Industrial_Production_Latam_CEIC_Python_Integration_Notebook.ipynb

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   "source": [
    "## Quick Guide\n",
    "\n",
    "\n",
    "The notebook works as a simple script that calls data from CDMNext using CEIC's Python package, extracts all series from the selected insight, gets all timepoints for these series, builds dataframe, and create a grid of interactive plots with the given dataframes.\n",
    "\n",
    "If you are reading the notebook via GitHub it is possible to run the code via the following link on Google Colab: https://colab.research.google.com/drive/18A0I_hNc6hEPQmGWimkDDcaJMJHfnsXI#scrollTo=DgHz20XdHPVJ\n",
    "\n",
    "Otherwise it is possible to download the notebook and run it in your pc.\n",
    "\n",
    "> #### Installation Guide\n",
    ">\n",
    "> 1. Install Python 3.9+\n",
    "> 2. Install the PyCEIC envorionment running the first lines:<br />\n",
    "> !pip install --extra-index-url https://downloads.ceicdata.com/python ceic_api_client <br />\n",
    ">!pip install --extra-index-url https://downloads.ceicdata.com/python ceic_api_client --upgrade\n",
    "\n",
    "---\n",
    "\n",
    "> #### Usage Guide\n",
    ">\n",
    "> **Import libraries** - cell to define libraries we need to set before running the code<br />\n",
    "> **Insight Information** - cell to define the insight we want to replicate<br />\n",
    "> **Function Definitions** - to read or change the existing logic used by the main program<br />\n",
    "> **DataFrames** - select the dataframes that are going to be plotted<br />\n",
    "> **Code Execution** - run the main program based on parameters and functions<br />\n",
    "> \n",
    ">\n",
    "> Visit **[CEIC Main Page](https://www.ceicdata.com/en)** for more information on how to use our data and the PyCEIC package.\n",
    ">\n",
    ">\n",
    ">\n",
    "> **Instructions**\n",
    "> 1. Run the 'Installing PyCEIC environment' code block to set up the connection with CDMNext\n",
    "> 2. Run section 'Import Libraries'\n",
    "> 3. In 'Credentials' section add your log-in credentials or select 'Not a client' to get a sample data\n",
    "> 4. Then you can run the rest of the code \n",
    "\n",
    "---\n",
    "> #### More info...\n",
    "> Request a demo of the platform to get access to all the data: https://www.ceicdata.com/en/ceic-request-a-d"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "DgHz20XdHPVJ",
   "metadata": {
    "id": "DgHz20XdHPVJ"
   },
   "source": [
    "## Intalling PyCEIC environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "IcosAKjmnQ6d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
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     "text": [
      "Looking in indexes: https://pypi.org/simple, https://downloads.ceicdata.com/python\n",
      "Requirement already satisfied: ceic_api_client in c:\\users\\necheverry\\anaconda3\\lib\\site-packages (2.8.14.262)\n",
      "Requirement already satisfied: python-dateutil in c:\\users\\necheverry\\anaconda3\\lib\\site-packages (from ceic_api_client) (2.8.2)\n",
      "Requirement already satisfied: certifi in c:\\users\\necheverry\\anaconda3\\lib\\site-packages (from ceic_api_client) (2021.10.8)\n",
      "Requirement already satisfied: urllib3>=1.15 in c:\\users\\necheverry\\anaconda3\\lib\\site-packages (from ceic_api_client) (1.26.9)\n",
      "Requirement already satisfied: six>=1.10 in c:\\users\\necheverry\\anaconda3\\lib\\site-packages (from ceic_api_client) (1.16.0)\n",
      "Looking in indexes: https://pypi.org/simple, https://downloads.ceicdata.com/python\n",
      "Requirement already satisfied: ceic_api_client in c:\\users\\necheverry\\anaconda3\\lib\\site-packages (2.8.14.262)\n",
      "Collecting ceic_api_client\n",
      "  Downloading https://downloads.ceicdata.com/python/ceic-api-client/ceic_api_client-2.9.2.278.tar.gz (156 kB)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\necheverry\\anaconda3\\lib\\site-packages (from ceic_api_client) (2021.10.8)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in c:\\users\\necheverry\\anaconda3\\lib\\site-packages (from ceic_api_client) (2.8.2)\n",
      "Requirement already satisfied: six>=1.10 in c:\\users\\necheverry\\anaconda3\\lib\\site-packages (from ceic_api_client) (1.16.0)\n",
      "Requirement already satisfied: urllib3>=1.23 in c:\\users\\necheverry\\anaconda3\\lib\\site-packages (from ceic_api_client) (1.26.9)\n",
      "Building wheels for collected packages: ceic-api-client\n",
      "  Building wheel for ceic-api-client (setup.py): started\n",
      "  Building wheel for ceic-api-client (setup.py): finished with status 'done'\n",
