Making a chart is often easier than deciding which chart answers the question. A small spreadsheet rarely needs a full dashboard, but choosing the first chart in a menu can hide the story in the data.
Here is the short decision guide I use, with three fictional CSV examples you can copy and test.
Start with the question, not the chart
| What do you want to see? | Use | Data shape |
|---|---|---|
| Change over time | Line chart | Date or ordered label + numeric series |
| Differences between categories | Bar chart | Category + one or more numeric series |
| Parts of one complete whole | Pie or donut chart | Category + non-negative value |
| A relationship between two measurements | Scatter plot | Numeric X + numeric Y |
| The distribution of raw measurements | Histogram | One numeric column of observations |
| Patterns across rows and columns | Heatmap | Labeled matrix or X/Y/value table |
| Pairwise linear relationships | Correlation matrix | Several numeric columns of observations |
These choices are about the question, not just the file format. For example, a bar chart compares named categories; a histogram groups raw numeric observations into intervals. They may look similar, but they answer different questions.
Example 1: Two sales series
This fictional table has a date and two values measured in the same unit:
Month,Online sales,Store sales
2026-01-01,120,96
2026-02-01,132,102
2026-03-01,128,110
2026-04-01,151,115
2026-05-01,164,126
2026-06-01,179,139
Use a line chart if you want to follow how each channel changes over time. Use a grouped bar chart if the main task is comparing online and store sales month by month. A heatmap can help you scan the whole month-by-channel table for high and low values.
The input is the same. The question changes the chart.
Example 2: Shares of one budget
Channel,Budget
Search,420
Social,240
Email,180
Events,160
This fictional budget totals 1,000. A pie or donut chart can show the four shares: 42%, 24%, 18%, and 16%. If your reader needs to compare two nearly equal categories precisely, use bars instead.
A pie chart only works when the categories are parts of the same whole. Independent conversion rates, for example, should not be added together to make a pie.
Example 3: Two measurements per campaign
Campaign,Ad spend,Sign-ups
A,100,12
B,140,18
C,180,17
D,220,29
E,260,34
F,300,31
Choose a scatter plot to inspect ad spend against sign-ups, with one point per campaign. You can also calculate a correlation matrix from the two numeric columns, but a correlation coefficient does not establish causation. With only six fictional observations, this example demonstrates the workflow—not a reliable marketing conclusion.
From table to export
After importing a spreadsheet, check the detected fields rather than assuming the first guess is correct. Then adjust the title and presentation, and export a PNG or SVG. Keep the source spreadsheet: a downloaded chart is not a substitute for the underlying data.
The fictional CSV files and screenshots are in this GitHub example repository. I made the screenshots with Charttoolbox, a browser-based chart tool that accepts CSV, TSV, XLS/XLSX, and pasted table data. Spreadsheet contents are processed locally in the browser for chart generation. Its optional analytics loads only with consent and does not receive the table contents.
This workflow is useful for a small, one-off chart. It does not replace a BI dashboard or statistical review. If you try the fictional examples, I would be interested to know where the chart choice or field selection still feels unclear.
Disclosure: I used AI assistance to prepare this article.
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