Simple example of numpy array splicing_python

This article mainly introduces a simple example of numpy array splicing, including an introduction to numpy arrays, properties of numpy arrays, etc. It has certain reference value and friends in need can refer to it.

NumPy array is a multidimensional array object called ndarray. It consists of two parts:

·The actual data

·Metadata describing these data

Most operations Only for metadata, without changing the underlying actual data.

There are a few things you need to know about NumPy arrays:

·The subscripts of NumPy arrays start from 0.

·The types of all elements in the same NumPy array must be the same.

NumPy array properties

Before introducing NumPy arrays in detail. First, let’s introduce the basic properties of NumPy arrays in detail. The dimensionality of a NumPy array is called rank. A one-dimensional array has a rank of 1, a two-dimensional array has a rank of 2, and so on. In NumPy, each linear array is called an axis (axes), and the rank actually describes the number of axes. For example, a two-dimensional array is equivalent to two one-dimensional arrays, where each element in the first one-dimensional array is another one-dimensional array. So a one-dimensional array is the axes (axes) in NumPy. The first axis is equivalent to the underlying array, and the second axis is the array in the underlying array. The number of axes, the rank, is the dimension of the array.

The more important ndarray object attributes in NumPy arrays are:

1.ndarray.ndim: The dimension of the array (that is, the number of array axes), which is equal to the rank. The most common is a two-dimensional array (matrix).

2.ndarray.shape: Dimensions of the array. is a tuple of integers representing the size of the array in each dimension. For example, in a two-dimensional array, it represents the "number of rows" and "number of columns" of the array. ndarray.shape returns a tuple whose length is the number of dimensions, that is, the ndim attribute.

3.ndarray.size: The total number of array elements is equal to the product of the tuple elements in the shape attribute.

4.ndarray.dtype: An object representing the type of elements in the array. You can use standard Python types to create or specify dtype. In addition, you can also use the data types provided by NumPy introduced in the previous article.

5.ndarray.itemsize: The byte size of each element in the array. For example, the itemsiz attribute value of an array whose element type is float64 is 8 (float64 occupies 64 bits, and each byte is 8 in length, so 64/8 takes up 8 bytes). Another example is an array whose element type is complex32. The array item attribute is 4 (32/8).

6.ndarray.data: Buffer containing actual array elements. Since elements are generally obtained through the index of the array, there is usually no need to use this attribute.

Array splicing method one

Idea: first convert the array into a list, and then use the list splicing functions append() and extend() Wait for splicing processing, and finally convert the list into an array.

Example 1:

>>> import numpy as np
>>> a=np.array([1,2,5])
>>> b=np.array([10,12,15])
>>> a_list=list(a)
>>> b_list=list(b)
>>> a_list.extend(b_list)
>>> a_list
[1, 2, 5, 10, 12, 15]
>>> a=np.array(a_list)
>>> a
array([ 1, 2, 5, 10, 12, 15])

This method is only suitable for simple one-dimensional array splicing, because the conversion process is very It is time-consuming and is generally not recommended for splicing large amounts of data.

Array splicing method two

Idea: numpy provides the numpy.append(arr, values, axis=None) function. For parameter specifications, there must be either one array and one value; or two arrays. Three or more arrays cannot be directly appended and spliced. The append function always returns a one-dimensional array.

Example 2:

>>> a=np.arange(5)
>>> a
array([0, 1, 2, 3, 4])
>>> np.append(a,10)
array([ 0, 1, 2, 3, 4, 10])
>>> a
array([0, 1, 2, 3, 4])
 
>>> b=np.array([11,22,33])
>>> b
array([11, 22, 33])
>>> np.append(a,b)
array([ 0, 1, 2, 3, 4, 11, 22, 33])
 
>>> a
array([[1, 2, 3],
    [4, 5, 6]])
>>> b=np.array([[7,8,9],[10,11,12]])
>>> b
array([[ 7, 8, 9],
    [10, 11, 12]])
>>> np.append(a,b)
array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])

numpy array does not have the function of dynamically changing the size, numpy.append() function Each time the entire array is reallocated and the original array is copied to the new array.

Array splicing method three

Idea: numpy provides numpy.concatenate((a1,a2,...),axis=0 )function. Able to complete the splicing of multiple arrays at one time. Where a1, a2,... are parameters of array type

Example 3:

>>> a=np.array([1,2,3])
>>> b=np.array([11,22,33])
>>> c=np.array([44,55,66])
>>> np.concatenate((a,b,c),axis=0) # 默认情况下，axis=0可以不写
array([ 1, 2, 3, 11, 22, 33, 44, 55, 66]) #对于一维数组拼接，axis的值不影响最后的结果
 
>>> a=np.array([[1,2,3],[4,5,6]])
>>> b=np.array([[11,21,31],[7,8,9]])
>>> np.concatenate((a,b),axis=0)
array([[ 1, 2, 3],
    [ 4, 5, 6],
    [11, 21, 31],
    [ 7, 8, 9]])
>>> np.concatenate((a,b),axis=1) #axis=1表示对应行的数组进行拼接
array([[ 1, 2, 3, 11, 21, 31],
    [ 4, 5, 6, 7, 8, 9]])

to numpy. Compare the running time of the two functions append() and numpy.concatenate()

Example 4:

>>> from time import clock as now
>>> a=np.arange(9999)
>>> b=np.arange(9999)
>>> time1=now()
>>> c=np.append(a,b)
>>> time2=now()
>>> print time2-time1
28.2316728446
>>> a=np.arange(9999)
>>> b=np.arange(9999)
>>> time1=now()
>>> c=np.concatenate((a,b),axis=0)
>>> time2=now()
>>> print time2-time1
20.3934997107

It can be seen that concatenate() is more efficient and suitable for large-scale data splicing

Summary

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