      "  Created wheel for ceic-api-client: filename=ceic_api_client-2.9.2.278-py3-none-any.whl size=438382 sha256=4ee0d480d2a1c3740586e2895525de76691a3a0d855ade1a2234eb3af99b3b7e\n",
      "  Stored in directory: c:\\users\\necheverry\\appdata\\local\\pip\\cache\\wheels\\ea\\60\\97\\fe38f2f6ac35c5659f8cd1c7f2720aad869afcdfb5e5c68232\n",
      "Successfully built ceic-api-client\n",
      "Installing collected packages: ceic-api-client\n",
      "  Attempting uninstall: ceic-api-client\n",
      "    Found existing installation: ceic-api-client 2.8.14.262\n",
      "    Uninstalling ceic-api-client-2.8.14.262:\n",
      "      Successfully uninstalled ceic-api-client-2.8.14.262\n",
      "Successfully installed ceic-api-client-2.9.2.278\n"
     ]
    }
   ],
   "source": [
    "!pip install --extra-index-url https://downloads.ceicdata.com/python ceic_api_client\n",
    "!pip install --extra-index-url https://downloads.ceicdata.com/python ceic_api_client --upgrade"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e70eb6e",
   "metadata": {
    "id": "9e70eb6e"
   },
   "source": [
    "### Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b10e4f43",
   "metadata": {
    "id": "b10e4f43"
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   "outputs": [],
   "source": [
    "# Call the PyCEIC functionalities \n",
    "from ceic_api_client.pyceic import Ceic \n",
    "\n",
    "import matplotlib.pyplot as plot \n",
    "import openpyxl\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import requests\n",
    "import math\n",
    "\n",
    "# Libraries nedded to include interactive widgets\n",
    "import ipywidgets as widgets\n",
    "from ipywidgets import DatePicker, interactive, HBox, VBox, Output\n",
    "from IPython.display import clear_output, display\n",
    "from datetime import datetime\n",
    "\n",
    "\n",
    "# Libraries needed to use bokeh python environment\n",
    "from bokeh.plotting import figure, show, ColumnDataSource\n",
    "from bokeh.models.widgets import DatePicker\n",
    "from bokeh.layouts import gridplot, column, Row\n",
    "from bokeh.io import output_notebook\n",
    "from bokeh.models import CustomJS, Label, CustomJSHover\n",
    "from bokeh.transform import dodge\n",
    "import bokeh.palettes\n",
    "from bokeh.models import HoverTool\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dea80ce4",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 111,
     "referenced_widgets": [
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      "2a88fd6bd4394ede9dc5853dd8ef37c1",
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      "adf3fda6a8244d7c8c4cb083ef979214",
      "ecfa55f5b77e4ebe82bcbd26a624307d"
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    "id": "dea80ce4",
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    "scrolled": false
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       "model_id": "13346d36bf58410196ac7b8929ada90a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(Button(description='Log In', icon='check', style=ButtonStyle(), tooltip='Log In'), Button(descr…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "6054aeb97c0b4ab88bdd320a01d83f8c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Text(value='', description='User name:', placeholder='Nombre de usuario')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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      "text/plain": [
       "Password(description='Password:', placeholder='Contraseña')"
      ]
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the widgets to display the log-in box.\n",
    "user = widgets.Text(value='', placeholder='Nombre de usuario', description='User name:', disabled=False)\n",
    "password = widgets.Password(value='', placeholder='Contraseña', description='Password:', disabled=False)\n",
    "\n",
    "login_button = widgets.Button(description='Log In', disabled=False, button_style='', tooltip='Log In', icon='check')\n",
    "not_client_button = widgets.Button(description=\"Not a client\", disabled=False, button_style='', tooltip='No soy cliente', icon='')\n",
    "\n",
    "# is_client = True\n",
    "\n",
    "def on_login_button_clicked(b):\n",
    "    global is_client\n",
    "    is_client = True\n",
    "\n",
    "def on_not_client_button_clicked(b):\n",
    "    global is_client\n",
    "    is_client = False\n",
    "\n",
    "login_button.on_click(on_login_button_clicked)\n",
    "not_client_button.on_click(on_not_client_button_clicked)\n",
    "\n",
    "display(widgets.HBox([login_button, not_client_button]), user, password)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea233e11",
