\n",
"
\n",
" "
],
"text/plain": [
"\n",
" \n",
" GoldenViz report\n",
" Automatic visual QA for the 25 Golden Rules.\n",
" \n",
"
\n",
" \n",
" PASS 25\n",
" WARNING 0\n",
" FAIL 0\n",
"
\n",
" \n",
"
\n",
" View warnings and failures
\n", " \n", "\n",
"
\n",
" \n",
" Axis: Revenue trend by year
\n",
" Needs attention (0)
\n",
" No warnings or failures for this axis.
\n",
" \n",
"
\n",
" Passing checks (25)
\n", "\n",
" \n",
" \n",
" \n",
"
\n",
" \n",
"
\n",
" | Rule | \n", "Status | \n", "Assessment | \n", "
|---|---|---|
| Clear title | \n", "\n", " PASS\n", " | \n", "\n",
" Title detected: 'Revenue trend by year'. \n",
" \n",
" | \n",
"
| Axis labels | \n", "\n", " PASS\n", " | \n", "\n",
" Both x-axis and y-axis labels are present. \n",
" \n",
" | \n",
"
| Units and scale clarity | \n", "\n", " PASS\n", " | \n", "\n",
" Numeric labels include detectable units or scale where applicable. \n",
" \n",
" | \n",
"
| Legend clarity | \n", "\n", " PASS\n", " | \n", "\n",
" No obvious legend clarity issue detected. \n",
" \n",
" | \n",
"
| Annotation context | \n", "\n", " PASS\n", " | \n", "\n",
" No obvious annotation context issue detected. \n",
" Revenue trend by year \n",
" | \n",
"
| Uncertainty cues | \n", "\n", " PASS\n", " | \n", "\n",
" No uncertainty cue issue detected. \n",
" Revenue trend by year \n",
" | \n",
"
| Readable labels and ticks | \n", "\n", " PASS\n", " | \n", "\n",
" No obvious readability issue detected. \n",
" \n",
" | \n",
"
| Color accessibility | \n", "\n", " PASS\n", " | \n", "\n",
" No obvious color accessibility issue detected. \n",
" \n",
" | \n",
"
| Direct labeling | \n", "\n", " PASS\n", " | \n", "\n",
" No direct-labeling opportunity detected. \n",
" \n",
" | \n",
"
| Avoid chartjunk | \n", "\n", " PASS\n", " | \n", "\n",
" No obvious chartjunk issue detected. \n",
" Revenue trend by year \n",
" | \n",
"
| Too many categories | \n", "\n", " PASS\n", " | \n", "\n",
" Category count appears manageable. \n",
" Revenue trend by year \n",
" | \n",
"
| Sort categorical bars | \n", "\n", " PASS\n", " | \n", "\n",
" No categorical bar sorting issue detected. \n",
" Revenue trend by year \n",
" | \n",
"
| Scatter overplotting | \n", "\n", " PASS\n", " | \n", "\n",
" No scatter overplotting issue detected. \n",
" Revenue trend by year \n",
" | \n",
"
| Decimal precision | \n", "\n", " PASS\n", " | \n", "\n",
" Tick precision appears readable. \n",
" Revenue trend by year \n",
" | \n",
"
| Date axis formatting | \n", "\n", " PASS\n", " | \n", "\n",
" No obvious date-axis formatting issue detected. \n",
" Revenue trend by year \n",
" | \n",
"
| Visual economy | \n", "\n", " PASS\n", " | \n", "\n",
" Chart appears visually economical. \n",
" Revenue trend by year \n",
" | \n",
"
| Appropriate scale | \n", "\n", " PASS\n", " | \n", "\n",
" No obvious scale issue detected. \n",
" \n",
" | \n",
"
| Chart type | \n", "\n", " PASS\n", " | \n", "\n",
" Detected chart type: line. \n",
" \n",
" | \n",
"
| Color map quality | \n", "\n", " PASS\n", " | \n", "\n",
" No problematic color map detected. \n",
" \n",
" | \n",
"
| Avoid dual axes | \n", "\n", " PASS\n", " | \n", "\n",
" No dual-axis layout detected. \n",
" Revenue trend by year \n",
" | \n",
"
| Area baseline | \n", "\n", " PASS\n", " | \n", "\n",
" No area baseline issue detected. \n",
" Revenue trend by year \n",
" | \n",
"
| Aspect ratio sanity | \n", "\n", " PASS\n", " | \n", "\n",
" Figure aspect ratio appears reasonable. \n",
" Revenue trend by year \n",
" | \n",
"
| Histogram bin quality | \n", "\n", " PASS\n", " | \n", "\n",
" No histogram bin issue detected. \n",
" Revenue trend by year \n",
" | \n",
"
| Category color consistency | \n", "\n", " PASS\n", " | \n", "\n",
" No category color issue detected. \n",
" Revenue trend by year \n",
" | \n",
"
| Diverging zero reference | \n", "\n", " PASS\n", " | \n", "\n",
" No diverging zero-reference issue detected. \n",
" Revenue trend by year \n",
" | \n",
"
\n",
"
"
]
},
{
"cell_type": "markdown",
"id": "e9af5881",
"metadata": {},
"source": [
"\n",
"
\n",
" \n",
"
\n",
" Bad chart code
\n", "import matplotlib.pyplot as plt\n", " \n", "months = ["Jan", "Feb", "Mar", "Apr", "May", "Jun"]\n", "revenue = [18, 20, 21, 23, 25, 28]\n", " \n", "fig, ax = plt.subplots(figsize=(6.4, 3.8))\n", "ax.plot(months, revenue, marker="o")\n", "ax.set_xlabel("Month")\n", "ax.set_ylabel("Revenue (k EUR)")\n", "plt.show()\n", "
\n",
" ![\"Rule](\"_static/example_notebook/rule_01_bad.png\")
\n",
"
\n",
"\n",
" \n",
"
"
]
},
{
"cell_type": "markdown",
"id": "f6d03c4b",
"metadata": {},
"source": [
"### Rule 2 · Axis labels\n",
"\n",
"Axis labels and units make the measurement unambiguous."
