As a new Data Scientist, you know that your path begins with programming languages you need to learn. Among all languages that you can select from Python is the most popular language for all Data Scientists. In this article, I will cover 7 reasons behind Python's popularity that will help you to understand why programmers love it.

作为一名新的数据科学家，您知道自己的道路始于需要学习的编程语言。 在所有Python中，您都可以从Python中选择最流行的语言。 在本文中，我将介绍Python流行的7个原因，它们将帮助您理解程序员为什么喜欢它。

1.简单性 (1. Simplicity)

Python is one of the easiest languages to start your journey. Also, its simplicity does not limit your functional possibilities.

Python是开始您的旅程的最简单的语言之一。 同样，它的简单性不限制您的功能可能性。

What gives Python such flexibility? There are multiple factors:

是什么赋予Python这样的灵活性？ 有多个因素：

• Python is a free and open-source language
  Python是一种免费的开源语言
• This is a high-level programming
  这是一个高级编程
• Python is interpreted
  解释了Python
• It has an enormous community
  它有一个庞大的社区

In addition, Python is fast in writing. Just compare these 2 examples written in Java and Python:

此外，Python的编写速度很快。 只需比较以下两个用Java和Python编写的示例：

This quick example shows how you can benefit from Python. Rather than type 3 code lines, you need to write 1 only. Just imagine how much time you can save with more complicated tasks.

这个简单的示例说明了如何从Python中受益。 无需键入3条代码行，只需编写1条即可。 试想一下，执行更复杂的任务可以节省多少时间。

1.可扩展性 (1. Scalability)

Python is a programming language that scales very fast. Among all available languages, Python is a leader in scaling. That means that Python has more and more possibilities.

Python是一种可快速扩展的编程语言。 在所有可用的语言中，Python是扩展的领导者。 这意味着Python具有越来越多的可能性。

Python flexibility is super useful for any problem in-app development

Python的灵活性对于任何有问题的应用内开发都非常有用

Any problem can be decided easily with new updates that are coming. Saying that Python provides the best options for newbies because there are many ways to decide the same issue.

即将推出的新更新可以轻松确定任何问题。 说Python为新手提供了最好的选择，因为有很多方法可以决定同一问题。

Even if you have a team of non-Python programmers, who knows C+ +design patterns, Python will be better for them in terms of time needed to develop and verify code correctness.

即使您有一个了解C ++设计模式的非Python程序员团队，Python在开发和验证代码正确性方面所需的时间也会对他们更好。

It happens fast because you don`t spend your time to find memory leaks, work for compilation or segmentation faults.

它之所以发生得很快，是因为您不花时间查找内存泄漏，进行编译或分段错误。

2.图书馆和框架 (2. Libraries and Frameworks)

Due to its popularity, Python has hundreds of different libraries and frameworks which is a great addition to your development process. They save a lot of manual time and can easily replace the whole solution.

由于其受欢迎程度，Python有数百种不同的库和框架，这对您的开发过程是一个很大的补充。 它们节省了大量的手动时间，并且可以轻松替换整个解决方案。

As a Data Scientist, you will find that many of these libraries will be focused on Data Analytics and Machine Learning. Also, there is a huge support for Big Data. I suppose there should be a strong pro why you need to learn Python as your first language.

作为数据科学家，您会发现其中许多库将专注于数据分析和机器学习。 此外，对大数据也有巨大的支持。 我认为应该有一个强大的专业人士，为什么您需要学习Python作为第一语言。

Some of these libraries are given below:

其中一些库如下所示：

• Pandas

  大熊猫

It is great for data analysis and data handling. Pandas provides data manipulation control.

非常适合数据分析和数据处理。 熊猫提供数据操纵控制。

• NumPy

  NumPy

NumPy is a free library for numerical computing. It provides high-level math functions along with data manipulations.

NumPy是一个免费的用于数值计算的库。 它提供了高级数学功能以及数据操作。

• SciPy

  科学

This library is related to scientific and technical computing. SciPy can be used for data optimization and modification, algebra, special functions, etc.

该库与科学技术计算有关。 SciPy可用于数据优化和修改，代数，特殊功能等。

3.网站开发 (3. Web Development)

To make your development process as easy as it is possible only, learn Python. There are a lot of Django and Flask libraries and frameworks that make your coding productive and speed up your work.

为了使开发过程尽可能简单，请学习Python。 有许多Django和Flask库和框架可提高您的编码效率并加快工作速度。

If you compare PHP and Python, you can find that the same task can be created within a few hours of code via PHP. But with Python, it will take only a few minutes. Just take a look at Reddit website — it was created with Python.

