机器学习\数据挖掘学习日记20160504

Python Numpy Reference 阅读

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np.array, np.dtype 和 array scalar type 概念与关系

NumPy provides an N-dimensional array type, the ndarray, which describes a collection of items of the same type.

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

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.

np.array

Base array and View array

View is An array that does not own its data, but refers to another array’s data instead.

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.

任何对于np.array的操作，先明确其操作对象，是array本身? array的一个view? 还是array的一个copy?

Creation

• Using ndarray constructor
• Using array creation routines
  Function XXX_like(an_array) Return a new array with the same shape and type as a given array

https://docs.scipy.org/doc/numpy-dev/reference/routines.array-creation.html#routines-array-creation

Indexing and Slicing

熟练地应用indexing 和 slicing 非常重要，可以对 data set 进行初步的处理

• Basic indexing/slicing
  x[selection object]
  All arrays generated by basic slicing are always views of the original array.
  You may use slicing to set values in the array, but (unlike lists) you can never grow the array.
  integers: postivie index, negative index
  slice object
  tuple for multi-dimensions
  ellipsis
  np.newaxis = np.None

• Field access
  Indexing x[‘field-name’] returns a new view to the array (same shape with x)

• Advanced indexing
  Advanced indexing always returns a copy of the data contrast with basic slicing that returns a view
  integer array indexing
  auto-broadcast
  Boolean array indexing
  If obj.ndim == x.ndim, x[obj] returns a 1-dimensional array filled with the elements of x corresponding to the True values of obj.

  np.ix_() function

• Flat Iterator indexing
  ndarray.flat : A 1-D iterator over the array
  This iterator object can also be indexed using basic slicing or advanced indexing as long as the selection object is not a tuple. This should be clear from the fact that x.flat is a 1-dimensional view. The shape of any returned array is therefore the shape of the integer indexing object.

https://docs.scipy.org/doc/numpy-dev/reference/arrays.indexing.html#flat-iterator-indexing