Numpy provides the wider range of numeric data types than that provided by the Python. It can be used as function.It can also be referred by character code.
A list of Numpy data types are given below:
- bool_: It is stored as bytes. It is used to indicate boolean value weather it is True or False.
- int_: It is used to store default type of integer. It is also identical to long type of C that contains 64 bit or 32 bit integer.
- intc: It is similar to the C integer, it is used to represent 32 or 64-bit int.
- intp: This type of integer are used for for indexing.
- int8: It is 8-bit integer identical to bytes. Its value range from -128 to 127.
- int16: It is 16-bit integer ranging from -32768 to 32767.
- int32: It is 32-bit integer ranging from -2147483648 to 2147483647.
- int64: It is 64-bit integer ranging from -9223372036854775808 to 9223372036854775807.
- uint8: It is 8-bit unsigned integer.
- uint16: It is 16-bit unsigned integer.
- uint32: It is 32-bit unsigned integer.
- uint64: It is 64-bit unsigned integer.
- float_: It is identical to float of 64 bit.
- float16: It is the half-precision float of 5 bits which is reserved for the exponent ,10 bits which is reserved for mantissa, and 1 bit which is reserved for the sign.
- float32: It is the single precision float o f8 bits which is reserved for the exponent, 23 bits which is reserved for mantissa, 1 bit is used for the sign.
- float64: It is the double precision float of 11 bits which is reserved for the exponent, 52 bits which is reserved for mantissa, 1 bit is used for the sign.
- complex_: It is identical to complex of 128-bits.
- complex64: This type of data type is used to represent the complex number where real and imaginary part shares 32 bits each.
- complex128: This type of data type is used to represent the complex number where real and imaginary part shares 64 bits each.
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