OWID/OWID_Visualize_wealth_distribution.ipynb

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    "<img width=\"8%\" alt=\"OWID.png\" src=\"https://raw.githubusercontent.com/jupyter-naas/awesome-notebooks/master/.github/assets/logos/OWID.png\" style=\"border-radius: 15%\">"
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   "source": [
    "# OWID - Visualize wealth distribuition of certain major economic powers\n",
    "<a href=\"https://bit.ly/3JyWIk6\">Give Feedback</a> | <a href=\"https://github.com/jupyter-naas/awesome-notebooks/issues/new?assignees=&labels=bug&template=bug_report.md&title=OWID+-+Visualize+wealth+distribuition+of+certain+major+economic+powers:+Error+short+description\">Bug report</a>"
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   "source": [
    "**Tags:** #dash #dashboard #plotly #naas #asset #analytics #dropdown #callback #bootstrap #snippet"
   ]
  },
  {
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   "id": "8b9eabab",
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   "source": [
    "**Author:** [Zihui Ouyang](https://www.linkedin.com/in/zihui-ouyang-539626227/)"
   ]
  },
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   "source": [
    "**Last update:** 2023-07-31 (Created: 2023-07-31)"
   ]
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  {
   "cell_type": "markdown",
   "id": "ab610df3",
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   "source": [
    "**Description:** This notebook creates an interactive plot using Dash app infrastructure with OWID's wealth distribution data. It shows the percentage of personal wealth obtained by the top 1% and the top 10%."
   ]
  },
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   "source": [
    "**References:**\n",
    "- https://github.com/owid/owid-datasets/tree/master/datasets/Top%20Net%20Personal%20Wealth%20Shares%20%E2%80%93%20WID%20(2018)\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",
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   "source": [
    "## Input"
   ]
  },
  {
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   "metadata": {
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   "source": [
    "### Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "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",
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   "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",
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   "source": [
    "DASH_PORT = 8050\n",
    "url = \"https://raw.githubusercontent.com/owid/owid-datasets/master/datasets/Top%20Net%20Personal%20Wealth%20Shares%20%E2%80%93%20WID%20(2018)/Top%20Net%20Personal%20Wealth%20Shares%20%E2%80%93%20WID%20(2018).csv\"\n",
    "title = \"Wealth distribution\""
   ]
  },
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   "cell_type": "markdown",
   "id": "f2d0f398",
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   "source": [
    "## Model"
   ]
  },
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   "id": "1f81b91e",
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    "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",
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   "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 = \"Wealth distribution\") 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": {},
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   "outputs": [],
   "source": [
    "contents_df = pd.read_csv(url, header = 0) \n",
    "entity = contents_df[\"Entity\"]\n",
    "year = contents_df[\"Year\"]\n",
    "one_pct = contents_df[\"Top 1% share of net personal wealth (WID 2018)\"]# sort the data\n",
    "ten_pct = contents_df[\"Top 10% share of net personal wealth (WID 2018)\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81c9c0a0",
   "metadata": {
    "papermill": {},
    "tags": []
   },
   "source": [
    "### Create new dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "68bfb55d",
   "metadata": {
    "papermill": {},
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   "outputs": [],
   "source": [
    "categories = []\n",
    "categories1 = []\n",
    "for i in range(len(entity)):\n",
    "    categories.append(\"Top 1%\")\n",
    "    categories1.append(\"Top 10%\")\n",
    "new_dict = {\"Country\": entity,\n",
    "           \"Year\": year,\n",
    "            \"Categories\": categories,\n",
    "           \"Wealth\": one_pct}\n",
    "new_df = pd.DataFrame(data=new_dict)\n",
    "new_dict1 = {\"Country\": entity,\n",
    "           \"Year\": year,\n",
    "            \"Categories\": categories1,\n",
    "           \"Wealth\": ten_pct}\n",
    "new_df1 = pd.DataFrame(data=new_dict1)\n",
    "new_df = pd.concat([new_df, new_df1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "560e3784",
   "metadata": {
    "papermill": {},
    "tags": []
   },
   "source": [
    "### Create Dash App"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2629798",
   "metadata": {
    "papermill": {},
    "tags": []
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   "outputs": [],
   "source": [
    "app.layout = html.Div(\n",
    "    [\n",
    "        html.H4(\"Wealth of top 1% vs wealth of top 10% as a percentage of total wealth for 5 countries\"),\n",
    "        html.P(\"Select country\"),\n",
    "        dcc.Dropdown(\n",
    "            id=\"country\",\n",
    "            options=new_df.Country.unique(),\n",
    "            value=\"China\"\n",
    "        ),\n",
    "        dcc.RangeSlider(id='slider', min=1807, max=2015, value=[1978, 2015],\n",
    "               marks={x: str(x) for x in [1807, 1857, 1907, 1957, 2007, 2015]}),\n",
    "        dcc.Graph(id=\"Wealth\", figure={}, style={'display': 'none'})\n",
    "    ]\n",
    ")\n",
    "\n",
    "@callback(\n",
    "    Output(\"Wealth\", 'figure'),\n",
    "    Output(\"Wealth\", 'style'),\n",
    "    Input('country', 'value'),\n",
    "    Input('slider', 'value')\n",
    ")\n",
    "\n",
    "def update_graph(country, year):\n",
    "    country_list = [country]\n",
    "    dff = (new_df[\"Country\"].isin(country_list)) & (new_df[\"Year\"] <= year[1]) & (new_df[\"Year\"] >= year[0]) & (new_df[\"Year\"] <= 2021)\n",
    "        \n",
    "    figures = px.line(new_df[dff], x='Year', y=\"Wealth\", color = \"Categories\", markers = True).update_layout(\n",
    "                                plot_bgcolor='rgba(0, 0, 0, 0)', height= 600)\n",
    "    styles = {'display': 'block'}   \n",
    "    return figures, styles"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d43335ed",
   "metadata": {
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   },
   "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",
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