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altair-example.ipynb
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"metadata": {
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"<a href=\"https://colab.research.google.com/gist/philipphager/f0f0cfa824755263a523d91995fbcf07/altair-example.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "VOhDpnr2hJ55"
},
"outputs": [],
"source": [
"!pip install --quiet altair, pandas"
]
},
{
"cell_type": "code",
"source": [
"import altair as alt\n",
"import pandas as pd"
],
"metadata": {
"id": "sVjToIrKhM5p"
},
"execution_count": 3,
"outputs": []
},
{
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"df = pd.DataFrame({\n",
" \"data\": [\"Dataset 1\", \"Dataset 1\", \"Dataset 1\", \"Dataset 1\", \"Dataset 1\", \"Dataset 1\", \"Dataset 1\", \"Dataset 1\", \"Dataset 1\", \"Dataset 2\", \"Dataset 2\", \"Dataset 2\", \"Dataset 2\", \"Dataset 2\", \"Dataset 2\", \"Dataset 2\", \"Dataset 2\", \"Dataset 2\"],\n",
" \"model\": [\"Model A\", \"Model A\", \"Model A\", \"Model B\", \"Model B\", \"Model B\", \"Model C\", \"Model C\", \"Model C\", \"Model A\", \"Model A\", \"Model A\", \"Model B\", \"Model B\", \"Model B\", \"Model C\", \"Model C\", \"Model C\"],\n",
" \"metric\": [0.8, 0.75, 0.85, 0.3, 0.29, 0.31, 0.7, 0.5, 0.3, 0.83, 0.65, 0.80, 0.9, 0.81, 0.94, 0.3, 0.25, 0.25]\n",
"})\n",
"\n",
"df.head()"
],
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"height": 206
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" data model metric\n",
"0 Dataset 1 Model A 0.80\n",
"1 Dataset 1 Model A 0.75\n",
"2 Dataset 1 Model A 0.85\n",
"3 Dataset 1 Model B 0.30\n",
"4 Dataset 1 Model B 0.29"
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"type": "dataframe",
"variable_name": "df",
"summary": "{\n \"name\": \"df\",\n \"rows\": 18,\n \"fields\": [\n {\n \"column\": \"data\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"Dataset 2\",\n \"Dataset 1\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"model\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"Model A\",\n \"Model B\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"metric\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.2636006337580527,\n \"min\": 0.25,\n \"max\": 0.94,\n \"num_unique_values\": 14,\n \"samples\": [\n 0.65,\n 0.81\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 15
}
]
},
{
"cell_type": "markdown",
"source": [
"## Altair\n",
"- Bar chart: https://altair-viz.github.io/gallery/simple_bar_chart.html\n",
"- Grouped bar chart: https://altair-viz.github.io/gallery/grouped_bar_chart.html\n",
"- Error bars: https://altair-viz.github.io/gallery/errorbars_with_ci.html"
],
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"# Create a basic bar chart:\n",
"bars = alt.Chart(df, width=300, height=400).mark_bar().encode(\n",
" x=\"model\",\n",
" y=\"mean(metric)\",\n",
" color=\"model\",\n",
")\n",
"\n",
"# Add a chart layer with bootstrapped 95% confidence interval:\n",
"confidence_intervals = alt.Chart(df, width=600, height=400).mark_errorbar(extent=\"ci\").encode(\n",
" x=\"model\",\n",
" y=\"metric\",\n",
" strokeWidth=alt.value(3),\n",
")\n",
"\n",
"# Layer bar chart and CIs on top of each other, lastly, split per dataset:\n",
"chart = (bars + confidence_intervals).facet(column=\"data\")\n",
"chart"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 544
},
"id": "6bb0tHlkiG-D",
"outputId": "01d49582-a56f-4d06-a639-eac638493978"
},
"execution_count": 23,
"outputs": [
{
"output_type": "execute_result",
"data": {
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"</script>"
],
"text/plain": [
"alt.FacetChart(...)"
]
},
"metadata": {},
"execution_count": 23
}
]
}
]
}
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