Data science has undisputedly become the most exciting field of science. It creates a systematic balance among quantitative skills, advanced statistics, and real-world programming aptitude to execute its essence. Although data science is a multidisciplinary field, computer science expertise – blended with mathematics, statistics, analytics, and business intelligence – remains the icing to this day. Computation skills are especially pertinent if your objective is to establish a successful career as a data scientist. (Costa and Santos, 2017)
Scientific computing in data science requires you to be good at at-least a couple of programming languages. Programming languages for data science are often regarded as data-centric languages. They are used to help data professionals in pre/re-processing of composite algorithms and analyze huge volumes of data to extract actionable insights. Out of more than 250 programming languages available today, Python has proven to be the most data-compatible language.
The latest Business Broadway survey reported that “of 24,000 data professionals, 23,000 out of which 78% identified as data scientists, used and recommended Python programming language.” The main reason behind the reliance of the data science community on Python is its multi-purpose and dynamic nature. Particularly for modern scientific computing and data mining procedures. Additionally, the advanced Python libraries and/or extensions provide high-level deep learning tools that are critical for data experts to smoothly execute complex tasks.
Python is a high-level cross-platform programming language. It is one of the most popular languages across the world. Python was created by Guido van Rossum and released in the year 1991. (Rocha and Ferreira, 2018)
Python is a free, open-source, and multipurpose computer programming language that can build several things due to its great variety of tools, extensions, and simple syntax called CPython in default implementation. Programmers and developers from around the world prefer Python for its procedural programming styles. It is no surprise that Python is also a leading language in the field of data science too. Python’s easy readability and interaction with high-performance algorithms built-in Fortran or C set it apart from R, SQL, etc. The key Python feature is its support for both procedure-oriented and object-oriented programming. Unlike other programming languages, e.g., Java, Python also supports multiple inheritances. (Shukla, Xitij and Parmar, 2016)
Overall python is an adaptable language, with a vast array of libraries to accommodate multiple roles. It has revolutionized the processes of data analysis, scientific computation, backend web development, and artificial intelligence. These processes are spread on various stages, such as data preprocessing, analysis, predictions, and visualization. (Jenkins, 2015)
Each of these stages requires a data-intensive custom library that Python has dedicated individually from its huge collection of extensions.
Following are some of the most commonly used Python extensions that deal with data:
NumPy
This open-source extension model is one of the principal sets in the scientific application areas. NumPy is designed for working with large multi-dimensional arrays and matrices. It is also intended to rapidly process the precompiled high-level mathematical routines and implementation methods. This allows Python to run various operations of data modeling and analysis.
With NumPy, you can perform standard numerical operations on a complete data set without writing different loop codes. Furthermore, if your data is built-in low-level programming languages, you can easily export it to external libraries and import the said data from there as NumPy arrays.
SciPy
SciPy is another Python core extension for scientific computing. This module is based on NumPy design capabilities that provide fast multidimensional array manipulation. Its main data structure has several user-friendly numerical routines along with BLAS and LAPACK functions. SciPy contains tools that assist with the optimization, probability theory, linear algebra, calculus integration, and other tasks related to data science.
Pandas
Pandas Python extension contains high-level data structures and several fast data analysis operational tools. The best feature of this module is the facility that allows quick translation of complex data operations by a single command. Its clean data alignment keeps the errors caused by raw uneven data in check and helps to retrieve the missing data through data munging tools.
Pandas extension comes with a great variety of built-in techniques that group, filter, and combining data with the time-series function. The ‘optimal still’ is the remarkable speed indicator the follows through the whole process and operation.
Matplotlib
Matplotlib extension is basically for visualization. It is a low-level module that is used for developing two-dimensional graphs, diagrams, diverse pie charts, histograms, scatterplots, or any other professional figures. You can even quickly make non-Cartesian coordinating graphs within Matplotlib. With its axes programmed alignment of axes and interactive features, you can customize your graphics to the last aspect. Matplotlib also has a colorblind-friendly color cycle.
Several other popular plotting libraries can jointly work with Matplotlib. The best feature of this extension is its support for different GUI back ends of all operating systems. Matplotlib also exports your diagrams to common vector or formats such as PDF, JPG, PNG, GIF, etc.
Statsmodels
StatsModels, true to its identification, allows you to conduct statistical data analysis, e.g., data exploration, statistical models estimation, statistical test execution, etc. With this Python module, you can apply different machine learning methods through the extensive list of descriptive statistics on any given type of data. You can also explore many plotting functions and result in statistics for an individual estimator.
