You might have seen different statistics on Python as one of the best learning languages. We are going to differ in our opinion here. Python is the best language to learn. The reason is that Python is closer to a human-interpretable language than machine-level languages like C++ or Java. It is an intuitive language that can be used across a wide range of applications.
Now, what does the term packages or library even mean? Python is a plus and play language. The idea is that if you are looking to implement a simple or even a complex logic, it is likely that someone has already done it before. This logic is then put in a form that makes it reusable this is known as a package or a library (the terms are used interchangeably). So, why this blog?
In the next section, let's go over the fundamentals of python packages and how we can leverage them to master Python, which can be done with the help of a full-time Data Science course.
What are Python Libraries?
The term "library" is used to collectively describe a reusable chunk of code. A python library consists of code that we can reuse while writing code for a given application. However, just to go a little bit in detail, a collection of modules is called a package, and a collection of packages is called a module. Now, a fundamental question comes to mind, when people are writing all this code, why would they build libraries for everyone to use? Let's understand a python module example.
This is one of the reasons Python has grown to be one of the most widely used languages in the world. Besides its ease of use and wide applications, there is an extremely supportive community around Python with millions of possible solutions for any issues you face. Python can be used for applications such as backend, frontend, middleware, data science, machine learning, artificial intelligence, deep learning, and even something as simple as mathematics!
In the next section, let's understand why we should leverage the libraries that are available in Python.
List of Python Libraries for Data Science in 2023
Now, we will go through different categories for the python modules list, ranging from Mathematics, data exploration and visualization, machine learning, data mining & data scraping, and natural language processing, and if you stick around till the end, we will also have bonus Python packages.
Now, remember, through this python libraries list exploration, our aim is to explore python libraries for data science that can help you in the field of Data Science and Data Analytics. And data science starts with one main thing – math! Using the python library example, let's dive into the Python libraries for mathematics.
A) Python Libraries for Math
In this section, we will go over the python packages list we use for mathematics.
Just like how we see the world in terms of visuals, smell, taste, and touch, machines see the world in terms of multi-dimensional arrays. As human beings, we can see and feel just 3 dimensions (X-Axis, Y-Axis, and Z-Axis). Machines can process and comprehend multiple dimensions, and this is represented by multi-dimensional arrays.
- NumPy is an abbreviation of numerical Python and is a package that is used to work with multi-dimensional arrays. It is a fundamental package for scientific computing with Python. It is one of the #1 packages used by almost everyone in the Data Science community.
- NumPy has functions in the domain of matrices, Fourier transformation, and of course, linear algebra
- NumPy is 50 times faster than traditional Python lists! This is because NumPy stores all of its arrays in one continuous memory, plus it is optimized to work with the newer CPU architectures
- NumPy is primarily written in C and C++ to enable super-fast computation, as C & C++ is a machine-level languages.
- Highly optimized: NumPy is a highly optimized package to perform scientific computation by working with numeric arrays, which makes it a fantastic tool for data scientists
- Efficient for use in popular packages; NumPy arrays are used as the input for many popular packages such as sci-kit-learn and TensorFlow
- The use of ndarray object: The array object, ndarray, provides a lot of supporting functions that make ndarray very efficient to use, such as elementwise addition and multiplication, the computation of Kronecker product, etc., which is not supported by Python lists
- The use of NaN: NumPy supports the use of Nan, which stands for "Not a Number," which is supported by NumPy, but not by as many packages. This makes it difficult to interpret and work with the user.
- Requires a continuous allocation of memory: When there is continuous memory allocated contiguously, the allocation and de-allocation of memory via insertion and deletion of memory becomes costly as it requires shifting.
- NumPy is leveraged to maintain minimal memory
- It is used as an alternative to arrays and lists in Python while working well for multi-dimensional arrays
- NumPy is used in cases where there is a requirement for faster runtime behavior
SciPy is an open-source package used for scientific and technical computing. It has modules for integration, optimization, interpolation, linear algebra, eigenvalue, statistics, multi-dimensional image processing, etc. Fun fact – SciPy uses NumPy underneath.
