NumPy Data Types

Data Types in Python

By default Python have these data types:

• strings - used to represent text data, the text is given under quote marks. e.g. "ABCD"
• integer - used to represent integer numbers. e.g. -1, -2, -3
• float - used to represent real numbers. e.g. 1.2, 42.42
• boolean - used to represent True or False.
• complex - used to represent complex numbers. e.g. 1.0 + 2.0j, 1.5 + 2.5j

Data Types in NumPy

NumPy has some extra data types, and refer to data types with one character, like i for integers, u for unsigned integers etc.

Below is a list of all data types in NumPy and the characters used to represent them.

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

Checking the Data Type of an Array

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

Example

  import numpy as np

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

print(arr.dtype)

Example

  import numpy as np

arr = np.array(['apple',
  'banana', 'cherry'])

print(arr.dtype)

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:

Example

  import numpy as np

arr = np.array([1, 2, 3, 4],
  dtype='S')

print(arr)
print(arr.dtype)

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

Example

  import numpy as np

arr = np.array([1, 2, 3, 4],
  dtype='i4')

print(arr)
print(arr.dtype)

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.

Note: ValueError: In Python ValueError is raised when the type of passed argument to a function is unexpected/incorrect.

Example

  import numpy as np

arr = np.array(['a', '2', '3'], dtype='i')

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 you 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 you can use the data type directly like float for float and int for integer.

Example

  import numpy as np

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

newarr = arr.astype('i')

print(newarr)
print(newarr.dtype)

Example

  import numpy as np

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

newarr = arr.astype(int)

print(newarr)
print(newarr.dtype)

Example

  import numpy as np

arr = np.array([1, 0, 3])

newarr = arr.astype(bool)

print(newarr)
print(newarr.dtype)