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Any 'pretty' data visualization libraries for Python?

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  • 106 5

    There are plenty of 'pretty-printing' visualization libraries for Javascript. E.g. those listed here.

    Googling for 'python visualization libraries' only turns up stuff like VTK and mayavi, which are primarily more for no-nonsense scientific use.

    So, do you know of any Python libraries similar to those Javascript ones in the above link? I particularly like the Javascript Infovis Toolkit.

      May 5, 2019 12:40 PM IST
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  • 102 9
    For Python there really isn't "one viz library to rule them all". There are different libraries and toolkits for different purposes. For graphs in Python you may find igraph useful. For other types of scientific or data visualizations matplotlib is also good
      May 5, 2019 12:53 PM IST
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  • 346 3
    I will go over four of the most popular Python libraries for data visualization: Matplotlib, Seaborn, Plotly Express, and Altair. To do so, I will create a simple bar plot and analyze how easy it is to use each library.
      September 4, 2021 12:41 PM IST
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  • 129 8

    Matplotlib Python Library is used to generate simple yet powerful visualizations. More than a decade old, it is the most widely-used library for plotting in the Python community. Matplotlib is used to plot a wide range of graphs– from histograms to heat plots.

    Matplotlob is the first Python data visualization library, therefore many other libraries are built on top of Matplotlib and are designed to work in conjunction with the analysis. Libraries like pandas and matplotlib are “wrappers” over Matplotlib allowing access to a number of Matplotlib’s methods with less code.

    The versatility of Matplotlib can be used to make visualization types such as:

    • Scatter plots
    • Bar charts and Histograms
    • Line plots
    • Pie charts
    • Stem plots
    • Contour plots
    • Quiver plots
    • Spectrograms

    You can create grids, labels, legends etc. with ease since everything is easily customizable.

      September 24, 2020 10:50 AM IST
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  • 63 1

    This list is an overview of 10 interdisciplinary Python data visualization libraries, from the well-known to the obscure. support three libraries on this list - matplotlib, Seaborn, and Plotly - and more than 60 others .

     

    1. Matplotlib
    2. Seaborn
    3. ggplot
    4. Bokeh
    5. pygal
    6. Plotly
    7. geoplotlib
    8. Gleam
    9. missingno
    10. Leather
    This post was edited by Jasmine Chacko at September 24, 2020 11:49 AM IST
      September 24, 2020 10:54 AM IST
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  • 126
    Some of the most famous visualization libraries and their application based on the project you need to fulfill (with our own little commentaries):
    • Data science neural network. The vast majority of tutorials for data science use matplotlib. This general-purpose visualization library is suitable for multiple projects as it serves as a base for applying other frameworks over it.
    • Data regression or classification model. It is highly likely that a coder doing regression or classification will gravitate toward Seaborn because of its correlation matrix. As the name suggests, what this matrix shows – at just one glance – is whether data elements have a mutual relationship or connection.
    • Stock ticker⁠. This kind of project typically requires a chart that can plot real-time data and respond when the user moves the cursor over the chart. Our recommended choices here are Plotly or Bokeh, two frameworks that support JavaScript.
    • Mapping. For instance, if a coder is assigned to work with a MongoDB database, which stores geographic and other data in JSON format, the task would most likely require drawing maps. In that case, our framework suggestion would be geoplotlib simply because it is designed especially for that purpose.
    • Applied statistics in a university or research setting. Coders doing research – which may include clinical trials for drug companies or utilizing algorithms to make the judicial process a lot less biased – are most likely going to use the R programming language. However, nowadays this trend is shifting towards the use of Python simply because this language has become widespread in terms of machine learning tasks. But considering that it’s easier for R programmers to use something they already know, the recommended solution would be ggplot since it can work as a Python front-end for the ggplot R-language visualization library.
     
    Of course, this is just the tip of the iceberg when it comes to data visualization libraries – it’s a field that’s constantly evolving and becoming more nuanced with each passing day.
      September 24, 2020 11:53 AM IST
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