SciPy has utility functions for signal processing, stats, and optimization.
- Used in scientific computing and mathematics
- SciPy comes under the umbrella of the NumPy stack, which includes packages such as matplotlib and pandas.
- SciPy has a full set of functions for linear algebra, while NumPy has comparatively fewer functions for linear algebra
- SciPy has featured in the domain of:
- Fourier Transformation
- Signal Processing
- Linear Algebra
- Multi-dimensional Image processing
- SciPy has classes for efficient visualization and data manipulation
- There is better cross-functionality with other Python libraries
- SciPy has the option for parallel programming for certain database and web routines
- SciPy is quick and simple to pick up
- The use of NaN: SciPy supports the use of Nan, which stands for "Not a Number," which is supported by NumPy, but not by as many packages. This makes it difficult to interpret and work with the user.
- It can be complex for someone with no mathematics background: SciPy is meant to be a tool that can aid scientific and mathematical exploration. However, if you do not have a fundamental knowledge of what you are looking to do, it may not be the best tool.
Mathematics! SciPy is used to perform tasks for research and scientific computation related to mathematical functions such as linear algebra, calculus, solving differential equations, and signal processing.
Theano is a python package built on top of NumPy to manipulate and evaluate mathematical expressions, specifically matrix-valued ones.
- Integration with NumPy: NumPy's ndarray objects are used by the Teano library as well
- Can calculate derivatives: Theano's class of libraries helps it to compute derivatives for one or more functions
- Dynamically generate C code: Theano can dynamically generate code in the programming language C to be able to evaluate expressions faster
- Efficient GPU use: Theano can perform operations that are data-intensive up to 140 times faster than on a CPU by leveraging a GPU
- Reliable and fast: Theano has been known to be stable and efficient while calculating expressions for large values of x
- Self-tests: Theano has tools to enable self-verification and unit testing, which can help catch potential problems early on in the analysis lifecycle.
- Newer, better versions now: Theano is considered to be the Godfather of machine learning libraries, specifically in the deep learning arena.
- Development stopped: The development of Theano stopped in late 2017. In fact, Google created Tensorflow to replace Theano
- Computer Vision: Theano is used in computer vision, such as recognizing handwriting and sparse coding
- Deep Learning: Considered the Godfather of Python packages, Theano was one of the first packages to leverage GPU optimization
B) Python Libraries for Data Exploration and Visualization
Let's review some of the python libraries for data analysis, also taught in the part-time Data Science Bootcamp.
Arguably the most used package by Data Scientists all over the world. Pandas is a software library that works with data structures and provides functions for data manipulation and analysis.
- Pandas library is used able to work with a large selection of IO tools such as CSV, JSON, SQL, BigQuery, and Excel files
- It has methods to perform functions such as object creation, viewing data, selection of data, analyzing missing data, operations such as merge, grouping, reshaping, time series, categorical values, and plotting
- Pandas have two main objects that it works with: Pandas Series and Dataframes
- Simple representation of data: Python has the ability to take multiple types of data and condense the information into a simple data frame. This facilitates us to visualize and understand the data more efficiently.
- Powerful features: Any command that is needed to manipulate data can be found within the Pandas library. From filtering to grouping to segmenting, Pandas can do it all!
- Handles large datasets: One of the main reason pandas was built was to handle large data frames efficiently
- Steep Learning Curve: Pandas have a steep learning curve, and users that are starting out with Pandas might take some time to get accustomed to the way that the Pandas library works
- Imperfect Documentation: Documentation is not the strong suit of pandas. This can perhaps be due to the sheer amount of capability of Pandas. However, if you know the application you are looking for, there are multiple use cases to refer to.
- Incompatibility with 3D matrices: One of the biggest drawbacks is Pandas' poor compatibility in handling 3D matrices. For applications that need to process multi-dimensional arrays, it is preferred to use packages such as NumPy.
