What are the most effective ways to present complex data using Python libraries?

Some libraries will be better for academic purpose, like Matplotlib or Seaborn. JavaScript based libraries like Plotly integrate better with web applications for BI or other reporting purposes. If you are doing quick visualizations just for you or your department (Exploratory Analysis with notebooks, and so on), you can use plotting functions that are built-in within other libraries, like Pandas (or more specific, like NetworkX or GeoPandas for maps). This is the quickest approach, but offers less personalization. Also, they are based on other libraries (Pandas plots with matplotlib)

Presenting complex data with Python involves leveraging powerful libraries. I use Matplotlib and Seaborn for clear and customisable visualisations, tailoring plots to data intricacies. Heatmaps and line charts help illustrate patterns, while interactive plots from Plotly enhance engagement. Jupyter Notebooks facilitate storytelling through code and explanations, promoting clarity. For large datasets, Pandas offers efficient data manipulation and preparation. Combining these tools ensures a comprehensive and compelling presentation of intricate data, enhancing understanding and decision-making for diverse audiences.

When presenting visualizations, it's crucial to communicate the complexity or lack of organization in the underlying data responsibly. Transparency about these aspects ensures that the visualizations accurately represent reality. It's essential to recognize that each dataset represents individuals, and the resulting visualizations can influence decisions that have both positive and negative impacts on people. Thus, maintaining awareness of the human dimension behind the data is vital for ethical and responsible decision-making.

Choosing the right Python visualization library really depends on several factors: First of all, how much you know the library (of course), how well documented it is and ease of use will be a first big selection criteria. Matplotlib is hard to learn but there are many ressources to use it and is well established. Vega Altair is declarative and way easier to use. Plotly (express) is easy to use and very popular as well. Also, think of the medium and the final user. Plotly is great for interactivity, and you can use it with tools like (Plotly) Dash, Datapane, Streamlit or Taipy. Or even in a DJango app... Seaborn can be a better option for academic looking static reports. Altair is also a great option for static visualizations.

Having knowledge of the library or being well-versed in accessing documentation is crucial for selecting the appropriate one based on the assigned task. This not only saves time but also enhances effectiveness in conveying the narrative within the data. It's essential to bear in mind that the story told through data can significantly influence the decisions of stakeholders and potentially impact the future outcomes for individuals.

When choosing between Matplotlib, Seaborn, or ggplot for data visualization, I prefer Seaborn. This is because Seaborn offers a high-level interface and default styles that are visually pleasing. It simplifies the process of creating complex visualizations by providing easy-to-use functions for standard statistical plots. Moreover, the integration of Seaborn with Pandas data structures makes it very convenient to work with DataFrame objects, consequently increasing the overall efficiency of data visualization workflows.

* Try to make your axes start at 0 (unless you have a good reason to not do it). Unfortunately, this is not the default in some very important libraries, like Plotly Express... * Take advantage of the colormap/cmap arguments (check the documentation for your specific library): Most (all?) libraries come with nice color palets for your visualizations, just make sure to use one that matches your type of data (continuous/discrete) * Add titles and set ax names * Use annotations if required. This is usually a bit complex, but this the kind of task where AI tools can help you greatly

Incorporating inclusivity into the creation of visualizations is integral to the professionalism and ethical standards of a data analyst. It is a shared responsibility to understand your audience and generate visualizations that are accessible to all individuals within it. Similarly, it's important to consistently consider that different types of graphs are suited for various storytelling needs.

For sure, everything involving the data science space is about iterations. While making data visualizations you should, I mean, is a must iterate between presenting and validating the story told by data. Asking for feedback while sharing your findings through data viz also is an important part of refining your final product. Internal stakeholders and team members can give you different perspectives that allow you to come up with enhanced data viz, ethical and inclusive.

It's easy to look at the out-of-the-box capabilities of something like matplotlib, and the examples published in its documentation, and think that that's all the library has to offer. It's true that these tools have quick and easy offerings for the most commonly used visualizations, but if you use them enough to understand them deeply, you'll find that you can really bend them to your will. At their core, they are simply tools for plotting shapes and text on a coordinate plane. Get creative and imaginative. Sketch your idea before you try to build it out. Then once you have your idea, figuring out how to create even highly non-standard plots to represent complex data ideas is usually the easy part.

We're all human, right? And as humans, we can't escape being influenced by our thoughts, past events, and experiences. Basically, it's in our nature to bring some bias into whatever task or analysis we're tackling. That's why it's crucial to keep hitting that validation button at every step in the data analysis process. Don't forget to loop others in and get their feedback too. It's all about staying on top of those biases and making sure our analyses are as clear and objective as possible.