A table can contain the answer to a business question without making it easy to see. Visualization turns selected values into charts so you can inspect patterns, comparisons, and changes. Choose the chart for the question—not just because a plotting tool offers it. A table of sales by product category and region, for example, invites comparison; monthly review counts invite a search for changes over time. section 3.3.1 section 2.6.2
Start with the decision you are trying to inform:
- To check whether activity changed, plot review counts by month. Look for phases, unusual values, and possible data-collection problems. An outlier might instead reflect a real business event, so investigate before cleaning it away. section 2.6.2
- To compare product categories across regions, try grouped bar plots or heatmaps of sales volume and revenue. A wide DataFrame may hold the figures but make their differences hard to inspect. section 3.3.1
- To examine whether review scores shift over time, group records by date or month, calculate each score’s proportion within the period, and plot the proportions as stacked bars. section 2.6.2
Ask for a chart, then test the answer
Given a regional sales DataFrame, ask an AI assistant: “Can you propose visualization methods for sales_by_region DataFrame, which would allow answering the posed research question?” Its suggestions can help you choose between bar plots and heatmaps, but run and inspect the generated Python. One attempted plot produced a deprecation warning and a `ValueError` because it could not interpret `customer_state`. Check the DataFrame’s actual columns and the chart’s readable output rather than accepting the proposed code or visual at face value. section 3.3.1
score_proportions.plot(kind='bar', stacked=True, figsize=(15, 6))
plt.xlabel('Month')
plt.ylabel('Proportion of Scores')
plt.title('Proportion of Review Scores Over Time (Monthly)')
plt.legend(title='Review Score', bbox_to_anchor=(1, 1))
plt.show()
This plotting step turns monthly review-score proportions into a stacked bar chart; inspect what the proportion axis says before drawing conclusions about raw review counts. section 2.6.2
Visualization belongs both to exploratory data analysis and to business analysis: revisit a chart as your understanding of the records changes. Use descriptive statistics and checks against the underlying data alongside the picture. A striking pattern can suggest a question, but a chart alone cannot establish its cause. section 2.6.2 section 3.3
