A brief discussion on several sorting methods of numpy array_python

This article mainly introduces several sorting methods of numpy arrays, including a brief introduction to numpy and how to create arrays. It has certain reference value. Friends who are interested in numpy can refer to it.

Brief introduction

The NumPy system is an open source array calculation extension for Python. This tool can be used to store and process large matrices much more efficiently than Python's own nested list structure (which can also be used to represent matrices).

Create an array

Create a 1-dimensional array:

data = np.array([1,3,4,8]) 　

View array dimensions

data.shape

View array type

data.dtype

Get or modify array elements by index

data[1] Get elements<br>data[1] = 'a' Modify Element　

Create a two-dimensional array

data = np.array([[1,2,3],[4, 5,6]]) Both elements are lists
2.data = np.arange(10) Like python’s range, range returns a list, and arange returns an array of array type
3.data2 = data.reshape(2,5) returns a 2*5 array. It does not copy the array but a reference. It just returns a different view of the array. If data changes, data2 will also change.

Create a special array

data = np.zeros((2,2)) Create a 2*2 2-dimensional array with all 0s<br>data = np.ones(( 2,3,3,)) Create a three-dimensional array with all 1s<br>data = np.eye(4) Create a 4*4 diagonal array, with diagonal elements being 1 and others being 0<br>

Array conversion

data = np.arange(16).reshape(4,4) Convert the shifted array of 0-16 to 4 *Array of 4

Sort method

Note: It is often necessary to sort arrays or lists, and python provides several Sorting function, the following describes the characteristics;

Two-dimensional array a:

1 4
3 1

1, ndarray.sort(axis= -1,kind='quicksort',order=None)

Usage method: a.sort

Parameter description:

axis: sort along the array Direction, 0 means by row, 1 means by column

kind: sorting algorithm, provides quick sort, mixed sort, heap sort

order: does not refer to the order, when used in the future Let’s analyze the effect of this

: Sort array a, and directly change a

after sorting. For example:

>>a.sort(axis=1)
>>print a

1 4
1 3

2、numpy.sort(a,axis=-1,kind='quicksort',order=None)

Usage ：numpy.sort(a)

Parameter description:

a: Array to be sorted, other effects are the same as 1

: For array a Sort, return a sorted array (same dimension as a), a unchanged

For example:

>>print numpy.sort(a,axis=1)
1 4
1 3
>>print a
1 4
3 1

3, numpy.argsort (a,axis=-1,kind='quicksort',order=None)

Usage method: numpy.argsort(a)

Parameter description: Same as 2

Effect: Sort array a and return a sorted index, a remains unchanged

For example:

>>print numpy.argsort(a,axis=1)
0 1
1 0

4, sorted (iterable,cmp=None,key=None,reverse=False)

Description: The built-in sorting function can be used for lists, dictionaries, etc.

iterable: iterable Type;

cmp: Function used for comparison, what is compared is determined by key, has a default value, and iterates an item in the collection;

key: uses a certain attribute and function of the list element As a keyword, proceed has a default value and is an item in the iterative collection;

reverse: sorting rule.reverse=True or reverse=False, the default is False (from small to large).

Return value: It is a sorted iterable type, the same as iterable;

For example: b is a dictionary

b:

{' a':2,'c':1,'b':3}

Sort b:

>>c=sorted(b.iteritems(),key=operator.itemgetter(1),reverse=False)
>>print c[('c', 1), ('a', 2), ('b', 3)]

Visible: return is a list

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