]
},
{
"cell_type": "markdown",
"id": "808c84b9",
"metadata": {},
"source": [
"\n",
"
\n",
" \n",
"
\n",
" Good chart code
\n", "import matplotlib.pyplot as plt\n", " \n", "months = ["Jan", "Feb", "Mar", "Apr", "May", "Jun"]\n", "revenue = [18, 20, 21, 23, 25, 28]\n", " \n", "fig, ax = plt.subplots(figsize=(6.4, 3.8))\n", "ax.plot(months, revenue, marker="o")\n", "ax.set_title("Monthly revenue increased from Jan to Jun 2026")\n", "ax.set_xlabel("Month")\n", "ax.set_ylabel("Revenue (k EUR)")\n", "plt.show()\n", "
\n",
" ![\"Rule](\"_static/example_notebook/rule_01_good.png\")
\n",
"
\n",
"\n",
" \n",
"
"
]
},
{
"cell_type": "markdown",
"id": "16c599ae",
"metadata": {},
"source": [
"\n",
"
\n",
" \n",
"
\n",
" Bad chart code
\n", "import matplotlib.pyplot as plt\n", " \n", "ad_spend = [8, 12, 16, 20, 24, 30, 34, 38]\n", "signups = [110, 140, 175, 205, 245, 310, 335, 390]\n", " \n", "fig, ax = plt.subplots(figsize=(6.4, 3.8))\n", "ax.scatter(ad_spend, signups)\n", "ax.set_title("Campaign performance")\n", "plt.show()\n", "
\n",
" ![\"Rule](\"_static/example_notebook/rule_02_bad.png\")
\n",
"
\n",
"\n",
" \n",
"
"
]
},
{
"cell_type": "markdown",
"id": "623eae39",
"metadata": {},
"source": [
"### Rule 3 · Units and scale clarity\n",
"\n",
"Large values should be scaled and labeled so the reader knows what the numbers mean."
]
},
{
"cell_type": "markdown",
"id": "73020e60",
"metadata": {},
"source": [
"\n",
"
\n",
" \n",
"
\n",
" Good chart code
\n", "import matplotlib.pyplot as plt\n", " \n", "ad_spend = [8, 12, 16, 20, 24, 30, 34, 38]\n", "signups = [110, 140, 175, 205, 245, 310, 335, 390]\n", " \n", "fig, ax = plt.subplots(figsize=(6.4, 3.8))\n", "ax.scatter(ad_spend, signups)\n", "ax.set_title("Campaign performance")\n", "ax.set_xlabel("Ad spend (k EUR)")\n", "ax.set_ylabel("New signups")\n", "plt.show()\n", "
\n",
" ![\"Rule](\"_static/example_notebook/rule_02_good.png\")
\n",
"
\n",
"\n",
" \n",
"
"
]
},
{
"cell_type": "markdown",
"id": "3cbdf810",
"metadata": {},
"source": [
"\n",
"
\n",
" \n",
"
\n",
" Bad chart code
\n", "import matplotlib.pyplot as plt\n", " \n", "years = [2022, 2023, 2024, 2025]\n", "revenue = [1250000, 1480000, 1730000, 1910000]\n", " \n", "fig, ax = plt.subplots(figsize=(6.4, 3.8))\n", "ax.plot(years, revenue, marker="o")\n", "ax.set_title("Revenue trend")\n", "ax.set_ylabel("Revenue")\n", "plt.show()\n", "
\n",
" ![\"Rule](\"_static/example_notebook/rule_03_bad.png\")
\n",
"
\n",
"\n",
" \n",
"
"
]
},
{
"cell_type": "markdown",
"id": "dc812695",
"metadata": {},
"source": [
"### Rule 4 · Legend clarity\n",
"\n",
"Multiple series need clear labels so readers can identify each line."
]
},
{
"cell_type": "markdown",
"id": "566c5dc6",
"metadata": {},
"source": [
"\n",
"
\n",
" \n",
"
\n",
" Good chart code
\n", "import matplotlib.pyplot as plt\n", " \n", "years = [2022, 2023, 2024, 2025]\n", "revenue = [1250000, 1480000, 1730000, 1910000]\n", "revenue_millions = [value / 1_000_000 for value in revenue]\n", " \n", "fig, ax = plt.subplots(figsize=(6.4, 3.8))\n", "ax.plot(years, revenue_millions, marker="o")\n", "ax.set_title("Revenue trend")\n", "ax.set_xlabel("Year")\n", "ax.set_ylabel("Revenue (million EUR)")\n", "plt.show()\n", "
\n",
" ![\"Rule](\"_static/example_notebook/rule_03_good.png\")
\n",
"
\n",
"\n",
" \n",
"
"
]
},
{
"cell_type": "markdown",
"id": "314e9f1e",
"metadata": {},
"source": [
"\n",
"
\n",
" \n",
"
\n",
" Bad chart code
\n", "import matplotlib.pyplot as plt\n", " \n", "years = [2021, 2022, 2023, 2024, 2025]\n", "series = {\n", " "Basic": [12, 14, 16, 18, 20],\n", " "Pro": [9, 13, 17, 22, 28],\n", " "Enterprise": [6, 8, 12, 17, 25],\n", "}\n", " \n", "fig, ax = plt.subplots(figsize=(6.4, 3.8))\n", "for values in series.values():\n", " ax.plot(years, values, marker="o")\n", "ax.set_title("Subscriptions by plan")\n", "ax.set_ylabel("Subscriptions (k)")\n", "plt.show()\n", "
\n",
" ![\"Rule](\"_static/example_notebook/rule_04_bad.png\")
\n",
"
\n",
"\n",
" \n",
"
"
]
},
{
"cell_type": "markdown",
"id": "74aa4054",
"metadata": {},
"source": [
"### Rule 5 · Annotation context\n",
"\n",
"Important events or takeaways should be visible when they explain the pattern."
]
},
{
"cell_type": "markdown",
"id": "280b4e3f",
"metadata": {},
"source": [
"\n",
"
\n",
" \n",
"
\n",
" Good chart code
\n", "import matplotlib.pyplot as plt\n", " \n", "years = [2021, 2022, 2023, 2024, 2025]\n", "series = {\n", " "Basic": [12, 14, 16, 18, 20],\n", " "Pro": [9, 13, 17, 22, 28],\n", " "Enterprise": [6, 8, 12, 17, 25],\n", "}\n", " \n", "fig, ax = plt.subplots(figsize=(6.4, 3.8))\n", "for label, values in series.items():\n", " ax.plot(years, values, marker="o", label=label)\n", "ax.legend(loc="center left", bbox_to_anchor=(1.02, 0.5))\n", "ax.set_title("Subscriptions by plan")\n", "ax.set_ylabel("Subscriptions (k)")\n", "plt.show()\n", "
\n",
" ![\"Rule](\"_static/example_notebook/rule_04_good.png\")
\n",
"
\n",
"\n",
" \n",
"
"
]
},
{
"cell_type": "markdown",
"id": "dd06b9ce",
"metadata": {},
"source": [
"\n",
"
\n",
" \n",
"
\n",
" Bad chart code
\n", "import matplotlib.pyplot as plt\n", " \n", "weeks = [1, 2, 3, 4, 5, 6, 7, 8]\n", "adoption = [12, 15, 18, 23, 34, 39, 43, 46]\n", " \n", "fig, ax = plt.subplots(figsize=(6.4, 3.8))\n", "ax.plot(weeks, adoption, marker="o")\n", "ax.set_title("Feature adoption")\n", "ax.set_xlabel("Week")\n", "ax.set_ylabel("Adoption (%)")\n", "plt.show()\n", "
\n",
" ![\"Rule](\"_static/example_notebook/rule_05_bad.png\")
\n",
"
\n",
"\n",
" \n",
"
"
]
},
{
"cell_type": "markdown",
"id": "0d931951",
"metadata": {},
"source": [
"### Rule 6 · Uncertainty cues\n",
"\n",
"Estimates should show uncertainty when the uncertainty matters."
