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yongshouzhang translating
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How to write your favorite R functions in Python
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======
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R or Python? This Python script mimics convenient R-style functions for doing statistics nice and easy.
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One of the great modern battles of data science and machine learning is "Python vs. R." There is no doubt that both have gained enormous ground in recent years to become top programming languages for data science, predictive analytics, and machine learning. In fact, according to a recent IEEE article, Python overtook C++ as the [top programming language][1] and R firmly secured its spot in the top 10.
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However, there are some fundamental differences between these two. [R was developed primarily][2] as a tool for statistical analysis and quick prototyping of a data analysis problem. Python, on the other hand, was developed as a general purpose, modern object-oriented language in the same vein as C++ or Java but with a simpler learning curve and more flexible demeanor. Consequently, R continues to be extremely popular among statisticians, quantitative biologists, physicists, and economists, whereas Python has slowly emerged as the top language for day-to-day scripting, automation, backend web development, analytics, and general machine learning frameworks and has an extensive support base and open source development community work.
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### Mimicking functional programming in a Python environment
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[R's nature as a functional programming language][3] provides users with an extremely simple and compact interface for quick calculations of probabilities and essential descriptive/inferential statistics for a data analysis problem. For example, wouldn't it be great to be able to solve the following problems with just a single, compact function call?
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* How to calculate the mean/median/mode of a data vector.
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* How to calculate the cumulative probability of some event following a normal distribution. What if the distribution is Poisson?
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* How to calculate the inter-quartile range of a series of data points.
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* How to generate a few random numbers following a Student's t-distribution.
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The R programming environment can do all of these.
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On the other hand, Python's scripting ability allows analysts to use those statistics in a wide variety of analytics pipelines with limitless sophistication and creativity.
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To combine the advantages of both worlds, you just need a simple Python-based wrapper library that contains the most commonly used functions pertaining to probability distributions and descriptive statistics defined in R-style. This enables you to call those functions really fast without having to go to the proper Python statistical libraries and figure out the whole list of methods and arguments.
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### Python wrapper script for most convenient R-functions
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[I wrote a Python script][4] to define the most convenient and widely used R-functions in simple, statistical analysis—in Python. After importing this script, you will be able to use those R-functions naturally, just like in an R programming environment.
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The goal of this script is to provide simple Python subroutines mimicking R-style statistical functions for quickly calculating density/point estimates, cumulative distributions, and quantiles and generating random variates for important probability distributions.
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To maintain the spirit of R styling, the script uses no class hierarchy and only raw functions are defined in the file. Therefore, a user can import this one Python script and use all the functions whenever they're needed with a single name call.
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Note that I use the word mimic. Under no circumstance am I claiming to emulate R's true functional programming paradigm, which consists of a deep environmental setup and complex relationships between those environments and objects. This script allows me (and I hope countless other Python users) to quickly fire up a Python program or Jupyter notebook, import the script, and start doing simple descriptive statistics in no time. That's the goal, nothing more, nothing less.
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If you've coded in R (maybe in grad school) and are just starting to learn and use Python for data analysis, you will be happy to see and use some of the same well-known functions in your Jupyter notebook in a manner similar to how you use them in your R environment.
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Whatever your reason, using this script is fun.
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### Simple examples
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To start, just import the script and start working with lists of numbers as if they were data vectors in R.
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```
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from R_functions import *
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lst=[20,12,16,32,27,65,44,45,22,18]
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<more code, more statistics...>
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```
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Say you want to calculate the [Tuckey five-number][5] summary from a vector of data points. You just call one simple function, **fivenum** , and pass on the vector. It will return the five-number summary in a NumPy array.
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```
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lst=[20,12,16,32,27,65,44,45,22,18]
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fivenum(lst)
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> array([12. , 18.5, 24.5, 41. , 65. ])
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```
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Maybe you want to know the answer to the following question:
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Suppose a machine outputs 10 finished goods per hour on average with a standard deviation of 2. The output pattern follows a near normal distribution. What is the probability that the machine will output at least 7 but no more than 12 units in the next hour?
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The answer is essentially this:
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You can obtain the answer with just one line of code using **pnorm** :
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```
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pnorm(12,10,2)-pnorm(7,10,2)
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> 0.7745375447996848
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```
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Or maybe you need to answer the following:
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Suppose you have a loaded coin with the probability of turning heads up 60% every time you toss it. You are playing a game of 10 tosses. How do you plot and map out the chances of all the possible number of wins (from 0 to 10) with this coin?
