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NumPy - Data Types
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 Half precision float: sign bit, 5 bits exponent, 10 bits mantissa |
| 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 (pttle-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 pttle-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, apgn, copy)
The parameters are −
Object − To be converted to data type object
Apgn − 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 appped 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)