NumPy Datatypes

The NumPy provides a higher range of numeric data types than that provided by the Python. A list of numeric data types is given in the following table.

NumPy dtype

All the items of a numpy array are data type objects also known as numpy dtypes. A data type object implements the fixed size of memory corresponding to an array.

We can create a dtype object by using the following syntax.

The constructor accepts the following object.

Object: It represents the object which is to be converted to the data type.

Align: It can be set to any boolean value. If true, then it adds extra padding to make it equivalent to a C struct.

Copy: It creates another copy of the dtype object.

Example 1

Output:

int32

Example 2

Output:

int32

Creating a Structured data type

We can create a map-like (dictionary) data type which contains the mapping between the values. For example, it can contain the mapping between employees and salaries or the students and the age, etc.

Consider the following example.

Example 1

Output:

[('salary', '

Example 2

Output:

[(10000.12,) (20000.5 ,)]