How to choose the right chart type

· Category: Data Science

Short answer

The right chart type highlights patterns in data without distorting or obscuring the underlying message.

Steps

  1. Identify the variables involved and whether they are categorical, ordinal, or continuous.
  2. Determine the relationship you want to show such as comparison, distribution, composition, or trend.
  3. Select a chart family: bars for comparison, lines for trends, scatter for correlation, pie for composition.
  4. Consider the audience's familiarity and the medium where the chart will be displayed.
  5. Iterate and seek feedback to ensure clarity and accuracy.

Tips

  • Avoid pie charts when comparing more than a few categories.
  • Use stacked bars instead of multiple pie charts for part-to-whole over time.
  • Log scales help visualize wide-ranging data but require clear labeling.
  • Small multiples often outperform complex single charts with many variables.

Common issues

  • Using 3D effects that distort proportions and impede interpretation.
  • Dual-axis charts misleading viewers by implying false correlations.
  • Overloading a single chart with too many series or colors.
  • Truncated y-axes exaggerating differences between values.

Example

import matplotlib.pyplot as plt
import seaborn as sns

sns.set_theme(style='whitegrid')
plt.figure(figsize=(10, 6))
sns.barplot(x='category', y='value', data=df)
plt.title('Sales by Category')
plt.show()

This snippet demonstrates how to configure aesthetics and create a publication-ready bar chart with labeled axes and a clear title.