When talking about the data science world, Python is increasingly becoming a go-to language and is one of the key aspects hiring managers are searching for in the skill set of a data scientist. It has been repeatedly ranked at the topmost position in global data science surveys and its universal success just keeps growing!

Python offers us easy-to-code, object-oriented, high-level language means. And then we have numerous libraries to do jobs like mathematics, data mining, data exploration, and visualization.

In this blog, we will be discussing these Python Libraries that are doing wonders in 2021:

1. NumPy

NumPy is among the most powerful scientific computation Python libraries and is used extensively for Machine Learning and Deep Learning apps. NumPy is short for NUMerical PYthon. Complex computational machine learning algorithms need multidimensional array operations. NumPy shows solutions for large objects with multidimensional arrays and different tools to function with them.

Features of NumPy

1. It is an open-source Python library.
2. It has matrix data structures and a multi-dimensional array.
3. It can be used to conduct a range of mathematical functions on arrays.
4. It is an extension of Numeric and numarray.
5. It also has random number generators.

2. Dear PyGui

Dear PyGui uses what is really considered the immediate mode paradigm, made popular in video games. This effectively implies that the dynamic GUI is separately created frame by frame, without the existence of any data. This allows this tool to be radically different from other GUI frameworks for Python. It is highly efficient and uses the GPU of your computer to promote the building of highly complex interfaces, as many have needed in applications for engineering, simulations, games, or data science.

Features of Dear PyGui

1. Dear PyGui has a drawing API to build custom drawings, plots, and even 2D games.
2. Offers easy built-in Asynchronous function support.
3. DearPyGui utilizes the immediate mode paradigm enabling extremely dynamic interfaces.
4. Allows developers to build and create fast and strong GUIs for scripts.

3. Scikit-learn

This is a Python library that is linked to NumPy and SciPy. Scikit-learn is known to be among the best libraries for dealing with complex data. In this library, there are a lot of modifications being made. The cross-validation function is one modification, offering the choice to use more than one metric. Few small changes have been made to many training approaches, such as logistics regression and nearest neighbours.

Features of Scikit-learn

1. It is an easy and effective tool for predictive data analysis.
2. Anyone can access it and reuse it in different contexts.
3. It is built on NumPy, SciPy, and matplotlib
4. It is open-source, commercially usable – BSD license

4. Keras

Keras is known as one of Python’s finest machine-learning libraries. It offers a simplified method for expressing neural networks. Keras also offers impressive utilities for compiling models, data-set analysis, graph visualization, and so much more.

Keras utilizes either Theano or TensorFlow internally within the backend. It is also possible to use some of the many common neural networks, including CNTK. When we contrast it with other machine learning libraries, Keras is relatively sluggish. Since, by using back-end infrastructure, it generates a computational graph and then uses it to perform tasks.

Features of Keras

1. Keras offers a lot of prelabeled datasets that can be imported and loaded directly.
2. Keras has many implemented layers and parameters, such as loss functions, optimizers, and metric evaluations.
3. It runs on both the CPU and the GPU smoothly.
4. Keras is a platform that is fully Python-based, making it simple to debug and explore.
5. The modular design of Keras is extremely expressive, versatile, and ideal for creative research.

5. SciPy

When it comes to scientific computing, SciPy (Scientific Python) is the go-to library that is used extensively in the realms of math, science, and engineering. It is similar to using a paid instrument called Matlab. As the manual states, SciPy offers many user-friendly and effective numerical routines such as numerical integration and optimization routines. It is built on the NumPy library.

Features of SciPy

1. SciPy implementation can be found in every complicated numerical computation.
2. It is an open-source Python library used to solve scientific and math problems.
3. It is built on the NumPy extension and enables the user to manipulate & visualize data.
4. It offers more utility features for optimization, stats and signal processing.

6. PyTorch

PyTorch is a massive library for machine learning that enables programmers to conduct GPU acceleration tensor computations, produce interactive computational graphs, and automatically calculate gradients. Other than that, PyTorch provides rich APIs to solve neural network-related application problems.

The basis of this machine learning library is Torch, which is an open-source machine library built in C with a wrapper in Lua. This machine library was released in Python in 2017, and the library has been getting popular and drawing a growing number of programmers of machine learning ever since its creation.

