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In the modern world, the information flow which befalls on a person is daunting. This led to a rather abrupt change in the basic principles of data perception. Therefore visualization is becoming the main tool for presenting information. With the help of visualization, information is presented to the audience in a more accessible, clear, visual form. Properly chosen method of visualization can make it possible to structure large data arrays, schematically depict elements that are insignificant in content, and make information more comprehensible. One of the most popular languages for data processing and analysis is Python, largely due to the high speed of creating and development of the libraries which grant basically unlimited possibilities for various data processing. The same is true for data visualization libraries. In this article, we will look at the basic tools of visualizing data that are used in the Python development environment. Matplotlib Matplotlib is perhaps the most widely known Python library for data visualization. Being easy to use, it offers ample opportunities to fine tune the way data is displayed. Polar area chart The library provides main visualization algorithms, including scatter plots, line plots, histograms, bar plots, box plots, and more. It is worth noting that the library has fairly extensive documentation, that makes it comfortable enough to work with even for beginners in the sphere of data processing and visualization. Multicategorical plot One of the main advantages of this library is a well-thought hierarchical structure. The highest level is represented by the functional interface called matplotlib.pyplot , which allows users to create complex infographics with just a couple of lines of code by choosing ready-made solutions from the functions offered by the interface. Histogram The convenience of creating visualizations using matplotlib is provided not only due to the presence of a number of built-in graphic commands but also due to the rich arsenal on the configuration of standard forms. Settings include the ability to set arbitrary colors, shapes, line type or marker, line thickness, transparency level, font size and type, and so on. Seaborn Despite the wide popularity of the Matplotlib library, it has one drawback, which can become critical for some users: the low-level API and therefore, in order to create truly complex infographics, you may need to write a lot of generic code. Hexbin plot Fortunately, this problem is successfully leveled by the Seaborn library, which is a kind of high-level wrapper over Matplotlib. With its help, users are able to create colorful specific visualizations: heat maps, time series, violin charts, and much more. Seaborn heatmap Being highly customizable, Seaborn allows users wide opportunities to add unique and fancy looks to their charts in a quite a simple way with no time costs. ggplot Those users who have experience with R, probably heard about ggplot2, a powerful data visualization tool within the R programming language environment. This package is recognized as one of the best tools for graphical presentation of information. Fortunately, the extensive capabilities of this library are now available in the Python environment due to porting the package, which is available there under the name ggplot . Box plot As we mentioned earlier, the process of data visualization has a deep internal structure. In other words, the process of creating a visualization is a clearly structured system, which largely influences the way of the thoughts in the process of creating infographics. And ggplot teaches the user to think in such a structured approach, to think according to this system so that in the process of consistently building commands, the user automatically starts detecting patterns in the data. Scatter plot Moreover, the library is very flexible. Ggplot provides users with ample opportunities for customizing how data will be displayed and preprocessing datasets before they are rendered. Bokeh Despite the rich potential of the ggplot library, some users may lack interactivity. Therefore, for those who need interactive data visualization, the Bokeh library has been created. Stacked area chart Bokeh is an open-source Javascript library with client-side for Python that allows users to create flexible, powerful and beautiful visualizations for web applications. With its help, users can create both simple bar charts and complex, highly detailed interactive visualizations without writing a single line in Javascript. Please have a look at this gallery to get an idea of the interactive features of Bokeh. plotly For those who need interactive diagrams, we recommend to check out the plotly library. It is positioned primarily as an online platform , on which the users can create and publish their own visualizations. However, the library can also be used offline without uploading the visualization to the plotly server. Contour plot Due to the fact that this library is positioned by developers mostly as an autonomous product, it is constantly being refined and expanded. So, it provides users truly unlimited possibilities for data visualization, whether it’s interactive graphics or contours. You can find some examples of Plotly through the link below and have a look at the features of the library. https://plot.ly/python/ Conclusion Over the past few years, data visualization tools available to Python developers have made a significant leap forward. Many powerful packages have appeared and are expanding in every possible way, implementing quite complex ways of graphical representation of information. This allows users not only to create various infographics but also to make them truly attractive and understandable to the audience.

