NumPy Datatype Exploration

Introduction

In this lab, we will cover datatypes in the NumPy library of Python. We will go through the dtype object syntax and its parameters.

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Understanding NumPy dtype Object

In NumPy, all the items of an array are data type objects that are also known as NumPy dtypes. The data type object is used to implement the fixed size of memory corresponding to an array.

It mainly provides us information about the following:

• The type of data (e.g., integer, float, or Python object)
• The size of data
• The Byte order (little-endian or big-endian)
• In the case of structured type, it tells us about the names of fields, the data type of each field, and part of the memory block taken by each field.
• In the case, if the data type is a subarray, it tells us about its shape and data type

Creating a NumPy dtype object

Given below is the required syntax used to create the dtype object:

numpy.dtype(object, align, copy)

Following is a description of arguments of the above mentioned constructor:

• object: This argument is used to represent the object which is to be converted to the data type.
• align: It is an optional argument. It is used to add padding to the fields to match what a C compiler would output for a similar C-struct. This argument can be set to any boolean value.
• copy: This argument is used to create a copy of dtype object and it is also an optional argument.

Numeric Data Types in NumPy

The NumPy library mainly provides a higher range of numeric data types than that provided by Python. The list of numeric data types is given in below table:

Characters used to represent dtype in NumPy

Here is the list of characters that are used to represent dtype in NumPy:

• i: integer
• b: boolean
• u: unsigned integer
• f: float
• c: complex float
• m: timedelta
• M: datetime
• O: object
• S: string
• U: Unicode string
• V: the fixed chunk of memory for other types (void)

Examples

Example 1: Finding Data Type of an Array

We will try to find out the data type of the array containing strings:

import numpy as np

ar1 = np.array(['chair', 'book', 'notebook'])
print(ar1.dtype)

Output:

<U8

Example 2: Constructing a dtype Object

We can create a dtype object using numpy.dtype function.

import numpy as np

dt1 = np.dtype(np.int64)
print (dt1)

Output:

int64

Example 3: Using Shorter Representation of Numeric Data Types

In the following example, we will use the shorter representation of numeric data types:

import numpy as np

a = np.dtype('i4')
print (a)

Output:

int32

Example 4: Creating a Structured dtype Object

We will create a structured data type and apply it to a ndarray object:

import numpy as np

## info with key and value
a = np.dtype([('rollno',np.int16)])
print(a)

a = np.array([(101,),(201,),(301,)], dtype=a)
print(a)

Output:

[('rollno', '<i2')]
[(101,) (201,) (301,)]

Example 5: Changing Data Type

In the following example, we will change the datatype from float to integer by using int as a parameter value:

import numpy as np

ar= np.array([1.1, 2.1, 3.1])

newarr = ar.astype(int)

print(newarr)
print(newarr.dtype)

Output:

[1 2 3]
int64

Summary

In this lab, we covered the concept of datatypes in an array. We saw how dtype object is used to specify the datatype of values, its syntax, and parameters required for the dtype object. We also covered various numeric data types and then a few examples for your understanding.