python numpy data types || data types || dtype || numpy ||python ||Ak

# using array-scalar type import numpy as np dt = np.dtype(np.int32) print (dt) #int8, int16, int32, int64 can be replaced by equivalent string 'i1', 'i2','i4', etc. import numpy as np dt = np.dtype('i4') print (dt) # using endian notation import numpy as np dt = np.dtype('i4') print (dt) #The following examples show the use of structured data type. #Here, the field name and the corresponding scalar data type is to be declared. # first create structured data type import numpy as np dt = np.dtype([('age',np.int8)]) print (dt) # now apply it to ndarray object import numpy as np dt = np.dtype([('age',np.int8)]) a = np.array([(10,),(20,),(30,)], dtype = dt) print (a) # file name can be used to access content of age column import numpy as np dt = np.dtype([('age',np.int8)]) a = np.array([(10,),(20,),(30,)], dtype = dt) print (a['age']) #The following examples define a structured data type called student #with a string field 'name', an integer field 'age' and a float field 'marks'. #This dtype is applied to ndarray object. import numpy as np student = np.dtype([('name','S20'), ('age', 'i1'), ('marks', 'f4')]) print (student) import numpy as np student = np.dtype([('name','S20'), ('age', 'i1'), ('marks', 'f4')]) a = np.array([('abc', 21, 50),('xyz', 18, 75)], dtype = student) print (a) thanxxxxxxxxxx