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+"""
+============
+Array basics
+============
+
+Array types and conversions between types
+=========================================
+
+Numpy supports a much greater variety of numerical types than Python does.
+This section shows which are available, and how to modify an array's data-type.
+
+==========  =========================================================
+Data type   Description
+==========  =========================================================
+bool        Boolean (True or False) stored as a byte
+int         Platform integer (normally either ``int32`` or ``int64``)
+int8        Byte (-128 to 127)
+int16       Integer (-32768 to 32767)
+int32       Integer (-2147483648 to 2147483647)
+int64       Integer (9223372036854775808 to 9223372036854775807)
+uint8       Unsigned integer (0 to 255)
+uint16      Unsigned integer (0 to 65535)
+uint32      Unsigned integer (0 to 4294967295)
+uint64      Unsigned integer (0 to 18446744073709551615)
+float       Shorthand for ``float64``.
+float32     Single precision float: sign bit, 8 bits exponent,
+            23 bits mantissa
+float64     Double precision float: sign bit, 11 bits exponent,
+            52 bits mantissa
+complex     Shorthand for ``complex128``.
+complex64   Complex number, represented by two 32-bit floats (real
+            and imaginary components)
+complex128  Complex number, represented by two 64-bit floats (real
+            and imaginary components)
+==========  =========================================================
+
+Numpy numerical types are instances of ``dtype`` (data-type) objects, each
+having unique characteristics.  Once you have imported NumPy using
+
+  ::
+
+    >>> import numpy as np
+
+the dtypes are available as ``np.bool``, ``np.float32``, etc.
+
+Advanced types, not listed in the table above, are explored in
+section `link_here`.
+
+There are 5 basic numerical types representing booleans (bool), integers (int),
+unsigned integers (uint) floating point (float) and complex. Those with numbers
+in their name indicate the bitsize of the type (i.e. how many bits are needed
+to represent a single value in memory).  Some types, such as ``int`` and
+``intp``, have differing bitsizes, dependent on the platforms (e.g. 32-bit
+vs. 64-bit machines).  This should be taken into account when interfacing
+with low-level code (such as C or Fortran) where the raw memory is addressed.
+
+Data-types can be used as functions to convert python numbers to array scalars
+(see the array scalar section for an explanation), python sequences of numbers
+to arrays of that type, or as arguments to the dtype keyword that many numpy
+functions or methods accept. Some examples::
+
+    >>> import numpy as np
+    >>> x = np.float32(1.0)
+    >>> x
+    1.0
+    >>> y = np.int_([1,2,4])
+    >>> y
+    array([1, 2, 4])
+    >>> z = np.arange(3, dtype=np.uint8)
+    array([0, 1, 2], dtype=uint8)
+
+Array types can also be referred to by character codes, mostly to retain
+backward compatibility with older packages such as Numeric.  Some
+documentation may still refer to these, for example::
+
+  >>> np.array([1, 2, 3], dtype='f')
+  array([ 1.,  2.,  3.], dtype=float32)
+
+We recommend using dtype objects instead.
+
+To convert the type of an array, use the .astype() method (preferred) or
+the type itself as a function. For example: ::
+
+    >>> z.astype(float)
+    array([0.,  1.,  2.])
+    >>> np.int8(z)
+    array([0, 1, 2], dtype=int8)
+
+Note that, above, we use the *Python* float object as a dtype.  NumPy knows
+that ``int`` refers to ``np.int``, ``bool`` means ``np.bool`` and
+that ``float`` is ``np.float``.  The other data-types do not have Python
+equivalents.
+
+To determine the type of an array, look at the dtype attribute::
+
+    >>> z.dtype
+    dtype('uint8')
+
+dtype objects also contain information about the type, such as its bit-width
+and its byte-order. See xxx for details.  The data type can also be used
+indirectly to query properties of the type, such as whether it is an integer::
+
+    >>> d = np.dtype(int)
+    >>> d
+    dtype('int32')
+
+    >>> np.issubdtype(d, int)
+    True
+
+    >>> np.issubdtype(d, float)
+    False
+
+
+Array Scalars
+=============
+
+Numpy generally returns elements of arrays as array scalars (a scalar
+with an associated dtype).  Array scalars differ from Python scalars, but
+for the most part they can be used interchangeably (the primary
+exception is for versions of Python older than v2.x, where integer array
+scalars cannot act as indices for lists and tuples).  There are some
+exceptions, such as when code requires very specific attributes of a scalar
+or when it checks specifically whether a value is a Python scalar. Generally,
+problems are easily fixed by explicitly converting array scalars
+to Python scalars, using the corresponding Python type function
+(e.g., ``int``, ``float``, ``complex``, ``str``, ``unicode``).
+
+The primary advantage of using array scalars is that
+they preserve the array type (Python may not have a matching scalar type
+available, e.g. ``int16``).  Therefore, the use of array scalars ensures
+identical behaviour between arrays and scalars, irrespective of whether the
+value is inside an array or not.  NumPy scalars also have many of the same
+methods arrays do.
+
+See xxx for details.
+
+"""