{
"cells": [
{
"cell_type": "markdown",
"id": "cd304e32-1231-4b6a-9be9-3e160c9981aa",
"metadata": {},
"source": [
"# Title of my notebook\n",
"\n",
"Some text.\n",
"\n",
"\n",
"\n",
"$E = mc^2$"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "8e336c21-ec93-4d81-adc3-c014521900ed",
"metadata": {},
"outputs": [],
"source": [
"def arithmetic_mean(sequence):\n",
" s = 0.0\n",
" for element in sequence:\n",
" s += element\n",
" n = len(sequence)\n",
" return s / n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "f0574887-56e6-4388-a730-9efc6b50bb23",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3.0"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"arithmetic_mean([1, 2, 3, 4, 5])"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "7154620e-1286-4f90-a484-8baaf2fe6b7d",
"metadata": {},
"outputs": [],
"source": [
"# this is here for google colab to update altair\n",
"import altair as alt\n",
"if not alt.__version__.startswith(\"5\"):\n",
" %pip install altair==5.3.0"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "fd726642-41ae-434c-80c7-2d250258807b",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
""
],
"text/plain": [
"alt.Chart(...)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# this example is from https://altair-viz.github.io/gallery/layered_histogram.html\n",
"\n",
"import pandas as pd\n",
"import altair as alt\n",
"import numpy as np\n",
"np.random.seed(42)\n",
"\n",
"# Generating Data\n",
"source = pd.DataFrame({\n",
" 'Trial A': np.random.normal(0, 0.8, 1000),\n",
" 'Trial B': np.random.normal(-2, 1, 1000),\n",
" 'Trial C': np.random.normal(3, 2, 1000)\n",
"})\n",
"\n",
"alt.Chart(source).transform_fold(\n",
" ['Trial A', 'Trial B', 'Trial C'],\n",
" as_=['Experiment', 'Measurement']\n",
").mark_bar(\n",
" opacity=0.3,\n",
" binSpacing=0\n",
").encode(\n",
" alt.X('Measurement:Q').bin(maxbins=100),\n",
" alt.Y('count()').stack(None),\n",
" alt.Color('Experiment:N')\n",
")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}