Introduction to NumPy Data Types

The data types are used for defining a variable with a specific type that is used for identifying the variable and allowing the given types of data. Numpy is a data type used on Python programming, and comes along with the python package that can be used for multiple scientific computational operations. A few of the commonly used NumPy data types are np.byte, np.short, np.int_, np.uintc, np.ubyte, np.bool_, np.longlong, np.single, np.half, np.single, np.double, np.csingle, np.int8, np.int64, np.int32, np.intp, np.unitp, np.float64, etc.

Numpy Data Types

The various data types supported by numpy are :

Numpy data type	Closely associated C data type	Storage Size	Description
np.bool_	bool	1 byte	can hold boolean values, like (True or False) or (0 or 1)
np.byte	signed char	1 byte	can hold values from 0 to 255
np.ubyte	unsigned char	1 byte	can hold values from -128 to 127
np.short	signed short	2 bytes	can hold values from -32,768 to 32,767
np.ushort	unsigned short	2 bytes	can hold values from 0 to 65,535
np.uintc	unsigned int	2 or 4 bytes	can hold values from 0 to 65,535 or 0 to 4,294,967,295
np.int_	long	8 bytes	can hold values from -9223372036854775808 to 9223372036854775807
np.uint	unsigned long	8 bytes	0 to 18446744073709551615
np.longlong	long long	8 bytes	can hold values from -9223372036854775808 to 9223372036854775807
np.ulonglong	unsigned long long	8 bytes	0 to 18446744073709551615
np.half / np.float16	—		allows half float precision with Format: sign bit, 5 bits exponent, 10 bits mantissa
np.single	float	4 bytes	allows single float precision Format: sign bit, 8 bits exponent, 23 bits mantissa
np.double	double	8 bytes	allows double float precision Format: sign bit, 11 bits exponent, 52 bits mantissa.
np.longdouble	long double	8 bytes	extension of float
np.csingle	float complex	8 bytes	can hold complex with real and imaginary parts up to single-precision float
np.cdouble	double complex	16 bytes	can hold complex with real and imaginary parts up to double-precision float
np.clongdouble	long double complex	16 bytes	extension of float for complex number
np.int8	int8_t	1 byte	can hold values from -128 to 127
np.int16	int16_t	2 bytes	can hold values from -32,768 to 32,767
np.int32	int32_t	4 bytes	can hold values from -2,147,483,648 to 2,147,483,647
np.int64	int64_t	8 bytes	can hold values from -9223372036854775808 to 9223372036854775807
np.uint8	uint8_t	1 byte	can hold values from 0 to 255
np.uint16	uint16_t	2 bytes	can hold values from 0 to 65,535
np.uint32	uint32_t	4 bytes	can hold values from 0 to 4,294,967,295
np.uint64	uint64_t	8 bytes	can hold values from 0 to 18446744073709551615
np.intp	intptr_t	4 bytes	a signed integer used for indexing
np.uintp	uintptr_t	4 bytes	an unsigned integer used for holding a pointer
np.float32	float	4 bytes	single float precision
np.float64	double	8 bytes	double float precision
np.complex64	float complex	8 bytes	single float precision in complex numbers
np.complex128	double complex	16 bytes	double float precision in complex numbers

Examples of NumPy Data Types

Now, let’s understand how a particular numpy data type is used.

Example #1

Creating a data type object

dt = np.dtype(np.int8)

Output:

Example #2

Finding the size of a data type

dt = np.dtype(np.int8) name = dt.name sizeoftype = dt.itemsize print('name:',name, 'size:',sizeoftype)

Output:

Example #3

Creating a data type object using unique symbols for each data type

Each data type in numpy has an associated character code that uniquely identifies it.

dt = np.dtype('i4')

Output:

Example #4

Using data types to create a structured array

employee_info = np.dtype([('name','S10'), ('age', 'i1'),('salary', 'f4'),('rating', 'f4')]) print(employee_info)

Output:

a = np.array([('Karthik',31,20000,3.84),('Rita',25,25123.34,4.41)], dtype = employee_info) print (a)

Output:

Conclusion

Numpy data types are more or less like the C data types. They can be roughly categorized into a bool, byte, int, float, double and complex. It is a must for good programmers to understand how data is stored and manipulated. This can be achieved by understanding data types effectively.

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