Library allows you to work with two-dimensional tables in Python

In the modern world, information is one of the greatest values. And I'm not just talking about the really important data like passwords or commercial secrets, but also about something simpler and more accessible, like information about the gender, age and place of residence of the website visitors, or the statistics of сryptocurrency rate fluctuations for a certain period.
At first glance, it may seem that such data is chaotic and has a very low value. And it might be the case if we evaluate it with the ordinary human brain. But the moment you apply the data analysis methods and data science groundwork to a large volumes of data, it turns out that the parameters you've thought werenжt connected actually correlate to each other in some way.
In this article we'll tell you about the most effective Python libraries for data science and data analysis.
1. NumPy
NumPy allows you to efficiently handle multidimensional arrays. Many other libraries are built on NumPy, and without it it'd be impossible to use pandas, Matplotlib, SciPy or scikit-learn, which is why it ranks first in the list.

Also, it has several well-implemented methods, for example, the random function, which is much better than the random number module from the standard library. The polyfit function fits perfectly for simple predictive analytics tasks, for example, linear or polynomial regression.

2. Pandas
Data analysts usually use flat tables, such as in SQL and Excel. Initially, this wasn't possible in Python. The pandas library allows you to work with two-dimensional tables in Python.

This high-level library allows you to build summary tables, allocate columns, use filters by parameters, perform grouping by parameters, run functions (addition, finding a median, the average, minimum, and maximum values), merge tables and much more. In pandas you can also create the multidimensional tables.
3. Matplotlib
Data visualization, obviously, allows to present it in a convenient visual form, study more precisely, which is harder to do in the usual format, and present it to other people in a more understandable way. Matplotlib is the best and most popular Python library for this purpose. It's not very easy to use, but with the help of 4-5 most common code blocks for simple line diagrams and point-to-point graphs you can learn to create them pretty quickly.