Turn analysis into charts people read correctly: visual principles, matplotlib and seaborn, small multiples, dashboards and a short data story.

Data Visualization & Storytelling

## Why it matters

A chart is a translation. You start with numbers, encode them as visual properties (position, length, colour, size), and your reader decodes those properties back into numbers in their head. Every encoding loses some accuracy in that round trip, and some lose far more than others. If you choose well, your reader sees the right answer in a second or two. If you choose badly, they misread the data or give up.

This lesson covers the two ideas that most chart advice rests on: the ranking of visual encodings, and pre-attentive attributes.

## Not all encodings are equal

In the 1980s the statisticians William Cleveland and Robert McGill ran experiments asking people to compare values shown in different ways. Later studies (including large online replications by Jeffrey Heer and Michael Bostock) found the same order. From most to least accurate for comparing quantities:

1. **Position on a common scale**: dots or bar ends lined up against the same axis.
2. **Position on non-aligned scales**: the same, but in separate panels.
3. **Length**: bars that start from the same baseline.
4. **Angle and slope**: pie slices, line steepness.
5. **Area**: bubbles, treemap rectangles.
6. **Colour intensity (saturation, lightness)**: heatmap cells, shaded maps.

That ranking explains a lot of everyday advice:

- A bar chart beats a pie chart for comparing categories, because length on a shared baseline is easier to judge than angle. In a pie with slices of 23% and 26%, most people cannot tell which is bigger. In a bar chart they can.
- Bubble charts exaggerate or hide differences, because people underestimate area. A circle with twice the area does not look twice as big.
- Heatmaps are good for spotting patterns (where is it hot?) but poor for reading exact values. Add the numbers as labels if precision matters.

**Rule of thumb:** put the comparison that matters most on position or length. Use colour, size and shape for secondary information.

## Pre-attentive attributes

Some visual differences are noticed before conscious attention, in roughly a quarter of a second. These are called **pre-attentive attributes**. Common ones:

- **Colour hue** (one orange bar among grey ones)
- **Intensity** (one dark item among light ones)
- **Size** (one large dot)
- **Orientation** (one tilted line)
- **Enclosure** (a box around one region)
- **Added marks** (an annotation or a bold label)

Try it mentally. Picture a page of 200 grey digits with every 7 printed in orange. You find the 7s instantly. Print them in the same grey and you have to read every digit.

This is a powerful tool and an easy one to misuse. Pre-attentive attributes work only when **one or two things** stand out. If every bar is a different bright colour, nothing stands out and the reader has to consult a legend for each bar.

## Worked example

You need to show that the North region missed its sales target while four other regions hit theirs.

**Weak version:** a pie chart of sales by region in five bright colours, with a legend on the right. The reader must match colours to the legend, compare angles, and still has no idea what the target was.

**Better version:**

- A horizontal bar chart sorted from highest to lowest (position and length on a shared baseline).
- All bars grey except North, which is orange (one pre-attentive highlight).
- A thin vertical line at the target value, labelled "Target".
- A title that states the finding: "North was the only region below target in Q3."

The data is identical. The second version answers the question before the reader even reads the axis labels.

## Colour with care

- **Categorical data** (regions, products): distinct hues, ideally no more than about six. Beyond that, group or highlight.
- **Ordered data** (low to high): one hue that runs from light to dark, called a sequential palette.
- **Data with a meaningful midpoint** (profit vs loss, above vs below average): two hues diverging from a neutral middle.
- **Accessibility:** about 1 in 12 men and 1 in 200 women have some colour vision deficiency, most often red-green. Avoid relying on red vs green alone; add labels, different lightness, or position. Tools such as ColorBrewer and the matplotlib "viridis" palette are designed to stay readable.

## Common mistakes

- **Truncated bar axes.** Bars encode value by length, so a bar axis must start at zero. Starting at 90 makes 92 look twice as big as 91. (Line charts and dot plots may zoom in, because they encode position, not length.)
- **3D effects.** Perspective distorts length and angle and hides values behind other bars.
- **Rainbow colour scales** for ordered data. The hue changes create false boundaries.
- **Too many highlights.** If three things are bold, none of them are.

## Quick recap

- Readers decode position and length most accurately; angle, area and colour intensity less so.
- Put your key comparison on a shared axis.
- Use one pre-attentive attribute, sparingly, to point at what matters.
- Choose a colour scheme that matches the data type, and never rely on colour alone.


Pick visual encodings that people decode quickly and accurately, and use pre-attentive attributes to direct attention.

