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

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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 )

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Checking the Data Type of an Array

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

ExampleGet your own Python Server

Get the data type of an array object:

import numpy as np

arr = np.array([1, 2, 3, 4])

print(arr.dtype)

Try it Yourself »

Example

Get the data type of an array containing strings:

import numpy as np

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

print(arr.dtype)

Try it Yourself »

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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

Create an array with data type string:

import numpy as np

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

print(arr)
print(arr.dtype)

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

Example

Create an array with data type 4 bytes integer:

import numpy as np

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

print(arr)
print(arr.dtype)

Try it Yourself »

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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.

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

Example

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')

Try it Yourself »

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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

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(newarr)
print(newarr.dtype)

Try it Yourself »

Example

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(newarr)
print(newarr.dtype)

Try it Yourself »

Example

Change data type from integer to boolean:

import numpy as np

arr = np.array([1, 0, 3])

newarr = arr.astype(bool)

print(newarr)
print(newarr.dtype)

Try it Yourself »

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Test Yourself With Exercises

Exercise:

NumPy uses a character to represent each of the following data types, which one?i = integer = boolean = unsigned integer = float = complex float = timedelta = datetime = object = string