This paper introduces the excellent data science and machine learning in the market Python library .

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According to the extensive needs of the current technical community , This article will focus on , Excellent in the market for data science and machine learning implementations Python Software ：

**Introduction to data science and machine learning****Why use Python Doing data science and machine learning ？****For data science and machine learning Python library**

Introduction to data science and machine learning

as everyone knows , We are in an era of big data , Data drives the development of machine models “ fuel ”.

actually , Data science and machine learning are both skills , It's not just two isolated technologies .

They require developers to have skills ： Get practical insights from the data , By building prediction models , And then the ability to solve problems .

As far as the literal definition is concerned ：

**Data Science ,**It's extracting useful information from data , The process of solving practical problems .**machine learning ,**How is it through the vast amount of data provided , To solve the problem .

Then the relationship between the two can be described as ： Machine learning is part of Data Science , It uses machine learning algorithms and other statistical techniques , To learn how data affects and develops the business .

Why use Python Doing data science and machine learning ？

Python Ranked first among the popular programming languages for machine learning and Data Science . Why is that ？

**① Easy to learn ：**Python It uses very simple syntax , Can be used to implement simple calculations .

for example ： Add two strings to the complex calculation process , To build a complex machine learning model .

**② Less code ：** Although there are many algorithms involved in data science and machine learning , But thanks to Python Support for predefined packages , We don't have to write algorithms from scratch .

meanwhile , To simplify ,Python It also provides a kind of “ Check when coding （check as you code）” Methods , Thus, the workload of testing code is effectively reduced .

**③ Pre built database ：**Python with 100 Various pre built libraries , It can be used to implement various machine learning and deep learning algorithms .

therefore , Every time a user runs an algorithm on a dataset , Just install and load the necessary packages with a single command .

among , Popular pre built libraries include ：NumPy、Keras、Tensorflow、 as well as Pytorch etc. .

**④ It's not about the platform ：**Python It can be run in include ：Windows、macOS、Linux、 as well as Unix On a variety of platforms .

When moving code from one platform to another , You can use things like PyInstaller Software packages like that , To solve all the dependency problems .

**⑤ A lot of community support ：** Besides having a lot of supporters ,Python There are also many communities and forums , All kinds of programmers can post their own errors in it , And help each other .

For data science and machine learning Python library

Python In artificial intelligence （AI） And the field of machine learning is widely used , One of the important reasons is ：Python Thousands of built-in libraries are available .

Through various built-in functions and methods , These libraries make it easy to analyze data 、 Handle 、 Arrangement 、 And modeling tasks .

Next, we will focus on the following types of task Libraries ：

**Statistical analysis****Data visualization****Data modeling and machine learning****Deep learning****natural language processing （NLP）**

** Statistical analysis **

Statistics is a foundation of data science and machine learning . All machine learning and deep learning （DL） Algorithm 、 And related technologies are based on the basic principles and concepts of Statistics . and Python It provides a large number of software libraries for statistical analysis .

Here it is , We will focus on the recommended packages and built-in functions that can perform complex statistical calculations .

They are ：

**NumPy****SciPy****Pandas****StatsModels**

**①NumPy**

NumPy、 Or called Numerical Python Is the most commonly used Python One of the Libraries . The main function of the library is ： Supports multidimensional arrays for mathematical and logical operations .

Users can NumPy Used to index 、 classification 、 plastic 、 Transmit images 、 And a multi-dimensional array of acoustic wave types .

Here is NumPy List of specific functions of ：

Perform mathematical and scientific calculations from simple to complex .

Powerful support for multidimensional array objects , A collection of functions and methods for handling array elements .

Provides Fourier transform and data processing routines .

Perform linear algebraic calculations , This is for including ： Linear regression 、 Logical regression 、 Naive Bayes and other machine learning algorithms , It's very necessary .

**②SciPy**

Based on the NumPy Above SciPy library , It's a collection of sub packages . It can help solve various basic problems related to statistical analysis .

Because it is suitable for processing, it uses NumPy Library defined array elements , therefore SciPy Libraries can usually be used to calculate those that use NumPy, Mathematical equations that are still impossible to complete .

Here is SciPy List of specific functions of ：

Through and with NumPy Arrays are used together , It provides a platform for numerical integration and optimization methods .

It comes with vector quantization 、 Fourier transformation 、 integral 、 A collection of sub packages such as interpolation .

Provides a complete stack of linear algebraic functions . These functions can be used such as k-means Algorithm , To do clustering and other advanced computing .

Provides for signal processing 、 data structure 、 Numerical algorithms 、 And the support of creating sparse matrix .

**③Pandas**

As another important statistical Library ,Pandas It is mainly used for statistics 、 Finance 、 economics 、 Data analysis and so on .