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You can obtain a nice bar chart with just a few lines of code using just one function, **dbinom** :
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```
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probs=[]
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import matplotlib.pyplot as plt
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for i in range(11):
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probs.append(dbinom(i,10,0.6))
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plt.bar(range(11),height=probs)
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plt.grid(True)
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plt.show()
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```
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### Simple interface for probability calculations
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R offers an extremely simple and intuitive interface for quick calculations from essential probability distributions. The interface goes like this:
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* **d** {distribution} gives the density function value at a point **x**
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* **p** {distribution} gives the cumulative value at a point **x**
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* **q** {distribution} gives the quantile function value at a probability **p**
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* **r** {distribution} generates one or multiple random variates
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In our implementation, we stick to this interface and its associated argument list so you can execute these functions exactly like you would in an R environment.
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### Currently implemented functions
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The following R-style functions are implemented in the script for fast calling.
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* Mean, median, variance, standard deviation
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* Tuckey five-number summary, IQR
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* Covariance of a matrix or between two vectors
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* Density, cumulative probability, quantile function, and random variate generation for the following distributions: normal, uniform, binomial, Poisson, F, Student's t, Chi-square, beta, and gamma.
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### Work in progress
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Obviously, this is a work in progress, and I plan to add some other convenient R-functions to this script. For example, in R, a single line of command **lm** can get you an ordinary least-square fitted model to a numerical dataset with all the necessary inferential statistics (P-values, standard error, etc.). This is powerfully brief and compact! On the other hand, standard linear regression problems in Python are often tackled using [Scikit-learn][6], which needs a bit more scripting for this use, so I plan to incorporate this single function linear model fitting feature using Python's [statsmodels][7] backend.
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If you like and use this script in your work, please help others find it by starring or forking its [GitHub repository][8]. Also, you can check my other [GitHub repos][9] for fun code snippets in Python, R, or MATLAB and some machine learning resources.
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If you have any questions or ideas to share, please contact me at [tirthajyoti[AT]gmail.com][10]. If you are, like me, passionate about machine learning and data science, please [add me on LinkedIn][11] or [follow me on Twitter. ][12]
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Originally published on [Towards Data Science][13]. Reposted under [CC BY-SA 4.0][14].
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--------------------------------------------------------------------------------
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via: https://opensource.com/article/18/10/write-favorite-r-functions-python
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作者：[Tirthajyoti Sarkar][a]
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选题：[lujun9972][b]
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译者：[译者ID](https://github.com/译者ID)
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校对：[校对者ID](https://github.com/校对者ID)
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本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
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[a]: https://opensource.com/users/tirthajyoti
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[b]: https://github.com/lujun9972
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[1]: https://spectrum.ieee.org/at-work/innovation/the-2018-top-programming-languages
