In the era of globalisation, a lot of data is produced daily. However, analysing and extracting information from raw data can be very challenging. Data visualisation with Python is a way to overcome this hurdle.
Data visualisation is a process that makes it easier to analyse, comprehend and observe the data by providing a better and more representative depiction of the data. It means the process of transforming complex data into easily understandable visual representations. Data visualisation helps in analysing specific trends and patterns for overcoming challenging situations.
Read along to know the multiple best practices when using Python to visualise data.
7 best practices through which data can be visualised with Python
No matter how necessary it is to create visualisations of complex data, there are some points that one should always keep in mind. Here are some of the best practices one cannot miss out on when visualising data with Python.
Select a suitable tool for data visualisation
Choosing the right tools after establishing the objectives of visualisation is important. There are several code libraries and no-code platforms that help in presenting data in a user-friendly format.
The most common and popular low-code data visualisation tools are Tableau, and Power BI. Tableau is a popular visual analytics platform that allows the designing of representative charts, graphs and dashboards. Power BI is a business intelligence platform enabling users to create multiple interactive representations.
If you’re directly working with Python code, ‘Jupyter Notebook’ might be the best tool for you.
Cleanse the data properly
Cleaning and processing the data is an integral step as it frees it from anomalies. However, many things are done to make the data error-free, such as standardising it, removing missing or duplicate figures, deleting unpaired data etc.
Let’s say that you have a table consisting of ‘date’, ‘products’, ‘product_id’, ‘number of units sold’ and the ‘name’ of the person who bought the product. You’re analysing the sales on a particular date. You can go ahead and remove the ‘name’ category, as it does not correlate to the outcome you’re trying to achieve through your analysis.
Choose the right visualisation type
One of the most important aspects of data visualisation is choosing the right chart or graph to represent your data. Python has many libraries like Matplotlib, Seaborn, and Plotly that provide a wide variety of chart types to choose from. Choose the one that best represents the data you are trying to convey.
The most used and easily readable type of data visualisation can be in the form of pie charts, line graphs, bar graphs, heatmaps and so on. The linear regression technique is best suited when you want to represent the data consisting of two continuous variables.
Employ brief titles and labels
The visualisation should have proper titles and labels that make the data easier to understand. Make sure to label your charts and graphs clearly so that your audience can understand what they are looking at. Using titles, axis labels, and legends makes data more accessible.
Try to make the visualisation more interactive
Python visualisation libraries like Plotly allow you to create interactive charts and graphs that can be more engaging for your audience. Consider adding interactive elements like hover-over text or clickable buttons to make your visualisation more engaging.
Consider using other interactive elements like sliders, dropdown menus, or zooming features. These can make your visualisation more dynamic and allow the audience to explore the data in more detail.
Make use of annotations
Annotations can be a powerful way to draw attention to specific parts of your visualisation. Use them to highlight important data points, provide additional context, or explain unusual or unexpected trends. Using clear and concise language in your annotations is a must, along with placing them in a way that does not clutter the visualisation.
Test your visualisation
In the process of creating data visualisation with Python, it is important to adopt an iterative approach that involves testing the visualisation as you go along. To refine your visualisation, use tools like Jupyter Notebook or Spyder to explore and manipulate the data in minute detail.
Once you have created your visualisation, it is important to thoroughly test it to identify potential issues or errors. This might involve checking the data for outliers, ensuring that the visualisation accurately represents the data, and making sure that any labels or annotations are clear and accurate.
Conclusion
The need for data visualisation can never be overlooked. Python is widely used for data visualisation in data science and machine learning fields. A data science course with job interview guarantee can help you understand the concepts of data visualisation with Python in the best way.
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