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 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'])
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Output:
int16
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 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)
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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 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'])
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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'])
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Output:
('John', [ 6., 7.])
Grades of John are: [ 6. 7.]
Names are: ['Sarah' 'John']References :