The GeneralizedPoisson, the zero-inflated model, NegativeBinomialP, and multivariate methods on factor analysis MANOVA, etc., are few of the upgraded features that have left time series and model count feature behind in term of extension incessant development.
NLTK
NLTK is used for natural language processing. A whole platform to develop a program to work with human language data. NLTK Python extension has a simple interface for different lexical resources like WordNet. It also has text processing libraries and wrapper that allows data classification, stem & tag, analysis, tokenization, and semantic reasoning to extract readable information. (Naur, Univ, and Denmark, 1975)
NLTK is a leading platform for developing research systems and prototypes. The best feature of NLTK is its CoreNLP interface, which enhances its compatibility and APIs performance.
Scikit-learn
The scikit-learn extension is a step forward from the SciPy extension we discussed above. It is one of the best Python module interfaces to implement machine learning algorithms and data mining processes, e.g., data clustering, classification, regression, variable reduction, and model selection.
With Scikit-learn, you can very easily and quickly implement algorithms on datasets through its consistent interface in conjunction with a cross-validation tool.
TensorFlow
TensorFlow extension is very popular due to its extensive framework for deep and machine learning. It is built-in Google Brain and provides a compatible platform to efficiently work with artificial neural networks of several data sets. The object identification, speech recognization, etc., are some of the most used applications of the TensorFlow Python module.
Theano
Theano is an extension strictly used for numerical computation. It is built on NumPy and has similar capabilities of working on large multidimensional arrays and matrices. Theano allows users to express, optimize, and analyze different numerical expressions through the fast processing of precompiled high-level routines and implementation methods. There are several other Python extensions like Pylearn2 that uses the Theano module as their core element for numerical computation.
Keras
Keras Python module is a high-level extension that works with neural networks. This library is built on TensorFlow and Theano and uses CNTK, MxNet, etc., back-ends for its smooth functionality. Keras is the best extension to simplify your tasks by moderating the repetitive code greatly. With the additional features like the Conv3DTranspose layer, MobileNet application, and automatic network normalization, it has become one of the best modules to execute artificial neural network learning algorithm for data translation.
Gensim
Gensim is an extension in Python library for strong semantic analysis and topic & vector-space modeling. This module is built on the NumPy and SciPy interface. Although, Gensim extension comes with its implementation, e.g., models.wrappers.fastest, it is used for the assistance of popular NLP algorithm implementation such as word2vec. Gensim’s ability to learn word vectors for out-of-vocabulary words makes it the best choice for word representation and sentence classification in fast text C implementation.
Seaborn
Seaborn Python extension is a higher-level application programming interface. This module is built on the Matplotlib library, but with more appropriate default settings for processing charts, graphs, diagrams, etc. Seaborn extension also comes with an in-built rich visualization gallery that includes different types for time series, violin diagrams, and joint plots. Its smart compatibility with FacetGrid or PairGrid and interactive backends like parameter editing or visualization selection is based on Matplotlib line.
Bokeh
The Bokeh extension works by using JavaScript widgets to create interactive and measurable visualizations in your browser. This Python module has a vast variety of graphs with several styling possibilities. The interaction abilities of Bokeh library, such as plots linkage, widgets addition, callbacks definitions, categorical tick labels rotation, etc., are constantly improved to enhance the user experience.
XGBoost / LightGBM / CatBoost
XGBoost, LightGBM, and CatBoost are especial Python extensions designed to assist the fast and easy implementation of gradient boosting. Gradient boosting is one of the well-known machine learning algorithms. It is used to develop a group of elementary models sequence such as decision trees. The XGBoost, LightGBM, and CatBoost extensions perform identically to solve complex problems to provide highly scalable, optimized, and swift execution of gradient boosting algorithms.
Plotly
Plotly library is very popular for its usability that allows users to develop advanced graphics and figures. This Python extension has been subjected to various modifications to be able to work with interactive web applications. It has some remarkable visualization features such as contour graphics, 3D charts, ternary plots, etc. Plotly’s support for multiple linked views, crosstalk integration, and animation makes it ideal for communicating the data discoveries.
Pydot
Pydot is also a graphical extension, but it has a different purpose than many of the above mentioned Python modules for visualization. Pydot is used for developing complex oriented as well as non-oriented graphs. Due to its Graphviz interface that is written in Python alone, Pydot is capable of illustrating the graphs’ structures, which are often crucial in neural networks and trees based algorithms development.
