![]() Print ( h ) # Prints a 2x2 matrix of random values random (( 2, 2 )) # Define a 2x2 matrix from the uniform distribution [0, 1) empty (( 2, 2 ), dtype = int ) # Define an int array without initializing entries empty (( 2, 2 )) # Define a float array without initializing entries eye ( 2 ) # Define a 2x2 identity matrixį = np. full (( 2, 2 ), 7 ) # Define a constant arrayĮ = np. ones (( 1, 2 )) # Define an array of all onesĭ = np. zeros (( 2, 2 )) # Define an array of all zerosĬ = np. Print ( a ) # Prints array(, dtype=float64)ī = np. NumPy arrays are designed for numerical (vector/matrix) operations, while lists are for more general purposes. Note the difference between a Python list and a NumPy array.array (, g ]) # Matrix initialized with both types array () # Matrix initialized with NumPy arrays array () # Define a NumPy array by passing in a list array () # Matrix initialized with listsĪ = np. # but note that the resulting matrix is always of type NumPy ndarray # NumPy arrays can be initialized using other NumPy arrays or lists array (, ]) # Define a rank 2 array using array ((( 1, 2, 3 ), ( 4, 5, 6 ))) # Define a rank 2 array using a nested tupleį = np. array (( 1, 2, 3 )) # Define a rank 1 array using a tupleĮ = np. array (, ]) # Define a rank 2 array (matrix) using a nested list array (]) # Define a rank 2 array (vector) using a nested listĬ = np. Print ( a, a, a ) # Prints (1, 2, 3)Ī = 5 # Change an element of the arrayī = np. size ) # Prints 3 equivalent to "np.prod(a.shape)" ![]() ndim ) # Prints 1 (the rank of the array) equivalent to "len(a.shape)" array () # Define a rank 1 array using a list We can initialize NumPy arrays from (nested) lists and tuples, and access elements using square brackets as array subscripts (similar to lists in Python).The size of an array is the number of elements it contains (which is equivalent to np.prod(.shape), i.e., the product of the array’s dimensions).The shape of an array is a tuple of integers giving the size of the array along each dimension.The rank of an array is the number of dimensions it contains.A NumPy array is a grid of values, all of the same type, and is indexed by a tuple of non-negative integers.If you’re already familiar with numerical processing in a different language like MATLAB and R, here are some recommended references:.It provides a high-performance multidimensional array object numpy.ndarray, and tools for operating on these arrays.It is informally known as the swiss army knife of the data scientist. NumPy is the core library for scientific computing in Python.ndarray.tolist(): Convert Multi Value Tensor to Scalar.em(): Convert Single Value Tensor to Scalar.
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