In this tutorial, we are going to learn about different numpy data types followed by how we can convert an existing numpy array to a different data type along with creating a numpy with a predefined data type array.

Numpy Data Types

Everytime you create an ndarray object (array), it’s always going to be associated with a certain type of data which decides what data type the array will have. Numpy supports the following data types:

Data type Description
bool_ Boolean stored as a byte.
int_ Default integer type.
intc Identical to C int.
intp Integer used for indexing.
int8 Byte.
int16 Integer.
int32 Integer.
int64 Integer.
uint8 Unsigned integer.
uint16 Unsigned integer.
uint32 Unsigned integer.
uint64 Unsigned integer
float_ Shorthand for float64.
float16 Half precision float.
float32 Single precision float.
float64 Double precision float.
complex_ Shorthand for complex128.
complex64 Complex number,
complex128 Complex number,

Syntax of Datatype

Let’s say you have an array to be converted to the required data type, then you need to provide with the required syntax of changing the data:

dtype(obj, align=False, copy=False)

So, let’s say we have an array of integers by default and we want to convert it to float data type.

arr = np.arange(10).reshape(2,5)


array([[0, 1, 2, 3, 4],
       [5, 6, 7, 8, 9]])

Now, let’s convert it to float datatype

arr = np.dtype(float)



As you can see now the data type is float64 (it can be float32 as well depending on your system).

You can convert the array to different data types based on your needs. You can even predefined the array data type before printing the array

arr = np.array([1,2,3,4,5], dtype=float)


array([1., 2., 3., 4., 5.])