{
"cells": [
{
"cell_type": "markdown",
"id": "ed186238",
"metadata": {},
"source": [
"# Streambook example"
]
},
{
"cell_type": "markdown",
"id": "83e965bb",
"metadata": {},
"source": [
"Streambook is a setup for writing live-updating notebooks\n",
"in any editor that you might want to use (emacs, vi, notepad)."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "14a4e11c",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 1,
"id": "7c768749",
"metadata": {
"execution": {
"iopub.execute_input": "2021-04-12T04:33:45.186839Z",
"iopub.status.busy": "2021-04-12T04:33:45.185793Z",
"iopub.status.idle": "2021-04-12T04:33:45.503548Z",
"shell.execute_reply": "2021-04-12T04:33:45.502548Z"
}
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import time"
]
},
{
"cell_type": "markdown",
"id": "4702566f",
"metadata": {},
"source": [
"## Main Code"
]
},
{
"cell_type": "markdown",
"id": "0690ed8d",
"metadata": {},
"source": [
"Notebook cells are separated by spaces. Comment cells are rendered\n",
"as markdown.\n",
"\n",
"See https://jupytext.readthedocs.io/en/latest/formats.html#the-light-format"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "c3781d0e",
"metadata": {
"execution": {
"iopub.execute_input": "2021-04-12T04:33:45.510026Z",
"iopub.status.busy": "2021-04-12T04:33:45.509117Z",
"iopub.status.idle": "2021-04-12T04:33:45.512247Z",
"shell.execute_reply": "2021-04-12T04:33:45.511341Z"
},
"lines_to_next_cell": 2
},
"outputs": [],
"source": [
"x = np.array([10, 20, 30])"
]
},
{
"cell_type": "markdown",
"id": "91c619fd",
"metadata": {},
"source": [
"Cells that end with an explicit variables are printed. \n",
"\n",
"See https://docs.streamlit.io/en/stable/api.html#magic-commands"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "e74a6cb8",
"metadata": {
"execution": {
"iopub.execute_input": "2021-04-12T04:33:45.521111Z",
"iopub.status.busy": "2021-04-12T04:33:45.520375Z",
"iopub.status.idle": "2021-04-12T04:33:45.524249Z",
"shell.execute_reply": "2021-04-12T04:33:45.525030Z"
},
"lines_to_next_cell": 2
},
"outputs": [
{
"data": {
"text/plain": [
"array([10, 20, 30])"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x"
]
},
{
"cell_type": "markdown",
"id": "0dc21cde",
"metadata": {},
"source": [
"Dictionaries are pretty-printed using streamlit and can be collapsed"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "f29c44e3",
"metadata": {
"execution": {
"iopub.execute_input": "2021-04-12T04:33:45.531718Z",
"iopub.status.busy": "2021-04-12T04:33:45.530819Z",
"iopub.status.idle": "2021-04-12T04:33:45.533983Z",
"shell.execute_reply": "2021-04-12T04:33:45.533200Z"
},
"lines_to_next_cell": 2
},
"outputs": [],
"source": [
"data = [dict(key1 = i, key2=f\"{i}\", key3=100 -i) for i in range(100)] "
]
},
{
"cell_type": "markdown",
"id": "e0b361f9",
"metadata": {
"lines_to_next_cell": 2
},
"source": [
"Pandas dataframe also show up in tables. "
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "11e558cd",
"metadata": {
"execution": {
"iopub.execute_input": "2021-04-12T04:33:45.545351Z",
"iopub.status.busy": "2021-04-12T04:33:45.544409Z",
"iopub.status.idle": "2021-04-12T04:33:45.558847Z",
"shell.execute_reply": "2021-04-12T04:33:45.558072Z"
},
"lines_to_next_cell": 2
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" key1 | \n",
" key2 | \n",
" key3 | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" 0 | \n",
" 0 | \n",
" 100 | \n",
"
\n",
" \n",
" | 1 | \n",
" 1 | \n",
" 1 | \n",
" 99 | \n",
"
\n",
" \n",
" | 2 | \n",
" 2 | \n",
" 2 | \n",
" 98 | \n",
"
\n",
" \n",
" | 3 | \n",
" 3 | \n",
" 3 | \n",
" 97 | \n",
"
\n",
" \n",
" | 4 | \n",
" 4 | \n",
" 4 | \n",
" 96 | \n",
"
\n",
" \n",
" | ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
"
\n",
" \n",
" | 95 | \n",
" 95 | \n",
" 95 | \n",
" 5 | \n",
"
\n",
" \n",
" | 96 | \n",
" 96 | \n",
" 96 | \n",
" 4 | \n",
"
\n",
" \n",
" | 97 | \n",
" 97 | \n",
" 97 | \n",
" 3 | \n",
"
\n",
" \n",
" | 98 | \n",
" 98 | \n",
" 98 | \n",
" 2 | \n",
"
\n",
" \n",
" | 99 | \n",
" 99 | \n",
" 99 | \n",
" 1 | \n",
"
\n",
" \n",
"
\n",
"
100 rows × 3 columns
\n",
"
![](\"example.gif\")
"
],
"text/plain": [
""
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Jupyter command\n",
"from IPython.display import HTML\n",
"HTML('')\n",
"# Streamlit command"
]
}
],
"metadata": {
"jupytext": {
"cell_metadata_filter": "-all"
},
"kernelspec": {
"display_name": "myenv",
"language": "python",
"name": "myenv"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}