如果比较PHP和Python，您会发现可以通过PHP在几小时的代码内创建相同的任务。 但是，使用Python只需几分钟。 只需查看Reddit网站-它是使用Python创建的。

Here are Pythons Full Stack frameworks for web development:

以下是用于Web开发的Pythons Full Stack框架：

• Django
  Django的
• Pyramid
  金字塔
• Web2py
  Web2py
• TurboGears
  涡轮齿轮

And here are Pythons micro-frameworks for web development:

以下是用于Web开发的Python微框架：

• Flask
  烧瓶
• Bottle
  瓶子
• CherryPy
  樱桃皮
• Hug
  拥抱

Also, there is an alternative framework you might want to consider:

另外，您可能要考虑一个替代框架：

• Tornado
  龙卷风

4.庞大的社区 (4. Huge Community)

As I have mentioned before, Python has a powerful community. You might think that it shouldn`t be one of the main reasons why you need to select Python. But the truth is vice versa.

如前所述，Python具有强大的社区。 您可能会认为这不是选择Python的主要原因之一。 但事实恰恰相反。

If you don`t get support from other specialists, your learning path can be difficult. That`s why you should know that this won`t happen with your Python learning journey.

如果您没有得到其他专家的支持，那么您的学习道路可能会很困难。 这就是为什么您应该知道在Python学习过程中不会发生这种情况的原因。

Here is a list of some Python communities:

以下是一些Python社区的列表：

官方Python有用链接： (Official Python helpful links:)

Official Tutorial: http://docs.python.org/tutorial/Language Reference: http://docs.python.org/reference/

官方教程： http : //docs.python.org/tutorial/语言参考： http : //docs.python.org/reference/

每日新闻和参与 (Daily news and engagement)

Pythonware Daily: http://www.pythonware.com/daily/Planet Python: http://planet.python.org/

每日Pythonware： http ：//www.pythonware.com/daily/ Planet Python： http ：//planet.python.org/

Video Tutorials: http://showmedo.com/videotutorials/python

视频教程： http ： //showmedo.com/videotutorials/python

Facts: http://www.ibiblio.org/swaroopch/byteofpython/read/

事实 ： http ： //www.ibiblio.org/swaroopch/byteofpython/read/

社区 (Communities)

Irc Node: http://www.python.org/community/irc/StackOverflow: http://stackoverflow.com/questions/tagged/python?sort=newest

Irc节点 ： http： //www.python.org/community/irc/ StackOverflow ： http ： //stackoverflow.com/questions/tagged/python ？ sort = newest

5.自动化 (5. Automation)

Using Python automation frameworks like PYunit gives you a lot of advantages:

使用PYunit之类的Python自动化框架可以为您带来很多好处：

• No additional modules are required to install. They come with the box
  无需安装其他模块。 他们随附盒子
• Even if you don`t have Python background you will find work with Unittest very comfortable. It is derivative and its working principle is similar to other xUnit frameworks.
  即使您没有Python背景，使用Unittest的工作也会非常舒适。 它是派生的，其工作原理类似于其他xUnit框架。
• You can run singular experiments in a more straightforward way. You should simply indicate the names on the terminal. The output is compact too, making the structure adaptable with regards to executing test cases.
  您可以以更直接的方式运行单个实验。 您只需在终端上指出名称。 输出也很紧凑，使得该结构适用于执行测试用例。
• The test reports are generated within milliseconds.
  测试报告在毫秒内生成。

5个用于自动化测试的Python框架： (5 Python Frameworks For Test Automation:)

1. Robot Framework

   机器人框架

2. UnitTest

2. 单元测试

3. Pytest

3. Pytest

4. Behave

4.表现

5. Lettuce

5.生菜

6.工作与成长 (6. Jobs and Growth)

Python is a unique language that has powerful growth and opens multiple career opportunities for Data Scientists. If you learn Python you can consider multiple additional jobs you might want to make the switch to in the future:

Python是一种独特的语言，具有强大的发展潜力，并为数据科学家提供了多种职业机会。 如果您学习Python，则可以考虑将来还要进行多项其他工作：

• Python Developer
  Python开发人员
• Product Manager
  产品经理
• Educator
  教育家
• Financial Advisors
  财务顾问
• Data Journalist
  数据记者

7.薪水 (7. Salary)

If you are looking for high paying opportunities, Python has massive options for you. Just check these stats:

如果您正在寻找高薪机会，Python为您提供了很多选择。 只需查看以下统计信息：

结论 (Conclusion)

Python is a base for any Data Scientist. There are many reasons to select this powerful programming language, so it’s up to you which reason will be main. You should definitely consider Python due to its possibilities and ongoing improvement, which will help you to build amazing products and help businesses.

Python是任何数据科学家的基础。 选择这种功能强大的编程语言的原因很多，因此取决于您的是哪个原因。 由于Python的可能性和持续改进，您绝对应该考虑使用Python，这将帮助您构建出色的产品并为企业提供帮助。