The software library mainly relies on NumPy Array , To deal with it Pandas Data objects for . After all ,NumPy、Pandas and SciPy Performing scientific calculations 、 And data processing , There's a deep interdependence .

Here is Pandas List of specific functions of ：

Use predefined and custom indexes , To quickly create effective DataFrame object .

Can be used to process large data sets , And execute the subset 、 Data slicing 、 And index operations .

Provides for creating Excel Chart , And the built-in capabilities to perform complex data analysis tasks , for example ： Descriptive statistical analysis , Data collation 、 transformation 、 operation 、 And visualization .

Provides support for processing temporal data .

** I personally think ：**Pandas It's an excellent software library for processing large amounts of data ;NumPy Excellent support for multidimensional arrays ; and Scipy It provides a set of sub packages for performing most of the statistical analysis tasks .

**④StatsModels**

Based on the NumPy and SciPy Above StatsModels Python software package , It's creating statistical models 、 The best choice for data processing and model evaluation .

Except for the use of SciPy In the library NumPy Beyond arrays and scientific models , It can also work with Pandas Phase integration , To achieve effective data processing .StatsModels Good at statistical calculation 、 Statistical testing and data exploration .

Here is StatsModels List of specific functions of ：

Make up for NumPy and SciPy Library defects , Ability to perform statistical tests and hypothesis tests .

Provides R-style The realization of the formula , For better statistical analysis . Statisticians can use R Language .

Because it can widely support statistical computation , Therefore, it can be used to implement generalized linear models （GLM,Generalised Linear Models） And ordinary least second order linear regression （OLM,Ordinaryleast-square Linear Regression） Model .

Support includes hypothesis testing （ Zero theory ,Null Theory） Internal statistical test .

** Data visualization **

Visualization is data visualization through , To effectively express key insights from data . It includes ： graphics 、 Chart 、 Mind mapping 、 Thermogram 、 Histogram 、 Density isograph , Then we study the correlation between various data variables .

Here it is , We'll focus on those that can be used with built-in functions , To study the dependence of various data Python Data visualization package .

They are ：

Matplotlib

Seaborn

Plotly

Bokeh

**①Matplotlib**

Matplotlib yes Python The most basic data visualization software package in . It supports things like ： Histogram 、 Bar chart 、 Power spectrum 、 Error chart and other graphics .

Through the 2D graphics library , Users can generate all kinds of clear graphics , For exploratory data （EDA） Is crucial .

Here is Matplotlib List of specific functions of ：

Users can target Matplotlib Choose the right line style 、 Font style 、 Format axis and other functions , In order to easily draw a variety of graphics .

As a tool for reasoning quantitative information , It can be created by creating graphics , To help users understand trends 、 Patterns and associations .

As Matplotlib One of the best features of a software package , Its Pyplot The module provides with MATLAB Very similar user interface .

Provides object-oriented API modular , Through things like Tkinter、wxPython、 as well as Qt etc. GUI Tools , Integrating graphics into applications .

**②Seaborn**

Although with Matplotlib Library Based , But with the Matplotlib comparison ,Seaborn Can be used to create more attractive and descriptive statistical charts .

In addition to providing extensive support for data visualization ,Seaborn It also comes with a built-in dataset oriented API, It can be used to study the relationship between multiple variables .

Here is Seaborn List of specific functions of ：

It can analyze and visualize univariate and bivariate data points , Provides options to compare the current data with other data subsets .

Linear regression models for various target variables , Support automatic statistical estimation and graphical representation .

By providing the execution of high-level abstract functions , Can build multi graph grid （multi-plotgrids） Complex visualization of .

Through various built-in themes , Can realize the style setting , And create Matplotlib chart .

**③Plotly**

As a well-known figure Python One of the Libraries ,Ploty Through interactive graphics , In order to facilitate users to understand the dependence between target variables and prediction variables .

It can be used to analyze and visualize Statistics , For Finance 、 Business and scientific data , Generate clear graphics 、 Subgraphs 、 Thermogram 、 as well as 3D Charts, etc. .

Here is Ploty List of specific functions of ：

have 30 Multiple chart types , Include ：3D Chart 、 Science and Statistics 、SVG Maps, etc , Can achieve clear visualization .

adopt Python API, You can create a diagram by 、 graphics 、 The text and Web The public of images / Private dashboard .

Can be created based on JSON Visual image of format serialization , Users can go to R、MATLAB、Julia Easy access to them on different platforms .

By named Plotly Grid The built-in API, Users can import data directly into Ploty Environmental Science .

**④Bokeh**

Bokeh yes Python One of the most interactive libraries in , Can be used for Web The browser builds a descriptive graphical representation .

It can easily handle large datasets , And build a generic diagram , In turn, it helps to implement a wide range of EDA.

By defining perfect features ,Bokeh Be able to build interactive charts 、 Dashboards and data applications .

