatype object is an instance of the numpy.dtype class and can be instantiated using numpy.dtype.

Parameters:

• obj: an object to convert to a datatype object.
• align : bool, optional
Padding the margins to match what the C compiler would output for a similar C structures.
• copy : bool, optional
Make a fresh copy of the datatype object. If False, the result can simply be a reference to an embedded data type object.

 ` # Python program to create data type object ` ` import ` ` numpy as np `   ` # np.int16 is converted to data type object. ` ` print ` ` (np.dtype (np.int16)) `

Exit :

`int16`

` `

` # Python program to create a data type object # contains a 32-bit big endian integer import numpy as np   # i4 represents integer a 4 byte number #" represents big endian byte order and "represents big endian encoding. # dt is a dtype object dt = np.dtype ( ’& gt; 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 various forms:

1. b1, i1, i2, i4, i8 , u1, u2, u4, u8, f2, f4, f8, c8, c16, a
(representing bytes, integers, unsigned integers, floating point numbers, complex and
fixed length strings of specified length in bytes )
2. int8,…, uint8,…, float16, float32, float64, complex64, complex128
(this time with bitwise dimensions)

Notes:

` 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) `

Exit:

` type is: dtype is: int32 `
2. Data type objects with structured arrays. Data type objects are useful for creating structured arrays. Structured array — this is the one that contains different types of data. Structured arrays can be accessed using fields.
A field is like giving a name to an object. In the case of structured arrays, the dtype will also be structured.

 ` # Python demo program ` ` # using fields ` ` import ` ` numpy as np `   ` # A structured data type containing a 16-character string (in the" name "field) ` ` # and a subarray of two 64-bit floating point numbers (in the & # 39; grades & # 39; field): ` ` `  ` dt ` ` = ` ` np.dtype ([(` ` ’name’ ` `, np.unicode_, ` ` 16 ` `), (` ` ’grades’ ` , np.float64, ( ` 2 ` `,))]) `   ` # Object data type with field ratings ` ` print ` ` (dt [` ` ’grades’ ` `]) `   ` # Object data type with field name ` ` print ` ` (dt [` ` ’name’ ` `]) `

Exit:

` (’"f8’, (2,)) `

` `

` # Python program for demonstration # using a structured array data type object. < code class = "keyword"> import numpy as np   dt = np.dtype ([( ’name’ , np.unicode_, 16 ), ( ’grades’ , np.float64, ( 2 ,))])   # x is a structured array with the names and marks of students. # Data type of student name np.unicode_ and # mark data type - 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’] `

This article is provided by Ayushi Astana . If you are as Python.Engineering and would like to contribute, you can also write an article using contribute.python.engineering or by posting an article contribute @ python.engineering. See my article appearing on the Python.Engineering homepage and help other geeks.

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