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[2]: https://www.coursera.org/lecture/r-programming/overview-and-history-of-r-pAbaE
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[3]: http://adv-r.had.co.nz/Functional-programming.html
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[4]: https://github.com/tirthajyoti/StatsUsingPython/blob/master/R_Functions.py
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[5]: https://en.wikipedia.org/wiki/Five-number_summary
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[6]: http://scikit-learn.org/stable/
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[7]: https://www.statsmodels.org/stable/index.html
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[8]: https://github.com/tirthajyoti/StatsUsingPython
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[9]: https://github.com/tirthajyoti?tab=repositories
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[10]: mailto:tirthajyoti@gmail.com
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[11]: https://www.linkedin.com/in/tirthajyoti-sarkar-2127aa7/
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[12]: https://twitter.com/tirthajyotiS
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[13]: https://towardsdatascience.com/how-to-write-your-favorite-r-functions-in-python-11e1e9c29089
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[14]: https://creativecommons.org/licenses/by-sa/4.0/
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@ -0,0 +1,147 @@
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如何在 Python 中写你喜爱的 R 函数
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======
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R 还是 Python ? 这个 Python 脚本模仿方便的 R 风格函数，使统计数据变得简单易行。
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“Python vs. R” 是数据科学和机器学习的现代战争之一。毫无疑问，近年来两者发展迅猛并成为数据科学，预测分析和机器学习领域的顶级编程语言。事实上，根据 IEEE 最近一篇文章，Python 已在 [最受欢迎编程语言排行榜][1] 中超越 C++ 并且 R 语言也稳居前 10 位。
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但是，这两者之间存在一些根本区别。[R] 主要作为统计分析和数据分析问题的快速原型设计的工具而开发的。另一方面，Python 开发为一种通用的，现代的面向对象语言，类似 C++ 或 Java，但具有更简单的学习曲线和更灵活的语言风格。因此，R 仍在统计学家，定量生物学家，物理学家和经济学家中备受青睐，而 Python 已逐渐成为日常脚本，自动化，后端Web开发，分析和通用机器学习框架的顶级语言并拥有广泛的支持基础和开源开发社区。
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###在 Python 环境中模仿函数式编程
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[R] 作为函数式编程语言的天性为用户提供了一个极其简单和紧凑的借口，用于快速计算概率和数据分析问题的基本描述/推论统计。例如，只用一个紧凑的函数调用来解决以下问题难道不是很好吗？
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* 如何计算数据向量的均值 / 中值 / 众数。
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* 如何计算某些服从正态分布事件的累积概率。如果服从 Poisson 分布会怎样？
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* 如何计算一系列数据点的四分差。
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* 如何生成服从学生 t 分布的一些随机数。
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R编程环境可以完成所有这些工作。
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另一方面，Python的脚本编写能力使分析师能够在各种分析流程中使用这些统计数据，具有无限的复杂性和创造力。
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要结合二者的优势，您只需要一个简单的基于 Python 的包装器库，其中包含与 R 风格定义的概率分布和描述性统计相关的最常用函数。 这使您可以非常快速地调用这些函数，而无需转到正确的 Python 统计库并理解整个方法和参数列表。
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### 便于调用 R 函数的 Python 包装脚本
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[我编写了一个Python脚本] [4] 在 Python 中定义了简单统计分析中方便广泛使用的 R 函数。
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此脚本的目标是提供简单的 Python 子例程，模仿 R 风格的统计函数，以快速计算密度/点估计，累积分布和分位数，并生成重要概率分布的随机变量。
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为了保持 R 风格的精髓，脚本不使用类层次结构，并且只在文件中定义原始函数。 因此，用户可以导入这个 Python 脚本，并在需要单个名称调用时使用所有功能。
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请注意，我使用 mimic 这个词。 在任何情况下，我都声称要模仿 R 的真正的函数式编程范例，该范式包括深层环境设置以及这些环境和对象之间的复杂关系。 这个脚本允许我（我希望无数其他 Python 用户）快速启动P ython 程序或 Jupyter 笔记本，导入脚本，并立即开始进行简单的描述性统计。 这就是目标，仅此而已。
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如果您已经写过 R 代码（可能在研究生院）并且刚刚开始学习并使用 Python 进行数据分析，那么您将很高兴看到并在 Jupyter 笔记本中以类似在 R 环境中一样使用一些相同的知名函数。
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无论出于何种原因，使用这个脚本很有趣。
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### 简单的例子
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首先，只需导入脚本并开始处理数字列表，就好像它们是 R 中的数据向量一样。
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```
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from R_functions import *
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lst=[20,12,16,32,27,65,44,45,22,18]
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<more code, more statistics...>
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```
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假设您想从数据点向量计算[Tuckey五数] [5]摘要。 你只需要调用一个简单的函数 ** fivenum **，然后传递向量。 它将返回 NumPy 数组中的五数摘要。
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```
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lst=[20,12,16,32,27,65,44,45,22,18]
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fivenum(lst)
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> array([12. , 18.5, 24.5, 41. , 65. ])
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```
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或许你想要知道下面问题的解答
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假设一台机器平均每小时输出 10 件成品，标准偏差为 2。输出模式遵循接近正态的分布。 机器在下一个小时内输出至少 7 个但不超过 12 个单位的概率是多少？