Here is Bokeh List of specific functions of ：

It can be done with a simple command , Help users quickly create complex statistical charts .

Support HTML、Notebook、 And output in server form . It also supports multi language binding , Include R、Python、lua、 as well as Julia etc. .

Through and with Flask and Django Integration of , You can express specific visualizations on the application .

By providing support for visualization files , Users can convert it to something like Matplotlib、Seaborn、 as well as ggplot And other libraries .

** machine learning **

Create results that can be accurately predicted 、 A machine learning model for solving specific problems , Is the most important part of any data science project .

However , Implementing machine learning and deep learning often involves thousands of lines of code . And when you need to solve complex problems through neural networks , The corresponding model becomes more cumbersome .

But fortunately , adopt Python A variety of self-contained software packages , We don't have to write any algorithms , It is easy to implement various machine learning technology applications .

Here it is , We'll focus on those that can be used with built-in functions , To achieve a variety of machine learning algorithms, highly recommended machine learning software package .

They are ：

**Scikit-learn****XGBoost****ELI5**

**①Scikit-learn**

As data modeling and model evaluation Python One of the Libraries ,Scikit-learn It comes with a variety of supervised and unsupervised machine learning algorithms .

meanwhile , It can be used in collective learning （Ensemble Learning） And promoting machine learning （Boosting Machine Learning） A clear definition of .

Here is Scikit List of specific functions of ：

By providing a standard data set （ Such as ：Iris and Boston House Price）, To assist users in machine learning .

Built in methods that can be used to perform supervised and unsupervised machine learning , Including parsing 、 clustering 、 classification 、 Return to 、 As well as the anomaly detection of various files .

With built-in features for feature extraction and feature selection , It can help identify important attributes in the data .

By performing cross validation , It provides different ways to evaluate model performance , The performance of the model can be optimized 、 And adjust the parameters .

**②XGBoost**

XGBoost That is to say “ Extreme gradient enhancement （Extreme Gradient Boosting）”, It belongs to Boosting Machine learning Python software package . By gradient enhancement ,XGBoost It can improve the performance and accuracy of machine learning model .

Here is XGBoost List of specific functions of ：

Because it uses C++ Compiling , therefore XGBoost It is considered to be the fastest way to improve the performance of machine learning models 、 And one of the effective software libraries .

because XGBoost The core algorithm of is parallelizable , Therefore, it can effectively utilize the performance of multi-core computers . meanwhile ,XGBoost Data sets can also be processed in large numbers 、 And can carry out network work across multiple data sets .

Provides that you can use to perform cross validation , Parameter adjustment , Regularization , And internal parameters that handle missing values , It can also provide with Scikit-learn Compatible API.

because XGBoost Often used in top data science and machine learning competitions , Therefore, it is generally considered to be superior to other algorithms .

**③ELI5**

As another Python library ,ELI5 It mainly focuses on improving the performance of machine learning models . Because it's relatively new , So it's usually associated with XGBoost、LightGBM、 as well as CatBoost To be used together , And then improve the accuracy of machine learning model .

Here is ELI5 List of specific functions of ：

Provide with Scikit-learn Integration of software packages , To represent the importance of characteristics , The decision tree and the integrated prediction based on the tree are explained .

Able to analyze and explain by XGBClassifier、XGBRegressor、LGBMClassifier、LGBMRegressor、CatBoostClassifier、CatBoostRegressor and Catboost The predictions made .

It provides support for implementing various algorithms , And be able to check the black box model . Its TextExplainer The module can interpret the predictions made by the text classifier .

Can assist in the analysis of those by linear regression 、 And the classifier gives Scikit Learn the general linear model （GLM,General Linear Models） Weight and prediction of .

** Deep learning **

The evolution of machine learning and artificial intelligence is inseparable from deep learning . With the introduction of deep learning , We can build complex models , And deal with huge datasets .

With Python A variety of deep learning packages available , We can easily build a variety of efficient neural networks .

Here it is , We'll focus on those that can be used with built-in functions , To realize the deep learning software package of complex neural network which is highly recommended .

They are ：

**TensorFlow****Pytorch****Keras**

**①TensorFlow**

As deep learning Python One of the Libraries ,TensorFlow Is an open source library for data stream programming across tasks .

TensorFlow Through a symbolic math library , To build powerful and accurate neural networks . It provides an intuitive multi platform programming interface , High scalability can be achieved in different domains .

Here is TensorFlow List of specific functions of ：

Facing large projects and data sets , It can build and train multiple neural networks .

In addition to supporting neural networks , It also provides various functions and methods for performing statistical analysis . for example ： It comes with the ability to create probabilistic models and Bayesian Networks （ Include ：Bernoulli、Chi2、Uniform、Gamma etc. ） Built in features of .

TensorFlow Provides layered components , These components can perform hierarchical operations on weights and deviations , And by implementing regularization techniques （ for example ：batch normalization、Dropout etc. ） To improve the performance of the model .