- Recommendation Systems: Websites like Netflix and Spotify leverage Pandas in the background for efficient processing of large volumes of data
- Advertising: Personalization via advertising has taken a huge leap, with software conglomerates streamlining the process of lead generation. Pandas help a lot of smaller companies streamline their efforts
- Natural Language Processing: With the help of packages such as Pandas and Scikit Learn, it has become simpler to create NLP models that can help with a plethora of applications.
Matplotlib is a Python package example that aids in visualizing and plotting data to make static, animated, and interactive visualizations.
- Enables a wide variety of visualizations such as line plots, subplots, images, histograms, paths, bar charts, pie charts, tables, scatter plots, filled curves, log plots, data handling, and stream plots
- It can be embedded in various IDEs as well as Jupyter Lab, and Graphical User Interfaces
- Images and visualizations can be exported to multiple file formats
- Based on NumPy, matplotlib is fairly simple for beginners to start off with
- Intuitive for folks who have worked with graph plotting tools such as Matlab
- High level of customization through code
- Not all visualizations from Matplotlib are interactive
- It is difficult to adjust the visuals from Matplotlib to look great as it is a low-level interface
- Plotting non-basic plots in matplotlib can get complex, as it can get code-heavy
- Used to make a lot of preliminary plots for large datasets, matplotlib is helpful in visualizing data
- Given that it uses NumPy in the backend, matplotlib is used extensively with multiple third-party extensions to get the fastest results
Perhaps the best plotting and graphing software in Python. Plotly enables the user to build low-code applications to build, scale and deploy data apps in Python.
- We are able to build full-fledged enterprise-grade applications with plotly or dash in the backend
- Plotly has all of the features of Matplotlib and more
- Interactive Plots: One of the biggest advantages of plotly over most other graphing and visualization tool is that the plots that we make in plotly are interactive
- Saves time: The interactivity helps the user save time and makes it easy to export and modify the plot
- Arguably the best plotting library: With customization and flexibility like none other, plotly is perhaps the best plotting library that exists
- Aesthetics: The ability to plot all of the charts from matplotlib and seaborn in a more aesthetically pleasing well, plotly has the best of all worlds
- Initial Setup: There is a teething period with plotly with an online account, and plotly is code-heavy for a lot of instances
- Extremely vast: When there is so much to keep up with, like Chart Studio, Express, etc., it is hard to keep everything up to date, so the documentation is out of date at times
We discussed that matplotlib has a low-level interface. Seaborn is built on top of matplotlib with a high-level interface to provide informative statistical graphs and draw attractive visualizations.
- Has plots such as relational plots, categorical plots, distribution plots, regression plots, matrix plots, multi-plot grids
- There are themes to style matplotlib visualizations
- Seaborn is able to plot linear regression models and statistical time series and works well with NumPy as well as Pandas data structures
- It is also fast at visualizing univariate and bivariate data
- Seaborn is simply faster as a visualization tool – we can pass the entire data, and seaborn does a lot of the work
- Seaborn has an interactive and informative representation that lets us visualize the data in a quick fashion
- Visualizations are not exactly interactive
- We are limited to the styles that seaborn has in terms of customization
Ggplot stands for the grammar of graphics. Ggplot is a package that was built with R in mind. It can be used in Python using the package plotnine
- Follows a format of data, x, y, and then the rest of the aesthetics
- It can be used to create complex plots from data present in a data frame
- It can provide a programmatic interface to work on the visualizations, the variables to represent, how to display them, and their corresponding visual properties
- Has components such as statistical transformations, scales, facets, coordinated systems, and themes
- The consistent underlying theme of the grammar of graphics means that you can do more visualization with lesser code
- The plots have a high level of abstraction ad are flexible
- The refinement has to lead to a mature and complete graphics system
- ggplot of slower as compared to more fundamental graphics solutions
- Even though the visuals that ggplot has to look nicer than the other libraries, it is difficult to change the default colors
- Gglot might require modifications to the structure of the data for certain plots
Altair is a declarative statistical visualization package that s based on Vega (which is a visualization grammar.