   "metadata": {
    "id": "ea233e11"
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   "source": [
    "### Insight information"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "018cf9b5",
   "metadata": {
    "id": "018cf9b5"
   },
   "source": [
    "Industrial production in Latin America already cooling after the 2021 revival\n",
    "  <br>\n",
    "id: ffe35493-6305-4ab8-83fc-bc18ff0f4baf  <br>\n",
    "Link:https://insights.ceicdata.com/Industrial-production-in-Latin-America-already-cooling-after-the-2021-revival"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0a6f1aa1",
   "metadata": {
    "id": "0a6f1aa1"
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   "outputs": [],
   "source": [
    "def get_series_to_df(id_insight, ini_date):\n",
    "    \n",
    "    series_dic = {}\n",
    "    label_dic = {}\n",
    "    \n",
    "    #Download the insight series\n",
    "    result = Ceic.insight_series(id_insight, start_date = ini_date)\n",
    "    #\n",
    "    for s in result.data.items:\n",
    "        column = s.insight_series.name\n",
    "        time_points_dict = dict((tp._date, tp.value) for tp in s.series_data.time_points)\n",
    "        series = pd.Series(time_points_dict)\n",
    "        series_dic[column] = series\n",
    "        label_dic[column] = column\n",
    "        \n",
    "    df = pd.DataFrame(series_dic)\n",
    "    \n",
    "    return df, result, series_dic, label_dic\n",
    "\n",
    "# Import fil from Drive\n",
    "def download_file_from_google_drive(id, destination):\n",
    "    URL = \"https://docs.google.com/uc?export=download\"\n",
    "\n",
    "    session = requests.Session()\n",
    "\n",
    "    response = session.get(URL, params={'id': id}, stream=True)\n",
    "    token = get_confirm_token(response)\n",
    "\n",
    "    if token:\n",
    "        params = {'id': id, 'confirm': token}\n",
    "        response = session.get(URL, params=params, stream=True)\n",
    "\n",
    "    save_response_content(response, destination)    \n",
    "\n",
    "def get_confirm_token(response):\n",
    "    for key, value in response.cookies.items():\n",
    "        if key.startswith('download_warning'):\n",
    "            return value\n",
    "\n",
    "    return None\n",
    "\n",
    "def save_response_content(response, destination):\n",
    "    CHUNK_SIZE = 32768\n",
    "\n",
    "    with open(destination, \"wb\") as f:\n",
    "        for chunk in response.iter_content(CHUNK_SIZE):\n",
    "            if chunk:\n",
    "                f.write(chunk)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "MjBuOY9Fgbry",
   "metadata": {
    "id": "MjBuOY9Fgbry"
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   "source": [
    "If you logged with your credentials you will be able to change the 'id' of the insight you want to call and also select the 'insight_date' to specifiy the initial date of your dataframe. If you are not a client the data would be between '2022-01-01' and '2023-01-01' by default."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c19652f9",
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    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "c19652f9",
    "outputId": "b79c7572-5ee2-475c-a993-0c286396e039",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Free Access\n"
     ]
    }
   ],
   "source": [
    "if is_client == True:\n",
    "\n",
    "  print('Client Access')\n",
    "\n",
    "  Ceic.login(user.value,password.value)\n",
    "  id = 'ffe35493-6305-4ab8-83fc-bc18ff0f4baf'\n",
    "  insight_date = '2021-01-01' # Change initial date\n",
    "\n",
    "  data, result, s, l = get_series_to_df(id, insight_date)\n",
    "  data\n",
    "\n",
    "else:\n",
    "  print('Free Access')\n",
    "  file_id = '1Y8yYPM_MYdvkzhB_-eBstMcMR2z2pmUs'\n",
    "  destination = 'my_excel_file.csv'\n",
    "  download_file_from_google_drive(file_id, destination)\n",
    "\n",
    "  data = pd.read_csv('my_excel_file.csv')\n",
    "  data.iloc[:,0] = pd.to_datetime(data.iloc[:,0])\n",
    "\n",
    "  first_column_name = data.columns[0]\n",
    "  data = data.set_index(first_column_name, drop=True)\n",
    "  data.index.name = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "LVt9u8uyiub3",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 814
    },
    "id": "LVt9u8uyiub3",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Industrial Production Index: Argentina</th>\n",
       "      <th>Industrial Production Index: Argentina Weighted</th>\n",
       "      <th>Industrial Production Index: Brazil Weighted</th>\n",
       "      <th>Industrial Production Index: Chile Weighted</th>\n",
