| Unit 22 | Transposing and Reshaping |
Unlike the great monuments of the past, numpy arrays are not carved in stone. They can easily change their shape and orientation without being called opportunists. Let’s build a one-dimensional array of some S&P stock symbols and twist it in every possible way:
| | # Some S&P stock symbols |
| | sap = np.array(["MMM", "ABT", "ABBV", "ACN", "ACE", "ATVI", "ADBE", "ADT"]) |
| => | array('MMM', 'ABT', 'ABBV', 'ACN', 'ACE', 'ATVI', 'ADBE', 'ADT'], |
| => | dtype='<U4') |
The function reshape(d0, d1, ...) changes the shape of an existing array. The arguments define the new dimensions. The total number of items in the old and new shapes must be equal: the conservation law still holds in numpyland!
| | sap2d = sap.reshape(2, ... |
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