]
},
{
"cell_type": "markdown",
"id": "bbfa96f8",
"metadata": {},
"source": [
"\n",
"
\n",
" \n",
"
\n",
" Good chart code
\n", "import matplotlib.pyplot as plt\n", " \n", "weeks = [1, 2, 3, 4, 5, 6, 7, 8]\n", "adoption = [12, 15, 18, 23, 34, 39, 43, 46]\n", " \n", "fig, ax = plt.subplots(figsize=(6.4, 3.8))\n", "ax.plot(weeks, adoption, marker="o")\n", "ax.annotate("Onboarding email launched", xy=(5, 34), xytext=(3.2, 42), arrowprops={"arrowstyle": "->"})\n", "ax.set_title("Feature adoption increased after onboarding email")\n", "ax.set_xlabel("Week")\n", "ax.set_ylabel("Adoption (%)")\n", "plt.show()\n", "
\n",
" ![\"Rule](\"_static/example_notebook/rule_05_good.png\")
\n",
"
\n",
"\n",
" \n",
"
"
]
},
{
"cell_type": "markdown",
"id": "61483f10",
"metadata": {},
"source": [
"\n",
"
\n",
" \n",
"
\n",
" Bad chart code
\n", "import matplotlib.pyplot as plt\n", " \n", "groups = ["A", "B", "C", "D"]\n", "mean = [52, 57, 61, 55]\n", "error = [4, 7, 3, 6]\n", " \n", "fig, ax = plt.subplots(figsize=(6.4, 3.8))\n", "ax.bar(groups, mean)\n", "ax.set_title("Average test result")\n", "ax.set_ylabel("Score")\n", "plt.show()\n", "
\n",
" ![\"Rule](\"_static/example_notebook/rule_06_bad.png\")
\n",
"
\n",
"\n",
" \n",
"
"
]
},
{
"cell_type": "markdown",
"id": "8d378c56",
"metadata": {},
"source": [
"## Readability rules\n",
"\n",
"Rules 7-16 check whether the chart can be read, scanned, and compared without unnecessary effort."
]
},
{
"cell_type": "markdown",
"id": "65369acf",
"metadata": {},
"source": [
"### Rule 7 · Readable labels\n",
"\n",
"Long labels should not collide or force the reader to decode a crowded axis."
]
},
{
"cell_type": "markdown",
"id": "9fe3d273",
"metadata": {},
"source": [
"\n",
"
\n",
" \n",
"
\n",
" Good chart code
\n", "import matplotlib.pyplot as plt\n", " \n", "groups = ["A", "B", "C", "D"]\n", "mean = [52, 57, 61, 55]\n", "error = [4, 7, 3, 6]\n", " \n", "fig, ax = plt.subplots(figsize=(6.4, 3.8))\n", "ax.bar(groups, mean, yerr=error, capsize=6)\n", "ax.set_title("Average test result with uncertainty")\n", "ax.set_ylabel("Score")\n", "plt.show()\n", "
\n",
" ![\"Rule](\"_static/example_notebook/rule_06_good.png\")
\n",
"
\n",
"\n",
" \n",
"
"
]
},
{
"cell_type": "markdown",
"id": "f32ffa19",
"metadata": {},
"source": [
"\n",
"
\n",
" \n",
"
\n",
" Bad chart code
\n", "import matplotlib.pyplot as plt\n", " \n", "segments = [\n", " "Returning enterprise customers",\n", " "New small business customers",\n", " "One-time promotional buyers",\n", " "Students using education plan",\n", " "Trial users awaiting onboarding",\n", "]\n", "counts = [180, 145, 96, 125, 72]\n", " \n", "fig, ax = plt.subplots(figsize=(6.4, 3.8))\n", "ax.bar(segments, counts)\n", "ax.set_title("Customers by segment")\n", "plt.show()\n", "
\n",
" ![\"Rule](\"_static/example_notebook/rule_07_bad.png\")
\n",
"
\n",
"\n",
" \n",
"
"
]
},
{
"cell_type": "markdown",
"id": "55cee0f6",
"metadata": {},
"source": [
"### Rule 8 · Color accessibility\n",
"\n",
"Charts should remain readable for people with color-vision deficiencies."
]
},
{
"cell_type": "markdown",
"id": "4cbd5455",
"metadata": {},
"source": [
"\n",
"
\n",
" \n",
"
\n",
" Good chart code
\n", "import matplotlib.pyplot as plt\n", "import textwrap\n", " \n", "segments = [\n", " "Returning enterprise customers",\n", " "New small business customers",\n", " "One-time promotional buyers",\n", " "Students using education plan",\n", " "Trial users awaiting onboarding",\n", "]\n", "counts = [180, 145, 96, 125, 72]\n", "labels = [textwrap.fill(s, 22) for s in segments]\n", "pairs = sorted(zip(counts, labels))\n", " \n", "fig, ax = plt.subplots(figsize=(6.4, 3.8))\n", "ax.barh([label for _, label in pairs], [count for count, _ in pairs])\n", "ax.set_title("Customers by segment")\n", "ax.set_xlabel("Customers")\n", "plt.show()\n", "
\n",
" ![\"Rule](\"_static/example_notebook/rule_07_good.png\")
\n",
"
\n",
"\n",
" \n",
"
"
]
},
{
"cell_type": "markdown",
"id": "be66cb18",
"metadata": {},
"source": [
"\n",
"
\n",
" \n",
"
\n",
" Bad chart code
\n", "import matplotlib.pyplot as plt\n", " \n", "segments = ["Home", "Food", "Auto", "Health", "Shopping"]\n", "spend = [42, 26, 18, 12, 8]\n", "colors = ["#d7191c", "#fdae61", "#ffffbf", "#a6d96a", "#1a9641"]\n", " \n", "fig, ax = plt.subplots(figsize=(6.4, 3.8))\n", "ax.bar(segments, spend, color=colors)\n", "ax.set_title("Household spend by category")\n", "ax.set_ylabel("Spend (%)")\n", "plt.show()\n", "
\n",
" ![\"Rule](\"_static/example_notebook/rule_08_bad.png\")
\n",
"
\n",
"\n",
" \n",
"
"
]
},
{
"cell_type": "markdown",
"id": "215fc162",
"metadata": {},
"source": [
"### Rule 9 · Direct labeling\n",
"\n",
"When lines are easy to label directly, readers should not have to bounce between the plot and legend."