       "      <th>Industrial Production Index: Colombia Weighted</th>\n",
       "      <th>Industrial Production Index: Mexico Weighted</th>\n",
       "      <th>Industrial Production Index: Peru Weighted</th>\n",
       "      <th>Industrial Production Index: Uruguay Weighted</th>\n",
       "      <th>Industrial Production Index: Latin America Weighted Average</th>\n",
       "      <th>Industrial Production Index: Brazil</th>\n",
       "      <th>Industrial Production Index: Chile</th>\n",
       "      <th>Industrial Production Index: Colombia</th>\n",
       "      <th>Industrial Production Index: Mexico</th>\n",
       "      <th>Industrial Production Index: Peru</th>\n",
       "      <th>Industrial Production Index: Uruguay</th>\n",
       "      <th>Industrial Production Index: Latin America</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>2023-01-01</th>\n",
       "      <td>6.253442</td>\n",
       "      <td>0.786206</td>\n",
       "      <td>0.105222</td>\n",
       "      <td>0.028809</td>\n",
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       "      <td>0.053596</td>\n",
       "      <td>0.008346</td>\n",
       "      <td>1.844532</td>\n",
       "      <td>0.269398</td>\n",
       "      <td>0.469939</td>\n",
       "      <td>0.796217</td>\n",
       "      <td>2.800173</td>\n",
       "      <td>1.077854</td>\n",
       "      <td>0.575253</td>\n",
       "      <td>1.748897</td>\n",
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       "    <tr>\n",
       "      <th>2022-12-01</th>\n",
       "      <td>-2.699487</td>\n",
       "      <td>-0.295879</td>\n",
       "      <td>-0.143791</td>\n",
       "      <td>-0.088544</td>\n",
       "      <td>0.049423</td>\n",
       "      <td>0.879549</td>\n",
       "      <td>-0.243209</td>\n",
       "      <td>0.016298</td>\n",
       "      <td>0.173846</td>\n",
       "      <td>-0.376290</td>\n",
       "      <td>-1.207549</td>\n",
       "      <td>0.670779</td>\n",
       "      <td>2.984193</td>\n",
       "      <td>-4.649946</td>\n",
       "      <td>1.146046</td>\n",
       "      <td>-0.590322</td>\n",
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       "      <th>2022-11-01</th>\n",
       "      <td>0.842822</td>\n",
       "      <td>0.092378</td>\n",
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       "      <td>-0.368101</td>\n",
       "      <td>0.174637</td>\n",
       "      <td>1.024452</td>\n",
       "      <td>-0.104836</td>\n",
       "      <td>-0.084522</td>\n",
       "      <td>1.046883</td>\n",
       "      <td>0.818770</td>\n",
       "      <td>-5.020079</td>\n",
       "      <td>2.370211</td>\n",
       "      <td>3.475831</td>\n",
       "      <td>-2.004373</td>\n",
       "      <td>-5.943554</td>\n",
       "      <td>-0.780053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-01</th>\n",
       "      <td>3.315654</td>\n",
       "      <td>0.363415</td>\n",
       "      <td>0.472882</td>\n",
       "      <td>-0.310842</td>\n",
       "      <td>0.243706</td>\n",
       "      <td>0.949543</td>\n",
       "      <td>0.007950</td>\n",
       "      <td>-0.059997</td>\n",
       "      <td>1.666656</td>\n",
       "      <td>1.237495</td>\n",
       "      <td>-4.239197</td>\n",
       "      <td>3.307630</td>\n",
       "      <td>3.221673</td>\n",
       "      <td>0.151988</td>\n",
       "      <td>-4.218937</td>\n",
       "      <td>0.396615</td>\n",
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       "    <tr>\n",
       "      <th>2022-09-01</th>\n",
       "      <td>4.070943</td>\n",
       "      <td>0.446199</td>\n",
       "      <td>-0.373529</td>\n",
       "      <td>-0.104607</td>\n",
       "      <td>0.324159</td>\n",
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       "      <td>0.038723</td>\n",
       "      <td>1.500159</td>\n",
       "      <td>-0.977496</td>\n",
       "      <td>-1.426611</td>\n",
       "      <td>4.399554</td>\n",
       "      <td>3.892250</td>\n",
       "      <td>0.421167</td>\n",
       "      <td>2.722993</td>\n",
       "      <td>1.871829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-01</th>\n",
       "      <td>7.540325</td>\n",
       "      <td>0.826463</td>\n",
       "      <td>0.670917</td>\n",
       "      <td>-0.370866</td>\n",
       "      <td>0.516185</td>\n",
       "      <td>1.145398</td>\n",
       "      <td>0.028198</td>\n",
       "      <td>0.132339</td>\n",
       "      <td>2.948634</td>\n",
       "      <td>1.755737</td>\n",
       "      <td>-5.057788</td>\n",
       "      <td>7.005781</td>\n",
       "      <td>3.886185</td>\n",