- The base code remains the same, and the user needs to only change the "mark" attribute to get various plots
- The code is short and simple as compared to other libraries. There is a higher focus on the relationship between the data columns than on the plot details
- It is easier to implement interactivity and faceting
- There is a limited amount of customization possible
- Plotting complex machine-learning models becomes difficult
- There is no 3D visualization with the Altair library for Python
Autoviz can make automatic visualizations of a dataset.
- Autoviz is able to analyze the dataset and make recommendations on how to clean your variables
- It is able to detect missing values, mixed data types, and rare categories and can help speed up data-cleaning activities
- Can be a part of MLOps pipelines and form word clouds
- Everything is done automatically! This is a huge boon if you are not sure what exactly you are analyzing in the dataset
- Autiviz is considerably fast in creating visualizations
- There is no bias in the visualizations, wherein a subject matter expert may even have a bias in the charts they select
Graphviz is an open-source visualization tool. It used an object called DOT, which is written in Python. Pydot is an interface to Graphviz.
- Used to manipulate dot files from Graphviz
- From an existing DOT string, a graph can be parsed
- NetworkX graphs can be converted to a Pydot graph
- Can add further nodes and edges along with being able to edit the attributes of graphs, nodes, and edges
C) Python Libraries for Machine Learning
Let's go over some of the python packages for data science and machine learning.
Keras provides an interface for Artificial Neural Networks (ANNs) and acts as an interface to the Tensorflow library. Keras is optimized to reduce cognitive load to help perform Deep Learning in a manner that requires a minimum number of user actions.
- Keras is simple, flexible, and powerful, and Keras is able to run experiments quickly and efficiently
- Keras is built on top of Tensorflow 2 and can scale to large settings for production quality outputs
- Keras can be deployed anywhere, such as websites, phones with Android, iOS, embedded devices, and even as a web API
- Since Keras is tightly integrated with TensorFlow 2, Keras is able to cover end-to-end machine learning solutions
- It is easy to use and is one of the best ways to get into deep learning
- Keras has pre-trained models and has multiple GPUs as well as TPU support
- Low-level API problems: Sometimes, while working with Keras, it is possible to get low-level backend problems, especially when we would like to perform operations that Keras was not designed for
- There are certain features, such as data pre-processing, basic machine learning algorithms, dynamic chart creation, etc., that Keras can improve on
- Thanks to Keras's user-friendliness, some applications sacrifice speed for their user-friendliness
Pre-trained models are especially helpful in applications such as image recognition, where we use models such as Xception, ResNet, MobileNet, ImageNet, etc.
Arguably the most popular machine learning library for modeling, Scikit learn is a machine learning library used for predictive data analysis. Scikit-learn has been built on open-source tools such as NumPy, SciPy, and Matplotlib.
- Supports predictive data analytics applications such as classification, regression, clustering, dimensionality reduction, model selection, pre-processing, etc.
- Scikit-learn supports algorithms such as logistic regression, decision trees, bagging, boosting, random forest, XGBoost, and Support Vector Machine (SVM), along with a whole host of classification metrics as well
- The package comes under a license that makes it free to use with minimum licensing and legal restrictions
- Scikit-learn is one of the most used packages for machine learning packages and is a great toolkit to work with modeling
- Scikit-learn is a great fundamental package; however, it is not the library of choice for in-depth machine learning
- Does not easily scale to large datasets
- One thing that becomes a barrier for data scientists is the confusion while working with NumPy as well as Pandas data frames. Scikit-learn does not work as well with Pandas
There is a wide range of applications, such as spam detection, image recognition, drug response, stock prices, customer segmentation, grouping experimentation, etc.
PyTorch is a machine learning framework that is based on the torch library by Meta that accelerates the path from prototyping & researching to production & deployment.
- PyTorch is built from the ground up to be production ready. There are easy tools to deploy the models in a way that is cloud agnostic.
