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According to the docs, it's possible to specify different array dtypes:
dt = np.dtype('u1') # 8-bit unsigned integer
dt = np.dtype('i4') # 32-bit signed integer
dt = np.dtype('f8') # 64-bit floating-point number
dt = np.dtype('c16') # 128-bit complex floating-point number
dt = np.dtype('a25') # 25-length zero-terminated bytes
dt = np.dtype('U25') # 25-character string
However, the smallest unsigned integer dtype is 8-bit. Is there a way to create a 2-bit unsigned integer dtype?
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Can an array store different data types? Yes, a numpy array can store different data String, Integer, Complex, Float, Boolean.
In order to change the dtype of the given array object, we will use numpy. astype() function. The function takes an argument which is the target data type. The function supports all the generic types and built-in types of data.
NumPy arrays have a fixed number of elements and all the elements have the same datatype, both specified when creating the array.
Data Types in NumPyNumPy 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.
According to the NumPy mailing list from November 2009, NumPy has 1-byte atomicity and so a 8-bit is the smallest unit. Even bool dtype uses a single byte.
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