机器学习python 库_最佳Python机器学习库

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![e2bcf87628d0fb8011896e91e2e30acf.png][]

机器学习python 库

In this article I am going to share some popular and best python machine learning libraries.

在本文中，我将分享一些流行且最佳的python机器学习库。

I will advise you to go through [Introduction to Machine Learning][] article (an introductory blogpost) to get better insights as we move further.

我建议您阅读[机器学习简介一][Introduction to Machine Learning]文(介绍性博文)，以便在我们进一步发展时获得更好的见解。

Here we will be focusing on some of the cool packages and libraries that we can use during our project life cycle in Machine Learning.

在这里，我们将重点介绍在机器学习的项目生命周期中可以使用的一些很酷的程序包和库。

## 最佳Python机器学习库 **(**Best Python Machine Learning Libraries**)** ##

Guys, primarily we need to opt a language for our journey with ML from R & Python, so based on the public interest and keeping various other factors in mind we will be continuing our rest of the session with Python as a language.

伙计们，首先，我们需要在R＆Python的ML之旅中选择一种语言，因此，出于公众利益的考虑，并牢记各种其他因素，我们将继续使用Python作为一种语言来完成本课程的其余部分。

![Best Python Machine Learning Libraries 1][]

[Image Source][]

[图片来源][Image Source]

Here we are going to discuss about some of the basic Python machine learning libraries and packages that some of you might have used during your projects and on the other hand some of the packages that are specific and beneficial for Machine Learning. So let’s start with discussing the importance of the packages and what functionalities do they have to offer.

在这里，我们将讨论一些基本的Python机器学习库和软件包，其中一些可能在您的项目期间使用过，另一方面，一些特定于机器学习的软件包对他们也很有用。 因此，让我们从讨论软件包的重要性以及它们必须提供的功能开始。

#### **NumPy** **(****NumPy****)** ####

NumPy (stands for Numerical Python) is one of the most famous and commonly used python package among data scientists and ML engineers. This is a part of Python’s SciPy Stack, which is basically a collection of software specially designed for scientific computations. However the stack mentioned above is pretty vast. In this post we’ll focus on some of the essential libraries pertaining to python.

NumPy(代表数值Python)是数据科学家和ML工程师中最著名，最常用的python软件包之一。 这是Python的SciPy Stack的一部分，而SciPy Stack基本上是专门为科学计算而设计的软件集合。 但是，上面提到的堆栈非常庞大。 在本文中，我们将重点介绍与python有关的一些基本库。

Talking about NumPy, it provides several features to work with n-dimensional arrays and matrices in python. This library provides vectorization of mathematical operations on the NumPy array type which adds up to the performance of the execution.

谈到NumPy，它提供了一些功能来使用python中的n维数组和矩阵。 该库提供了对NumPy数组类型的数学运算的向量化，从而增加了执行性能。

#### **大熊猫** **(****Pandas****)** ####

The Pandas library is too a well-known library in the world of Analytics and Data Sciences. This package is primarily designed to work with simple and relational data. This is one of the favorite libraries among the data scientists for easy data manipulation, visualization as well as aggregation.

熊猫图书馆在分析和数据科学领域也是著名的图书馆。 该软件包主要用于处理简单和相关的数据。 这是数据科学家中最喜欢的库之一，它易于进行数据处理，可视化以及汇总。

If talking about the data structures, there are basically two prime data structures available in the library which are Series (one-dimensional) & Data Frames ( two-dimensional) and we think these are not that significant to talk about as of now.

如果谈论数据结构，那么库中基本上有两个主要的数据结构，分别是“系列”(一维)和“数据帧”(二维)，并且我们认为到目前为止，讨论这些并不重要。

Let’s see some of the basic functionalities that Pandas has to offer:

让我们看一下熊猫提供的一些基本功能：

*  We can very easily delete as well as add a columns from DataFrame
    
    我们可以很容易地从DataFrame中删除以及添加列
 *  Pandas can be used to convert the Data Structures in to DataFrame objects.
    
    熊猫可用于将数据结构转换为DataFrame对象。
 *  If we have any redundancy in the dataset in the form of missing data represented as ‘NaN’, this is the perfect tool to remove that
    
    如果我们在数据集中存在任何冗余(以表示为“ NaN”的缺失数据的形式)，则这是删除该数据的理想工具
 *  Can be used for grouping of the attributes based strictly on their functionality.
    
    可以严格根据其功能将其用于属性分组。

#### **科学** **(****SciPy****)** ####

This is a SciPy library, do not get confused with SciPy Stack that we have mentioned earlier. SciPy is a library that contains modules for Liner Algebra, Statistics, Optimization & Integration. This fact cannot be denied that the main functionality of SciPy is built upon NumPy.

