There are reasons why Python is among the most loved programming languages in data science. The more you explore it, the more it will be your favorite as a data scientist. Though it is easy to learn and implement from scratch, it has vast libraries to reduce your complexity and simplify your output with minimal effort.
This free and open-source language is one of the top-most reasons why data science is so popular today. But in this blog, we get in-depth knowledge of 10 Python libraries for data visualization and how Python takes data-human relationships a step further.
Data science is all about statistics, data, and numbers. The prime purpose is to analyze data, manipulate if necessary and provide the optimum output based on requirements. But the whole process has never been so simple, that made us feel while reading. There were many steps and sub-steps involved, along with intelligent programming to simplify the process and get output with higher accuracy.
It’s a human tendency to skim and read only the crucial part to decide whether to read the full or not; the same happens with data. As data is humongous, it’s always challenging to process and analyze data for professionals in the data-driven industry. And at the same time, data visualization has many advantages too. It helps make decisions based on the output, identifying patterns, finding errors, exploring business insights, doing better analysis, and much more.
Using Python, you always have advantages on your side; you don’t have to write each step for creating a visualization. There are always predefined libraries to limit your writing.
The only difference is if you want to focus solely on presenting and making data interactive, Tableau and Power BI are the way to go. If you want to handle some powerful but probably more niche tasks with the data, you might benefit from a little Python coding.
One of the best Python libraries for data visualization is matplotlib, which creates graphical visualizations, plotting, etc., using python programming. The best part is that it supports various GUI toolkits like GTKm Tkinter and a general-purpose API for interaction with multiple applications. It can even provide numeral and matrix operations using NumPy.
It is the graphical visualization, plotting, and charting library in Python, and as per data scientists, it is the best library for data visualization in Python programming. It can even give a MATLAB-like interface and visualization, and with this, you can know why it is the best data visualization library in Python.
Seaborn is a top-notch data visualization library that has many built-in functions, and by using which you can create beautiful plots using simple lines of code. The best is it is well-advanced and provides advanced visualization plots with simple syntax, including box plots, violin plots, dist plots, pair plots, heatmaps, etc.
Using Seaborn, you can quickly determine the relationship between two variables. Again, you can differentiate when analyzing univariate and bivariate distributions, provide multi-grid plotting. And plot linear regression models for dependent variables.
Installation: pip install seaborn
gg stands for the grammar of graphics, and Ggplot works utterly different from matplotlib. It gives you multiple options and freedom to add extra components to create complete graphs, and plots like you can create various axes and mark other components as trend lines.
It’s essential to store data in the data frame before using the ggplot library to reduce the complexity and fill the transparency.
Installation: pip install ggplot
If you ever wanted to create interactive dashboards online and offline, then Plotly is your go-to library that acts like a python analytics library. The graph using Plotly is highly interactive, which means using Plotly, you can easily find the value at one particular point on the charts.
Plotly makes an advanced library that is super-easy to generate dashboards using 3D and SVG graphs and deploy them on the server with the support of Python, R, and Julia.
Bokeh is a Python visualization tool that allows you to develop multiple statistical charts using simple code. The benefit is the output is in multiple formats, including HTML, notebook, and others using the Bokeh library.
On the other hand, it can integrate various programming languages like Java, Python, Django, Flask, and others. And the most significant advantage with Bokeh is it allows you to transform the visualization code in different libraries like seaborn, matplotlib, ggplot.
Want to visualize the geographical data creating your maps? Then there is a library for that in Python. Not only can you make normal maps, but advanced-level maps include heatmaps, dot-density maps, geographical maps, and others.
Installation guide: pip installation geoplotlib()
Ever thought how wonderful it could be to turn some graphs into amazing web apps using just Python codes? This feature is highly beneficial for people who don’t have the domain knowledge of HTML and CSS as languages. Being a data scientist or into data-driven industries, you never need to know these two languages.
Website developers need to have them. Indeed it will help them to create unique websites with better UI/UX. Although it is not a visualization library, it works with any data visualization library as it is inspired by R’s shiny packages that convert the stats into visually appealing graphs.
When it’s all about finding the correct information from the given set of data and making it available for everyone, then the best way to do it is data visualization and to find out the missing numbers and display them in a beautiful graphical representation in a real-world dataset with just one line of code.
The visualization it supports is a heatmap, bars, charts, dendrograms, etc.
Installation: pip install missingno
Suppose you look into the world of data in the Python lenses. In that case, data visualization is way powerful with simple libraries and visually appealing images that plot so accurately only if you know where to use which one.
Therefore, if you look into the core and work on the niche data analysis, these top eight Python libraries will come in handy and do almost everything for you in fewer lines of code.
On the other hand, Tableau and Power BI are easy-going tools with an easy interface that simplifies the data analysis process through data visualization.
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