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答案基本上是这样的：
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您可以使用 **pnorm** 只用一行代码获得答案：
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```
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pnorm(12,10,2)-pnorm(7,10,2)
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> 0.7745375447996848
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```
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或者您可能需要回答以下问题：
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假设你有一个不正的硬币，每次投它时有 60％ 可能正面朝上。 你正在玩10次投掷游戏。 你如何绘制并给出这枚硬币所有可能的胜利数（从0到10）？
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只需使用一个函数 **dbinom** 就可以获得一个只有几行代码的漂亮条形图：
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```
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probs=[]
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import matplotlib.pyplot as plt
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for i in range(11):
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probs.append(dbinom(i,10,0.6))
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plt.bar(range(11),height=probs)
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plt.grid(True)
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plt.show()
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```
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### 简单概率计算接口
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R 提供了一个非常简单直观的界面，可以从基本概率分布中快速计算。 接口如下：
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* **d** {distribution} 给出点 **x** 处的密度函数值
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* **p** {distribution} 给出 **x**点的累积值
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* **q** {distribution} 以概率 **p**给出分位数函数值
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* **r** {distribution} 生成一个或多个随机变量
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在我们的实现中，我们坚持使用此接口及其关联的参数列表，以便您可以像在 R 环境中一样执行这些函数。
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### 目前实现的函数
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脚本中实现了以下R风格函数，以便快速调用。
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* 均值，中位数，方差，标准差
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* Tuckey 五数总结，IQR
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* 矩阵的协方差或两个向量之间的协方差
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* 以下分布的密度，累积概率，分位数函数和随机变量生成：正态，均匀，二项式，泊松，F，学生t，卡方，β和伽马
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### 进行中的工作
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显然，这是一项正在进行的工作，我计划在此脚本中添加一些其他方便的R函数。 例如，在 R 中，单行命令 ** lm ** 可以为数字数据集提供一个普通的最小二乘拟合模型，其中包含所有必要的推理统计量（P 值，标准误差等）。 这是有力的简洁和紧凑！ 另一方面，Python 中的标准线性回归问题经常使用 [Scikit-learn] [6] 来处理，这需要更多的脚本用于此用途，所以我打算使用 Python 的 [statsmodels] 合并这个单函数线性模型拟合特征 [7]后端。
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如果您喜欢并在工作中使用此脚本，请通过主演或分析其 [GitHub存储库] [8]帮助其他人找到它。 另外，您可以查看我的其他 [GitHub repos] [9]，了解 Python，R 或 MATLAB 中的有趣代码片段以及一些机器学习资源。
|
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如果您有任何问题或想法要分享，请通过 [tirthajyoti [AT] gmail.com] [10]与我联系。 如果你像我一样热衷于机器学习和数据科学，请 [加我在LinkedIn] [11]或[在Twitter上关注我。] [12]
|
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|
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最初发表于[走向数据科学] [13]。 转载于[CC BY-SA 4.0] [14]。
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|
||||
--------------------------------------------------------------------------------
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||||
|
||||
via: https://opensource.com/article/18/10/write-favorite-r-functions-python
|
||||
|
||||
作者：[Tirthajyoti Sarkar][a]
|
||||
选题：[lujun9972][b]
|
||||
译者：[yongshouzhang](https://github.com/yongshouzhang)
|
||||
校对：[校对者ID](https://github.com/校对者ID)
|
||||
|
||||
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
|
||||
|
||||
[a]: https://opensource.com/users/tirthajyoti
|
||||
[b]: https://github.com/lujun9972
|
||||
[1]: https://spectrum.ieee.org/at-work/innovation/the-2018-top-programming-languages
|
||||
[2]: https://www.coursera.org/lecture/r-programming/overview-and-history-of-r-pAbaE
|
||||
[3]: http://adv-r.had.co.nz/Functional-programming.html
|
||||
[4]: https://github.com/tirthajyoti/StatsUsingPython/blob/master/R_Functions.py
|
||||
[5]: https://en.wikipedia.org/wiki/Five-number_summary
|
||||
[6]: http://scikit-learn.org/stable/
|
||||
[7]: https://www.statsmodels.org/stable/index.html
|
||||
[8]: https://github.com/tirthajyoti/StatsUsingPython
|
||||
[9]: https://github.com/tirthajyoti?tab=repositories
|
||||
[10]: mailto:tirthajyoti@gmail.com
|
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[11]: https://www.linkedin.com/in/tirthajyoti-sarkar-2127aa7/
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[12]: https://twitter.com/tirthajyotiS
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[13]: https://towardsdatascience.com/how-to-write-your-favorite-r-functions-in-python-11e1e9c29089
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[14]: https://creativecommons.org/licenses/by-sa/4.0/
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