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60 lines
1.3 KiB
Markdown
60 lines
1.3 KiB
Markdown
## Numpy Resources
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### Arrays
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* Grid of values, all of the same type.
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* The number of dimensions is the rank of the array.
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* The shape of an array is a tuple of integers giving the size of the array along each dimension.
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```
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a = np.array([1, 2, 3]) # Create a rank 1 array
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print a.shape # Prints "(3,)"
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```
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```
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numpy.asarray([])
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numpy.asarray([]).shape
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```
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* Many functions to create arrays:
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```
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a = np.zeros((2,2)) # Create an array of all zeros
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b = np.ones((1,2)) # Create an array of all ones
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c = np.full((2,2), 7) # Create a constant array
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d = np.eye(2) # Create a 2x2 identity matrix
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e = np.random.random((2,2)) # Create an array filled with random values
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```
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* Products:
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```
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x = np.array([[1,2],[3,4]])
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v = np.array([9,10])
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w = np.array([11, 12])
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# Inner product of vectors
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print v.dot(w)
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print np.dot(v, w)
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# Matrix / vector product
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print x.dot(v)
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print np.dot(x, v)
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```
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* Sum:
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```
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print np.sum(x) # Compute sum of all elements
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print np.sum(x, axis=0) # Compute sum of each column
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print np.sum(x, axis=1) # Compute sum of each row
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```
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* Broadcasting is a mechanism that allows numpy to work with arrays of different shapes when performing arithmetic operations. Frequently we have a smaller array and a larger array, and we want to use the smaller array multiple times to perform some operation on the larger array.
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