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    NumPy – Data Types

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

    Sr.No. Data Types & Description
    1

    bool_

    Boolean (True or False) stored as a byte

    2

    int_

    Default integer type (same as C long; normally either int64 or int32)

    3

    Intc

    Identical to C int (normally int32 or int64)

    4

    Intp

    Integer used for indexing (same as C ssize_t; normally either int32 or int64)

    5

    int8

    Byte (-128 to 127)

    6

    int16

    Integer (-32768 to 32767)

    7

    int32

    Integer (-2147483648 to 2147483647)

    8

    int64

    Integer (-9223372036854775808 to 9223372036854775807)

    9

    uint8

    Unsigned integer (0 to 255)

    10

    uint16

    Unsigned integer (0 to 65535)

    11

    uint32

    Unsigned integer (0 to 4294967295)

    12

    uint64

    Unsigned integer (0 to 18446744073709551615)

    13

    float_

    Shorthand for float64

    14

    float16

    Shorthand for float64

    15

    float32

    Single precision float: sign bit, 8 bits exponent, 23 bits mantissa

    16

    float64

    Double precision float: sign bit, 11 bits exponent, 52 bits mantissa

    17

    complex_

    Shorthand for complex128

    18

    complex64

    Complex number, represented by two 32-bit floats (real and imaginary components)

    19

    complex128

    Complex number, represented by two 64-bit floats (real and imaginary components)

    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 
    importnumpyasnp
    dt=np.dtype(np.int64)
    printdt
    

    The output is as follows −

    Int64

    Example 2

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

    The output is as follows −

    int32

    Example 3

    # using endian notation 
    importnumpyasnp
    dt=np.dtype('>i4')
    printdt
    

    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 
    importnumpyasnp
    dt=np.dtype([('age',np.int8)])
    printdt
    

    The output is as follows −

    [(‘age’, ‘i1’)]

    Example 5

    # now apply it to ndarray object 
    importnumpyasnp
    
    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

    importnumpyasnp

    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.

    importnumpyasnp
    student=np.dtype([('name','S20'),('age','i1'),('marks','f4')])
    print student
    

    The output is as follows −

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

    Example 8

    importnumpyasnp
    
    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)]

     

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