NumPy Data Types

Machine Learning using Python 3

• Data Types in Python

strings: used to represent text data under quote marks like "ABCD".

integer: used to represent integer numbers like 1, 2, 3, -1, -2, -3.

float: used to represent real numbers like 1.2, 42.42.

boolean: used to represent True or False.

complex: used to represent complex numbers like 1.0 + 2.0j, 1.5 + 2.5j .

• Data Types in NumPy

NumPy has some extra data types and refers them with one character, like i for integers, u for unsigned integers etc. List of all data types in NumPy and the characters used to represent them is as follows:

• Checking the Data Type of an Array

The NumPy array object has a property called dtype that returns the data type of the array.

Program 1: Get the data type of an array object.

import numpy as np
arr = np.array([1, 2, 3, 4])
print("The data type of arr is:", arr.dtype)

Output:

The data type of arr is: int32

Program 2: Get the data type of an array containing strings.

import numpy as np
arr = np.array(['apple', 'banana', 'cherry'])
print("The data type of arr is:", arr.dtype)

Output:

The data type of arr is: <U6

• Creating Arrays With a Defined Data Type

We use the array() function to create arrays, this function can take an optional argument: dtype that allows us to define the expected data type of the array elements.

Program 3: Create an array with data type string.

import numpy as np
arr = np.array([1, 2, 3, 4], dtype='S')
print("The value of arr is", arr)
print("The data type of arr is:", arr.dtype)

Output:

The value of arr is [b'1' b'2' b'3' b'4']
The data type of arr is: |S1

For i, u, f, S and U we can define size as well.

Program 4: Create an array with data type 4 bytes integer.

import numpy as np
arr = np.array([1, 2, 3, 4], dtype='i4')
print("The value of arr is:", arr)
print("The data type of arr is:", arr.dtype)

Output:

The value of arr is: [1 2 3 4]
The data type of arr is: int32

• What if a Value Can Not Be Converted?

If a type is given in which elements can't be casted then NumPy will raise a ValueError. In Python, ValueError is raised when the type of passed argument to a function is unexpected/incorrect.

Program 5: A non integer string like 'a' can not be converted to integer (will raise an error).

import numpy as np
arr = np.array(['a', '2', '3'], dtype='i')
print("The value of arr is:", arr)

Output:

ValueError                     Traceback (most recent call last)
<ipython-input-5-bc5f30a52b19> in <module>
      1 import numpy as np
----> 2 arr = np.array(['a', '2', '3'], dtype='i')
      3 print(arr)

ValueError: invalid literal for int() with base 10: 'a'

• Converting Data Type on Existing Arrays

The best way to change the data type of an existing array, is to make a copy of the array with the astype() method. The astype() function creates a copy of the array, and allows us to specify the data type as a parameter. The data type can be specified using a string, like 'f' for float, 'i' for integer etc. or we can use the data type directly like float for float and int for integer.

Program 6: Change data type from float to integer by using 'i' as parameter value.

import numpy as np
arr = np.array([1.1, 2.1, 3.1])
newarr = arr.astype('i')
print("The value of newarr is:", newarr)
print("The data type of newarr is:", newarr.dtype)

Output:

The value of newarr is: [1 2 3]
The data type of newarr is: int32

Program 7: Change data type from float to integer by using int as parameter value.

import numpy as np
arr = np.array([1.1, 2.1, 3.1])
newarr = arr.astype(int)
print("The value of newarr is:", newarr)
print("The data type of newarr is:", newarr.dtype)

Output:

The value of newarr is: [1 2 3]
The data type of newarr is: int32

Program 8: Change data type from integer to boolean.

import numpy as np
arr = np.array([1, 0, 3])
newarr = arr.astype(bool)
print("The value of newarr is:", newarr)
print("The data type of newarr is:", newarr.dtype)

Output:

The value of newarr is: [ True False  True]
The data type of newarr is: bool