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)
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