4 Python libraries aspiring data scientists should know about in 2022 

Python, one of the most popular and widely used programming languages, used in the field of data science. So, it is essential for aspiring data scientists to learn Python in great details. However, just learning the programming language and its functions is not enough. They also need to be acquainted with Python libraries where all kinds of coding and programming resources with respect to Python can be found and used for reference. Here is a list of top 4 Python libraries that can help budding data scientists enhance their coding skills.  

Pandas 
Pandas is one of the most popular open-source Python libraries suitable for entry level users, that is not only used in data science but also in machine learning. The speciality of this library is data manipulation and analysis tools that are mainly used for analysing data resources and structures especially in case of numerical tables and time series.  

Statsmodels 
Statsmodels is a multipurpose Python library that is primarily used for statistical analysis and data reconfiguration. This library is particularly popular as it is a combination of other Python libraries and their functions, such as Matplotlib for its graphical operations, Pandas for its data handling, Pasty for its R-based calculations (R is a popular coding language), and Numpy and Scipy for their data foundations. Statsmodels is widely used for developing various kinds of statistical models, such as OLS, alongside running statistical tests.  

Requests 
Requests is not a typical Python library and is used essentially for sending HTTP requests, from which it derives its name. Apart from sending such requests, Requests library module is also used to support operations such as adding headers and footers, forming data, accessing and analysing responsive data objects, for instance content data, encoding data and status.  

TensorFlow 
Just like Pandas, this too is an open-source Python library that was custom made for deep learning applications by Google’s Brain Team in 2015. TensorFlow is widely used for numeric computations, and also provides vast range of rich as well as flexible community tools and resources for data scientists and software developers to employ during creation of machine learning, software development and data science applications.