]
},
{
"cell_type": "markdown",
"id": "6e71abb8",
"metadata": {},
"source": [
"\n",
"
\n",
" \n",
"
\n",
" Good chart code
\n", "import matplotlib.pyplot as plt\n", " \n", "segments = ["Home", "Food", "Auto", "Health", "Shopping"]\n", "spend = [42, 26, 18, 12, 8]\n", "colors = ["#0072B2", "#E69F00", "#56B4E9", "#009E73", "#CC79A7"]\n", "hatches = ["", "//", "\\\\", "..", "xx"]\n", " \n", "fig, ax = plt.subplots(figsize=(6.4, 3.8))\n", "bars = ax.bar(segments, spend, color=colors)\n", "for bar, hatch in zip(bars, hatches):\n", " bar.set_hatch(hatch)\n", "ax.set_title("Household spend by category")\n", "ax.set_ylabel("Spend (%)")\n", "plt.show()\n", "
\n",
" ![\"Rule](\"_static/example_notebook/rule_08_good.png\")
\n",
"
\n",
"\n",
" \n",
"
"
]
},
{
"cell_type": "markdown",
"id": "48f5cc9e",
"metadata": {},
"source": [
"\n",
"
\n",
" \n",
"
\n",
" Bad chart code
\n", "import matplotlib.pyplot as plt\n", " \n", "years = [2021, 2022, 2023, 2024, 2025]\n", "series = {\n", " "Basic": [12, 14, 16, 18, 20],\n", " "Pro": [9, 13, 17, 22, 28],\n", " "Enterprise": [6, 8, 12, 17, 25],\n", " "Education": [4, 7, 10, 13, 19],\n", "}\n", " \n", "fig, ax = plt.subplots(figsize=(6.4, 3.8))\n", "for label, values in series.items():\n", " ax.plot(years, values, marker="o", label=label)\n", "ax.legend()\n", "ax.set_title("Subscriptions by plan")\n", "ax.set_ylabel("Subscriptions (k)")\n", "plt.show()\n", "
\n",
" ![\"Rule](\"_static/example_notebook/rule_09_bad.png\")
\n",
"
\n",
"\n",
" \n",
"
"
]
},
{
"cell_type": "markdown",
"id": "2909f1dc",
"metadata": {},
"source": [
"### Rule 10 · Avoid chartjunk\n",
"\n",
"Decoration should not compete with the data."
]
},
{
"cell_type": "markdown",
"id": "c5ac6aa2",
"metadata": {},
"source": [
"\n",
"
\n",
" \n",
"
\n",
" Good chart code
\n", "import matplotlib.pyplot as plt\n", " \n", "years = [2021, 2022, 2023, 2024, 2025]\n", "series = {\n", " "Basic": [12, 14, 16, 18, 20],\n", " "Pro": [9, 13, 17, 22, 28],\n", " "Enterprise": [6, 8, 12, 17, 25],\n", " "Education": [4, 7, 10, 13, 19],\n", "}\n", " \n", "fig, ax = plt.subplots(figsize=(6.4, 3.8))\n", "for label, values in series.items():\n", " ax.plot(years, values, marker="o")\n", " ax.text(years[-1] + 0.05, values[-1], label, va="center")\n", "ax.set_xlim(2021, 2025.9)\n", "ax.set_title("Subscriptions by plan")\n", "ax.set_ylabel("Subscriptions (k)")\n", "plt.show()\n", "
\n",
" ![\"Rule](\"_static/example_notebook/rule_09_good.png\")
\n",
"
\n",
"\n",
" \n",
"
"
]
},
{
"cell_type": "markdown",
"id": "21781b2c",
"metadata": {},
"source": [
"\n",
"
\n",
" \n",
"
\n",
" Bad chart code
\n", "import matplotlib.pyplot as plt\n", " \n", "regions = ["North", "South", "East", "West"]\n", "profit = [18, 12, 15, 21]\n", " \n", "fig, ax = plt.subplots(figsize=(6.4, 3.8))\n", "ax.set_facecolor("#f3d9a5")\n", "ax.bar(regions, profit, edgecolor="black", linewidth=2)\n", "ax.grid(True, axis="both")\n", "ax.set_title("!!! PROFIT !!!")\n", "ax.set_ylabel("Profit (k EUR)")\n", "plt.show()\n", "
\n",
" ![\"Rule](\"_static/example_notebook/rule_10_bad.png\")
\n",
"
\n",
"\n",
" \n",
"
"
]
},
{
"cell_type": "markdown",
"id": "1613b022",
"metadata": {},
"source": [
"### Rule 11 · Too many categories\n",
"\n",
"Too many categories make comparison slow and crowded."
]
},
{
"cell_type": "markdown",
"id": "cf8464c3",
"metadata": {},
"source": [
"\n",
"
\n",
" \n",
"
\n",
" Good chart code
\n", "import matplotlib.pyplot as plt\n", " \n", "regions = ["North", "South", "East", "West"]\n", "profit = [18, 12, 15, 21]\n", " \n", "fig, ax = plt.subplots(figsize=(6.4, 3.8))\n", "ax.bar(regions, profit)\n", "ax.grid(True, axis="y")\n", "ax.set_title("Profit by region")\n", "ax.set_ylabel("Profit (k EUR)")\n", "plt.show()\n", "
\n",
" ![\"Rule](\"_static/example_notebook/rule_10_good.png\")
\n",
"
\n",
"\n",
" \n",
"
"
]
},
{
"cell_type": "markdown",
"id": "33c00507",
"metadata": {},
"source": [
"\n",
"
\n",
" \n",
"
\n",
" Bad chart code
\n", "import matplotlib.pyplot as plt\n", " \n", "categories = [f"C{i}" for i in range(1, 19)]\n", "values = [34, 28, 26, 22, 20, 18, 16, 12, 11, 10, 9, 8, 7, 6, 5, 5, 4, 3]\n", " \n", "fig, ax = plt.subplots(figsize=(6.4, 3.8))\n", "ax.bar(categories, values)\n", "ax.set_title("Requests by category")\n", "ax.set_ylabel("Requests")\n", "plt.show()\n", "
\n",
" ![\"Rule](\"_static/example_notebook/rule_11_bad.png\")
\n",
"
\n",
"\n",
" \n",
"
"
]
},
{
"cell_type": "markdown",
"id": "cf8ecf29",
"metadata": {},
"source": [
"### Rule 12 · Sort categorical bars\n",
"\n",
"Sorted bars make ranking and comparison faster."
