【Numpy】array操作总结

官方Document: https://www.numpy.org/devdocs/reference/routines.array-manipulation.html
开发测试环境
- Win10
- Python 3.6.4
- NumPy 1.14.2

Basic operations

函数原型	作用
[`copyto`](dst, src[, casting, where])	Copies values from one array to another, broadcasting as necessary.

Changing array shape

函数原型	作用
[`reshape`](a, newshape[, order])	Gives a new shape to an array without changing its data.
[`ravel`](a[, order])	Return a contiguous flattened array.
[`ndarray.flat`]	A 1-D iterator over the array.
`ndarray.flatten`	Return a copy of the array collapsed into one dimension.

Transpose-like operations

函数原型	作用
[`moveaxis`](a, source, destination)	Move axes of an array to new positions.
[`rollaxis`](a, axis[, start])	Roll the specified axis backwards, until it lies in a given position.
[`swapaxes`](a, axis1, axis2)	Interchange two axes of an array.
[`ndarray.T`]	Same as self.transpose(), except that self is returned if self.ndim < 2.
[`transpose`](a[, axes])	Permute the dimensions of an array.

Changing number of dimensions

函数原型	作用
`atleast_1d`(*arys)	Convert inputs to arrays with at least one dimension.
`atleast_2d`(*arys)	View inputs as arrays with at least two dimensions.
`atleast_3d`(*arys)	View inputs as arrays with at least three dimensions.
`broadcast`	Produce an object that mimics broadcasting.
`broadcast_to`(array, shape[, subok])	Broadcast an array to a new shape.
`broadcast_arrays`(args, *kwargs)	Broadcast any number of arrays against each other.
`expand_dims`(a, axis)	Expand the shape of an array.
`squeeze`(a[, axis])	Remove single-dimensional entries from the shape of an array.

expand_dims

扩展array的shape，插入一个新的轴，该轴将出现在扩展阵列形状的轴位置

>>> x = np.array([1,2])
>>> x.shape
(2,)

以下操作相当于 x[np.newaxis,:] or x[np.newaxis]:

>>> y = np.expand_dims(x, axis=0)
>>> y
array([[1, 2]])
>>> y.shape
(1, 2)

>>> y = np.expand_dims(x, axis=1)  # Equivalent to x[:,np.newaxis]
>>> y
array([[1],
       [2]])
>>> y.shape
(2, 1)

注意在一些列子中使用None替换np.newaxis

>>> np.newaxis is None
True

squeeze

从数组的形状中移除一维条目
- a : array
- axis: None或者整数或者整数元组，默认是None
选择shape单维度的条目，若选取的axis的shape条目不为1，则会抛出异常

>>> x = np.array([[[0], [1], [2]]])
>>> x.shape
(1, 3, 1)
>>> np.squeeze(x).shape
(3,)
>>> np.squeeze(x, axis=0).shape
(3, 1)
>>> np.squeeze(x, axis=1).shape
Traceback (most recent call last):
...
ValueError: cannot select an axis to squeeze out which has size not equal to one
>>> np.squeeze(x, axis=2).shape
(1, 3)

axis参数输入为整数列表时，起作用相当于axis=None时的作用

Changing kind of array

函数原型	作用
[`asarray`](a[, dtype, order])	Convert the input to an array.
[`asanyarray`](a[, dtype, order])	Convert the input to an ndarray, but pass ndarray subclasses through.
[`asmatrix`](data[, dtype])	Interpret the input as a matrix.
[`asfarray`](a[, dtype])	Return an array converted to a float type.
[`asfortranarray`](a[, dtype])	Return an array laid out in Fortran order in memory.
[`ascontiguousarray`](a[, dtype])	Return a contiguous array in memory (C order).
[`asarray_chkfinite`](a[, dtype, order])	Convert the input to an array, checking for NaNs or Infs.
`asscalar`	Convert an array of size 1 to its scalar equivalent.
[`require`](a[, dtype, requirements])	Return an ndarray of the provided type that satisfies requirements.

Joining arrays

函数原型	作用
`concatenate`((a1, a2, …)[, axis, out])	Join a sequence of arrays along an existing axis.
`stack`(arrays[, axis, out])	Join a sequence of arrays along a new axis.
`column_stack`(tup)	Stack 1-D arrays as columns into a 2-D array.
`dstack`(tup)	Stack arrays in sequence depth wise (along third axis).
`hstack`(tup)	Stack arrays in sequence horizontally (column wise).
`vstack`(tup)	Stack arrays in sequence vertically (row wise).
`block`(arrays)	Assemble an nd-array from nested lists of blocks.

concatenate

沿着指定的维度进行合并，结果是该维度上shape增加

a1, a2, … : array序列，除了axis对应的维度外，其他shape相同
axis : 沿着axis维进行结合，结合后这个维的shape是序列这个维的shape之和

>>> a = np.array([[1, 2], [3, 4]])
>>> b = np.array([[5, 6]])
>>> a.shape, b.shape
((2, 2), (1, 2))
>>> c = np.concatenate((a, b), axis=0) # (2,2) (1,2) =>(3,2)
>>> c.shape
(3, 2)
>>> c
array([[1, 2],
       [3, 4],
       [5, 6]])
>>> d = np.concatenate((a, b.T), axis=1)
>>> d
array([[1, 2, 5],
       [3, 4, 6]])
>>> d.shape
(2, 3)

Splitting arrays

函数原型	作用
[`split`](ary, indices_or_sections[, axis])	Split an array into multiple sub-arrays.
[`array_split`](ary, indices_or_sections[, axis])	Split an array into multiple sub-arrays.
[`dsplit`](ary, indices_or_sections)	Split array into multiple sub-arrays along the 3rd axis (depth).
[`hsplit`](ary, indices_or_sections)	Split an array into multiple sub-arrays horizontally (column-wise).
[`vsplit`](ary, indices_or_sections)	Split an array into multiple sub-arrays vertically (row-wise).

Tiling arrays

函数原型	作用
[`tile`](A, reps)	Construct an array by repeating A the number of times given by reps.
[`repeat`](a, repeats[, axis])	Repeat elements of an array.

Adding and removing elements

函数原型	作用
[`delete`](arr, obj[, axis])	Return a new array with sub-arrays along an axis deleted.
[`insert`](arr, obj, values[, axis])	Insert values along the given axis before the given indices.
[`append`](arr, values[, axis])	Append values to the end of an array.
[`resize`](a, new_shape)	Return a new array with the specified shape.
[`trim_zeros`](filt[, trim])	Trim the leading and/or trailing zeros from a 1-D array or sequence.
[`unique`](ar[, return_index, return_inverse, …])	Find the unique elements of an array.

Rearranging elements

函数原型	作用
[`flip`](m[, axis])	Reverse the order of elements in an array along the given axis.
`fliplr`	Flip array in the left/right direction.
`flipud`	Flip array in the up/down direction.
[`reshape`](a, newshape[, order])	Gives a new shape to an array without changing its data.
[`roll`](a, shift[, axis])	Roll array elements along a given axis.
[`rot90`](m[, k, axes])	Rotate an array by 90 degrees in the plane specified by axes.