User guide

CHAPTER
TWO
NUMPY BASICS
2.1 Data types
See Also:
Data type objects
2.1.1 Array types and conversions between types
Numpy supports a much greater variety of numerical types than Python does. This section shows which are available,
and how to modify an array’s data-type.
Data type Description
bool_
Boolean (True or False) stored as a byte
int_
Default integer type (same as C long; normally either int64 or int32)
intc Identical to C int (normally int32 or int64)
intp Integer used for indexing (same as C ssize_t; normally either int32 or int64)
int8 Byte (-128 to 127)
int16 Integer (-32768 to 32767)
int32 Integer (-2147483648 to 2147483647)
int64 Integer (-9223372036854775808 to 9223372036854775807)
uint8 Unsigned integer (0 to 255)
uint16 Unsigned integer (0 to 65535)
uint32 Unsigned integer (0 to 4294967295)
uint64 Unsigned integer (0 to 18446744073709551615)
float_
Shorthand for float64.
float16 Half precision float: sign bit, 5 bits exponent, 10 bits mantissa
float32 Single precision float: sign bit, 8 bits exponent, 23 bits mantissa
float64 Double precision float: sign bit, 11 bits exponent, 52 bits mantissa
complex_
Shorthand for complex128.
complex64 Complex number, represented by two 32-bit floats (real and imaginary components)
complex128 Complex number, represented by two 64-bit floats (real and imaginary components)
Additionally to intc the platform dependent C integer types short, long, longlong and their unsigned versions
are defined.
Numpy numerical types are instances of dtype (data-type) objects, each having unique characteristics. Once you
have imported NumPy using
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