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Top 5 Python Libraries for Data Visualization

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Data visualization is the pictorial representation of data. With the help of graphs and stats, it becomes easy to take complex business decisions. With visualization, they can recognize new patterns and get valuable insights from data. Interactive detailed data visualization can provide charts and graphs with deep information which helps in quickly identifying key details and insights from them.

Python has maintained its leading position among other competitors like Julia, R in data science challenges. If you are planning to join the analysis race, you can go through Data Scientist Course and develop deep programming corner of your brain.  In this article, I’m going to discuss the best data visualization libraries offered by Python in data science.

1). ggplot :

ggplot is designed to assist users in designing professional looking plots with minimal coding involvement. It is based on the ggplot2 developed in R programming and The Grammar of Graphics. Plotting requires time and code for adding coloured points, drawing lines etc. Instead of repeating code all the time, you can use high-level APIs offered by ggplot. It allows you to layer components for complete plot creation.

It is not designed for high-level graphics customization. It is only limited to simple plotting methods. ggplot and Pandas is tightly coupled with each other. Thus, you can add your ggplot data in DataFrame.

2). Matplotlib :

Matplotlib library of the Python is one of the most famous libraries among developers for 2D plotting. It’s interactive and user-friendly environment allows you to get high-quality figures with various file extensions. You can use Matplotlib in web servers, Jupyter notebooks, Python script, shell. It is very easy to learn and use as it requires a few lines of code to draw plots, histograms, charts, error charts etc.

Here, you will have complete control over the fonts, line, styles, axes properties with same functionalities like MATLAB. Various third-party packages are developed through it such as seaborn, holoviews etc. It also includes multiple add-on toolkits like mplot3d, axesgrid1 etc.

3). Bokeh :

Same as ggplot, Bokeh is also based on The Grammar of Graphics. Here you can leverage the support of real-time data and streaming. The best part is that Bokeh created web plots can easily provide JSON objects, HTML files or web applications as a result. For multiple user requirements, it offers three kinds of interfaces with variable degrees of control. Topmost degree is designed for creating charts in the fastest way. Here, you can select various methods for creating regular chart types like box or bar plotting, histograms design etc. At the middle level, one can control common building blocks of the generated chart such as controlling dots in scatter charts. The bottom level of the Bokeh is designed for developers. Here, they define each parameter or rules for the chart.

4). geoplotlib :

Geoplotlib is one of the handy tools used for creating geographical maps. It is designed to develop various types of map like heatmaps, choropleths, density maps etc. For using it, you need to install an object-oriented programming interface called Pyglet. It provides a set of inbuilt tools for performing regular tasks like spatial graphs, shapefiles, density distribution visualizing etc.

5). Seaborn :

Seaborn delivers beautiful charts in just a few lines of code. It is developed on the basis of matplotlib. It’s user-friendly, and the high-level interface allows you to draw informative and attractive statistical graphs in Python. It is strongly coupled with PyData stack, numpy and Pandas. Data scientists prefer Seaborn for creating heat maps, histograms, density estimates, box plots or time series etc. Here, one can visualize complex models like bivariate distribution or regression models. For complex visualization models, Seaborn has emerged as a gift.

So these python libraries will help you to visualize your company or research data through a customizable data visualization platform. Thus, your team can collect valuable insights by further putting those data into complex machine learning models.

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