Plotly is a popular Python library for creating interactive and expressive data visualizations. However, as with any tool, there are some common mistakes that can affect the quality and clarity of your plots. In this article, we will discuss some of these mistakes and how to avoid them.
One of the most fundamental decisions you have to make when working with Plotly is what type of chart to use for your data. Plotly offers a wide range of options, from basic line and bar charts to more complex and specialized ones like treemaps and sunbursts. However, not every chart type is suitable for every data set or question. For example, if you want to compare the distribution of a numerical variable across different groups, a box plot or a violin plot might be more informative than a pie chart or a scatter plot. Similarly, if you want to show the relationship between two numerical variables, a scatter plot or a line plot might be more appropriate than a bar chart or a histogram. Therefore, you should always consider the nature and purpose of your data before choosing a chart type.
Often presenting information with the correct type of chart can set you apart positively. An example of best practice regarding pie charts is to use them when you have few categories for comparison, around 5 or 6.
Some are common mistakes in plotly :- 1. Understand and analyze the data whether the data is suitable for correct chart or not. Wrong chart type can harmful. 2. Check the data is clean or not. It is correctly formatted or not. Is there any null or blank values or not. 3. The chart may me too much informative and congested with labels. 4. May be use various color to show it more colorful.
Common Plotly mistakes include: not using correct data types, leading to errors; ignoring the layout object, which customizes plots; overplotting, which can clutter charts; not handling missing data, as Plotly doesn't do this implicitly; ignoring interactivity, a key Plotly feature; using wrong plot types, which can misrepresent data; not updating plots dynamically when data changes; forgetting to use hover information for better data insights; neglecting to customize axis labels and titles for clarity; not optimizing large datasets, which can slow down rendering; and not using subplots for complex visualizations, which can simplify data interpretation.
When working with Plotly, it's crucial to avoid using inappropriate chart types for your data. Common mistakes include selecting overly complex charts for simple data, misinterpreting categorical vs. continuous data, neglecting to customize axes and labels for clarity, and overcrowding the visualization with unnecessary elements. Always ensure that the chosen chart type effectively communicates the intended message and insights from your data without overwhelming the viewer.
Plotly espera que os dados estejam em uma estrutura específica, como listas ou arrays, dependendo do tipo de gráfico que você está criando. Certifique-se de entender como formatar seus dados adequadamente antes de tentar criar um gráfico. A documenta??o do Plotly é extensa e contém exemplos úteis para praticamente tudo que você precisa fazer. N?o se esque?a de consultar a documenta??o para entender melhor como usar diferentes recursos e evitar erros. Se seus dados contiverem valores ausentes, certifique-se de lidar com eles adequadamente antes de plotar o gráfico. Caso contrário, isso pode levar a erros ou a interpreta??es incorretas dos dados.
Another common mistake when working with Plotly is to try to cram too much information into one chart. This can make your chart look cluttered, confusing, and hard to read. For example, if you use too many colors, markers, annotations, or axes, you might distract your audience from the main message or insight of your chart. Moreover, if you use too many dimensions or variables in your chart, you might create misleading or spurious patterns or correlations. Therefore, you should always aim for simplicity and clarity in your charts, and use only the essential elements that convey your data story.
If you want to make your client get lost in your charts without any clue, make fancy and multidimensional charts. On the other hand, if you want to expres the main point, be simple, plain and focus on one thing in your chart.
I experienced this issue once I developed dashboard, I overloaded the charts with too much information to gain more canvas space in the page and to present as much as information I could but unfortunately I suffered from slow updates on some charts, misinterpreted figures from stakeholders in addition to not good looking dashboard so I learnt that a careful planning of developing the dashboard along with more reading about best practices might save more time to get perfect results.
A leitura facilitada de um gráfico é um favor importante. Nem todo mundo que vai ler o gráfico ou dashboard entende de dados. Portanto quanto mais facilitado melhor. Um gráfico que é constantemente sobrecarregado e muitas vezes utilizado de forma errada, é o gráfico de pizza ou de rosca, uma péssima escolha para muitas informa??es
- Choose wisely: Match your chart type to your data (scatter plots for trends, histograms for distributions) and avoid complexity that can mislead. - Clarity reigns: Keep it simple and focus on clear labels and essential elements to prevent overwhelming viewers. - Think user-friendly: Design for accessibility and consider interactive features to engage your audience.