]
},
{
"cell_type": "markdown",
"id": "640f2c9d",
"metadata": {},
"source": [
"\n",
"
\n",
" \n",
"
\n",
" Good chart code
\n", "import matplotlib.pyplot as plt\n", " \n", "categories = [f"C{i}" for i in range(1, 19)]\n", "values = [34, 28, 26, 22, 20, 18, 16, 12, 11, 10, 9, 8, 7, 6, 5, 5, 4, 3]\n", "top_labels = categories[:7] + ["Other"]\n", "top_values = values[:7] + [sum(values[7:])]\n", "pairs = sorted(zip(top_values, top_labels))\n", " \n", "fig, ax = plt.subplots(figsize=(6.4, 3.8))\n", "ax.barh([label for _, label in pairs], [value for value, _ in pairs])\n", "ax.set_title("Requests by category")\n", "ax.set_xlabel("Requests")\n", "plt.show()\n", "
\n",
" ![\"Rule](\"_static/example_notebook/rule_11_good.png\")
\n",
"
\n",
"\n",
" \n",
"
"
]
},
{
"cell_type": "markdown",
"id": "b7a8a6e5",
"metadata": {},
"source": [
"\n",
"
\n",
" \n",
"
\n",
" Bad chart code
\n", "import matplotlib.pyplot as plt\n", " \n", "teams = ["Ops", "Sales", "Support", "Product", "Finance"]\n", "hours = [42, 18, 31, 24, 36]\n", " \n", "fig, ax = plt.subplots(figsize=(6.4, 3.8))\n", "ax.barh(teams, hours)\n", "ax.set_title("Average resolution time")\n", "ax.set_xlabel("Hours")\n", "plt.show()\n", "
\n",
" ![\"Rule](\"_static/example_notebook/rule_12_bad.png\")
\n",
"
\n",
"\n",
" \n",
"
"
]
},
{
"cell_type": "markdown",
"id": "c5cacebd",
"metadata": {},
"source": [
"### Rule 13 · Scatter overplotting\n",
"\n",
"Repeated or dense points should reveal density instead of hiding it."
]
},
{
"cell_type": "markdown",
"id": "fc3579d0",
"metadata": {},
"source": [
"\n",
"
\n",
" \n",
"
\n",
" Good chart code
\n", "import matplotlib.pyplot as plt\n", " \n", "teams = ["Ops", "Sales", "Support", "Product", "Finance"]\n", "hours = [42, 18, 31, 24, 36]\n", "pairs = sorted(zip(hours, teams))\n", " \n", "fig, ax = plt.subplots(figsize=(6.4, 3.8))\n", "ax.barh([team for _, team in pairs], [hour for hour, _ in pairs])\n", "ax.set_title("Average resolution time")\n", "ax.set_xlabel("Hours")\n", "plt.show()\n", "
\n",
" ![\"Rule](\"_static/example_notebook/rule_12_good.png\")
\n",
"
\n",
"\n",
" \n",
"
"
]
},
{
"cell_type": "markdown",
"id": "1856f46c",
"metadata": {},
"source": [
"\n",
"
\n",
" \n",
"
\n",
" Bad chart code
\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", " \n", "rng = np.random.default_rng(42)\n", "centers = np.array([[2, 2], [3, 3], [4, 2.5], [4.5, 4]])\n", "points = np.repeat(centers, [35, 45, 25, 30], axis=0)\n", "points = points + rng.normal(0, 0.04, size=(135, 2))\n", " \n", "fig, ax = plt.subplots(figsize=(6.4, 3.8))\n", "ax.scatter(points[:, 0], points[:, 1], s=28)\n", "ax.set_xlabel("Value score")\n", "ax.set_ylabel("Satisfaction score")\n", "plt.show()\n", "
\n",
" ![\"Rule](\"_static/example_notebook/rule_13_bad.png\")
\n",
"
\n",
"\n",
" \n",
"
"
]
},
{
"cell_type": "markdown",
"id": "e0fb2b26",
"metadata": {},
"source": [
"### Rule 14 · Decimal precision\n",
"\n",
"Labels should avoid unnecessary decimals that add noise without adding meaning."
]
},
{
"cell_type": "markdown",
"id": "49815ffb",
"metadata": {},
"source": [
"\n",
"
\n",
" \n",
"
\n",
" Good chart code
\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", " \n", "centers = np.array([[2, 2], [3, 3], [4, 2.5], [4.5, 4]])\n", "counts = np.array([35, 45, 25, 30])\n", " \n", "fig, ax = plt.subplots(figsize=(6.4, 3.8))\n", "ax.scatter(centers[:, 0], centers[:, 1], s=counts * 18, alpha=0.55, edgecolor="black")\n", "ax.set_xlabel("Value score")\n", "ax.set_ylabel("Satisfaction score")\n", "ax.set_title("Point size shows repeated observations")\n", "plt.show()\n", "
\n",
" ![\"Rule](\"_static/example_notebook/rule_13_good.png\")
\n",
"
\n",
"\n",
" \n",
"
"
]
},
{
"cell_type": "markdown",
"id": "da35303d",
"metadata": {},
"source": [
"\n",
"
\n",
" \n",
"
\n",
" Bad chart code
\n", "import matplotlib.pyplot as plt\n", " \n", "products = ["A", "B", "C", "D"]\n", "conversion = [12.3421, 11.9874, 13.2251, 12.7718]\n", " \n", "fig, ax = plt.subplots(figsize=(6.4, 3.8))\n", "bars = ax.bar(products, conversion)\n", "ax.bar_label(bars, fmt="%.4f%%")\n", "ax.set_ylim(0, 15)\n", "ax.set_ylabel("Conversion (%)")\n", "plt.show()\n", "
\n",
" ![\"Rule](\"_static/example_notebook/rule_14_bad.png\")
\n",
"
\n",
"\n",
" \n",
"
"
]
},
{
"cell_type": "markdown",
"id": "1f1d4f06",
"metadata": {},
"source": [
"### Rule 15 · Date axis formatting\n",
"\n",
"Date ticks should be formatted at a readable interval."
]
},
{
"cell_type": "markdown",
"id": "0a8cabcb",
"metadata": {},
"source": [
"\n",
"
\n",
" \n",
"
\n",
" Good chart code
\n", "import matplotlib.pyplot as plt\n", " \n", "products = ["A", "B", "C", "D"]\n", "conversion = [12.3421, 11.9874, 13.2251, 12.7718]\n", " \n", "fig, ax = plt.subplots(figsize=(6.4, 3.8))\n", "bars = ax.bar(products, conversion)\n", "ax.bar_label(bars, fmt="%.1f%%")\n", "ax.set_ylim(0, 15)\n", "ax.set_ylabel("Conversion (%)")\n", "plt.show()\n", "
\n",
" ![\"Rule](\"_static/example_notebook/rule_14_good.png\")
\n",
"
\n",
"\n",
" \n",
"
"
]
},
{
"cell_type": "markdown",
"id": "55103355",
"metadata": {},
"source": [
"\n",
"
\n",
" \n",
"
\n",
" Bad chart code
\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", " \n", "dates = [np.datetime64("2026-01-01") + np.timedelta64(i * 14, "D") for i in range(10)]\n", "visits = [120, 135, 128, 150, 162, 158, 170, 181, 190, 205]\n", " \n", "fig, ax = plt.subplots(figsize=(6.4, 3.8))\n", "ax.plot(dates, visits, marker="o")\n", "ax.set_title("Website visits")\n", "ax.set_ylabel("Visits (k)")\n", "plt.show()\n", "
\n",
" ![\"Rule](\"_static/example_notebook/rule_15_bad.png\")
\n",
"
\n",
"\n",
" \n",
"
"
]
},
{
"cell_type": "markdown",
"id": "bf7f1050",
"metadata": {},
"source": [
"### Rule 16 · Visual economy\n",
"\n",
"Use enough visual encoding to explain the data, but avoid redundant styling."
