Libraries for Data Science

Libraries for Data Science

When it comes to data science projects written, there are thousands of libraries to pick and choose from. However, they’re not all on the same level of code quality, diversity, or size.

To help you choose, here are the most useful libraries to use. You can also access the documentation of the relevant library by clicking on it.

Data mining is the process of sorting through large data sets to identify patterns and relationships that can help solve business problems through data analysis.

Data mining techniques and tools enable enterprises to predict future trends and make more-informed business decisions.

Scrapy helps to build crawling programs (spider bots) that can retrieve structured data from the web — for example, URLs or contact info. It’s a great tool for scraping data used in.

Developers use it for gathering data from APIs. This full-fledged framework follows the Don’t Repeat Yourself principle in the design of its interface. As a result, the tool inspires users to write universal code that can be reused for building and scaling large crawlers.

BeautifulSoup is another really popular library for web crawling and data scraping. If you want to collect data that’s available on some website but not via a proper CSV or API, BeautifulSoup can help you scrape it and arrange it into the format you need.

Selenium is an open-source web-based automation tool. Selenium primarily used for testing in the industry but It can also be used for web scraping. You can use the Chrome browser but also you try on any browser.

Data processing manipulation of data by a computer. It includes the conversion of raw data to machine-readable form, flow of data through the CPU and memory to output devices, and formatting or transformation of output. Any use of computers to perform defined operations on data can be included under data processing. In the commercial world, data processing refers to the processing of data required to run organizations and businesses.

Data modeling is the process of creating a visual representation of either a whole information system or parts of it to communicate connections between data points and structures. The goal is to illustrate the types of data used and stored within the system, the relationships among these data types, the ways the data can be grouped and organized and its formats and attributes.

NumPy is a perfect tool for scientific computing and performing basic and advanced array operations. The library offers many handy features performing operations on n-arrays and matrices. It helps to process arrays that store values of the same data type and makes performing math operations on arrays (and their vectorization) easier.

In fact, the vectorization of mathematical operations on the NumPy array type increases performance and accelerates the execution time.

This useful library includes modules for linear algebra, integration, optimization, and statistics. Its main functionality was built upon NumPy, so its arrays make use of this library. SciPy works great for all kinds of scientific programming projects (science, mathematics, and engineering).

It offers efficient numerical routines such as numerical optimization, integration, and others in submodules. The extensive documentation makes working with this library really easy.

Pandas is a library created to help developers work with “labeled” and “relational” data intuitively. It’s based on two main data structures: “Series” (one-dimensional, like a list of items) and “Data Frames” (two-dimensional, like a table with multiple columns).

Pandas allows converting data structures to DataFrame objects, handling missing data, and adding/deleting columns from DataFrame, imputing missing files, and plotting data with histogram or plot box. It’s a must-have for data wrangling, manipulation, and visualization.

Keras is a great library for building neural networks and modeling. It’s very straightforward to use and provides developers with a good degree of extensibility. The library takes advantage of other packages, (Theano or TensorFlow) as its backends. Moreover, Microsoft integrated CNTK (Microsoft Cognitive Toolkit) to serve as another backend.

It’s a great pick if you want to experiment quickly using compact systems — the minimalist approach to design really pays off!

This is an industry-standard for data science projects . Scikits is a group of packages in the SciPy Stack that were created for specific functionalities — for example, image processing. Scikit-learn uses the math operations of SciPy to expose a concise interface to the most common machine learning algorithms.

Data scientists use it for handling standard machine learning and data mining tasks such as clustering, regression, model selection, dimensionality reduction, and classification. Another advantage? It comes with quality documentation and offers high performance.

PyTorch is a framework that is perfect for data scientists who want to perform deep learning tasks easily. The tool allows performing tensor computations with GPU acceleration. It’s also used for other tasks — for example, for creating dynamic computational graphs and calculating gradients automatically.

PyTorch is based on Torch, which is an open-source deep learning library implemented in C, with a wrapper in Lua.

TensorFlow is a popular framework for machine learning and deep learning, which was developed at Google Brain. It’s the best tool for tasks like object identification, speech recognition, and many others. It helps in working with artificial neural networks that need to handle multiple data sets.

The library includes various layer-helpers (tflearn, tf-slim, skflow), which make it even more functional. TensorFlow is constantly expanded with its new releases — including fixes in potential security vulnerabilities or improvements in the integration of TensorFlow and GPU.

Use this library to implement machine learning algorithms under the Gradient Boosting framework. XGBoost is portable, flexible, and efficient. It offers parallel tree boosting that helps teams to resolve many data science problems.

Another advantage is that developers can run the same code on major distributed environments such as Hadoop, SGE, and MPI.

Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.

In the world of Big Data, data visualization tools and technologies are essential to analyze massive amounts of information and make data-driven decisions.

This is a standard data science library that helps to generate data visualizations such as two-dimensional diagrams and graphs (histograms, scatterplots, non-Cartesian coordinates graphs).

Matplotlib is one of those plotting libraries that are really useful in data science projects — it provides an object-oriented API for embedding plots into applications. However, developers need to write more code than usual while using this library for generating advanced visualizations. Note that popular plotting libraries work seamlessly with Matplotlib.

Seaborn is based on Matplotlib and serves as a useful machine learning tool for visualizing statistical models — heatmaps and other types of visualizations that summarize data and depict the overall distributions.

When using this library, you get to benefit from an extensive gallery of visualizations (including complex ones like time series, joint plots, and violin diagrams).

This library is a great tool for creating interactive and scalable visualizations inside browsers using JavaScript widgets. Bokeh is fully independent of Matplotlib. It focuses on interactivity and presents visualizations through modern browsers — similarly to Data-Driven Documents.

It offers a set of graphs, interaction abilities (like linking plots or adding JavaScript widgets), and styling.

This web-based tool for data visualization that offers many useful out-of-box graphics — you can find them on the Plot.ly website. The library works very well in interactive web applications.

Its creators are busy expanding the library with new graphics and features for supporting multiple linked views, animation, and crosstalk integration.

Which is better between libraries should not be compared. It is necessary to use each of them with maximum efficiency in solving the related problem by finding the aspects that are better than the other.

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