How people read charts

Data Visualization: Preattentive Attributes

## Why it matters

"What chart should I use?" is the wrong first question. The right first question is "what does my reader need to find out?" Once you can name the question, the chart type nearly chooses itself. Most business and research questions fall into five families, and each family has one or two charts that answer it well.

## The five question families

| Question | Example | Good first choice | Also consider |
|---|---|---|---|
| **Comparison** across categories | Which product sold most? | Bar chart (horizontal if labels are long) | Dot plot, lollipop chart |
| **Change over time** | How did monthly sign-ups change? | Line chart | Column chart for few periods, area chart for totals |
| **Distribution** | How long do deliveries usually take? | Histogram | Box plot, density plot, strip plot |
| **Relationship** between two measures | Do longer sessions mean more purchases? | Scatter plot | Scatter with trend line, bubble chart, heatmap for many points |
| **Part-to-whole** | What share of revenue comes from each channel? | Stacked bar (100%) or simple bar of shares | Pie only for 2 to 3 slices; treemap for hierarchies |

A sixth family, **geography** ("where?"), uses maps, but only when location itself matters. A map of sales by state is often better as a sorted bar chart, because large, empty states dominate a map visually.

## Worked examples

**1. "Which of our 12 support teams resolves tickets fastest?"**
This is a comparison across categories. Use a horizontal bar chart of median resolution time, sorted from fastest to slowest. Sorting matters: an alphabetical order forces the reader to hunt for the ranking.

**2. "Is our weekly active user count growing?"**
Change over time, so use a line chart with weeks on the x-axis. Time almost always goes left to right. If you have many series (say one per country), do not draw 30 tangled lines; highlight one or two and grey the rest, or use small multiples (Module 2).

**3. "Are most orders small, or do a few large orders dominate?"**
A question about distribution, so use a histogram of order value. An average alone would hide the shape: a mean of 50 could come from many orders near 50, or from many orders near 10 plus a few at 500. If you need to compare distributions across several groups, side-by-side box plots save space.

**4. "Do customers who use the app more spend more?"**
Relationship, so use a scatter plot with usage on x and spend on y. Each dot is one customer. With thousands of points, reduce dot opacity or use a 2D histogram so overlapping points remain visible. Remember that a pattern shows association, not cause.

**5. "What share of support tickets come from each channel?"**
Part-to-whole. With three channels a pie can work, but a bar chart of percentages sorted descending is easier to read, and it still works with eight channels. To show how the mix changes over time, use a 100% stacked bar per month, and put the most important category on the baseline so its changes are easiest to read.

## Tables are allowed

When readers need to look up exact values ("what was March revenue in Spain?"), a well-formatted table beats any chart. Charts show shape and pattern; tables show precise values. A dashboard often needs both.

## Questions to ask before you pick

1. **What is the one question?** Write it as a sentence. If you have three questions, you may need three charts.
2. **What type is each variable?** Categorical (region), ordinal (small, medium, large), continuous (revenue), or time.
3. **How many categories or points?** Five bars are easy; 50 bars need sorting, grouping or filtering.
4. **Who is reading, and where?** An executive skimming a slide needs one obvious message. An analyst exploring in a notebook can handle a dense scatter matrix.

## Common mistakes

- **Line charts for categories.** A line implies continuity, so connecting "Sales, Marketing, Support" with a line suggests a trend that does not exist.
- **Pie charts with many slices.** Beyond three or four slices, angles become impossible to compare.
- **Dual y-axes.** Two scales on one chart let you make any two series look correlated by stretching one axis. Use two aligned charts instead.
- **Unsorted bars.** Sort by value unless the categories have a natural order (months, age bands, survey scales).
- **Choosing for novelty.** Radial charts, word clouds and Sankey diagrams look impressive but are hard to read accurately. Use them only when their special strength (flows for Sankey, for example) is the point.

## Quick recap

- Start from the reader's question, not the chart gallery.
- Comparison: bars. Time: lines. Distribution: histograms or box plots. Relationship: scatter. Part-to-whole: sorted bars or 100% stacked bars.
- Sort categories, keep time left to right, and use a table when exact values matter.


Match the chart type to the question: comparison, change over time, distribution, relationship or part-to-whole.