]
},
{
"cell_type": "markdown",
"id": "0ae1fd2d",
"metadata": {},
"source": [
"\n",
"
\n",
" \n",
"
\n",
" Good chart code
\n", "import matplotlib.pyplot as plt\n", "import matplotlib.dates as mdates\n", "import numpy as np\n", " \n", "dates = [np.datetime64("2026-01-01") + np.timedelta64(i * 14, "D") for i in range(10)]\n", "visits = [120, 135, 128, 150, 162, 158, 170, 181, 190, 205]\n", " \n", "fig, ax = plt.subplots(figsize=(6.4, 3.8))\n", "ax.plot(dates, visits, marker="o")\n", "ax.set_title("Website visits")\n", "ax.set_ylabel("Visits (k)")\n", "ax.xaxis.set_major_locator(mdates.MonthLocator())\n", "ax.xaxis.set_major_formatter(mdates.DateFormatter("%b %Y"))\n", "ax.tick_params(axis="x", rotation=30)\n", "plt.show()\n", "
\n",
" ![\"Rule](\"_static/example_notebook/rule_15_good.png\")
\n",
"
\n",
"\n",
" \n",
"
"
]
},
{
"cell_type": "markdown",
"id": "92ede337",
"metadata": {},
"source": [
"\n",
"
\n",
" \n",
"
\n",
" Bad chart code
\n", "import matplotlib.pyplot as plt\n", " \n", "products = ["A", "B", "C", "D", "E", "F"]\n", "values = [18, 25, 22, 31, 27, 20]\n", " \n", "fig, ax = plt.subplots(figsize=(6.4, 3.8))\n", "ax.bar(products, values, hatch="//", edgecolor="black")\n", "ax.plot(products, values, marker="D", color="red")\n", "ax.grid(True, axis="both")\n", "ax.set_title("Product sales with redundant styling")\n", "plt.show()\n", "
\n",
" ![\"Rule](\"_static/example_notebook/rule_16_bad.png\")
\n",
"
\n",
"\n",
" \n",
"
"
]
},
{
"cell_type": "markdown",
"id": "7cb55cf5",
"metadata": {},
"source": [
"## Integrity rules\n",
"\n",
"Rules 17-25 check whether the chart avoids misleading scale, encoding, or comparison choices."
]
},
{
"cell_type": "markdown",
"id": "aeb17191",
"metadata": {},
"source": [
"### Rule 17 · Appropriate scale\n",
"\n",
"For bars, a truncated baseline can exaggerate small differences."
]
},
{
"cell_type": "markdown",
"id": "08aa7e6a",
"metadata": {},
"source": [
"\n",
"
\n",
" \n",
"
\n",
" Good chart code
\n", "import matplotlib.pyplot as plt\n", " \n", "products = ["A", "B", "C", "D", "E", "F"]\n", "values = [18, 25, 22, 31, 27, 20]\n", " \n", "fig, ax = plt.subplots(figsize=(6.4, 3.8))\n", "ax.bar(products, values)\n", "ax.grid(True, axis="y")\n", "ax.set_title("Product sales")\n", "ax.set_ylabel("Sales (k EUR)")\n", "plt.show()\n", "
\n",
" ![\"Rule](\"_static/example_notebook/rule_16_good.png\")
\n",
"
\n",
"\n",
" \n",
"
"
]
},
{
"cell_type": "markdown",
"id": "2da5e72e",
"metadata": {},
"source": [
"\n",
"
\n",
" \n",
"
\n",
" Bad chart code
\n", "import matplotlib.pyplot as plt\n", " \n", "branches = ["North", "Central", "South"]\n", "satisfaction = [82, 86, 88]\n", " \n", "fig, ax = plt.subplots(figsize=(6.4, 3.8))\n", "ax.bar(branches, satisfaction)\n", "ax.set_ylim(80, 90)\n", "ax.set_title("Customer satisfaction by branch")\n", "ax.set_ylabel("Satisfied customers (%)")\n", "plt.show()\n", "
\n",
" ![\"Rule](\"_static/example_notebook/rule_17_bad.png\")
\n",
"
\n",
"\n",
" \n",
"
"
]
},
{
"cell_type": "markdown",
"id": "14158f6e",
"metadata": {},
"source": [
"### Rule 18 · Suitable chart type\n",
"\n",
"Categorical comparisons are easier to read as bars than as a line that implies sequence."
]
},
{
"cell_type": "markdown",
"id": "6127ddb6",
"metadata": {},
"source": [
"\n",
"
\n",
" \n",
"
\n",
" Good chart code
\n", "import matplotlib.pyplot as plt\n", " \n", "branches = ["North", "Central", "South"]\n", "satisfaction = [82, 86, 88]\n", " \n", "fig, ax = plt.subplots(figsize=(6.4, 3.8))\n", "ax.bar(branches, satisfaction)\n", "ax.set_ylim(0, 100)\n", "ax.set_title("Customer satisfaction by branch")\n", "ax.set_ylabel("Satisfied customers (%)")\n", "plt.show()\n", "
\n",
" ![\"Rule](\"_static/example_notebook/rule_17_good.png\")
\n",
"
\n",
"\n",
" \n",
"
"
]
},
{
"cell_type": "markdown",
"id": "2711c750",
"metadata": {},
"source": [
"\n",
"
\n",
" \n",
"
\n",
" Bad chart code
\n", "import matplotlib.pyplot as plt\n", " \n", "products = ["Shoes", "Bags", "Watches", "Perfume", "Jewelry"]\n", "sales = [54, 31, 46, 28, 62]\n", " \n", "fig, ax = plt.subplots(figsize=(6.4, 3.8))\n", "ax.plot(products, sales, marker="o")\n", "ax.set_title("Sales by product category")\n", "ax.set_ylabel("Sales (k EUR)")\n", "plt.show()\n", "
\n",
" ![\"Rule](\"_static/example_notebook/rule_18_bad.png\")
\n",
"
\n",
"\n",
" \n",
"
"
]
},
{
"cell_type": "markdown",
"id": "2b0df070",
"metadata": {},
"source": [
"### Rule 19 · Color map quality\n",
"\n",
"Continuous color should use a perceptual scale and a labeled color bar."
