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actions-user committed Aug 7, 2023
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{
"cells": [
{
"cell_type": "markdown",
"id": "b1d69ab6",
"metadata": {
"papermill": {},
"tags": []
},
"source": [
"<img width=\"10%\" alt=\"Naas\" src=\"https://landen.imgix.net/jtci2pxwjczr/assets/5ice39g4.png?w=160\"/>"
]
},
{
"cell_type": "markdown",
"id": "267d5bbf",
"metadata": {
"papermill": {},
"tags": []
},
"source": [
"# OWID - Visualize world population growth\n",
"<a href=\"https://app.naas.ai/user-redirect/naas/downloader?url=https://raw.githubusercontent.com/jupyter-naas/awesome-notebooks/master/OWID/OWID_world_population_growth.ipynb\" target=\"_parent\"><img src=\"https://naasai-public.s3.eu-west-3.amazonaws.com/Open_in_Naas_Lab.svg\"/></a><br><br><a href=\"https://bit.ly/3JyWIk6\">Give Feedbacks</a> | <a href=\"https://app.naas.ai/user-redirect/naas/downloader?url=https://raw.githubusercontent.com/jupyter-naas/awesome-notebooks/master/Naas/Naas_Start_data_product.ipynb\" target=\"_parent\">Generate Data Product</a>"
]
},
{
"cell_type": "markdown",
"id": "5231f49b",
"metadata": {
"papermill": {},
"tags": []
},
"source": [
"**Tags:** #dash #dashboard #plotly #naas #asset #analytics #dropdown #callback #bootstrap #snippet"
]
},
{
"cell_type": "markdown",
"id": "8b9eabab",
"metadata": {
"papermill": {},
"tags": []
},
"source": [
"**Author:** [Zihui Ouyang](https://www.linkedin.com/in/zihui-ouyang-539626227/)"
]
},
{
"cell_type": "markdown",
"id": "acb27e96-54ec-49d8-8eb8-e8d94dd9ef94",
"metadata": {
"papermill": {},
"tags": []
},
"source": [
"**Last update:** 2023-07-31 (Created: 2023-07-31)"
]
},
{
"cell_type": "markdown",
"id": "ab610df3",
"metadata": {
"papermill": {},
"tags": []
},
"source": [
"**Description:** This notebook creates an interactive plot using Dash app infrastructure with OWID's world population growth data."
]
},
{
"cell_type": "markdown",
"id": "37a304c0",
"metadata": {
"papermill": {},
"tags": []
},
"source": [
"**References:**\n",
"- https://github.com/owid/owid-datasets/tree/master/datasets/Annual%20world%20population%20growth%20rate%20-%20OWID\n",
"- https://stackoverflow.com/questions/70886359/dash-python-making-subplots-when-multiple-parameters-are-selected\n",
"- https://dash-example-index.herokuapp.com/line-charts"
]
},
{
"cell_type": "markdown",
"id": "af4fbd09",
"metadata": {
"papermill": {},
"tags": []
},
"source": [
"## Input"
]
},
{
"cell_type": "markdown",
"id": "61bb4b6a-3622-4fc8-a64b-f58c3061298e",
"metadata": {},
"source": [
"### Import libraries"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0649176e",
"metadata": {
"papermill": {},
"tags": []
},
"outputs": [],
"source": [
"try:\n",
" import dash\n",
" import os\n",
"except:\n",
" !pip install dash --user\n",
" import dash\n",
"try:\n",
" import dash_bootstrap_components as dbc\n",
"except:\n",
" !pip install dash_bootstrap_components --user\n",
" import dash_bootstrap_components as dbc\n",
"import pandas as pd\n",
"import numpy as np\n",
"from dash import Dash, html, dcc, callback, Output, Input\n",
"import plotly.express as px"
]
},
{
"cell_type": "markdown",
"id": "cabe4219",
"metadata": {
"papermill": {},
"tags": []
},
"source": [
"### Setup variables\n",
"- `DASH_PORT`: specify a port number for Dash\n",
"- `url`: URL to get data from Excel\n",
"- `title`: App title"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c9ffa9db",
"metadata": {
"papermill": {},
"tags": []
},
"outputs": [],
"source": [
"DASH_PORT = 8050\n",
