![]() # while using only slices yields an array of the same rank as the # Mixing integer indexing with slices yields an array of lower rank, # Two ways of accessing the data in the middle row of the array. array (,, ]) # Basic slicingĪ # Select rows 0, 1 and 2, all columnsĪ # Select rows 0 and 1, column 1Ī # Select row 0, all columns (same as a)Ī # Select row 1, all columns Import numpy as np # Define the following rank 2 array with shape (3, 4)Ī = np. To “select” a particular row or column in an array, NumPy offers similar functionality as Python lists:.NumPy arrays can be indexed by integers, a tuple of nonnegative integers, by booleans or by another array.You can read about other methods of array creation in the NumPy documentation.# np.ones_like(): Return a new array with the same shape and type as a given array, filled with ones.Į = np. # np.ones(): Return a new array of given shape and type, filled with ones. # Note the difference between np.ones() and np.ones_like() below. empty_like ( a ) # Uninitialized arrayĬ = np. To create a new array with the same shape and type as a given array, NumPy offers the following methods:Ī = (, ) # Python list More on this in the section on standard normal. On the other hand, np.random.random() accepts its shape argument as a single tuple containing all dimensions. Note that with np.random.randn(), the length of each dimension of the output array is an individual argument. ![]() Print ( k ) # Prints a 2x2 matrix of random values # General form: stddev * np.random.randn(.) + mean Print ( j ) # Prints a 2x2 matrix of random values randn ( 2, 2 ) # Sample a 2x2 matrix from the standard normal distribution Print ( i ) # Prints a 2x2 matrix of random values # Sample from Unif[a, b), b > a: (b - a) * random_sample() + a random_sample (( 2, 2 )) - 5 # Sample 2x2 matrix from Unif[-5, 0) 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.
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