Python for Scientists PDF 2024.
Data Science with Python:
Python is one of the most popular languages used by scientists and software developers for tasks related to data science (Data Science).
It can be used to predict results, automate tasks, streamline processes and provide business intelligence information. Here is a non-exhaustive list of 15 Python libraries for data science you need to know.
It is possible to work with data in Python alone, but there are a number of open-source libraries that make Python data manipulation tasks much, much easier.
You have certainly heard of it, but is there a useful library that you may miss? Below is a list of the most important Python libraries for data science tasks, covering areas such as data processing, modeling and visualization.
15 Python Libraries for Data Science you need to know:
Data extraction: 1. Scrapy Scrapy (one of the most popular data science libraries in Python) helps build spider bots that can retrieve structured data from the web - for example, URLs or contact information. It is an excellent tool for scraping data used, for example, in Python Machine Learning models. Developers use it to collect data from APIs. This full-fledged framework follows the "Don't Repeat Yourself" principle in its interface design. As a result, the tool encourages users to write universal code that can be reused for building and scaling large crawlers. (link to the scrapy github) 2. BeautifulSoup BeautifulSoup is another very popular library for web crawling and data scraping. If you want to collect data that is available on a website but not via a CSV file or an API, BeautifulSoup can help you scrape and organize it into the format you need. (link to BeautifulSoup documentation) Data processing and modeling 3. NumPy NumPy (for Numerical Python) is a perfect tool for scientific computing and performing basic and advanced operations with arrays. The library offers many convenient features for performing operations on arrays (n-arrays) and matrices in Python. It allows you to process arrays that store values of the same data type and makes it easy to perform mathematical operations on arrays (and their vectorization). In fact, vectorizing mathematical operations on the NumPy array type increases performance and speeds up execution time. (NumPy github link) If you want to learn more, check out this NumPy guide. 4. SciPy Its main functionality was built on NumPy, so its tables use this library. SciPy works perfectly for all kinds of scientific programming projects (science, math and engineering). It offers efficient numerical routines such as numerical optimization, integration and others in sub-modules. The extensive documentation makes working with this library really easy (SciPy github link) 5. Pandas Pandas is a library created to help developers work intuitively with "labeled" and "relational" data. It is based on two main data structures: "Series" (one-dimensional, like a Python list) and "Dataframe" (two-dimensional, like a multi-column array). Pandas allows you to convert data structures into DataFrame objects, handle missing data and add/remove DataFrame columns, impute missing files, and plot data with a histogram or a whisker box. It is an indispensable tool for data manipulation and visualization. (github Pandas link) If you want to learn more, check out this Pandas guide or these 10 Pandas tips. 6. Keras Keras is an excellent library for building neural networks and modeling. It is very easy to use and offers developers a good degree of extensibility. The library takes advantage of other packages (Theano or TensorFlow) as endpoints. In addition, Microsoft has integrated CNTK (Microsoft Cognitive Toolkit) to serve as another backend. This is a great choice if you want to experiment quickly using compact systems - the minimalist approach to design is really top notch! (github Keras link) 7. Scikit-Learn Scikits are a group of SciPy packages that have been created for specific functionality - for example, image processing. Scikit-learn uses SciPy's mathematical operations to expose a concise interface to popular machine learning algorithms. Data scientists use it to handle standard Machine Learning and data mining tasks such as clustering, regression, model selection, dimensionality reduction and classification. Another advantage? It comes with great documentation and offers high performance. (github link scikit-learn)
15 Python Libraries for Data Science you need to know:
8. PyTorch PyTorch is a framework that is perfect for data scientists who want to easily perform Deep Learning tasks. The tool allows you to perform tensor calculations with GPU acceleration. It is also used for other tasks - for example, to create dynamic computational graphs and automatically calculate gradients. PyTorch is based on Torch, which is an open-source Deep Learning library, implemented in C, with a Lua wrapper (github link PyTorch). 9. TensorFlow TensorFlow is a popular Python framework for Machine Learning and Deep Learning, which was developed at Google Brain. It is the best tool for tasks like object identification, speech recognition and many others. It allows you to work with artificial neural networks that need to handle multiple data sets. The library includes several layer helpers (tflearn, tf-slim, skflow), which make it even more functional. TensorFlow is constantly being enhanced with new versions, including fixing possible security flaws or improving the integration of TensorFlow and the GPU. (TensorFlow github link). 10. XGBoost Use this library to implement Machine Learning algorithms in the Gradient Boosting framework. XGBoost is portable, flexible and efficient. It offers parallel tree boosting that helps teams solve many data science problems. 11. Matplotlib Matplotlib is a standard data science library that helps generate data visualizations such as two-dimensional charts and graphs (histograms, scatterplots, non-Cartesian coordinate graphs). Matplotlib is one such plot library that is really useful in data science projects - it provides an object-oriented API for embedding plots in applications. It is because of this library that Python can compete with scientific tools like MatLab or Mathematica. However, developers have to write more code than usual using this library to generate advanced visualizations. Note that popular plot libraries work seamlessly with Matplotlib (github Matplotlib link) To go further, see the guide to data visualizations with Matplotlib and Seaborn. 12. Seaborn Seaborn is based on Matplotlib and serves as a useful Python Machine Learning tool for visualizing statistical models - heat maps and other types of visualizations that summarize data and depict overall distributions. Using this library, you get a vast gallery of visualizations (including complex visualizations like time series, joint plots, and fiddle diagrams). (Seaborn github link). 13. Bokeh This library is an excellent tool for creating interactive and scalable visualizations inside browsers using JavaScript widgets. Bokeh is completely independent from Matplotlib. It focuses on interactivity and presents visualizations across modern browsers - similar to data-driven documents (d3.js). It offers a set of graphics, interaction capabilities (such as linking plots or adding JavaScript widgets) and styling (github Bokeh link). 14. Plotly This web-based data visualization tool that offers many useful ready-made charts - you can find them at Plot.ly. The library works in web applications. Its creators are working on enriching the library with new graphics and features to support multiple linked views, animation and cross-talk integration. (github link Plotly).
Conclusion on Python libraries for Data Science:
15. pydot This library allows to generate oriented and non-oriented graphs. to Graphviz FOR written in pure Python. You can easily show the structure of graphs with this library. This comes in handy when you develop algorithms based on neural networks and decision trees. (github pydot link) Conclusion on Python libraries for Data Science: This list is of course incomplete! The Python ecosystem offers many other tools that can be useful for data science work. Data scientists and software engineers involved in data science projects that use Python will use many of these tools, as they are essential for building successful Machine Learning models in Python.
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