Choosing the chart

How to Choose the Best Chart for Your Data Visualization: Ask Yourself These 3 Key Questions

How To Choose The Right Graph (Types of Graphs and When To Use Them)

## Why it matters

Every mark on a chart costs the reader a little attention: gridlines, borders, tick marks, legends, background shading, decimal places. Some of those marks help. Many do not. Edward Tufte called the useless ones **chart junk** and argued for a high **data-ink ratio**: most of the ink should show data. Cole Nussbaumer Knaflic's *Storytelling with Data* turns this into a practical habit: remove everything that does not help the message, then add back emphasis where it does.

Decluttering is not about minimalism for its own sake. It is about lowering the effort it takes to see the point.

## A decluttering checklist

Work through these in order on any default chart from Excel, Google Sheets or matplotlib:

1. **Remove the chart border and background colour.** White space separates the chart well enough.
2. **Remove or lighten gridlines.** If you need them for reading values, make them thin and light grey.
3. **Remove the legend when you can label directly.** Put the series name at the end of each line, or label bars on the axis. A legend forces eye travel back and forth.
4. **Drop redundant axis labels.** If the title says "Monthly revenue ($k)", the y-axis does not need "Revenue in thousands of dollars" as well.
5. **Simplify numbers.** Use 1.2M instead of 1,200,000.00, and round labels to the precision that matters.
6. **Sort and group.** Sort bars by value. Fold a long tail of tiny categories into "Other".
7. **Use colour on purpose.** Make everything grey, then colour only the one series or bar you want people to look at.
8. **Write a takeaway title.** Replace "Tickets by category" with "Shipping problems drive the most support tickets".

## Worked example

A default chart of support tickets by category has: a grey background, a box border, eight bars in eight different colours, a legend repeating the category names, gridlines every 50 tickets, and values labelled with two decimals.

After decluttering:

- Bars sorted from most to fewest tickets, with the four smallest categories folded into "Other" (because nobody will act on a bar for 22 tickets).
- All bars grey except "Shipping", in orange, because that is the category the team will fix next quarter.
- No legend (the category names are on the axis), no border, no background, no gridlines; each bar has a whole-number label at its end.
- Title: "Shipping issues are our largest ticket category".

Here is how that chart looks in matplotlib. The data preparation, which you will write in the activity, happens first:

```python
import matplotlib.pyplot as plt

spec = [("Shipping", 530), ("Login", 412), ("Billing", 287), ("Returns", 198), ("Other", 218)]
labels = [label for label, _ in spec]
values = [value for _, value in spec]
colors = ["#d95f02" if label == "Shipping" else "#bbbbbb" for label in labels]

fig, ax = plt.subplots(figsize=(7, 4))
bars = ax.barh(labels, values, color=colors)
ax.invert_yaxis()                      # largest bar at the top
ax.bar_label(bars, padding=3)          # direct labels instead of gridlines
for side in ["top", "right", "bottom"]:
    ax.spines[side].set_visible(False) # remove the box
ax.xaxis.set_visible(False)            # the labels carry the values
ax.set_title("Shipping issues are our largest ticket category", loc="left")
plt.tight_layout()
plt.show()
```

Notice what each line does: `invert_yaxis` keeps the sort order reading top to bottom, `bar_label` puts values where the eye already is, and removing spines strips the frame.

## When not to remove things

- **Keep the zero baseline** on bar charts.
- **Keep units and sources.** A chart without units or a data source is not trustworthy. A small grey footnote ("Source: Zendesk export, Q3 2026") costs almost nothing.
- **Keep context the reader needs**, such as a target line or the previous year for comparison. Context is not clutter if it changes the interpretation.
- **Exploratory charts** for yourself can stay rough. Declutter the charts other people will see.

## Common mistakes

- Removing axis labels without labelling the data directly, so no values can be read at all.
- Folding important categories into "Other" just to tidy up. Decide the cut-off from what the audience needs, not from aesthetics.
- Highlighting with red and green only, which many readers cannot distinguish.
- Keeping the default title, which describes the chart instead of stating the insight.

## Quick recap

- Default charts carry clutter: borders, heavy gridlines, legends, extra decimals, rainbow colours.
- Declutter in order: frame, gridlines, legend, labels, numbers, sorting, colour, title.
- Grey everything, then highlight one thing.
- Keep baselines, units, sources and context.

## Try it

Open the **Do** tab to write the data preparation behind a decluttered bar chart: sort, fold the tail into "Other", and pick highlight colours.


Remove chart junk step by step so the data and the message are the first things a reader sees.

Decluttering

How to declutter data visualizations (5 steps)

How to eliminate clutter from Excel graphs

Learn how people decode charts, pick the chart that answers the question, and strip away clutter so the message is obvious.