In Google’s ‘Year in Search’ Report 2020, Python stood out as the most searched programming language in 2020. And this is not the first time Python has achieved such an accolade. Python has been the most popular and the most loved programming language for many years now. It is very easy to learn, has a very simple syntax, and above all, has extremely useful libraries that can help users accomplish a lot of their tasks quickly without a hassle and much less code.
One of the most common places Python finds a lot of applications in is the field of data science and machine learning. Working with Python is almost a mandatory skill for most data science jobs. The reason Python is the language of choice for data science professionals is because of the ample libraries in Python that are relevant and helpful for data science tasks. Moreover, Python is an easy to code, object-oriented, high-level language.
So, what are the top Python libraries that are commonly used in data science?
If you are looking to work in the scientific computing, machine learning, or deep learning space, NumPy is a library that you will definitely get to use. ‘NumPy’ stands for ‘Numerical Python’. It provides support for large multi-dimensional array operations and also offers various tools to work with them. Such is the utility of this library that there are many other useful libraries in Python, such as, Pandas, Matplotlib, and Scikit-learn, that are built on top of NumPy. A good data science training would definitely include NumPy in its course content.
Like ‘NumPy’ stands for ‘Numerical Python’, ‘SciPy’ stands for ‘Scientific Python’. This is the go-to library for scientific computing like that used in mathematics or engineering or science. In some ways, it is almost equivalent to using MATLAB, though MATLAB is a paid tool. SciPy provides user-friendly and efficient numerical routines like routines for numerical integration and optimization. Plus, it is built on the NumPy library. If you’re looking to get certified in data science, make sure you learn SciPy.
Scrapy is the library to use for data mining. This library helps build crawling programs or what is called spider bots which would help retrieve structured data from the web. In essence, as the name suggests, Scrapy helps scrap data that is being used. It is also used for gathering data from APIs. Also, Scrapy is a very useful tool if you are looking to build and scale large crawlers. If you are going to be working on the data mining side of things, make sure when you learn data science, you cover Scrapy.
BeautifulSoup is another commonly used Python library for data mining. Like Scrapy, it is also commonly used for web crawling and data scraping. BeautifulSoup is especially useful if you are looking for data that is available on some website or at some source where it is not organized in a proper CSV or API format, then you can use BeautifulSoup to scrape the data for you and arrange it in the format you require.
The Pandas library helps developers work with labeled and relational data. It is primarily based on series and data frames. With Pandas, you can convert data structure to data frame objects, handle missing data, add or delete columns from the data frame, input the missing files, as well as plot the data with a histogram or a plot box. If your work involves data wrangling, manipulation, and visualization using Python, you will definitely be required to use the Pandas library.
Data science projects based in Python almost always use Scikit-Learn, consider it as a sort of industry standard. Scikits is a group of packages in the SciPy stack that have been created for serving specific functionalities. From these, the Scikit-Learn uses the math operations of SciPy to expose a concise interface to the most common machine learning algorithms. With scikit-learn, you can accomplish the usual machine learning and data mining tasks like clustering, regression, model selection, dimension reduction, classification, etc. A good Data Science with Python Training will definitely include Scikit-Learn as part of its curriculum.
PyTorch is a very helpful library for users who need to perform deep learning tasks. PyTorch helps with performing tensor computations with GPU acceleration. It can also help you to create dynamic computation graphs and automatic calculation of gradients. PyTorch is based on Torch – an open-source deep library in C for deep learning and has a wrapper in Lua. Again, if you are looking for the best data science with Python training, make sure you choose one that teaches PyTorch.
Developed by Google Brain, TensorFlow is commonly used in machine learning and deep learning, especially for tasks like object identification and speech recognition. It is very useful when you are working with artificial neural networks and have to work with multiple data sets. All in all, a very helpful framework.
Matplotlib is a data science library that helps generate data visualization like 2D diagrams and graphs. It also provides an object-oriented API for embedding plots into applications. Matplotlib is majorly responsible for helping Python compete against more scientific tools like MATLAB or Mathematica. This is also a standard requirement if you plan to get certified in Python and data science.
This is indeed a very, very short list of libraries since there are so many more useful tools available in Python for different purposes and functions. Data scientists, developers, software engineers, machine learning engineers, etc. use a host of tools to accomplish different tasks. To understand more about their roles, we would strongly recommend undergoing thorough data science training. Cognixia – the world’s digital talent transformation company offers an intensive, hands-on instructor-led Data Science with Python Certification course. Delivered by experts, this online data science with Python course is perfect for data enthusiasts who would like to upskill and learn valuable data science & Python skills.