- Supports features such as metrics, logging, multi-model serving, and the creation of RESTful endpoints
- Training for the model can be done in a distributed fashion & has a robust ecosystem
- Even though PyTorch has a C++ frontend, it also has a Pythonic frontend to extend PyTorch functionalities when desired
- PyTorch is easy to learn, has a strong community, and is easy to debug
- It has support for CPU as well as GPU and can scale very well
- PyTorch is fairly new and is not as widely known in the community – it was released in 2016
- There is no monitoring and dashboard tool like Tensorflow has a tensor board
- The developer community s relatively smaller as compared to the other frameworks
PyCaret is a low-code machine-learning library that is used to automate machine learning workflows.
- The three primary features that PyCare pushes are:
Fast + Scalable + Explainable
- PyCaret recommends you spend less time coding and more time on analysis due to its automation.
- It has automated workflows in the field of exploratory data analysis, data pre-processing, model training, model explainability, and mlops
- PyCaret does end-to-end machine learning – all the way from eda to deployment, where it has advanced features such as
- Experiment Tracking
- Creating ML Applications
- Building Docker Images
- Creating REST APIs
- GPU Support
- Models can be created with just a line of code – this can make modeling really approachable
- PyCaret automatically tunes the model, removing all of the labor that goes into hyperparameter tuning
- Evaluation is also a line of code
- PyCaret is on its way to democratizing data. But it definitely won't be happening that soon as it is not mature and there are a lot of bugs to iron out
- AutoML means we are not able to see what is happening at the backend. So, it would not be advisable for beginners who are looking to learn
- AutoML libraries are great places to start our experimentation because they can do a lot more in much less time, and this can give us solid direction
- PyCaret leads to increased productivity, is easy to use, and is business ready
Tensorflow is a world-renowned package with a focus on the training and inference of deep neural networks. It is an opens source package for machine learning and Data Science.
- Tensorflow is used to prepare data, build ML models, deploy models and implement MLOps
- Tensorflow enables ease of use via pre-trained models, research with state-of-the-art models, and helps build your own models
- Tensorflow can be deployed on the web, on mobile and edge, and on servers
- Models are easy to build with Tensorflow using the high-level Keras API
- Tensorflow enables Robust ML Production
- Tensorflow is scalable, enables easy debugging, has extensive scalable architectural support, and has fantastic library management support
- Tensorflow is not that exceptional on Windows
- As compared to other frameworks, Tensorflow is relatively slow and inconsistent
- Tensorflow has architectural limitations, where it only allows the execution of models and does not allow its training
- Airbnb leverages Tensorflow to classify images and detect objects
- Airbus leverages Tensorflow to extract information from satellite images to deliver insights to clients
- GE leverages Tensorflow to identify the anatomy on MRIs of brains
Requests is an HTTP library that allows the user to send and receive HTTP requests easily. It has over 30 million downloads per week.
- Requests have features like keep-alive, connection pooling, international domains, sessions with cookie persistence, browser-style SSL verification, content decoding, authentication, automatic decompression, HTTP(s) proxy support, multipart file uploads, streaming downloads, connection timeouts, etc.
- The requests module allows us to send HTTP requests using Python and returns a response object with the response data such as content, encoding, and data
- Easy to use, this library can also be used for web scraping
- With the requests module, it is possible to get, post, delete and update the data in a particular link
- Handling cookies and sessions are easy. The security is also taken care of by the authentications module
D) Python Libraries for Data Mining and Data Scrapping
Python scrapy is a framework that is open source and collaborative for obtaining the information you require from websites. Quickly, easily, and extensively. It can be applied to a variety of purposes, including data mining, monitoring, and automated testing.
- Scrapy is capable of exporting feeds in formats such as JSON, CSV, and XML
- Scrapy consists of robust encoding support and auto-detection, which enables to deal with non-standard, broken, and foreign encoding declarations
- With helper methods to extract utilizing regular expressions, expanded CSS selectors and XPath expressions are supported for choosing and extracting data from HTML/XML sources.
