{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%load_ext tsumiki"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Work with jinja2 template."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"first_name = \"Yuki\"\n",
"last_name = \"Nagato\""
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
" \n",
"
Yuki Nagato is a fictional character in the Haruhi Suzumiya franchise.
\n",
"
"
],
"text/plain": [
""
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%tsumiki -r\n",
"\n",
":Markdown:\n",
"**{{ first_name }} {{ last_name }}** is a fictional character in the Haruhi Suzumiya franchise."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"sos_member = [\"Haruhi\", \"Koizumi\", \"Mikuru\", \"Nagato\", \"Kyon\"]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
"
SOS Brigade members
\n",
"
* Haruhi\n",
"* Koizumi\n",
"* Mikuru\n",
"* Nagato\n",
"* Kyon\n",
"
\n",
"
"
],
"text/plain": [
""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%tsumiki -r\n",
"\n",
":Markdown:\n",
"### SOS Brigade members\n",
"{% for member in sos_member %}\n",
" * {{ member }}\n",
"{% endfor %}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### with pandas DataFrame"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"df1 = pd.DataFrame(np.random.rand(5, 5))\n",
"df2 = pd.DataFrame(np.random.rand(5, 5))\n",
"df1_html = df1.to_html()\n",
"df2_html = df2.to_html()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" 0.982298 | \n",
" 0.577508 | \n",
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" 0.947929 | \n",
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" 3 | \n",
" 0.879355 | \n",
" 0.259316 | \n",
" 0.561044 | \n",
" 0.003871 | \n",
" 0.548311 | \n",
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" 4 | \n",
" 0.061514 | \n",
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" 0 | \n",
" 0.576741 | \n",
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" 0.775877 | \n",
" 0.334813 | \n",
" 0.938785 | \n",
"
\n",
" \n",
" 1 | \n",
" 0.111955 | \n",
" 0.626392 | \n",
" 0.081983 | \n",
" 0.386245 | \n",
" 0.411804 | \n",
"
\n",
" \n",
" 2 | \n",
" 0.127789 | \n",
" 0.714526 | \n",
" 0.352714 | \n",
" 0.674443 | \n",
" 0.069859 | \n",
"
\n",
" \n",
" 3 | \n",
" 0.486995 | \n",
" 0.747210 | \n",
" 0.396503 | \n",
" 0.729041 | \n",
" 0.949199 | \n",
"
\n",
" \n",
" 4 | \n",
" 0.154404 | \n",
" 0.427610 | \n",
" 0.267587 | \n",
" 0.411358 | \n",
" 0.645386 | \n",
"
\n",
" \n",
"
\n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
""
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%tsumiki -r\n",
"\n",
":HTML::\n",
"{{ df1_html }}\n",
"\n",
":HTML::\n",
"{{ df2_html }}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### with matplotlib"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"from io import StringIO\n",
"import matplotlib.pyplot as plt\n",
"\n",
"svg_data = StringIO()\n",
"\n",
"fig, ax = plt.subplots()\n",
"ax.plot(df1)\n",
"fig.savefig(svg_data, format=\"SVG\")\n",
"plt.close(fig)\n",
"svg_data.seek(0)\n",
"\n",
"plot_data = svg_data.read()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
"
DataFrame and Plot
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" 4 | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 0.111053 | \n",
" 0.503947 | \n",
" 0.507890 | \n",
" 0.681142 | \n",
" 0.860380 | \n",
"
\n",
" \n",
" 1 | \n",
" 0.597266 | \n",
" 0.896991 | \n",
" 0.519128 | \n",
" 0.326821 | \n",
" 0.609664 | \n",
"
\n",
" \n",
" 2 | \n",
" 0.982298 | \n",
" 0.577508 | \n",
" 0.328038 | \n",
" 0.824749 | \n",
" 0.947929 | \n",
"
\n",
" \n",
" 3 | \n",
" 0.879355 | \n",
" 0.259316 | \n",
" 0.561044 | \n",
" 0.003871 | \n",
" 0.548311 | \n",
"
\n",
" \n",
" 4 | \n",
" 0.061514 | \n",
" 0.768419 | \n",
" 0.120131 | \n",
" 0.371848 | \n",
" 0.449520 | \n",
"
\n",
" \n",
"
\n",
"
\n",
" \n",
"
\n",
" \n",
"\n",
"\n",
"
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"
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" \n",
"
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"
"
],
"text/plain": [
""
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%tsumiki -r\n",
"\n",
":Markdown:\n",
"# DataFrame and Plot\n",
"\n",
":HTML::\n",
"{{ df1_html }}\n",
":HTML::\n",
"{{ plot_data }}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"hide_input": false,
"kernelspec": {
"display_name": "Python 3",
"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": "3.6.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}