]
},
{
"cell_type": "markdown",
"id": "b61c3d23",
"metadata": {},
"source": [
"\n",
"
\n",
" \n",
"
\n",
" Good chart code
\n", "import matplotlib.pyplot as plt\n", " \n", "products = ["Shoes", "Bags", "Watches", "Perfume", "Jewelry"]\n", "sales = [54, 31, 46, 28, 62]\n", "pairs = sorted(zip(sales, products))\n", " \n", "fig, ax = plt.subplots(figsize=(6.4, 3.8))\n", "ax.barh([p for _, p in pairs], [s for s, _ in pairs])\n", "ax.set_title("Sales by product category")\n", "ax.set_xlabel("Sales (k EUR)")\n", "plt.show()\n", "
\n",
" ![\"Rule](\"_static/example_notebook/rule_18_good.png\")
\n",
"
\n",
"\n",
" \n",
"
"
]
},
{
"cell_type": "markdown",
"id": "54479f28",
"metadata": {},
"source": [
"\n",
"
\n",
" \n",
"
\n",
" Bad chart code
\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", " \n", "heat = np.outer(np.linspace(0, 1, 12), np.linspace(0.2, 1, 12))\n", " \n", "fig, ax = plt.subplots(figsize=(6.4, 3.8))\n", "image = ax.imshow(heat, cmap="rainbow")\n", "ax.set_title("Demand intensity")\n", "fig.colorbar(image, ax=ax)\n", "plt.show()\n", "
\n",
" ![\"Rule](\"_static/example_notebook/rule_19_bad.png\")
\n",
"
\n",
"\n",
" \n",
"
"
]
},
{
"cell_type": "markdown",
"id": "e39be5a0",
"metadata": {},
"source": [
"### Rule 20 · Avoid dual axes\n",
"\n",
"Dual axes can imply relationships that come from scale choices rather than data."
]
},
{
"cell_type": "markdown",
"id": "662a8c36",
"metadata": {},
"source": [
"\n",
"
\n",
" \n",
"
\n",
" Good chart code
\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", " \n", "heat = np.outer(np.linspace(0, 1, 12), np.linspace(0.2, 1, 12))\n", " \n", "fig, ax = plt.subplots(figsize=(6.4, 3.8))\n", "image = ax.imshow(heat, cmap="viridis")\n", "ax.set_title("Demand intensity")\n", "colorbar = fig.colorbar(image, ax=ax)\n", "colorbar.set_label("Orders per store")\n", "plt.show()\n", "
\n",
" ![\"Rule](\"_static/example_notebook/rule_19_good.png\")
\n",
"
\n",
"\n",
" \n",
"
"
]
},
{
"cell_type": "markdown",
"id": "7aa4815a",
"metadata": {},
"source": [
"\n",
"
\n",
" \n",
"
\n",
" Bad chart code
\n", "import matplotlib.pyplot as plt\n", " \n", "months = [1, 2, 3, 4, 5, 6]\n", "revenue = [1.2, 1.5, 1.8, 2.1, 2.4, 2.7]\n", "churn = [9, 8, 7, 6, 5, 4]\n", " \n", "fig, ax1 = plt.subplots(figsize=(6.4, 3.8))\n", "ax2 = ax1.twinx()\n", "ax1.plot(months, revenue, marker="o", label="Revenue")\n", "ax2.plot(months, churn, marker="o", color="red", label="Churn")\n", "ax1.set_ylabel("Revenue (M EUR)")\n", "ax2.set_ylabel("Churn (%)")\n", "plt.show()\n", "
\n",
" ![\"Rule](\"_static/example_notebook/rule_20_bad.png\")
\n",
"
\n",
"\n",
" \n",
"
"
]
},
{
"cell_type": "markdown",
"id": "0a9abb09",
"metadata": {},
"source": [
"### Rule 21 · Area baseline\n",
"\n",
"Filled areas should use an honest baseline because area size carries meaning."
]
},
{
"cell_type": "markdown",
"id": "baec913a",
"metadata": {},
"source": [
"\n",
"
\n",
" \n",
"
\n",
" Good chart code
\n", "import matplotlib.pyplot as plt\n", " \n", "months = [1, 2, 3, 4, 5, 6]\n", "revenue = [1.2, 1.5, 1.8, 2.1, 2.4, 2.7]\n", "churn = [9, 8, 7, 6, 5, 4]\n", " \n", "fig, axes = plt.subplots(2, 1, figsize=(6.4, 4.2), sharex=True)\n", "axes[0].plot(months, revenue, marker="o")\n", "axes[0].set_ylabel("Revenue (M EUR)")\n", "axes[1].plot(months, churn, marker="o", color="red")\n", "axes[1].set_ylabel("Churn (%)")\n", "axes[1].set_xlabel("Month")\n", "plt.show()\n", "
\n",
" ![\"Rule](\"_static/example_notebook/rule_20_good.png\")
\n",
"
\n",
"\n",
" \n",
"
"
]
},
{
"cell_type": "markdown",
"id": "07de0fd2",
"metadata": {},
"source": [
"\n",
"
\n",
" \n",
"
\n",
" Bad chart code
\n", "import matplotlib.pyplot as plt\n", " \n", "years = [2020, 2021, 2022, 2023, 2024, 2025]\n", "share = [62, 64, 66, 67, 69, 70]\n", " \n", "fig, ax = plt.subplots(figsize=(6.4, 3.8))\n", "ax.fill_between(years, share, 60)\n", "ax.plot(years, share, marker="o")\n", "ax.set_ylim(60, 72)\n", "ax.set_ylabel("Share (%)")\n", "plt.show()\n", "
\n",
" ![\"Rule](\"_static/example_notebook/rule_21_bad.png\")
\n",
"
\n",
"\n",
" \n",
"
"
]
},
{
"cell_type": "markdown",
"id": "e0e43e67",
"metadata": {},
"source": [
"### Rule 22 · Aspect ratio sanity\n",
"\n",
"Extreme aspect ratios can flatten or exaggerate trends."
]
},
{
"cell_type": "markdown",
"id": "aa2b34af",
"metadata": {},
"source": [
"\n",
"
\n",
" \n",
"
\n",
" Good chart code
\n", "import matplotlib.pyplot as plt\n", " \n", "years = [2020, 2021, 2022, 2023, 2024, 2025]\n", "share = [62, 64, 66, 67, 69, 70]\n", " \n", "fig, ax = plt.subplots(figsize=(6.4, 3.8))\n", "ax.fill_between(years, share, 0)\n", "ax.plot(years, share, marker="o")\n", "ax.set_ylim(0, 100)\n", "ax.set_ylabel("Share (%)")\n", "plt.show()\n", "
\n",
" ![\"Rule](\"_static/example_notebook/rule_21_good.png\")
\n",
"
\n",
"\n",
" \n",
"
"
]
},
{
"cell_type": "markdown",
"id": "21a39443",
"metadata": {},
"source": [
"\n",
"
\n",
" \n",
"
\n",
" Bad chart code
\n", "import matplotlib.pyplot as plt\n", " \n", "quarters = [1, 2, 3, 4, 5, 6, 7, 8]\n", "index = [12, 14, 15, 16, 18, 19, 21, 22]\n", " \n", "fig, ax = plt.subplots(figsize=(7.8, 2.2))\n", "ax.plot(quarters, index, marker="o")\n", "ax.set_title("Compressed aspect ratio hides the trend")\n", "plt.show()\n", "
\n",
" ![\"Rule](\"_static/example_notebook/rule_22_bad.png\")
\n",
"
\n",
"\n",
" \n",
"
"
]
},
{
"cell_type": "markdown",
"id": "4ab25f1c",
"metadata": {},
"source": [
"### Rule 23 · Histogram bin quality\n",
"\n",
"A histogram needs enough bins to reveal the distribution shape without creating noise."
