Learn numpy slicing techniques to simplify large data processing

Master the Numpy slicing operation method and easily process large-scale data. Specific code examples are required

Summary:
Use appropriate tools when processing large-scale data Very important. Numpy is a commonly used library in Python that provides high-performance numerical calculation tools. This article will introduce Numpy's slicing operation method, and use code examples to demonstrate how to easily operate and extract data when processing large-scale data.

1. Introduction
   Numpy is a commonly used numerical calculation library in Python, providing efficient data processing tools. The slicing operation is a very powerful function in Numpy, which can be used to quickly access and operate the elements of an array. The slicing operation can perform flexible operations on one-dimensional, two-dimensional, and multi-dimensional arrays, saving the process of writing loops and improving the operation speed.
2. One-dimensional array slicing
   First, let’s look at the slicing operation method of one-dimensional array. Suppose we have a one-dimensional array a containing 10 elements:

import numpy as np

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

We can use colon: to specify the range of the slice. The sample code is as follows:

# 切片操作
b = a[2:6]  # 从下标2到下标5的元素
print(b)  # 输出：[2 3 4 5]

c = a[:4]  # 从开头到下标3的元素
print(c)  # 输出：[0 1 2 3]

d = a[6:]  # 从下标6到末尾的元素
print(d)  # 输出：[6 7 8 9]

e = a[::3]  # 每隔2个元素取一个
print(e)  # 输出：[0 3 6 9]

3. Two-dimensional array slicing
   Next, let’s look at the slicing operation method of the two-dimensional array. Suppose we have a 2x3 two-dimensional array b:

b = np.array([[0, 1, 2],
              [3, 4, 5]])

We can specify the range of the slice by using commas. The sample code is as follows:

# 切片操作
c = b[0]  # 提取第0行的元素
print(c)  # 输出：[0 1 2]

d = b[:, 1]  # 提取所有行的第1列元素
print(d)  # 输出：[1 4]

e = b[:2, 1:]  # 提取前两行以及第二列之后的元素
print(e)  # 输出：[[1 2]
           #       [4 5]]

4. Multidimensional array slicing
   Slicing operations are also very convenient when processing multidimensional arrays. Suppose we have a 3x3x3 three-dimensional array c:

c = np.array([[[0, 1, 2],
               [3, 4, 5],
               [6, 7, 8]],
              [[9, 10, 11],
               [12, 13, 14],
               [15, 16, 17]],
              [[18, 19, 20],
               [21, 22, 23],
               [24, 25, 26]]])

We can specify the range of the slice by increasing the number of commas. The sample code is as follows:

# 切片操作
d = c[0]  # 提取第0个二维数组
print(d)  # 输出：[[0 1 2]
           #       [3 4 5]
           #       [6 7 8]]

e = c[:, 1, :]  # 提取所有二维数组的第1行的元素
print(e)  # 输出：[[ 3  4  5]
           #       [12 13 14]
           #       [21 22 23]]

f = c[:, :, ::2]  # 提取所有二维数组的每隔一个元素的列
print(f)  # 输出：[[[ 0  2]
           #        [ 3  5]
           #        [ 6  8]]
           #       [[ 9 11]
           #        [12 14]
           #        [15 17]]
           #       [[18 20]
           #        [21 23]
           #        [24 26]]]

5. Summary
   This article introduces Numpy's slicing operation method, and illustrates through specific code examples how to use slicing operations to easily process large-scale data. Slicing operations can perform flexible operations on one-dimensional, two-dimensional, and multi-dimensional arrays, which can greatly improve the efficiency of data processing and the readability of code. Once you master the Numpy slicing operation method, it will become easier to process large-scale data.

References:

• Travis E, Oliphant. (2006). A guide to NumPy. USA: Trelgol Publishing
• https://numpy .org/doc/stable/reference/
• https://numpy.org/doc/stable/user/quickstart.html

Code example:

import numpy as np

# 一维数组切片
a = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
b = a[2:6]
c = a[:4]
d = a[6:]
e = a[::3]

# 二维数组切片
b = np.array([[0, 1, 2],
              [3, 4, 5]])
c = b[0]
d = b[:, 1]
e = b[:2, 1:]

# 多维数组切片
c = np.array([[[0, 1, 2],
               [3, 4, 5],
               [6, 7, 8]],
              [[9, 10, 11],
               [12, 13, 14],
               [15, 16, 17]],
              [[18, 19, 20],
               [21, 22, 23],
               [24, 25, 26]]])
d = c[0]
e = c[:, 1, :]
f = c[:, :, ::2]

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