It comes with a device called TensorBoard The visualization program of , The visualization program can create interactive and visual graphics , To understand the dependence of data characteristics .

**②Pytorch**

Pytorch It's based on Python Open source software package for scientific computing , Deep data sets can be implemented on large neural networks .

Facebook Use this software library to develop its neural network , Then, the tasks such as face recognition and automatic marking are realized .

Here is Pytorch List of specific functions of ：

Provides easy to use API, It can be integrated with other data science and machine learning frameworks .

Be similar to NumPy,Pytorch Provide what is called Tensors Multidimensional array of , And can be used in GPU On .

It can not only be used to model large neural networks , And it also provides an interface , Support up to 200 A variety of mathematical operations that can be used for statistical analysis .

The code can be executed on each node , To create dynamic calculation graphs , And then assist in time series analysis , And be able to forecast sales in real time .

**③Keras**

Also as Python One of the best deep learning libraries in China ,Keras Be able to build 、 analysis 、 Evaluating and improving neural networks provides comprehensive support .

Keras Is based on Theano and TensorFlow Python Library built . It provides a variety of additional functions needed to build complex large-scale deep learning models .

Here is Keras List of specific functions of ：

Support for the construction of all types of neural networks , Include ： Complete connection 、 Convolution 、 Pooling 、 loop 、 And embedding . Able to target large datasets and problems , By further combining the various models , To create a complete neural network .

It has built-in functions to perform neural network calculations , Include ： Define layers and goals , Activate function ; Through optimizer and lots of tools , To easily process image and text data .

It comes with some preprocessed data sets and trained models , Include ：MNIST、VGG、Inception、SqueezeNet、 as well as ResNet etc. .

extensible , It can support new functions and methods .

** natural language processing **

Google Application Alexa To accurately predict what users are searching for , And in the Siri Other chat robots will use natural language processing （NLP） technology .

NLP In the design AI In the system , Played a huge role . This system helps to describe the interaction between human language and computer .

Here it is , We'll focus on those that can be used with built-in functions , To achieve advanced AI The system is a highly recommended natural language processing package .

They are ：

**NLTK****spaCy****Gensim**

**①NLTK（ Natural language toolkit ,Natural Language ToolKit）**

NLTK Considered to be excellent at analyzing human language and behavior Python software package . As the first choice for most data scientists ,NLTK The library provides an easy-to-use interface , It includes 50 A variety of corpora and lexical resources , Help to describe the interaction between people , And building recommendation engines AI System .

Here is NLTK List of specific functions of ：

Provides a complete set of data and text processing methods , Can be used for text analysis classification 、 Mark 、 Word stem 、 Parsing and semantic reasoning .

Included for industrial use NLP The wrapper for the library , By building complex systems , To assist in text categorization , And look for behavioral trends and patterns of human speech .

It comes with the implementation of computational linguistics （Computational Linguistics） A comprehensive guide to 、 And complete API Documentation guide , Can help novice programmers to use NLP.

It has a huge community of users and professionals , Can provide a comprehensive tutorial and quick guide , It is convenient for users to learn how to use Python Computational linguistics .

**②spaCy**

As a free Python Open source library ,spaCy It can be used to implement advanced natural language processing （NLP） Related technology .

When you're dealing with a lot of text , Can pass spaCy To easily learn the morphological meaning of the text , And how to classify it into human intelligible language .

Here is spaCy List of specific functions of ：

Besides language computing ,spaCy Separate modules are also available , Can be used to build 、 Training and testing various statistical models , It can help users understand the meaning of words better .

It comes with various built-in language annotations , It can help to analyze the grammatical structure of sentences . This not only helps to understand the various tests , It also helps to find out the relationship between different words in a sentence .

Can be used for complex nested tags that contain abbreviations and multiple punctuation marks （nestedtokens）, To achieve tokenization .

Besides its powerful function and efficiency ,spaCy And support 51 More than one language .

**③Gensim**

Gensim It's another open source Python software package , This modeling aims to extract semantic topics from large documents and texts , To deal with by means of statistical models and linguistic calculations , And then analyze and predict human behavior .

Whether it's raw data or unstructured data , It has the ability to handle and deal with huge data sets .

Here is Genism List of specific functions of ：

By understanding the statistical semantics of each word , To build an effective classification document model .

It comes with something like Word2Vec、FastText、 Latent semantic analysis （Latent Semantic Analysis） Text processing algorithms like this .

These algorithms can study statistical co-occurrence patterns in documents , By filtering out unnecessary words , And then build a model with only important features .

Provide for import 、 And support a variety of data formats I/O Wrappers and readers .

Its simple and intuitive interface , It can be used by beginners easily . meanwhile , Its API The learning curve is gentle , Therefore, it is loved by developers from all walks of life .