"url = \"https://raw.githubusercontent.com/owid/owid-datasets/master/datasets/Annual%20world%20population%20growth%20rate%20-%20OWID/Annual%20world%20population%20growth%20rate%20-%20OWID.csv\"\n",
"title = \"World population growth\""
]
},
{
"cell_type": "markdown",
"id": "f2d0f398",
"metadata": {
"papermill": {},
"tags": []
},
"source": [
"## Model"
]
},
{
"cell_type": "markdown",
"id": "1f81b91e",
"metadata": {
"papermill": {},
"tags": []
},
"source": [
"### Initialize Dash app\n",
"The `os.environ.get(\"JUPYTERHUB_USER\")` is used to access the environment variable `JUPYTERHUB_USER` already stored into your Naas Lab."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4d1f3df3",
"metadata": {
"papermill": {},
"tags": []
},
"outputs": [],
"source": [
"app = dash.Dash(\n",
" title=title,\n",
" requests_pathname_prefix=f'/user/{os.environ.get(\"JUPYTERHUB_USER\")}/proxy/{DASH_PORT}/',\n",
" external_stylesheets=[dbc.themes.BOOTSTRAP],\n",
" meta_tags=[\n",
" {\"name\": \"viewport\", \"content\": \"width=device-width, initial-scale=1.0\"}\n",
" ],\n",
")\n",
"\n",
"#app = dash.Dash(title = \"World population growth\") if you are not in Naas"
]
},
{
"cell_type": "markdown",
"id": "7d73d825",
"metadata": {
"papermill": {},
"tags": []
},
"source": [
"### Get Data"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "aad6eb74",
"metadata": {
"papermill": {},
"tags": []
},
"outputs": [],
"source": [
"contents_df = pd.read_csv(url, header = 0) \n",
"new_df = contents_df[contents_df['Year'] > 1949]"
]
},
{
"cell_type": "markdown",
"id": "560e3784",
"metadata": {
"papermill": {},
"tags": []
},
"source": [
"### Create Dash App"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c2629798",
"metadata": {
"papermill": {},
"tags": []
},
"outputs": [],
"source": [
"app = Dash(title = \"Population growth in the world\")\n",
"app.layout = html.Div(\n",
" [\n",
" html.H4(\"Population growth from 1950 to 2015\"),\n",
" dcc.RangeSlider(id='slider', min=1950, max=2015, value=[1950, 2015],\n",
" marks={x: str(x) for x in [1950, 1965, 1980, 1995, 2015]}),\n",
" dcc.Graph(id=\"Annual population growth rate (OWID)\", figure={}, style={'display': 'none'})\n",
" ]\n",
")\n",
"\n",
"@callback(\n",
" Output(\"Annual population growth rate (OWID)\", 'figure'),\n",
" Output(\"Annual population growth rate (OWID)\", 'style'),\n",
" Input('slider', 'value')\n",
")\n",
"\n",
"def update_graph(year):\n",
" dff = (new_df[\"Year\"] <= year[1]) & (new_df[\"Year\"] >= year[0])\n",
" \n",
" figures = px.line(new_df[dff], x='Year', y=\"Annual population growth rate (OWID)\", markers = True).update_layout(\n",
" plot_bgcolor='rgba(0, 0, 0, 0)', height= 600)\n",
" styles = {'display': 'block'} \n",
" return figures, styles\n",
"if __name__ == \"__main__\":\n",
" app.run_server(debug=True, port=8049, use_reloader=False)"
]
},
{
"cell_type": "markdown",
"id": "d43335ed",
"metadata": {
"papermill": {},
"tags": []
},
"source": [
"## Output"
]
},
{
"cell_type": "markdown",
"id": "4f1ab927",
"metadata": {
"papermill": {},
"tags": []
},
"source": [
"### Generate URL and show logs"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2cb37b0c",
"metadata": {
"papermill": {},
"tags": []
},
"outputs": [],
"source": [
"if __name__ == \"__main__\":\n",
" app.run_server(proxy=f\"http://127.0.0.1:{DASH_PORT}::https://app.naas.ai\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8c8917e7-a31c-4934-b851-41ad402b502f",
"metadata": {
"papermill": {},
"tags": []
},
"outputs": [],
"source": []
}
],
"metadata": {
"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": "3.9.6"
},
"naas": {
"notebook_id": "b536a539006dced496f5dee0a0054d535f4880f440631c3b26f6f8d5f626c7d6",