Features of PyTorch

1. PyTorch enables fast, flexible experimentation and efficient production.
2. It is concise and easy to use and provides you with the ability to deploy computational graphs.
3. It makes use of Python integrations combined with a data science stack.
4. It provides an easy interface with APIs.

7. Matplotlib

Matplotlib is by far the most common library in the Python community for exploration and data visualization. This library is the foundation of every other library. It provides countless charts and customization, from histograms to scatter plots, to customize and configure your plots, matplotlib sets down a variety of colors, themes, palettes, and other possibilities. If you are doing data analysis for a machine learning project or producing a report for stakeholders, matplotlib is certainly the most functional library.

Features of Matplotlib

1. It offers an object-oriented API for integrating plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK+.
2. It has quite an active development community.
3. It is open-source and free.

8. Plotly

Plotly is a visualization library that is free and open-source. Developers love this library because of its top-quality, publication-ready and immersive charts. A few instances of the charts that are available are Boxplot, heatmaps, and bubble charts. Built on top of the D3.js, HTML, and CSS visualization library, it is one of the greatest data visualization tools accessible. It is developed using the Django framework and Python.

Features of Plotly

1. It helps in the creation of interactive graphs.
2. It is involved in the development of data analytics and visualization tools such as Dash and Chart Studio.
3. You can easily import data to a chart.
4. It helps you make beautiful slide decks and dashboards.

9. PyCaret

PyCaret is an open-source machine learning library that assists you in functions like data preparation and deployment of models. Being a low-code library, it allows you to save loads of time. It is a machine learning library that is simple to understand and use that will assist you to conduct end-to-end machine learning tests, whether it is inferring missing values, interpreting categorical data, engineering features, tuning hyperparameters, or creating ensemble models.

Features of PyCaret

1. PyCaret is a low-code library that helps you become more efficient.
2. It is a simple and easy-to-use ML library.
3. It enables you to prototype quickly and efficiently from your choice of notebook environment.
4. It provides a business-ready solution.

10. LightGBM

Gradient Boosting is among the oldest and most effective libraries for machine learning, which allows programmers to use redefined elementary models and decision trees to create new algorithms. There are also unique libraries that are available to apply this approach easily and efficiently. LightGBM, XGBoost, and CatBoost are such libraries. All these libraries are competitors to each other that attempt to overcome a similar problem and can be used in virtually the same way.

Features of LightGBM

1. It offers optimal speed and memory usage.
2. It gives better accuracy.
3. It is capable of handling large-scale data.
4. It is highly efficient and supports GPU learning.

11. Tensor Flow

In terms of machine learning and deep learning, TensorFlow, created by the Google Brain team, has picked up steam and become the most happening library. Back in 2015, TensorFlow had its very first public disclosure. At the moment, Caffe and Theano were consuming the emerging deep learning environment for programmers & researchers. TensorFlow drew considerable attention as a deep learning library in a short period of time.

TensorFlow is an end-to-end machine learning library that provides research group tools, databases, and resources to drive the state of the art in deep learning and business developers to create ML & DL-driven applications.

Features of Tensor Flow

1. It is an open-source framework developed by Google.
2. It supports deep learning networks and ML principles.
3. It is easy to run and allows faster debugging.
4. It offers a prediction of stocks, products, and more.

12. Scalene

Scalene is a Python script CPU and memory profiler equipped to handle multi-threaded code correctly and distinguish between the time spent running Python versus native code. There’s no requirement to change your code as you can straight away execute your script from the scalene command line, and it will produce a text or HTML document for you, displaying CPU and memory use for each line of your code.

Features of Scalene

1. Scalene is fast and precise.
2. Scalene supports memory usage.
3. It produces per-line memory profiles, making it easier to track down leaks.
4. Scalene separates out time spent running in Python

Conclusion

Python is among the most common languages used for Data science activities by both data scientists and programmers. It could be used to predict results, automate operations, streamline procedures, and provide insights into business intelligence.

Working with data in Vanilla Python is feasible, however, there are also a few open-source libraries that render Python data activities quite simpler. This list is by no means exhaustive! Several other tools that can be useful for data science work are offered by the Python community. Several of these tools would be used by data scientists and programmers involved in data science projects using Python, as they are important for building high-performance ML models in Python.

python has been in-demand for quite some time and developers have been loving working around the language. Hiring an expert Python Developer will make things easier for you and upgrade your project quality.

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