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NumPy - dtypes
The data type or dtype is a special object containing the information (or metadata,
data about data) the ndarray needs to interpret a chunk of memory as a particular
type of data:
arr = np.array([1,2,3],dtype="float64")
print(arr.dtype) # dtype('float64')dtypes are a source of NumPy’s flexibility for interacting with data coming from other
systems. In most cases they provide a mapping directly onto an underlying disk or
memory representation, which makes it easy to read and write binary streams of data
to disk
Don’t worry about memorizing the NumPy dtypes, especially if
you’re a new user. It’s often only necessary to care about the general
kind of data you’re dealing with, whether floating point, complex,
integer, boolean, string, or general Python object. When you need
more control over how data are stored in memory and on disk,
especially large datasets, it is good to know that you have control
over the storage type.
| Type | Type Code | Description |
|---|---|---|
| int8, uint8 | i1, u1 | Signed and unsigned 8-bit (1 byte) integer types |
| int16, uint16 | i2, u2 | Signed and unsigned 16-bit integer types |
| int32, uint32 | i4, u4 | Signed and unsigned 32-bit integer types |
| int64, uint64 | i8, u8 | Signed and unsigned 64-bit integer types |
| float16 | f2 | Half-precision floating point |
| float32 | f4 or f | Standard single-precision oating point; compatible with C oat |
| float64 | f8 or d | Standard double-precision oating point; compatible with C double and Python float object |
| float128 | f16 or g | Extended-precision oating point |
| complex64 | c8, c16 | Complex numbers represented by two 32 floats |
| complex128 | c32 | Complex numbers represented by two 64 float |
| complex256 | c32 | Complex numbers represented by two 128 float |
| bool | ? | Boolean type storing True and False values |
| object | O | Python object type; a value can be any Python object |
| string_ | S | Fixed-length ASCII string type (1 byte per character); for example, to create a string dtype with length 10, use 'S10' |
| unicode_ | U | Fixed-length Unicode type (number of bytes platform specific); same specification semantics as string_ (e.g., 'U10') |
Note -
It’s important to be cautious when using the numpy.string_
type,as string data in NumPy is fixed size and may
truncate input without warning. pandas has more intuitive
out-of-the-box behavior on non-numeric data.
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