Python Data Structure 简明教程
Python - Matrix
矩阵是二维数组的一种特殊情况，其中每个数据元素的大小完全相同。因此，每个矩阵均是二维数组，但反之不然。
Matrix is a special case of two dimensional array where each data element is of strictly same size. So every matrix is also a two dimensional array but not vice versa.
矩阵是对许多数学和科学计算非常重要的数据结构。正如我们在上一章已经讨论过二维数组数据结构一样，本章将重点介绍针对矩阵的数据结构操作。
Matrices are very important data structures for many mathematical and scientific calculations. As we have already discussed two dimnsional array data structure in the previous chapter we will be focusing on data structure operations specific to matrices in this chapter.
我们还会使用 numpy 包进行矩阵数据操作。
We also be using the numpy package for matrix data manipulation.
Matrix Example
考虑记录一周内在上午、中午、晚上和午夜测得的温度的情况。可以使用数组和 numpy 中提供的 reshape 方法，以 7X5 矩阵来表示。
Consider the case of recording temprature for 1 week measured in the morning, mid-day, evening and mid-night. It can be presented as a 7X5 matrix using an array and the reshape method available in numpy.
from numpy import *
a = array([['Mon',18,20,22,17],['Tue',11,18,21,18],
['Wed',15,21,20,19],['Thu',11,20,22,21],
['Fri',18,17,23,22],['Sat',12,22,20,18],
['Sun',13,15,19,16]])
m = reshape(a,(7,5))
print(m)Output
可以用二维数组表示上述数据，如下所示：
The above data can be represented as a two dimensional array as below −
[
['Mon' '18' '20' '22' '17']
['Tue' '11' '18' '21' '18']
['Wed' '15' '21' '20' '19']
['Thu' '11' '20' '22' '21']
['Fri' '18' '17' '23' '22']
['Sat' '12' '22' '20' '18']
['Sun' '13' '15' '19' '16']
]Accessing Values
可以使用索引访问矩阵中的数据元素。访问方法与在二维数组中访问数据的方法相同。
The data elements in a matrix can be accessed by using the indexes. The access method is same as the way data is accessed in Two dimensional array.
Adding a row
使用以下提到的代码为矩阵添加一行。
Use the below mentioned code to add a row in a matrix.
Example
from numpy import *
m = array([['Mon',18,20,22,17],['Tue',11,18,21,18],
['Wed',15,21,20,19],['Thu',11,20,22,21],
['Fri',18,17,23,22],['Sat',12,22,20,18],
['Sun',13,15,19,16]])
m_r = append(m,[['Avg',12,15,13,11]],0)
print(m_r)Output
执行上述代码后，将生成以下结果 −
When the above code is executed, it produces the following result −
[
['Mon' '18' '20' '22' '17']
['Tue' '11' '18' '21' '18']
['Wed' '15' '21' '20' '19']
['Thu' '11' '20' '22' '21']
['Fri' '18' '17' '23' '22']
['Sat' '12' '22' '20' '18']
['Sun' '13' '15' '19' '16']
['Avg' '12' '15' '13' '11']
]Adding a column
我们可以使用 insert() 方法为矩阵添加一列。在此，我们必须指定要添加列的位置，以及一个包含所添加列的新值的数组。在以下示例中，我们在起始位置添加了一列。
We can add column to a matrix using the insert() method. here we have to mention the index where we want to add the column and a array containing the new values of the columns added.In the below example we add t a new column at the fifth position from the beginning.
Example
from numpy import *
m = array([['Mon',18,20,22,17],['Tue',11,18,21,18],
['Wed',15,21,20,19],['Thu',11,20,22,21],
['Fri',18,17,23,22],['Sat',12,22,20,18],
['Sun',13,15,19,16]])
m_c = insert(m,[5],[[1],[2],[3],[4],[5],[6],[7]],1)
print(m_c)Output
执行上述代码后，将生成以下结果 −
When the above code is executed, it produces the following result −
[
['Mon' '18' '20' '22' '17' '1']
['Tue' '11' '18' '21' '18' '2']
['Wed' '15' '21' '20' '19' '3']
['Thu' '11' '20' '22' '21' '4']
['Fri' '18' '17' '23' '22' '5']
['Sat' '12' '22' '20' '18' '6']
['Sun' '13' '15' '19' '16' '7']
]Delete a row
我们可以使用 delete() 方法从矩阵中删除一行。我们必须指定该行的索引以及轴值，行的轴值为 0，而列的轴值为 1。
We can delete a row from a matrix using the delete() method. We have to specify the index of the row and also the axis value which is 0 for a row and 1 for a column.
Delete a column
我们可以使用 delete() 方法从矩阵中删除一列。我们必须指定该列的索引以及轴值，行的轴值为 0，而列的轴值为 1。
We can delete a column from a matrix using the delete() method. We have to specify the index of the column and also the axis value which is 0 for a row and 1 for a column.
Update a row
为了更新矩阵中行的值，我们只需重新指定行索引处的值。以下示例中，所有星期四的数据值都标记为零。该行的索引为 3。
To update the values in the row of a matrix we simply re-assign the values at the index of the row. In the below example all the values for thrusday’s data is marked as zero. The index for this row is 3.