"notebook_path": "OWID/OWID_Visualize_GDP_per_capita_through_the_years.ipynb"
},
"papermill": {
"default_parameters": {},
"environment_variables": {},
"parameters": {},
"version": "2.4.0"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
{"cells": [{"cell_type": "markdown", "id": "b1d69ab6", "metadata": {"papermill": {}, "tags": []}, "source": "<img width=\"10%\" alt=\"Naas\" src=\"https://landen.imgix.net/jtci2pxwjczr/assets/5ice39g4.png?w=160\"/>"}, {"cell_type": "markdown", "id": "267d5bbf", "metadata": {"papermill": {}, "tags": []}, "source": "# OWID - Visualize world population growth\n<a href=\"https://app.naas.ai/user-redirect/naas/downloader?url=https://raw.githubusercontent.com/jupyter-naas/awesome-notebooks/master/OWID/OWID_world_population_growth.ipynb\" target=\"_parent\"><img src=\"https://naasai-public.s3.eu-west-3.amazonaws.com/Open_in_Naas_Lab.svg\"/></a><br><br><a href=\"https://bit.ly/3JyWIk6\">Give Feedbacks</a> | <a href=\"https://app.naas.ai/user-redirect/naas/downloader?url=https://raw.githubusercontent.com/jupyter-naas/awesome-notebooks/master/Naas/Naas_Start_data_product.ipynb\" target=\"_parent\">Generate Data Product</a>"}, {"cell_type": "markdown", "id": "5231f49b", "metadata": {"papermill": {}, "tags": []}, "source": "**Tags:** #dash #dashboard #plotly #naas #asset #analytics #dropdown #callback #bootstrap #snippet"}, {"cell_type": "markdown", "id": "8b9eabab", "metadata": {"papermill": {}, "tags": []}, "source": "**Author:** [Zihui Ouyang](https://www.linkedin.com/in/zihui-ouyang-539626227/)"}, {"cell_type": "markdown", "id": "acb27e96-54ec-49d8-8eb8-e8d94dd9ef94", "metadata": {"papermill": {}, "tags": []}, "source": "**Last update:** 2023-08-07 (Created: 2023-08-07)"}, {"cell_type": "markdown", "id": "ab610df3", "metadata": {"papermill": {}, "tags": []}, "source": "**Description:** This notebook creates an interactive plot using Dash app infrastructure with OWID's world population growth data."}, {"cell_type": "markdown", "id": "37a304c0", "metadata": {"papermill": {}, "tags": []}, "source": "**References:**\n- https://github.com/owid/owid-datasets/tree/master/datasets/Annual%20world%20population%20growth%20rate%20-%20OWID\n- https://stackoverflow.com/questions/70886359/dash-python-making-subplots-when-multiple-parameters-are-selected\n- https://dash-example-index.herokuapp.com/line-charts"}, {"cell_type": "markdown", "id": "af4fbd09", "metadata": {"papermill": {}, "tags": []}, "source": "## Input"}, {"cell_type": "markdown", "id": "61bb4b6a-3622-4fc8-a64b-f58c3061298e", "metadata": {"tags": [], "papermill": {}}, "source": "### Import libraries"}, {"cell_type": "code", "execution_count": null, "id": "0649176e", "metadata": {"papermill": {}, "tags": []}, "outputs": [], "source": "try:\n import dash\n import os\nexcept:\n !pip install dash --user\n import dash\ntry:\n import dash_bootstrap_components as dbc\nexcept:\n !pip install dash_bootstrap_components --user\n import dash_bootstrap_components as dbc\nimport pandas as pd\nimport numpy as np\nfrom dash import Dash, html, dcc, callback, Output, Input\nimport plotly.express as px"}, {"cell_type": "markdown", "id": "cabe4219", "metadata": {"papermill": {}, "tags": []}, "source": "### Setup variables\n- `DASH_PORT`: specify a port number for Dash\n- `url`: URL to get data from Excel\n- `title`: App title"}, {"cell_type": "code", "execution_count": null, "id": "c9ffa9db", "metadata": {"papermill": {}, "tags": []}, "outputs": [], "source": "DASH_PORT = 8050\nurl = \"https://raw.githubusercontent.com/owid/owid-datasets/master/datasets/Annual%20world%20population%20growth%20rate%20-%20OWID/Annual%20world%20population%20growth%20rate%20-%20OWID.csv\"\ntitle = \"World population growth\""}, {"cell_type": "markdown", "id": "f2d0f398", "metadata": {"papermill": {}, "tags": []}, "source": "## Model"}, {"cell_type": "markdown", "id": "1f81b91e", "metadata": {"papermill": {}, "tags": []}, "source": "### Initialize Dash app\nThe `os.environ.get(\"JUPYTERHUB_USER\")` is used to access the environment variable `JUPYTERHUB_USER` already stored into your Naas Lab."