       "      <td>0.539114</td>\n",
       "      <td>9.306036</td>\n",
       "      <td>3.567913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-01</th>\n",
       "      <td>5.422013</td>\n",
       "      <td>0.594284</td>\n",
       "      <td>-0.136854</td>\n",
       "      <td>-0.372654</td>\n",
       "      <td>0.309339</td>\n",
       "      <td>0.831231</td>\n",
       "      <td>0.123533</td>\n",
       "      <td>-0.014791</td>\n",
       "      <td>1.334088</td>\n",
       "      <td>-0.358137</td>\n",
       "      <td>-5.082168</td>\n",
       "      <td>4.198423</td>\n",
       "      <td>2.820257</td>\n",
       "      <td>2.361836</td>\n",
       "      <td>-1.040082</td>\n",
       "      <td>1.188877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-01</th>\n",
       "      <td>7.254484</td>\n",
       "      <td>0.795133</td>\n",
       "      <td>-0.230323</td>\n",
       "      <td>-0.113172</td>\n",
       "      <td>0.650224</td>\n",
       "      <td>1.083917</td>\n",
       "      <td>0.286616</td>\n",
       "      <td>0.051368</td>\n",
       "      <td>2.523764</td>\n",
       "      <td>-0.602736</td>\n",
       "      <td>-1.543413</td>\n",
       "      <td>8.824992</td>\n",
       "      <td>3.677589</td>\n",
       "      <td>5.479845</td>\n",
       "      <td>3.612164</td>\n",
       "      <td>3.814703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-01</th>\n",
       "      <td>11.708497</td>\n",
       "      <td>1.283318</td>\n",
       "      <td>-0.109517</td>\n",
       "      <td>0.116981</td>\n",
       "      <td>2.199880</td>\n",
       "      <td>1.058144</td>\n",
       "      <td>0.091467</td>\n",
       "      <td>0.170235</td>\n",
       "      <td>4.810509</td>\n",
       "      <td>-0.286598</td>\n",
       "      <td>1.595362</td>\n",
       "      <td>29.857277</td>\n",
       "      <td>3.590143</td>\n",
       "      <td>1.748772</td>\n",
       "      <td>11.970859</td>\n",
       "      <td>8.597759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-01</th>\n",
       "      <td>4.573717</td>\n",
       "      <td>0.501306</td>\n",
       "      <td>-0.036747</td>\n",
       "      <td>-0.257479</td>\n",
       "      <td>0.673796</td>\n",
       "      <td>0.739513</td>\n",
       "      <td>0.169428</td>\n",
       "      <td>0.055581</td>\n",
       "      <td>1.845396</td>\n",
       "      <td>-0.096165</td>\n",
       "      <td>-3.511445</td>\n",
       "      <td>9.144909</td>\n",
       "      <td>2.509069</td>\n",
       "      <td>3.239319</td>\n",
       "      <td>3.908415</td>\n",
       "      <td>2.823974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-01</th>\n",
       "      <td>3.420861</td>\n",
       "      <td>0.374946</td>\n",
       "      <td>-0.740070</td>\n",
       "      <td>0.067917</td>\n",
       "      <td>0.605683</td>\n",
       "      <td>0.773335</td>\n",
       "      <td>0.221200</td>\n",
       "      <td>0.078135</td>\n",
       "      <td>1.381145</td>\n",
       "      <td>-1.936705</td>\n",
       "      <td>0.926233</td>\n",
       "      <td>8.220472</td>\n",
       "      <td>2.623822</td>\n",
       "      <td>4.229140</td>\n",
       "      <td>5.494428</td>\n",
       "      <td>3.282607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-01</th>\n",
       "      <td>8.464188</td>\n",
       "      <td>0.927724</td>\n",
       "      <td>-0.988857</td>\n",
       "      <td>-0.234075</td>\n",
       "      <td>0.555502</td>\n",
       "      <td>0.727698</td>\n",
       "      <td>0.139858</td>\n",
       "      <td>0.162861</td>\n",
       "      <td>1.290710</td>\n",
       "      <td>-2.587762</td>\n",
       "      <td>-3.192266</td>\n",
       "      <td>7.539397</td>\n",
       "      <td>2.468984</td>\n",
       "      <td>2.673956</td>\n",
       "      <td>11.452309</td>\n",
       "      <td>3.831258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
       "      <td>-1.248513</td>\n",
       "      <td>-0.136844</td>\n",
       "      <td>-2.131596</td>\n",
       "      <td>-0.086464</td>\n",
       "      <td>0.753130</td>\n",
       "      <td>1.167374</td>\n",
       "      <td>-0.082423</td>\n",
       "      <td>0.142943</td>\n",
       "      <td>-0.373880</td>\n",
       "      <td>-5.578220</td>\n",
       "      <td>-1.179176</td>\n",
       "      <td>10.221646</td>\n",
       "      <td>3.960747</td>\n",
       "      <td>-1.575848</td>\n",
       "      <td>10.051679</td>\n",
       "      <td>2.093188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Industrial Production Index: Argentina  \\\n",
       "2023-01-01                                6.253442   \n",
