Array objects

NumPy provides an N-dimensional array type, the ndarray, which describes a collection of “items” of the same type. The items can be indexed using for example N integers.

All ndarrays are homogenous: every item takes up the same size block of memory, and all blocks are interpreted in exactly the same way. How each item in the array is to be interpreted is specified by a separate data-type object, one of which is associated with every array. In addition to basic types (integers, floats, etc.), the data type objects can also represent data structures.

An item extracted from an array, e.g., by indexing, is represented by a Python object whose type is one of the array scalar types built in NumPy. The array scalars allow easy manipulation of also more complicated arrangements of data.

• The N-dimensional array (ndarray)
  • Constructing arrays
  • Indexing arrays
  • Internal memory layout of an ndarray
  • Array attributes
  • Array methods
  • Arithmetic, matrix multiplication, and comparison operations
  • Special methods
• Scalars
  • Built-in scalar types
  • Attributes
  • Indexing
  • Methods
  • Defining new types
• Data type objects (dtype)
  • Specifying and constructing data types
  • dtype
• Indexing
  • Basic Slicing and Indexing
  • Advanced Indexing
  • Detailed notes
  • Field Access
  • Flat Iterator indexing
• Iterating Over Arrays
  • Single Array Iteration
  • Broadcasting Array Iteration
  • Putting the Inner Loop in Cython
• Standard array subclasses
  • Special attributes and methods
  • Matrix objects
  • Memory-mapped file arrays
  • Character arrays (numpy.char)
  • Record arrays (numpy.rec)
  • Masked arrays (numpy.ma)
  • Standard container class
  • Array Iterators
• Masked arrays
  • The numpy.ma module
  • Using numpy.ma
  • Examples
  • Constants of the numpy.ma module
  • The MaskedArray class
  • MaskedArray methods
  • Masked array operations
• The Array Interface
  • Python side
  • C-struct access
  • Type description examples
  • Differences with Array interface (Version 2)
• Datetimes and Timedeltas
  • Basic Datetimes
  • Datetime and Timedelta Arithmetic
  • Datetime Units
  • Business Day Functionality
  • Changes with NumPy 1.11
  • Differences Between 1.6 and 1.7 Datetimes