- It is a cross-platform application framework that can be used across Windows, Linux, and Mac OS
- The requests on scrapy are processed in an asynchronous manner, meaning it can load several pages in parallel
- Large volumes of data can be scraped using scrapy while consuming little memory and CPU space
- Python version of 2.7 or greater is required for using scrapy
- Different operating systems have different installation processes for scrapy
- Web Scraping
- Data Extraction using APIs
- Web crawler for different websites
Python's Beautiful Soup package is used to extract data from HTML and XML files for web scraping purposes. The source code of the website generates a parse tree that can be utilized to extract information and data in a hierarchical and more comprehensible way.
- Consists of different parsing tools, which are Html.parser, lxml, and HTML5lib enabling different parsing methods
- BeautifulSoup permits the processing of parallel requests
- BeautifulSoup is easy to use
- BeautifulSoup only requires a few lines of code, making it widely popular among many developers
- Reliable online community for BeautifulSoup, which for resolution of questions at a quick turnaround
- It is not easy to set BeautifulSoup
- It lags in speed and performance in comparison to Scrapy
- It is limited to smaller web scraping tasks with less amount of data
SQLAlchemy is a python SQL toolkit and an object relationship mapper that enables a user the full flexibility and power to use SQL in Python. It is widely popular for its object-relational mapper (ORM), which provides a data mapper pattern where classes are mapped to the database in multiple, open-ended ways that allow the object model and the database schema to develop in a cleanly decouples method
- It is compatible with Python 2.5x - 3. x versions and also supports both Jython (Python implementation in Java) and Pypy
- The Core is a fully featured SQL abstraction toolkit consisting of DBAPI implementations and SQL expression language
- The ORM method ensures clean decoupled development between the object model and database schema from the inception
- Supports a wide range of databases such as SQLite, PostgreSQL, MySQL, Oracle, etc.
- It is open source and hence can be used by just installing the package
- It allows users to write Python code to map from the DB schema to the python object, meaning SQL knowledge is not necessarily required
- Not always efficient due to the layer of abstractions
- SQLalchemy facilitates the communication between Python and databases
- A user can create python code to interact with the databases using the SQLalchemy package
E) Python Libraries For Natural Language Processing
A collection of libraries and applications for statistical language processing can be found in the NLTK (Natural Language Toolkit) library. One of the most potent NLP libraries, it includes tools that allow computers to comprehend human language and respond when it is used. This quite popular package in Python finds its application quite often in education and research
- Tokenization: If we break down a text paragraph into smaller chunks, a single chunk is called a token
- NLTK offers sentiment analysis through its built-in classifier, which enables tagging or determining the ratio of positive to negative engagements about a specific topic
- Stop words and names can be removed in a recommendation system with NLTK
- The difficulty level of using this package is on the higher side
- NLTK package is comparatively slower than similar packages due to which it doesn't match the demands of real-world production usage
- NLTK can be used for performing sentiment analysis on online reviews
- Chatbots can be built using the nltk.chat module
An advanced NLP library called SpaCy is accessible in Python and Cython. It is designed to operate alongside deep learning frameworks like TensorFlow or PyTorch and is performance-oriented. Convolutional neural network models for tagging, parsing, and named entity recognition are included, along with tokenization for more than 50 languages.
- Similar to NLTK, the spacy library consists of tokenization too
- Part of speech (POS) tagging can be performed using SpaCy, where a word's POS is determined for nouns, verbs, adjectives, etc.
