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, -3float– used to represent real numbers. e.g. 1.2, 42.42boolean– 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– integerb– booleanu– unsigned integerf– floatc– complex floatm– from timeM– datetimeO– objectS– stringU– unicode stringV– 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
Get the data type of an array object:
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import numpy as np arr = np.array([1, 2, 3, 4]) print(arr.dtype)
Example
Get the data type of an array containing strings:
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import numpy as np arr = np.array(['apple', 'banana', 'cherry']) print(arr.dtype)
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:
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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
Create an array with data type 4 bytes integer:
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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.
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):
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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
Change data type from float to integer by using 'i' as parameter value:
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import numpy as np arr = np.array([1.1, 2.1, 3.1]) newarr = arr.astype('i') print(newarr)print(newarr.dtype)
Example
Change data type from float to integer by using int as parameter value:
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import numpy as np arr = np.array([1.1, 2.1, 3.1]) newarr = arr.astype(int) print(newarr)print(newarr.dtype)
Example
Change data type from integer to boolean:
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import numpy as np arr = np.array([1, 0, 3]) newarr = arr.astype(bool) print(newarr)print(newarr.dtype)
