NumPy arrays are the basis of all computations performed by the NumPy library. They are simple Python lists with a few additional properties. A NumPy array has the
dtype attribute, which specifies the data type of all the elements in the array.
If the array contains elements of different data types, all the elements are cast into the largest type (a process known as upcasting). A NumPy array can also be a copy or a reference to an existing NumPy array.
import numpy as np # A 2D array arr1 = np.array([[10, 20, 30], [-4, -5, -6]], dtype = np.double) # All the elements are doubles print("2D NumPy array of doubles \n" + repr(arr1) + "\n") arr2 = np.array([10, 'hello', 5.8], dtype = np.str) # All the elements are strings print("1D NumPy array of string \n" + repr(arr2) + "\n") # Creating a reference to a NumPy array arr3 = arr2 arr3 = 50 print("The new value of arr2: " + arr2) # Creating a Copy arr4 = arr2.copy() arr4 = '100' # arr2 will not be affected print("The value of arr2 is not altered: " + arr2) # Assigning an empty value to an index arr4 = np.nan print("Assigning NaN to an index: " + arr4)
When working with large amounts of data, hardcoding an array isn’t always possible. We can use the
arange method. It can take up to 3 parameters which will indicate the nature of the data:
import numpy as np # Creating an array from 0 to 9 arr = np.arange(10) print("An array from 0 to 9\n" + repr(arr) + "\n") # Creating an array of floats arr = np.arange(10.1) print("An array of floats\n" + repr(arr) + "\n") # The first and second arguments specify the start and end points arr = np.arange(-5, 5) print("An array from -5 to 5\n" + repr(arr) + "\n") # Third argument specifies the step arr = np.arange(-10, 10, 4) print("An array from -10 to 10 with a step of 4\n" + repr(arr))
np.arange performs upcasting. It also has the
dtype keyword argument to manually cast the array.
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