- SpaCy also enables Named Entity Recognition (NER) which helps in identifying and classifying named entities
- It is faster than NLTK
- It is a library that is easy to learn and use
- It uses Neural networks for training models
- It is used for analyzing online reviews as well as sentiment analysis
- Automated summarization of resumes with Named Entity Recognition
- Search autocomplete, and autocorrect can be done with SpaCy
Gensim, which is short for Generate Similar, is an open-source, popular NLP language library used for topic modeling. Genism uses modern statistical ML for performing complex tasks, including corpora, topic identification, etc.,
- Genism easily processes large and web-scale corpora through its online training algorithms
- It is highly scalable, meaning the input corpus doesn't have to reside in RAM at any given time. Algorithms part of Genism are memory-independent
- Genism is platform agnostic (Windows, Linux and Mac OS)
- Genism enables effective and efficient multicore implementations for high-speed processing and retrieval
- Enables us to handle large text files even without loading them to the memory
- Due to its use of unsupervised models, it doesn't require annotations or hand tagging documents
- Genism is limited to unsupervised text modeling only
- It doesn't possess the capacity to implement an NLP pipeline fully
- Has been used and cited in thousands of academic, and commercial applications and research papers, as well
- Includes streamlined implementations for fastText (Word embedding text classification), Word2vec (for reconstructing linguistic context)
- For applications such as Latent Semantic, Latent Dirichlet Allocation, term frequency-inverse document frequency (TF-IDF)
F) Bonus Python Libraries!
Now that we have gone over python libraries used for data science let's go over some bonus Python libraries.
This is a library that is dedicated to applications of computer vision, machine learning, and image processing. For the usage of various machine learning and computer vision skills like object identification and facial recognition, OpenCV provides access to over 2,500 methods.
- Supports a wide variety of programming languages (Python, C++, Java, etc.)
- It can identify objects, faces, or handwriting by processing images and videos
- It is an open-source, quick, and often easy to implement and integrate
- The library's code is customizable, which can be performed to meet business needs
- More than 2500 modern and classic algorithms can be accessed for performing various tasks
- OpenCV is extensively used across the industry, which makes the community very accessible for assistance for all users
- It takes advantage of hardware acceleration and multicore systems to deploy, which provides algorithmic efficiency
Within the facial recognition system, there are many limitations for OpenCV, such as being highly sensitive to pose variations, and occlusion interference.
- OpenCV can be used to remove watermarks on images
- Backgrounds from images can be removed/cleaned using OpenCV
- OpenCV can be used for facial detection and recognition and similarly for objects as well
Mahotas is a Python module for computer vision and image processing. Many of the algorithms are speed-oriented C++ implementations that use NumPy arrays and a fairly clear Python interface. Currently, Mahotas has over 100 image processing and computer vision functions.
- It has numerous operations for image processing which include cropping images, finding eccentricity and roundness, finding tonal distribution using histograms, etc.
- It has functions for wavelet decompositions and local feature computations
- Mahotas has a comprehensive automated unit test suite that verifies all functionality and contains a number of regression tests for the Quality Control of module
- It is faster in processing than libraries such as pymorph, scikits-image
- It is available on different operating systems such as Linux, Mac OS, and windows
- For more complex methods such as watershed, the pure python approach is considered to be very inefficient
- It is dependent on NumPy to be present and installed
SimpleITK is a comprehensive toolkit for image analysis that supports a variety of filtering operations as well as picture segmentation and registration.
- Up to 20 image file types, including JPG, PNG, and DICOM, are supported and convertible through its image file I/O.
- Otsu, level sets, and watersheds are just a few of the image segmentation workflow filters that are offered.
- It understands images as spatial objects rather than an array of pixels.
- It is available in most programming languages such as Python, R, Java, C#, etc
- The documentation for SimpleITK is good and extensive for high-level guides and instructions to build toolkits and examples for SimpleIKT applications
The standard image processing package for the Python language is the Python Imaging Library (extension of PIL). It includes simple image processing capabilities that help with image creation, editing, and saving. PIL will be replaced going forward by Pillow, it was stated. BMP, PNG, JPEG, and TIFF are just a few of the several image file types that Pillow supports.
- Provides features for image processing such as obtaining information for color mode, size, and format of the image, rotating images, etc.,
- It supports different types of formats of images such as jpeg, png, gif, tiff, etc.
