Pie charts are widely used for visualizing
categorical data. However, there are some common mistakes that people often make when using pie charts in Python.Here are a few examples along with code snippets to illustrate these mistakes:
1. Using too many categories in a single pie chart:
In the incorrect approach, the code attempts to create a pie chart with numerous categories, resulting in a cluttered and confusing visualization.To avoid this mistake, it is recommended to limit the number of categories in a pie chart which is done in the example below:
To handle too many categories in a pie chart, the code groups categories with low values into an “Other” category.The top categories, up to a defined threshold, are individually displayed in the pie chart. The remaining categories beyond the threshold are combined into the “Other” category, providing a more concise representation of the data.
2. Not sorting the categories by value:
In the incorrect approach, the code creates a pie chart without sorting the categories by their values, leading to an unclear and uninterpretable representation.Sorting the categories beforehand is crucial for accurate interpretation.This can be done using the
sort_values() function in Pandas.The correct approach for sorting the data is shown in the example code below:
3. Using a pie chart for non-proportional data:
The incorrect approach tries to use a pie chart for non-proportional data, which can lead to misleading interpretations.Pie charts are best suited for proportional data, and alternative visualizations like bar charts should be considered for non-proportional data.The correct approach for using a pie chart for non-proportional data is shown in the example code below:
By avoiding these common mistakes,you can create more accurate and effective pie charts in Python.