       "2022-12-01                               -2.699487   \n",
       "2022-11-01                                0.842822   \n",
       "2022-10-01                                3.315654   \n",
       "2022-09-01                                4.070943   \n",
       "2022-08-01                                7.540325   \n",
       "2022-07-01                                5.422013   \n",
       "2022-06-01                                7.254484   \n",
       "2022-05-01                               11.708497   \n",
       "2022-04-01                                4.573717   \n",
       "2022-03-01                                3.420861   \n",
       "2022-02-01                                8.464188   \n",
       "2022-01-01                               -1.248513   \n",
       "\n",
       "            Industrial Production Index: Argentina Weighted  \\\n",
       "2023-01-01                                         0.786206   \n",
       "2022-12-01                                        -0.295879   \n",
       "2022-11-01                                         0.092378   \n",
       "2022-10-01                                         0.363415   \n",
       "2022-09-01                                         0.446199   \n",
       "2022-08-01                                         0.826463   \n",
       "2022-07-01                                         0.594284   \n",
       "2022-06-01                                         0.795133   \n",
       "2022-05-01                                         1.283318   \n",
       "2022-04-01                                         0.501306   \n",
       "2022-03-01                                         0.374946   \n",
       "2022-02-01                                         0.927724   \n",
       "2022-01-01                                        -0.136844   \n",
       "\n",
       "            Industrial Production Index: Brazil Weighted  \\\n",
       "2023-01-01                                      0.105222   \n",
       "2022-12-01                                     -0.143791   \n",
       "2022-11-01                                      0.312875   \n",
       "2022-10-01                                      0.472882   \n",
       "2022-09-01                                     -0.373529   \n",
       "2022-08-01                                      0.670917   \n",
       "2022-07-01                                     -0.136854   \n",
       "2022-06-01                                     -0.230323   \n",
       "2022-05-01                                     -0.109517   \n",
       "2022-04-01                                     -0.036747   \n",
       "2022-03-01                                     -0.740070   \n",
       "2022-02-01                                     -0.988857   \n",
       "2022-01-01                                     -2.131596   \n",
       "\n",
       "            Industrial Production Index: Chile Weighted  \\\n",
       "2023-01-01                                     0.028809   \n",
       "2022-12-01                                    -0.088544   \n",
       "2022-11-01                                    -0.368101   \n",
       "2022-10-01                                    -0.310842   \n",
       "2022-09-01                                    -0.104607   \n",
       "2022-08-01                                    -0.370866   \n",
       "2022-07-01                                    -0.372654   \n",
       "2022-06-01                                    -0.113172   \n",
       "2022-05-01                                     0.116981   \n",
       "2022-04-01                                    -0.257479   \n",
       "2022-03-01                                     0.067917   \n",
       "2022-02-01                                    -0.234075   \n",
       "2022-01-01                                    -0.086464   \n",
       "\n",
       "            Industrial Production Index: Colombia Weighted  \\\n",
       "2023-01-01                                        0.055843   \n",
       "2022-12-01                                        0.049423   \n",
       "2022-11-01                                        0.174637   \n",
       "2022-10-01                                        0.243706   \n",
       "2022-09-01                                        0.324159   \n",
       "2022-08-01                                        0.516185   \n",
       "2022-07-01                                        0.309339   \n",
       "2022-06-01                                        0.650224   \n",
       "2022-05-01                                        2.199880   \n",
