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