Eli5
Eli5 is essentially a helper extension. It works through tracking and debugging machine learning models and algorithm performance to generate relatively clear predictions. Eli5 provides support for many other libraries such as Scikit-learn, XGBoost, LightGBM, lightning, and sklearn-crfsuite and executes different commands for them individually.
Python is a high-level cross-platform programming language. It is one of the most popular languages across the world. Python was created by Guido van Rossum and released in the year 1991. (Rocha and Ferreira, 2018)
Python is a free, open-source, and multipurpose computer programming language that can build several things due to its great variety of tools, extensions, and simple syntax called CPython in default implementation. The programmers and developers from around the world prefer Python for its procedural programming styles. It is no surprise that Python is also a leading language in the field of data science too. Python’s easy readability and interaction with high-performance algorithms built-in Fortran or C set it apart from R, SQL, etc. The key Python feature is its support for both procedure-oriented and object-oriented programming. Unlike other programming languages, e.g., Java, Python also supports multiple inheritances. (Shukla, Xitij and Parmar, 2016)
Overall python is an adaptable language, with a vast array of libraries to accommodate multiple roles. It has revolutionized the processes of data analysis, scientific computation, backend web development, and artificial intelligence. These processes are spread on various stages, such as data preprocessing, analysis, predictions, and visualization. (Jenkins, 2015)
Each of these stages requires a data-intensive custom library that Python has dedicated individually from its huge collection of extensions.
Following are some of the most commonly used Python extensions that deal with data:
NumPy
This open-source extension model is one of the principal sets in the scientific application areas. NumPy is designed for working with large multi-dimensional arrays and matrices. It is also intended to rapidly process the precompiled high-level mathematical routines and implementation methods. This allows Python to run various operations of data modeling and analysis.
With NumPy, you can perform standard numerical operations on a complete data set without writing different loop codes. Furthermore, if your data is built-in low-level programming languages, you can easily export it to external libraries and import the said data from there as NumPy arrays.
SciPy
SciPy is another Python core extension for scientific computing. This module is based on NumPy design capabilities that provide fast multidimensional array manipulation. Its main data structure has several user-friendly numerical routines along with BLAS and LAPACK functions. SciPy contains tools that assist with the optimization, probability theory, linear algebra, calculus integration, and other tasks related to data science.
Pandas
Pandas Python extension contains high-level data structures and several fast data analysis operational tools. The best feature of this module is the facility that allows quick translation of complex data operations by a single command. Its clean data alignment keeps the errors caused by raw uneven data in check and helps to retrieve the missing data through data munging tools.
Pandas extension comes with a great variety of built-in techniques that group, filter, and combining data with the time-series function. The ‘optimal still’ is the remarkable speed indicator the follows through the whole process and operation.
Matplotlib
Matplotlib extension is basically for visualization. It is a low-level module that is used for developing two-dimensional graphs, diagrams, diverse pie charts, histograms, scatterplots, or any other professional figures. You can even quickly make non-Cartesian coordinating graphs within Matplotlib. With its axes programmed alignment of axes and interactive features, you can customize your graphics to the last aspect. Matplotlib also has a colorblind-friendly color cycle.
Several other popular plotting libraries can jointly work with Matplotlib. The best feature of this extension is its support for different GUI back ends of all operating systems. Matplotlib also exports your diagrams to common vector or formats such as PDF, JPG, PNG, GIF, etc.
Statsmodels
StatsModels, true to its identification, allows you to conduct statistical data analysis, e.g., data exploration, statistical models estimation, statistical test execution, etc. With this Python module, you can apply different machine learning methods through the extensive list of descriptive statistics on any given type of data. You can also explore many plotting functions and result in statistics for an individual estimator.
The GeneralizedPoisson, the zero-inflated model, NegativeBinomialP, and multivariate methods on factor analysis MANOVA, etc., are few of the upgraded features that have left time series and model count feature behind in term of extension incessant development.
NLTK
NLTK is used for natural language processing. A whole platform to develop a program to work with human language data. NLTK Python extension has a simple interface for different lexical resources like WordNet. It also has text processing libraries and wrapper that allows data classification, stem & tag, analysis, tokenization, and semantic reasoning to extract readable information. (Naur, Univ, and Denmark, 1975)
NLTK is a leading platform for developing research systems and prototypes. The best feature of NLTK is its CoreNLP interface, which enhances its compatibility and APIs performance.
