{
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{
"cell_type": "markdown",
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"source": [
"# `break_width` Parameter in Position Scales\n",
"\n",
"The `break_width` parameter specifies a fixed distance between axis breaks.\n",
"\n",
"If the scale has an associated transformation, the distance is measured in transformed units. \\\n",
"For example, on a log10 scale, `break_width=1` places breaks at every power of 10, and `break_width=0.5` — at every half-power."
]
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{
"cell_type": "code",
"execution_count": 1,
"id": "27c90744-c88e-4500-92bd-eb26a90aec56",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from lets_plot import *"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "8ca59e39-0241-4f96-97a1-3d24c28df552",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
" \n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"LetsPlot.setup_html()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "6e2e328b-eb16-4b82-9525-b0502a84a4a0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(5000, 10)\n"
]
}
],
"source": [
"df = (pd.read_csv(\"https://raw.githubusercontent.com/JetBrains/lets-plot-docs/refs/heads/master/data/diamonds.csv\")\n",
" .sample(n=5000, random_state=42))\n",
"print(df.shape)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "f3ea8933-bb78-4dc8-b77f-afc11578e782",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ggplot(df, aes(x='carat', y='price')) + \\\n",
" geom_point(alpha=0.3) + \\\n",
" scale_y_log10() # <-- a log10 scale with auto-selected breaks"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "39742733-2bc9-449b-8a47-b5c04bf36851",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ggplot(df, aes(x='carat', y='price')) + \\\n",
" geom_point(alpha=0.3) + \\\n",
" scale_y_log10(break_width=0.5) # <-- breaks every 0.5 powers of 10\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
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"name": "ipython",
"version": 3
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.23"
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