Data types of Python Numpy multi dimensional array

There are more variety of data types in Numpy than in Python. We can assign chars to each data types and here is the list.

?

Boolean

b

(Signed) byte

B

(UnSigned) byte

i

(Signed) Integer

u

(UnSigned) Integer

f

Floating point

c

Complex floating point

m

TimeDelta

M

Datetime

O

(Python) Objects

S, a

Zero terminated Bytes

U

Unicode String

V

raw data (void)

Using dtype to get the Data type of the Numpy array.

import numpy as np
npr1=np.array(['a','b','c'])
npr2=np.array([1,2,5])
npr3=np.array([True,True,False,True])

print(npr1.dtype) # <U1
print(npr2.dtype) # int64
print(npr3.dtype) # bool

Specifying data type while creating the array

npr4 = np.array([1, 2, 3, 4], dtype='?') 
print(npr4.dtype) # bool
npr5 = np.array([1, 2, 3, 4], dtype='U') 
print(npr5.dtype) # <U1

astype()

We can change the data type of an array by using astype(), here one Boolean data type array ( originally ) changed to integer or int32 data type.

npr4 = np.array([1, 2, 3, 4], dtype='?')  
print(npr4.dtype) # bool
npr4= npr4.astype(np.int32)
print(npr4.dtype) # int32

While changing the data may change to match the new requirment. Here float data type is changed to integer type.

npr1=np.array([1.4,3,4.9,9])
print(npr1.dtype) #float64
npr1=npr1.astype(np.int8) # changing to int8 data type
print(npr1.dtype) # int8
print(npr1) # [1 3 4 9] # Output after changing the data type

When we increase the size there will not be any change of data.

npr2=np.array(['a','b','c'])
print(npr2.dtype) #<U1
npr2=npr2.astype('<U2') # changed data type to U2
print(npr2.dtype) # <U2
print(npr2) # ['a' 'b' 'c']

When we decrease the size there will be data loss.

npr2=np.array(['a12','b123','c1234'])
print(npr2.dtype) # <U5
npr2=npr2.astype('<U2') # change data type to U2 
print(npr2)  # ['a1' 'b1' 'c1']

Note in above lines we are specifying the size while defining the data type.

Creating structured data

By using our defined data type we can create structured data to store columns with data.

import numpy as np
student=np.dtype([('id','i'),('name','S20')])
print(type(student)) # <class 'numpy.dtype[void]'>

data=np.array([(1,'Alex'),(2,'Rabi')]) # adding data 
print(data)

Output

<class 'numpy.dtype[void]'>
[['1' 'Alex']
 ['2' 'Rabi']]

More Example on using structured data.

import numpy as np
student=np.dtype([('id','i'),('name','S20')])

data=np.array([(1,'Alex'),(2,'Rabi')]) # adding data 
print(data[1]) # ['2' 'Rabi']
print(data[0][1]) # Alex

Example of getting dtype from the structured data array.

import numpy as np
student=np.dtype([('id','i'),('name','S20')])
data=np.array([(1,'Alex'),(2,'Rabi')]) # adding data 

name=data[:,1] # Name column 
id=data[:,0] #id column

print(id.dtype) # <U21
id=id.astype(np.int32) # converting the data type to integer 
print(id.dtype) # int32

Create a Numpy array using Student table.

Types of Data supported by Numpy

Source : Numpy Manual

Numpy type	C type	Description
`numpy.bool_`	`bool`	Boolean (True or False) stored as a byte
`numpy.byte`	`signed char`	Platform-defined
`numpy.ubyte`	`unsigned char`	Platform-defined
`numpy.short`	`short`	Platform-defined
`numpy.ushort`	`unsigned short`	Platform-defined
`numpy.intc`	`int`	Platform-defined
`numpy.uintc`	`unsigned int`	Platform-defined
`numpy.int_`	`long`	Platform-defined
`numpy.uint`	`unsigned long`	Platform-defined
`numpy.longlong`	`long long`	Platform-defined
`numpy.ulonglong`	`unsigned long long`	Platform-defined
`numpy.half` `numpy.float16`		Half precision float: sign bit, 5 bits exponent, 10 bits mantissa
`numpy.single`	`float`	Platform-defined single precision float: typically sign bit, 8 bits exponent, 23 bits mantissa
`numpy.double`	`double`	Platform-defined double precision float: typically sign bit, 11 bits exponent, 52 bits mantissa.
`numpy.longdouble`	`long double`	Platform-defined extended-precision float
`numpy.csingle`	`float complex`	Complex number, represented by two single-precision floats (real and imaginary components)
`numpy.cdouble`	`double complex`	Complex number, represented by two double-precision floats (real and imaginary components).
`numpy.clongdouble`	`long double complex`	Complex number, represented by two extended-precision floats (real and imaginary components).

Numpy random.randint , Random Integers

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Data types Numpy

astype()

Creating structured data

Types of Data supported by Numpy

Subhendu Mohapatra