numpy.ndarray Operation

Context:
- It can (typically) involve a numpy.ndarray Method.
Example(s)
- Create an empty array:
  - ... .
- Create a prepopulated array:
  - ... .
- Update array rows (or add row to an array):
  - ... .
- Query an array:
  - ... .
- Add a columns to an array:
  - ... .
- Delete array rows.
  - ... .
- Query an array's structure:
  - ... .
- Query an array's metadata:
  - ... .
- Modify an array's structure.
  - ... .
- Iterate over an array.
  - ... .
- Delete an array:
  - ... .
- …
Counter-Example(s)
- a pandas.Series Operation, on a pandas.Series.
- a pandas.DataFrame Operation, on a pandas.DataFrame.
- a SciPy Sparse Array Operation.
- a Python Array Operation.
- a Python List Operation.
- a Perl Associative Array Operation.
- a Perl Array Operation.
- a SQL Table Operation.
- an R Array Operation (R DataFrame Operation).
See: pandas.DataFrame Attribute.

References

http://docs.scipy.org/doc/numpy/reference/arrays.ndarray.html
- QUOTE: An ndarray is a (usually fixed-size) multidimensional container of items of the same type and size. The number of dimensions and items in an array is defined by its shape, which is a tuple of N positive integers that specify the sizes of each dimension. The type of items in the array is specified by a separate data-type object (dtype), one of which is associated with each ndarray.
  As with other container objects in Python, the contents of an ndarray can be accessed and modified by indexing or slicing the array (using, for example, N integers), and via the methods and attributes of the ndarray.
  Different ndarrays can share the same data, so that changes made in one ndarray may be visible in another. That is, an ndarray can be a “view” to another ndarray, and the data it is referring to is taken care of by the “base” ndarray. ndarrays can also be views to memory owned by Python strings or objects implementing the buffer or array interfaces.