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書名： Getting Started with Python Data Analysis
作者名： Phuong Vo.T.H Martin Czygan
本章字?jǐn)?shù)： 279字
更新時間： 2021-07-09 21:02:33

Array functions

Many helpful array functions are supported in NumPy for analyzing data. We will list some part of them that are common in use. Firstly, the transposing function is another kind of reshaping form that returns a view on the original data array without copying anything:

>>> a = np.array([[0, 5, 10], [20, 25, 30]])
>>> a.reshape(3, 2)
array([[0, 5], [10, 20], [25, 30]])
>>> a.T
array([[0, 20], [5, 25], [10, 30]])

In general, we have the swapaxes method that takes a pair of axis numbers and returns a view on the data, without making a copy:

>>> a = np.array([[[0, 1, 2], [3, 4, 5]], 
 [[6, 7, 8], [9, 10, 11]]])
>>> a.swapaxes(1, 2)
array([[[0, 3],
 [1, 4],
 [2, 5]],
 [[6, 9],
 [7, 10],
 [8, 11]]])

The transposing function is used to do matrix computations; for example, computing the inner matrix product XT.X using np.dot:

>>> a = np.array([[1, 2, 3],[4,5,6]])
>>> np.dot(a.T, a)
array([[17, 22, 27],
 [22, 29, 36],
 [27, 36, 45]])

Sorting data in an array is also an important demand in processing data. Let's take a look at some sorting functions and their use:

>>> a = np.array ([[6, 34, 1, 6], [0, 5, 2, -1]])

>>> np.sort(a) # sort along the last axis
array([[1, 6, 6, 34], [-1, 0, 2, 5]])

>>> np.sort(a, axis=0) # sort along the first axis
array([[0, 5, 1, -1], [6, 34, 2, 6]])

>>> b = np.argsort(a) # fancy indexing of sorted array
>>> b
array([[2, 0, 3, 1], [3, 0, 2, 1]])
>>> a[0][b[0]]
array([1, 6, 6, 34])

>>> np.argmax(a) # get index of maximum element
1

See the following table for a listing of array functions:

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Getting Started with Python Data Analysis

Array functions