这是一个SciPy库，不要与我们前面提到的SciPy Stack混淆。 SciPy是一个包含Liner代数，统计，优化和集成模块的库。 不能否认SciPy的主要功能是建立在NumPy之上的。

The purpose mentioned above like statistics, optimization is served by this library with the help of its specific sub-modules (in which the functions are well documented).

上面提到的目的(如统计信息，优化)由该库借助其特定的子模块(其中功能有据可查)来实现。

**Note:** These three libraries that we’ve mentioned above are the core libraries, i.e. they can be frequently used in the python programming as well as for highly specific tasks like Data Analysis and Machine Learning.

**注意：**我们上面提到的这三个库是核心库，即它们可以在python编程以及数据分析和机器学习等高度特定的任务中频繁使用。

*** ***Let us now see some of the more great libraries that add up to the beauty of python when working with data.

*** *** 现在让我们来看一些更出色的库，这些库在处理数据时增加了python的美感。

### **数据可视化库** **(****Libraries for Data Visualization****)** ###

*  Matplotlib
    
    Matplotlib
 *  Seaborn
    
    Seaborn
 *  Bokeh
    
    散景
 *  Plotly
    
    密谋

These are the libraries that are frequently being used in Data Sciences preferably for data visualization. We do not need to explain them right now but will be using in the script whenever required.

这些是数据科学中经常用于数据可视化的库。 我们不需要立即解释它们，但是在需要时将在脚本中使用它们。

### **机器学习图书馆** **(****Libraries for Machine Learning****)** ###

*  Scikit-Learn
    
    Scikit学习
 *  Keras
    
    凯拉斯
 *  Theano
    
    茶野
 *  TensorFlow
    
    TensorFlow

### **自然语言处理图书馆** **(****Libraries for Natural Language Processing****)** ###

*  NLTK (Natural Language Toolkit)
    
    NLTK(自然语言工具包)
 *  Gensim
    
    Gensim

### **数据挖掘与统计图书馆** **(****Libraries for Data Mining & Statistics****)** ###

*  Scrapy
    
    cra草
 *  Statsmodels
    
    统计模型

These are some of the most familiar machine learning libraries in python that are being preferred and used by the data scientists and engineers. You can also find some other packages/libraries useful depending on your needs.

这些是python中一些最熟悉的机器学习库，它们是数据科学家和工程师首选并使用的库。 您还可以根据需要找到其他有用的软件包/库。

Let’s see now what Github has to say for the use of different libraries throughout:

现在让我们看看Github在使用不同的库时要说些什么：

![Best Python Machine Learning Libraries 2][]

**Bonus Tip:** As some of our readers might be working on their ML projects with the help of R language. So here I am attaching a screenshot for them to choose a best performing package based on downloads.

**温馨提示：**正如我们的某些读者可能会在R语言的帮助下从事ML项目一样。 所以在这里，我为他们附加了一个屏幕截图，以便他们根据下载情况选择性能最佳的软件包。

![Best R Machine Learning Libraries and Packages][]

[Image Source][Image Source 1]

[图片来源][Image Source 1]

Alright guys that is all for today. We hope you enjoyed learning with us. We will be coming with such articles on regular intervals. Stay tuned.

好了，今天就这些。 希望您喜欢和我们一起学习。 我们将定期发布此类文章。 敬请关注。

> 翻译自: [https://www.thecrazyprogrammer.com/2017/11/best-python-machine-learning-libraries.html][https_www.thecrazyprogrammer.com_2017_11_best-python-machine-learning-libraries.html]

机器学习python 库

[e2bcf87628d0fb8011896e91e2e30acf.png]: /images/20221123/5d7722602d5e4035a10b72ad6d659017.png
[Introduction to Machine Learning]: https://www.thecrazyprogrammer.com/2017/11/introduction-machine-learning.html
[Best Python Machine Learning Libraries 1]: /images/20221123/c7962dd81f2d473c8bd46c9299238f78.png
[Image Source]: http://stamfordresearch.com/essential-python-packages/
[Best Python Machine Learning Libraries 2]: /images/20221123/f7626a5fc4d4497898b1cb5d1e2ab943.png
[Best R Machine Learning Libraries and Packages]: /images/20221123/a118540df5c544639bc1960430e9ad8a.png
[Image Source 1]: https://www.kdnuggets.com/2015/06/top-20-r-machine-learning-packages.html
[https_www.thecrazyprogrammer.com_2017_11_best-python-machine-learning-libraries.html]: https://www.thecrazyprogrammer.com/2017/11/best-python-machine-learning-libraries.html