The more carefully you process the data and go into details, the more valuable information you can get for your benefit. Data visualization is an efficient and handy tool for gaining insights from data. Moreover, you can make the data far more understandable, colorful and pleasant with the help of visualization tools. As data is changing every second, it is an urgent task to investigate it carefully and get the insights as fast as possible. Data visualization tools cover a full scope of opportunities and additional functions which are called upon to facilitate the visualization process for you. Thus, we attempted to make an overview of the most popular and useful libraries for data visualization in R. Ggplot2 Ggplot2 is a system for creating charts based on the Grammar of Graphics. It proved to be one of the best R libraries for visualization. Ggplot2 works with both univariate and multivariate numerical and categorical data. Thus, it is very flexible. The plot specification is at a high level of abstraction and has complete graphics system. It contains a variety of labels, themes, different coordinate systems, etc. Therefore, you get the opportunity to: control data values with scales option filter, sort, summarize datasets create complex plots. However, some activities are not available with ggplot2 such as 3d graphics, graph-theory type graphs, and interactive graphics. Here are several examples of the visualization plots made with the help of Ggplot2. Density plot Boxplot Scatterplot Plotly Plotly is an online platform for data visualization, available in R and Python. This package creates interactive web-based plots using plotly.js library. Its advantage is that it can build contour plots, candlestick charts, maps, and 3D charts, which cannot be created using most packages. In addition, it has 30 repositories available. Plotly gives you an opportunity to interact with graphs, change their scale and point out the necessary record. The library also supports graph hovering. Moreover, you can easily add Plotly in knitr/R Markdown or Shiny apps. Have a look at several plots and charts created with Plotly. Contour plot Candlestick chart 3d scatterplot Dygraphs Dygraphs is an R interface to the JavaScript charting library. This library proved to be fast and flexible in its application. It facilitates the work with dense data. Dygraphs is a useful tool for charting time-series data in R. The benefits of this library include the support of visualizing xts objects, support of graph covering such as shaded regions, event lines, and point annotations, interaction with graphs, showing upper/lower bars, synchronization and the range selector, and more. You can also easily add Dygraphs in knitr/R Markdown or Shiny apps. Moreover, huge datasets with millions of points don’t affect its speed. Also, you can use RColorBrewer with Dygraphs to increase the range of colors. Below you can see a vivid representation of the data visualization with Dygraphs package. Leaflet Leaflet is a well-known package based on JavaScript libraries for interactive maps. It is widely used for mapping and working with the customization and design of interactive maps. Besides, Leaflet provides an opportunity to make these maps mobile-friendly. It's abilities include: interaction with plots, and the change in their scale map design (Markers, Pop-ups, GeoJSON) easy integration with knitr/R Markdown or Shiny apps work with latitude/longitude columns support of the Shiny logic using map bounds and mouse events visualization of maps in non-spherical Mercator projections. Rgl Rgl package may become a perfect fit for creating interactive 3D plots in R. It offers a variety of 3D shapes, lighting effects, different types of the objects, and even the ability to make an animation of your 3D scene. Rgl contains high-graphics commands and low-level structure. The plot types include meaning points, lines, segments from z=0, and spheres. Moreover, with Rgl, you can: interact with graphs apply various decorative functions easily add Rgl in knitr/R Markdown or Shiny apps create new shapes Conclusion To sum up, data visualization is more than a charming picture of your data. It's a chance to see the data under the hood. R is one of the powerful visualization tools. Using R you can build a variety of charts from a simple pie chart to more sophisticated such as 3d graphs, interactive graphs, maps, etc. Of course, this list is not complete and there exist many other great visualization tools which can bring their specific benefits to your data visualization. Nevertheless, we compiled this list from our experience. Summarizing everything mentioned before, Plotly, Dygraph, and Leaflet support zooming, moving your graphs. If you are plotting time series, you can filter dates using a range selector. For building 3d models it is highly suitable to use Rgl. Do your best with handy R visualization tools!  

Companies have a growing demand to visualize their data with business intelligence tools. We compared the salaries from across 10 different European countries using   Glassdoor , which offers self-reported salary information by location and employer, giving us some key insights into the salaries of people with “Business Intelligence” in their job title. Switzerland with the highest salary for Business Intelligence There are few reliable resources available to track salaries in the workplace. Most people are private about how much they earn and a lot of companies choose not to share them. Glassdoor allows access to self-reported salary information by location and employer, and can provide us with some insights into the salaries of the job titles containing “business intelligence.” We collected the salary data from 10 different European countries, including Austria, Belgium, France, Germany, Ireland, Italy, Netherland, Spain, Switzerland, and the UK. To make salaries comparable, we changed them into annual salaries in Euro (GBP/EUR: 1.12, CHF/EUR: 0.88). The figure plots the average nominal salaries per year by country and ranking. Junior level salaries range from EUR 24,900 in Italy to EUR 108,400 in Switzerland. Germany and Ireland follow closely behind with EUR 62,300 and EUR 51,900. For senior positions in Switzerland, wages are EUR 125,000, making it the clear lead in Europe.     When the cost of living is adjusted, the difference between Germany and Switzerland decreases These nominal salaries don’t tell us much about the underlying purchasing power. European cities like Geneva and Zurich are famous for being expensive places to live. To take into account the difference in the cost of living, and to compute real wages, we use the  OECD price level index . The table below shows what the average annual salary for junior level positions is, with the prices adjusted. It shows that the cost of living in Switzerland is 70% higher than Spain, and so some of the differences in salaries for business intelligence can be explained by the cost of living. However even with these adjustments, there’s not that much that changes in the ranking. Switzerland still stands out clearly with the highest wage, while Italy remains at the lowest. The difference between Germany and Switzerland however, shrinks from around EUR 45,000 per year, to around EUR 12,700 per year.   Country     Annual Salary in 1000 EUR     OECD Price level     Adjusted Salary   Italy 24.9 91 27.3 Spain 28.1 83 33.9 United Kingdom 41 108 38.0 Netherlands 44.9 103 43.6 France 46.9 101 46.4 Belgium 47.8 101 47.3 Ireland 51.9 102 50.8 Germany 62.3 98 63.6 Switzerland 108.4 142 76.3