What is the numpy slicing operation method?

numpy slicing operation method: 1. One-dimensional array slicing, you can use a method similar to list slicing in Python to perform slicing operations; 2. Two-dimensional array slicing, you can use two index values ​​​​to perform slicing operations Operation, the first index value represents a row, and the second index value represents a column; 3. Multi-dimensional array slicing, multiple index values ​​can be used to perform slicing operations, each index value corresponds to a dimension; 4. Boolean index, through Boolean values ​​are used to filter; 5. Conditional index slicing is a way to filter through conditional expressions, etc.

Operating system for this tutorial: Windows 10 system, Python version 3.11.4, Dell G3 computer.

numpy is an open source numerical calculation library that provides rich array operation functions. Among them, slicing operation is one of the commonly used functions in numpy. The slicing operation can obtain a subset of the array through indexing, and can perform operations such as slicing, dicing, and row cutting on the array. This article will introduce in detail the slicing operation method of numpy.

In numpy, slicing operations can be used for one-dimensional arrays, two-dimensional arrays and multi-dimensional arrays. The slicing operation methods in these three cases are introduced below.

One-dimensional array slicing operation:

For one-dimensional arrays, you can perform slicing operations in a manner similar to list slicing in Python.

import numpy as np
a = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
# 获取数组中的前三个元素
b = a[:3]
print(b)  # 输出: [0 1 2]
# 获取数组中的第三个到第六个元素
c = a[2:6]
print(c)  # 输出: [2 3 4 5]
# 获取数组中的倒数三个元素
d = a[-3:]
print(d)  # 输出: [7 8 9]

Two-dimensional array slicing operation:

For two-dimensional arrays, you can use two index values ​​​​to perform slicing operations. The first index value represents the row, and the first index value represents the row. The two index values ​​represent columns.

import numpy as np
a = np.array([[0, 1, 2, 3],
              [4, 5, 6, 7],
              [8, 9, 10, 11]])
# 获取数组的第一行
b = a[0, :]
print(b)  # 输出: [0 1 2 3]
# 获取数组的第二列
c = a[:, 1]
print(c)  # 输出: [1 5 9]
# 获取数组的前两行和前三列
d = a[:2, :3]
print(d)  # 输出: [[0 1 2]
          #        [4 5 6]]

Multi-dimensional array slicing operation:

For multi-dimensional arrays, multiple index values ​​can be used to perform slicing operations, each index value corresponding to one dimension.

import numpy as np
a = np.array([[[0, 1, 2],
               [3, 4, 5],
               [6, 7, 8]],
              [[9, 10, 11],
               [12, 13, 14],
               [15, 16, 17]]])
# 获取数组的第一个元素
b = a[0, :, :]
print(b)  # 输出: [[0 1 2]
          #        [3 4 5]
          #        [6 7 8]]
# 获取数组的第二个元素的第一行和第二行
c = a[1, :2, :]
print(c)  # 输出: [[ 9 10 11]
          #        [12 13 14]]

In addition to using integer indexes for slicing operations, you can also use Boolean indexes and conditional indexes for slicing operations.

Boolean index slicing operation:

Boolean index is a way to filter by Boolean values, which can be used to obtain elements in an array that meet certain conditions.

import numpy as np
a = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
# 获取数组中大于5的元素
b = a[a > 5]
print(b)  # 输出: [6 7 8 9]

Conditional index slicing operation:

Conditional index is a way to filter through conditional expressions, which can be used to obtain items that meet certain conditions in an array. element.

import numpy as np
a = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
# 获取数组中大于5的元素的索引值
b = np.where(a > 5)
print(b)  # 输出: (array([6, 7, 8, 9]),)

Numpy's slicing operation provides a flexible and efficient way to obtain a subset of an array. Whether it is a one-dimensional array, a two-dimensional array, or a multi-dimensional array, you can use slicing operations to extract and filter data. Slicing operations not only support integer indexes, but also Boolean indexes and conditional indexes, which can meet various needs. By rationally using numpy's slicing operations, the efficiency and flexibility of data processing can be improved.

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