Scikit-learn
The scikit-learn extension is a step forward from the SciPy extension we discussed above. It is one of the best Python module interfaces to implement machine learning algorithms and data mining processes, e.g., data clustering, classification, regression, variable reduction, and model selection.
With Scikit-learn, you can very easily and quickly implement algorithms on datasets through its consistent interface in conjunction with a cross-validation tool.
TensorFlow
TensorFlow extension is very popular due to its extensive framework for deep and machine learning. It is built-in Google Brain and provides a compatible platform to efficiently work with artificial neural networks of several data sets. The object identification, speech recognization, etc., are some of the most used applications of the TensorFlow Python module.
Theano
Theano is an extension strictly used for numerical computation. It is built on NumPy and has similar capabilities of working on large multidimensional arrays and matrices. Theano allows users to express, optimize, and analyze different numerical expressions through the fast processing of precompiled high-level routines and implementation methods. There are several other Python extensions like Pylearn2 that uses the Theano module as their core element for numerical computation.
Keras
Keras Python module is a high-level extension that works with neural networks. This library is built on TensorFlow and Theano and uses CNTK, MxNet, etc., back-ends for its smooth functionality. Keras is the best extension to simplify your tasks by moderating the repetitive code greatly. With the additional features like the Conv3DTranspose layer, MobileNet application, and automatic network normalization, it has become one of the best modules to execute artificial neural network learning algorithm for data translation.
Gensim
Gensim is an extension in Python library for strong semantic analysis and topic & vector-space modeling. This module is built on the NumPy and SciPy interface. Although, Gensim extension comes with its implementation, e.g., models.wrappers.fastest, it is used for the assistance of popular NLP algorithm implementation such as word2vec. Gensim’s ability to learn word vectors for out-of-vocabulary words makes it the best choice for word representation and sentence classification in fast text C implementation.
Seaborn
Seaborn Python extension is a higher-level application programming interface. This module is built on the Matplotlib library, but with more appropriate default settings for processing charts, graphs, diagrams, etc. Seaborn extension also comes with an in-built rich visualization gallery that includes different types for time series, violin diagrams, and joint plots. Its smart compatibility with FacetGrid or PairGrid and interactive backends like parameter editing or visualization selection is based on Matplotlib line.
Bokeh
The Bokeh extension works by using JavaScript widgets to create interactive and measurable visualizations in your browser. This Python module has a vast variety of graphs with several styling possibilities. The interaction abilities of Bokeh library, such as plots linkage, widgets addition, callbacks definitions, categorical tick labels rotation, etc., are constantly improved to enhance the user experience.
XGBoost / LightGBM / CatBoost
XGBoost, LightGBM, and CatBoost are especial Python extensions designed to assist the fast and easy implementation of gradient boosting. Gradient boosting is one of the well-known machine learning algorithms. It is used to develop a group of elementary models sequence such as decision trees. The XGBoost, LightGBM, and CatBoost extensions perform identically to solve complex problems to provide highly scalable, optimized, and swift execution of gradient boosting algorithms.
Plotly
Plotly library is very popular for its usability that allows users to develop advanced graphics and figures. This Python extension has been subjected to various modifications to be able to work with interactive web applications. It has some remarkable visualization features such as contour graphics, 3D charts, ternary plots, etc. Plotly’s support for multiple linked views, crosstalk integration, and animation makes it ideal for communicating the data discoveries.
Pydot
Pydot is also a graphical extension, but it has a different purpose than many of the above mentioned Python modules for visualization. Pydot is used for developing complex oriented as well as non-oriented graphs. Due to its Graphviz interface that is written in Python alone, Pydot is capable of illustrating the graphs’ structures, which are often crucial in neural networks and trees based algorithms development.
Eli5
Eli5 is essentially a helper extension. It works through tracking and debugging machine learning models and algorithm performance to generate relatively clear predictions. Eli5 provides support for many other libraries such as Scikit-learn, XGBoost, LightGBM, lightning, and sklearn-crfsuite and executes different commands for them individually.
Many Python extensions deal with data processes, but we have tried to put together a comprehensive list that contains the most frequently used libraries. Especially keeping in mind our aspiring data scientists, we made sure to include a detailed description with each module for you to get started as per your requirement.
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