]
},
{
"cell_type": "markdown",
"id": "3f08ca11",
"metadata": {},
"source": [
"\n",
"
\n",
" \n",
"
\n",
" Good chart code
\n", "import matplotlib.pyplot as plt\n", " \n", "quarters = [1, 2, 3, 4, 5, 6, 7, 8]\n", "index = [12, 14, 15, 16, 18, 19, 21, 22]\n", " \n", "fig, ax = plt.subplots(figsize=(6.4, 3.8))\n", "ax.plot(quarters, index, marker="o")\n", "ax.set_title("Balanced aspect ratio shows the trend clearly")\n", "plt.show()\n", "
\n",
" ![\"Rule](\"_static/example_notebook/rule_22_good.png\")
\n",
"
\n",
"\n",
" \n",
"
"
]
},
{
"cell_type": "markdown",
"id": "789ddc56",
"metadata": {},
"source": [
"\n",
"
\n",
" \n",
"
\n",
" Bad chart code
\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", " \n", "rng = np.random.default_rng(42)\n", "scores = rng.normal(72, 9, 420)\n", " \n", "fig, ax = plt.subplots(figsize=(6.4, 3.8))\n", "ax.hist(scores, bins=3)\n", "ax.set_title("Exam scores")\n", "ax.set_xlabel("Score")\n", "ax.set_ylabel("Students")\n", "plt.show()\n", "
\n",
" ![\"Rule](\"_static/example_notebook/rule_23_bad.png\")
\n",
"
\n",
"\n",
" \n",
"
"
]
},
{
"cell_type": "markdown",
"id": "a7503db9",
"metadata": {},
"source": [
"### Rule 24 · Category color consistency\n",
"\n",
"The same category should keep the same color throughout a chart."
]
},
{
"cell_type": "markdown",
"id": "61a8cd33",
"metadata": {},
"source": [
"\n",
"
\n",
" \n",
"
\n",
" Good chart code
\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", " \n", "rng = np.random.default_rng(42)\n", "scores = rng.normal(72, 9, 420)\n", " \n", "fig, ax = plt.subplots(figsize=(6.4, 3.8))\n", "ax.hist(scores, bins=18)\n", "ax.set_title("Exam scores")\n", "ax.set_xlabel("Score")\n", "ax.set_ylabel("Students")\n", "plt.show()\n", "
\n",
" ![\"Rule](\"_static/example_notebook/rule_23_good.png\")
\n",
"
\n",
"\n",
" \n",
"
"
]
},
{
"cell_type": "markdown",
"id": "2c028bb4",
"metadata": {},
"source": [
"\n",
"
\n",
" \n",
"
\n",
" Bad chart code
\n", "import matplotlib.pyplot as plt\n", " \n", "quarters = ["Q1", "Q2", "Q3"]\n", "desktop = [42, 45, 48]\n", "mobile = [31, 34, 38]\n", " \n", "fig, ax = plt.subplots(figsize=(6.4, 3.8))\n", "ax.plot(quarters, desktop, marker="o", color="blue", label="Desktop")\n", "ax.plot(quarters, mobile, marker="o", color="orange", label="Mobile")\n", "ax.scatter(["Q2"], [45], color="orange", s=90)\n", "ax.scatter(["Q2"], [34], color="blue", s=90)\n", "ax.legend()\n", "plt.show()\n", "
\n",
" ![\"Rule](\"_static/example_notebook/rule_24_bad.png\")
\n",
"
\n",
"\n",
" \n",
"
"
]
},
{
"cell_type": "markdown",
"id": "fbc5e2b4",
"metadata": {},
"source": [
"### Rule 25 · Diverging zero reference\n",
"\n",
"Positive and negative values need a clear zero reference."
]
},
{
"cell_type": "markdown",
"id": "5a569bb8",
"metadata": {},
"source": [
"\n",
"
\n",
" \n",
"
\n",
" Good chart code
\n", "import matplotlib.pyplot as plt\n", " \n", "quarters = ["Q1", "Q2", "Q3"]\n", "desktop = [42, 45, 48]\n", "mobile = [31, 34, 38]\n", " \n", "fig, ax = plt.subplots(figsize=(6.4, 3.8))\n", "ax.plot(quarters, desktop, marker="o", color="blue", label="Desktop")\n", "ax.plot(quarters, mobile, marker="o", color="orange", label="Mobile")\n", "ax.legend()\n", "ax.set_title("Traffic by device")\n", "ax.set_ylabel("Sessions (k)")\n", "plt.show()\n", "
\n",
" ![\"Rule](\"_static/example_notebook/rule_24_good.png\")
\n",
"
\n",
"\n",
" \n",
"
"
]
},
{
"cell_type": "markdown",
"id": "82ca9bb3",
"metadata": {},
"source": [
"\n",
"
\n",
" \n",
"
\n",
" Bad chart code
\n", "import matplotlib.pyplot as plt\n", " \n", "departments = ["Ops", "Sales", "Support", "Product"]\n", "delta = [-8, 12, -5, 9]\n", " \n", "fig, ax = plt.subplots(figsize=(6.4, 3.8))\n", "ax.bar(departments, delta)\n", "ax.set_title("Change vs target")\n", "ax.set_ylabel("Change (%)")\n", "plt.show()\n", "
\n",
" ![\"Rule](\"_static/example_notebook/rule_25_bad.png\")
\n",
"
\n",
"\n",
" \n",
"
"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"pygments_lexer": "ipython3"
},
"nbsphinx": {
"execute": "never"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
\n",
"
\n",
" \n",
"
\n",
" Good chart code
\n", "import matplotlib.pyplot as plt\n", " \n", "departments = ["Ops", "Sales", "Support", "Product"]\n", "delta = [-8, 12, -5, 9]\n", "colors = ["#b42318" if value < 0 else "#2f855a" for value in delta]\n", " \n", "fig, ax = plt.subplots(figsize=(6.4, 3.8))\n", "ax.bar(departments, delta, color=colors)\n", "ax.axhline(0, color="black", linewidth=1.2)\n", "ax.set_title("Change vs target")\n", "ax.set_ylabel("Change (%)")\n", "plt.show()\n", "
\n",
" ![\"Rule](\"_static/example_notebook/rule_25_good.png\")
\n",
"
\n",
"\n",
"