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All about Python packages for data science in 2021 you can find here. Python is an interactive, interpreted, and object-oriented programming language. It is a general-purpose programming language that can run seamlessly on various Unix variants including Linux and macOS, as well as Windows. Python is widely used in computer vision, hacking, 3D machine learning, data visualization, robotics, and data science. It is the most preferred language of developers worldwide.
Python is one of the most widely used programming languages among Data Scientists across the world.
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Do you know why?
Python Packages for Data Science
Apart from being open-source, easy to learn, and use, the most prominent reason Python is widely used is that there are hundreds of packages and frameworks in Python. There are extraordinary libraries in Python that are used for solving problems in Data Science.
It is the most sought-after programming language and is widely used by AI Engineers, Machine Learning Engineers, and Data Scientists. The average annual salary of Python Developers is around USD 115,000. This makes clear that with Python you can make a lucrative career in varying fields.
This is why professionals around the world are looking for Data Science with Python certification to make a bright career in Data Science.
Today we will discuss Python libraries that you should learn to improve your skills in Data Science in 2021.
Top 10 Python Libraries for Data Science
TensorFlow
TensorFlow is developed by Google Brain Team and is an open-source library that is used for deep learning applications. It is the most widely used Python library that is used for high-performance numeric computations that have around 35000 comments and an active community of around 1500 contributors. It is used across different scientific fields.
Features:
- Parallel computing that allows executing complex models
- Seamless library management maintained by Google
- Error reduction
- Better computational visualizations
TensorFlow is used for video detection, text-based applications, time-series applications, speech and image recognition.
NumPy(Numerical Python)
The name makes it clear that this fundamental package is useful when you do numeric computations. It has a powerful N-dimensional array object, 18000 comments on GitHub, and a vibrant community of 700 contributors. It is an array-processing package that contains tools for working with high-performance multidimensional objects called arrays.
Features
- Fast, precompiled functions
- Better efficiency with array-oriented computing
- Object-oriented approach
- Vectorization makes faster and compact computations easy
NumPy is extensively used in data analysis and forms the base of other packages such as sci-kit learn and SciPy.
SciPy (Scientific Python)
Another free package in Python, SciPy is used for high-level computations. It is used extensively for scientific and technical computations. As it extends NumPy, it can provide many efficient and user-friendly routines required for scientific calculations.
Features:
- High-level commands make data visualization and manipulation easy.
- Algorithms for optimization
- Can be used for linear algebra
- Built-in functions allow easy solutions to differential equations
Matplotlib
This is popular for its powerful and beautiful visualizations. It is extensively used for data visualization as it has an excellent collection of graphs and plots. It provides object-oriented APIs that help in embedding those plots into applications.
Features:
- It can be used in place of MATLAB, with the benefit of being open-source and free.
- Better runtime behavior and low memory consumption.
- It supports many backends and output types so that you don’t care about the operating system.
Pandas (Python data analysis)
This is the most important package for data science. It is used along with NumPy in Matplotlib. It is used for data analysis and cleaning. Pandas provide flexible and fast data structures designed to work with structured data easily and intuitively.
Features:
- Great syntax and functionalities that allows you to deal with missing data
- High-level abstraction
- It lets you create your own functions and execute them across a series of data
Pandas library is used for data wrangling and data cleaning as well. It supports ETL jobs for data analysis and storage. It is heavily used in commercial areas such as finance, statistics, and neuroscience.
Sci-kit Learn
It is a machine learning package that provides nearly all machine learning algorithms that you may need. It is so designed that it can be interpolated into SciPy and NumPy.
This package is extensively used for applications such as classification, clustering, regression, dimensionality reduction, and model selection.
Keras
Keras, similar to TensorFlow, is another popular package used for deep learning and neural network modules. It can support both the Theano and TensorFlow backends, allowing you to work without diving deep into the details of TensorFlow.
Features:
- It provides various prelabeled datasets to be used to directly import and load.
- There are different implemented parameters and layers used for configuration, construction, training, as well as evaluation of neural networks.
The most significant application of Keras is in deep learning models, available with their pretrained parameters.
Scrapy
Scrapy is one of the most popular, open-source, fast web crawling frameworks written in Python. Generally, it is used to extract the data from the web page by using selectors based on XPath.
Scrapy helps in building spider bots or crawling programs to retrieve structured data from the web. It can also be used to collect data from APIs and follows a principle of “Don’t Repeat Yourself” in the design of its interface. It also influences users to write codes to be reused for creating and scaling huge crawlers.
PyTorch
It is another popular Python package that is a scientific computing package utilizing the power of graphics processing units. It is one of the most commonly preferred deep learning research platforms developed to provide maximum speed and flexibility.
PyTorch is popular for providing two of the most high-level features: tensor computations that have strong GPU extension support; creating deep neural networks on the basis of a tape autograd system.
Theano
It is a computational package for machine learning, meant for computing multidimensional arrays. It works similar to TensorFlow, as it can be used to work in parallel or distributed environments.
Features:
- Tightly integrated with NumPy
- Efficient symbolic differentiation
- Transparency in the usage of GPU
- Large-scale unit-testing and self-verification
Theano is regarded as an industry standard when it comes to Deep Learning Research and Development.
Other popular libraries in Python that are extensively used in Data Science are:
- Eli5
- LightGBM
- BeautifulSoup
- Seaborn
- Pycaret
- XGBoost
- Plotly
- pydot
- Bokeh
And many more.
Conclusion
After coming across powerful libraries in Python, you now realize the versatility of Python. The Data scientists who know well how to use Python in different scenarios are the most preferred candidates for recruiters.
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