Plotly is a powerful and flexible library that allows you to customize and enhance your charts in many ways. However, this does not mean that you can ignore the basic principles and best practices for data visualization. For example, you should always use appropriate and consistent scales, labels, legends, and titles for your charts, and avoid using misleading or inaccurate data or representations. You should also use colors, shapes, and sizes that are meaningful and contrast well, and avoid using too many or too similar ones. Furthermore, you should follow the guidelines for visual perception and cognition, and use the most effective and intuitive ways to encode and present your data. For instance, you should use position, length, and angle to show quantitative data, rather than area, volume, or color.
Keep plots clean and focused. Avoid cluttering with unnecessary data or annotations. Optimize performance by minimizing unnecessary computations and rendering. Large datasets may require aggregation or sampling. Ensure plots are responsive across devices. Test and adjust layout and sizing accordingly. Document your code and plots for clarity and reproducibility. Include comments and relevant information about data sources and plot configuration. Choose colors wisely for better readability and accessibility. Avoid overly bright or clashing color schemes. Incorporate tooltips, hover effects, and click interactions where applicable. Handle missing or erroneous data appropriately. Explore Plotly's Full Potential.
Existem diversas práticas recomendadas para uma boa visualiza??o de dados. Respeitar a paleta de cores do gráfico ou dashboard, ainda em cores, respeitar a psicologia das cores. Respeitar e padronizar o espa?amento entre barras e entre diferentes gráficos, esse espa?o também é chamado de linha de respiro. N?o saturar os gráficos com diversas informa??es, e respeitar a sugest?o de cada gráfico.
While Plotly offers customization freedom, remember these core principles: Clarity over complexity: Prioritize clear, well-labeled charts that avoid visual clutter and misleading elements. Data integrity: Ensure accurate data and representations to build trust with viewers. Perceptual best practices: Use color, size, and position effectively for clear data encoding.
The final common mistake when working with Plotly is to assume that your charts are perfect and ready to share as soon as you create them. However, this is rarely the case, as there might be errors, inconsistencies, or room for improvement in your charts. Therefore, you should always test and refine your charts before publishing or presenting them. For example, you should check your charts for accuracy, completeness, and relevance of your data and analysis. You should also check your charts for readability, usability, and accessibility of your design and layout. Moreover, you should check your charts for impact, appeal, and engagement of your message and story. To do this, you can use various tools and techniques, such as debugging, reviewing, feedback, and storytelling.
Testing mechanism should be maintained overtime through update the related procedures and tools. The main factors in this stage are the end user test feedback and the real users’ feedback issues which supposed to be including in the testing and refining action plan.
Muitas vezes, um bom storytelling com dados precisa ser testado pelos usuários finais. Ninguém melhor que o usuário para reportar as dificuldades de análise que um gráfico pode trazer.
Common mistakes to avoid when using Plotly include overcrowding visuals with too many traces or elements, neglecting responsiveness across different devices, and ignoring performance optimization, which can lead to slow loading times and decreased interactivity, particularly with large datasets.
Don't let your first draft be your last! Avoid the mistake of neglecting these final steps: - Test & refine: Scrutinize your charts for accuracy, clarity, and accessibility before sharing. - Seek feedback: Utilize reviews and testing to ensure your visualizations effectively communicate your message. - Refine for impact: Polish your charts for maximum appeal and engagement with your audience. By following these tips, you'll transform your Plotly visualizations from good to great, leaving a lasting impression on your viewers.
Plotly may not handle missing values gracefully. Ensure that your data is clean and handle missing values appropriately before creating plots.
Ensure your data is clean, well-formatted, and correctly structured for the chosen chart type. Missing values, inconsistent formatting, or typos can lead to unexpected results or errors.
Users should be mindful about Plotly's performance when working with large or complex data sets, as it takes some time for plotly to generate an output. Also, it is essential to make sure that the data is structured appropriately for the type of plot desired.
Algumas boas práticas sugeridas para uma visualiza??o de dados assertiva: . Respeitar a paleta de cores. . Respeitar as linhas de respiro entre gráficos. . Entender qual gráfico utilizar de acordo com a análise. . N?o saturar dashboards ou gráficos com muitas informa??es.
- Expand your visualization expertise: Explore new chart types, interactive features, and customization options to enhance your storytelling. - Stay ahead of the curve: Leverage the latest advancements to create even more captivating and impactful visualizations. - Embrace continuous learning: Foster a growth mindset by staying curious and exploring the ever-expanding possibilities of Plotly.