}, {"cell_type": "code", "execution_count": null, "id": "4d1f3df3", "metadata": {"papermill": {}, "tags": []}, "outputs": [], "source": "app = dash.Dash(\n title=title,\n requests_pathname_prefix=f'/user/{os.environ.get(\"JUPYTERHUB_USER\")}/proxy/{DASH_PORT}/',\n external_stylesheets=[dbc.themes.BOOTSTRAP],\n meta_tags=[\n {\"name\": \"viewport\", \"content\": \"width=device-width, initial-scale=1.0\"}\n ],\n)\n\n#app = dash.Dash(title = \"World population growth\") if you are not in Naas"}, {"cell_type": "markdown", "id": "7d73d825", "metadata": {"papermill": {}, "tags": []}, "source": "### Get Data"}, {"cell_type": "code", "execution_count": null, "id": "aad6eb74", "metadata": {"papermill": {}, "tags": []}, "outputs": [], "source": "contents_df = pd.read_csv(url, header = 0) \nnew_df = contents_df[contents_df['Year'] > 1949]"}, {"cell_type": "markdown", "id": "560e3784", "metadata": {"papermill": {}, "tags": []}, "source": "### Create Dash App"}, {"cell_type": "code", "execution_count": null, "id": "c2629798", "metadata": {"papermill": {}, "tags": []}, "outputs": [], "source": "app = Dash(title = \"Population growth in the world\")\napp.layout = html.Div(\n [\n html.H4(\"Population growth from 1950 to 2015\"),\n dcc.RangeSlider(id='slider', min=1950, max=2015, value=[1950, 2015],\n marks={x: str(x) for x in [1950, 1965, 1980, 1995, 2015]}),\n dcc.Graph(id=\"Annual population growth rate (OWID)\", figure={}, style={'display': 'none'})\n ]\n)\n\n@callback(\n Output(\"Annual population growth rate (OWID)\", 'figure'),\n Output(\"Annual population growth rate (OWID)\", 'style'),\n Input('slider', 'value')\n)\n\ndef update_graph(year):\n dff = (new_df[\"Year\"] <= year[1]) & (new_df[\"Year\"] >= year[0])\n \n figures = px.line(new_df[dff], x='Year', y=\"Annual population growth rate (OWID)\", markers = True).update_layout(\n plot_bgcolor='rgba(0, 0, 0, 0)', height= 600)\n styles = {'display': 'block'} \n return figures, styles\nif __name__ == \"__main__\":\n app.run_server(debug=True, port=8049, use_reloader=False)"}, {"cell_type": "markdown", "id": "d43335ed", "metadata": {"papermill": {}, "tags": []}, "source": "## Output"}, {"cell_type": "markdown", "id": "4f1ab927", "metadata": {"papermill": {}, "tags": []}, "source": "### Generate URL and show logs"}, {"cell_type": "code", "execution_count": null, "id": "2cb37b0c", "metadata": {"papermill": {}, "tags": []}, "outputs": [], "source": "if __name__ == \"__main__\":\n app.run_server(proxy=f\"http://127.0.0.1:{DASH_PORT}::https://app.naas.ai\")"}, {"cell_type": "code", "execution_count": null, "id": "8c8917e7-a31c-4934-b851-41ad402b502f", "metadata": {"papermill": {}, "tags": []}, "outputs": [], "source": ""}], "metadata": {"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": "3.9.6"}, "naas": {"notebook_id": "b536a539006dced496f5dee0a0054d535f4880f440631c3b26f6f8d5f626c7d6", "notebook_path": "OWID/OWID_Visualize_GDP_per_capita_through_the_years.ipynb"}, "papermill": {"default_parameters": {}, "environment_variables": {}, "parameters": {}, "version": "2.4.0"}}, "nbformat": 4, "nbformat_minor": 5}

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