How to choose the right numpy version to optimize your data science workflow

numpy is a commonly used mathematical operation library in Python. It provides powerful array operations and numerical calculation functions. However, as numpy versions are constantly updated, how users choose the appropriate version has become an important issue. Choosing the right numpy version can optimize your data science workflow and improve the maintainability and readability of your code. This article will introduce how to choose the numpy version and provide actual code examples for readers' reference.

1. Understand the characteristics of different versions of numpy

The numpy library is updated very quickly, and the latest version is 1.21.2. When using numpy, understanding the changes and characteristics between different versions can help us choose the appropriate numpy version and improve the efficiency and maintainability of the code. The main versions of numpy include 1.11, 1.12, 1.13, 1.14, 1.15, 1.16, 1.17, 1.18, 1.19, 1.20 and 1.21. The main changes between different versions are:

Version features
1.11 - Introduced np.random.choice and np.random.permutation functions

• Addednp.histogramddFunction
• Improved performance and stability
  1.12 - Introduced support for reading and writing MATLAB format files
• Optimized support for Structured Arrays
• Making performance significantly improved in some cases
  1.13 - Introduced support for an improved version of UMFPACK
• Addednp.iscloseFunction
• Improved support for Polynomials
  1.14 - Removed some obsolete functions and properties
• Introduced support for multi-threaded calculations np.matmulFunction
• Documentation optimizations
  1.15 - Introduced compatibility enhancements for Pandas
• Improvementsnp.loadtxtand np.genfromtxtFunction
• Improved segmentation and slicing operations of multi-dimensional arrays
  1.16 - Introduced mask array of boolean type
• Added np.piecewiseFunction
• Improved performance and stability
  1.17 - Introducednp.stackFunction
• Added new features for Structured arrays
• Documentation and performance optimizations
  1.18 - Introduced np.moveaxisFunction
• Added np.copytoFunction
• Improved np.count_nonzero and np.bincount functions
  1.19 - Introduced np.compressfunction
• Added np.isinfunction
• Improved np.promote_typesfunction
  1.20 - Introduced np.histogram_bin_edgesfunction
• Added np.searchsorted function
• Improved performance of np.unique function
  1.21 - Introduced np.linalg.lstsqFunctionrcondParameters
• Introduced np.cellFunction
• Introduced np.format_float_positionalFunction

As can be seen from the above table, each version of numpy has different changes and optimizations. When choosing a numpy version, you need to select the corresponding version based on specific needs and usage scenarios. If you need to use a new feature or solve a specific problem, you can choose a newer version. If you consider stability and backward compatibility, you can choose an older version.

2. How to change the numpy version

In Python, you can use the pip command to install and change the numpy version. The following are the steps to change the numpy version:

• First, you can view the currently installed numpy version through the pip list command. For example, use the following command to check the numpy version:

!pip list | grep numpy

Output:

numpy                1.19.5

The result shows that the currently installed numpy version is 1.19.5.

• In order to change the numpy version, you need to uninstall the current version first, and then install the new version. You can use the following code to install and uninstall numpy:

# 卸载numpy
!pip uninstall -y numpy 

# 安装新的numpy版本
!pip install numpy==1.20

In the code, numpy==1.20 means installing version 1.20. Readers can choose the appropriate version number to install according to their needs.

3. Use numpy optimization techniques

In addition to choosing an appropriate numpy version, you can also use some numpy optimization techniques to improve the efficiency and reliability of your code for specific data science problems. Readability. The following are several examples of practical numpy optimization techniques:

(1) Vectorized calculations using numpy

numpy makes vectorized calculations very easy. When working with large amounts of data, vectorized calculations are faster than looping over elements one by one. The following is an example to implement element-by-element summation of two arrays:

import numpy as np

# 生成两个向量
a = np.array([1,2,3,4])
b = np.array([5,6,7,8])

# 使用循环计算元素和
c = np.zeros(len(a))
for i in range(len(a)):
    c[i] = a[i] + b[i]

# 使用向量化计算元素和
d = a + b

# 输出结果
print(c)   # [ 6.  8. 10. 12.]
print(d)   # [ 6  8 10 12]

As can be seen from the above example, using vectorized calculations can greatly simplify the code and improve efficiency at the same time.

(2) Use the broadcast function of numpy

The broadcast function of numpy is a very powerful tool that allows mathematical calculations to be performed between arrays of different shapes. Broadcasting rules can make some calculations very simple. Here is an example of adding two arrays of different shapes:

import numpy as np

# 生成两个数组
a = np.array([[ 0.0,  0.0,  0.0],
              [10.0, 10.0, 10.0],
              [20.0, 20.0, 20.0],
              [30.0, 30.0, 30.0]])
b = np.array([1.0, 2.0, 3.0])

# 使用广播计算元素和
c = a + b

# 输出结果
print(c)

This code snippet treats the numbers 1, 2, and 3 as a column vector and adds them to the aarray Each row of . The broadcast mechanism allows numpy to automatically infer which axes to perform broadcast operations on, making calculations very simple.

(3) Use numpy’s slicing and indexing functions

numpy提供了切片和索引的功能，使得对数组中特定元素的访问变得非常方便。例如，如果想要选择数组中的一个子集，可以使用切片：

import numpy as np

# 生成一个数组
a = np.array([[ 0,  1,  2,  3],
              [10, 11, 12, 13],
              [20, 21, 22, 23],
              [30, 31, 32, 33],
              [40, 41, 42, 43]])

# 切片选择子数组
b = a[:, 1:3]

# 输出子数组
print(b)

该代码片段选择了数组a中第2列和第3列的所有行作为子数组，结果如下：

[[ 1  2]
 [11 12]
 [21 22]
 [31 32]
 [41 42]]

除了切片，numpy还提供了强大的索引功能，可以使用它来选择特定的元素或子数组：

import numpy as np

# 生成一个数组
a = np.array([[ 0,  1,  2,  3],
              [10, 11, 12, 13],
              [20, 21, 22, 23],
              [30, 31, 32, 33],
              [40, 41, 42, 43]])

# 使用索引选择特定元素
b = a[[0, 1, 2, 3], [1, 2, 3, 0]]

# 输出选中的元素
print(b)

该代码片段选择了数组a中的4个元素，分别是(0,1)、(1,2)、(2,3)和(3,0)，结果如下：

[ 1 12 23 30]

4.结语

选择合适的numpy版本和使用优化技巧是提高数据科学工作效率的有效方法。通过与具体的场景结合，使用numpy的向量化计算、广播、切片和索引等优化技巧，能够简化代码、提高效率、降低资源消耗。读者可以基于本文提供的实际代码示例，进一步探索numpy的强大功能。

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