NumPy - Data Types

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NumPy supports a much greater variety of numerical types than Python does. The following table shows different scalar data types defined in NumPy.

NumPy numerical types are instances of dtype (data-type) objects, each having unique characteristics. The dtypes are available as np.bool_, np.float32, etc.

Data Type Objects (dtype)

A data type object describes interpretation of fixed block of memory corresponding to an array, depending on the following aspects −

• Type of data (integer, float or Python object)

• Size of data

• Byte order (little-endian or big-endian)

• In case of structured type, the names of fields, data type of each field and part of the memory block taken by each field.

• If data type is a subarray, its shape and data type

The byte order is decided by prefixing '<' or '>' to data type. '<' means that encoding is little-endian (least significant is stored in smallest address). '>' means that encoding is big-endian (most significant byte is stored in smallest address).

A dtype object is constructed using the following syntax −

numpy.dtype(object, align, copy)

The parameters are −

• Object − To be converted to data type object

• Align − If true, adds padding to the field to make it similar to C-struct

• Copy − Makes a new copy of dtype object. If false, the result is reference to builtin data type object

Example 1

# using array-scalar type 
import numpy as np 
   dt = np.dtype(np.int32) 
   print dt

The output is as follows −

int32

Example 2

#int8, int16, int32, int64 can be replaced by equivalent string 'i1', 'i2','i4', etc. 
import numpy as np 
   dt = np.dtype('i4')
   print dt 

The output is as follows −

int32

Example 3

# using endian notation 
import numpy as np 
   dt = np.dtype('>i4') 
   print dt

The output is as follows −

>i4

The following examples show the use of structured data type. Here, the field name and the corresponding scalar data type is to be declared.

Example 4

# first create structured data type 
import numpy as np 
   dt = np.dtype([('age',np.int8)]) 
   print dt 

The output is as follows −

[('age', 'i1')] 

Example 5

# 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

The output is as follows −

[(10,) (20,) (30,)]

Example 6

# 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 output is as follows −

[10 20 30]

Example 7

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

The output is as follows −

[('name', 'S20'), ('age', 'i1'), ('marks', '<f4')])

Example 8

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

The output is as follows −

[('abc', 21, 50.0), ('xyz', 18, 75.0)]

Each built-in data type has a character code that uniquely identifies it.

• 'b' − boolean
• 'i' − (signed) integer
• 'u' − unsigned integer
• 'f' − floating-point
• 'c' − complex-floating point
• 'm' − timedelta
• 'M' − datetime
• 'O' − (Python) objects
• 'S', 'a' − (byte-)string
• 'U' − Unicode
• 'V' − raw data (void)