- It allows getting general statistics of the images, which can be used for statistical analysis and automatic contrast enhancement
- It has a wide variety of actions that can be performed on images
- It works on devices such as Raspberry Pi zero, where modules such as OpenCV don't
- It lacks optimization of codes even though it is simple and easy to pick up
- Feature extraction from images is a limitation of Pillow
- It is used for various operations such as creating thumbnails, merging images, cropping, blurring, and resizing images
- It can be used for creating watermarks for images
Python web browser interaction can be automated with the help of the selenium module. For many testers around the world, Selenium is the first choice for any tasks relating to executing automated tests. It allows the user to automatically define and detect the tests on a pre-decided browser.
- Selenium is capable of interacting with different browsers, such as Chrome and Safari. IE, Opera, Edge, etc.
- Due to its WebDriver component within Selenium, it is able to execute the test cases with high performance and speed
- Selenium is an open-source website that can be easily downloaded from its website
- It can work across different Operating Systems such as Linux, Windows, Mac OS, etc.
- Server installation is not necessary as Selenium interacts directly with the browser
- Incomplete solution: Third-party frameworks are required to automate the testing of web applications completely
- Code modification is hard: Since the scripts are written in Mandarin, it makes it non-user friendly and hard to modify
- Automated Testing: Selenium enables automated testing, which saves a lot of time and effort for web testers
- Users can create automation scripts to test and view the results from the automation test results
PyTest is a plugin-based, feature-rich ecosystem for testing your Python code. Common activities can be completed with PyTest with less code, and more complex jobs can be completed using a range of time-saving commands and plug-ins.
- PyTest provides multiple options for running tests from the command line
- It is easy to start with and uses simple syntax, making it easier for one to pick up
- The community of PyTest has huge test plug-ins for extending its functionality
- PyTest is open source and does not associate with any licensing cost
- It is easy and quick to learn due to its simple syntax
- Can execute multiple test cases simultaneously, which reduces the duration of execution
- PyTest doesn't guarantee to uncover every bug
- More time investment is required at times when one has to write multiple lines of code to test one line of code
- Errors in integration are not identified as PyTest only tests sets of data and its functionality
Python unit testing is used to find defects early in the application development process when fixing them will be easier and less expensive. For the automated testing of the code, PyUnit includes fixtures, test cases, test suites, and a test runner. You can group test cases into suites in PyUnit that share the same fixtures.
- Test cases can be organized as suites using the same fixtures
- It includes test cases, test suites, and a test runner, which enables automatic running for the testing of the code
- It is not suitable for high-level testing, which is also called large test suites
- Group testing is not available in PyUnit
- HTML reports cant be created using PyUnit
Why Use Python Libraries for Data Science
Let's take a simple, fundamental example of a function and extrapolate the use of python libraries from there. Let's say we are trying to add two numbers, and we need to use this in ten places in our code.
a = 1, b = 6
We will mention c = a + b in ten places in our code.
We can define a function,
Def calculation_function(a, b):
c = a + b
Now, we will mention this function is ten places in our code, instead of the code directly like in Method A.
Now, let's say that the business changes the logic, and we need to make it multiplication (*)instead of addition (+).
In Method A, you will have to go to ten places and change the code manually. This is error-prone and inefficient.
In Method B, you will have to change one character in the function. This will apply in 10 places in a consistent and efficient manner.
Now, let's scale this up to thousands, maybe even millions of lines of code. Every time you try to implement a new logic, would you rather re-write so many lines of code that are error-prone, or would you rather use near-perfect, well-documented, versioned code compliant with world-class global coding standards? Unless you are doing something extraordinarily unique, the best route for 99% of people is to use packages in Python.
- Ease of learning
- Less Code
- Prebuilt Libraries
- Platform Independent
- Massive Community Support
In this detailed blog, we were able to understand a wide variety of packages. We first went over the fundaments of Python Packages and why we use them. From there, we explored packages for mathematics, data exploration, visualization, machine learning, data mining, natural language processing, and even some bonus Python packages, which are taught in KnowledgeHut’s full-time Data Science course.
Packages are the backbone of why Python is the best language in the world today, and having this knowledge in your toolkit will make you stand out as an accomplished data scientist.