A NumPy array can be created using multiple techniques. The following code creates a new NumPy array object from a Python list:
In [4]: # a simple array a1 = np.array([1, 2, 3, 4, 5]) a1 Out[4]: array([1, 2, 3, 4, 5]) In [5]: # what is its type? type(a1) Out[5]: numpy.ndarray In [6]: # how many elements? np.size(a1) Out[6]: 5
In NumPy, n-dimensional arrays are denoted as ndarray
, and this one contains five elements, as is reported by the np.size()
function.
NumPy arrays must have all of their elements of the same type. If you specify different types in the list, NumPy will try to coerce all the items to the same type. The following code example demonstrates using integer and floating-point values to initialize the array, which are then converted to floating-point numbers by NumPy:
In [7]: # any float in the sequences makes # it an array of floats a2 = np.array([1, 2, 3, 4.0, 5.0]) a2...