       "2022-04-01                                        0.673796   \n",
       "2022-03-01                                        0.605683   \n",
       "2022-02-01                                        0.555502   \n",
       "2022-01-01                                        0.753130   \n",
       "\n",
       "            Industrial Production Index: Mexico Weighted  \\\n",
       "2023-01-01                                      0.806509   \n",
       "2022-12-01                                      0.879549   \n",
       "2022-11-01                                      1.024452   \n",
       "2022-10-01                                      0.949543   \n",
       "2022-09-01                                      1.147186   \n",
       "2022-08-01                                      1.145398   \n",
       "2022-07-01                                      0.831231   \n",
       "2022-06-01                                      1.083917   \n",
       "2022-05-01                                      1.058144   \n",
       "2022-04-01                                      0.739513   \n",
       "2022-03-01                                      0.773335   \n",
       "2022-02-01                                      0.727698   \n",
       "2022-01-01                                      1.167374   \n",
       "\n",
       "            Industrial Production Index: Peru Weighted  \\\n",
       "2023-01-01                                    0.053596   \n",
       "2022-12-01                                   -0.243209   \n",
       "2022-11-01                                   -0.104836   \n",
       "2022-10-01                                    0.007950   \n",
       "2022-09-01                                    0.022029   \n",
       "2022-08-01                                    0.028198   \n",
       "2022-07-01                                    0.123533   \n",
       "2022-06-01                                    0.286616   \n",
       "2022-05-01                                    0.091467   \n",
       "2022-04-01                                    0.169428   \n",
       "2022-03-01                                    0.221200   \n",
       "2022-02-01                                    0.139858   \n",
       "2022-01-01                                   -0.082423   \n",
       "\n",
       "            Industrial Production Index: Uruguay Weighted  \\\n",
       "2023-01-01                                       0.008346   \n",
       "2022-12-01                                       0.016298   \n",
       "2022-11-01                                      -0.084522   \n",
       "2022-10-01                                      -0.059997   \n",
       "2022-09-01                                       0.038723   \n",
       "2022-08-01                                       0.132339   \n",
       "2022-07-01                                      -0.014791   \n",
       "2022-06-01                                       0.051368   \n",
       "2022-05-01                                       0.170235   \n",
       "2022-04-01                                       0.055581   \n",
       "2022-03-01                                       0.078135   \n",
       "2022-02-01                                       0.162861   \n",
       "2022-01-01                                       0.142943   \n",
       "\n",
       "            Industrial Production Index: Latin America Weighted Average  \\\n",
       "2023-01-01                                           1.844532             \n",
       "2022-12-01                                           0.173846             \n",
       "2022-11-01                                           1.046883             \n",
       "2022-10-01                                           1.666656             \n",
       "2022-09-01                                           1.500159             \n",
       "2022-08-01                                           2.948634             \n",
       "2022-07-01                                           1.334088             \n",
       "2022-06-01                                           2.523764             \n",
       "2022-05-01                                           4.810509             \n",
       "2022-04-01                                           1.845396             \n",
       "2022-03-01                                           1.381145             \n",
       "2022-02-01                                           1.290710             \n",
       "2022-01-01                                          -0.373880             \n",
       "\n",
       "            Industrial Production Index: Brazil  \\\n",
       "2023-01-01                             0.269398   \n",
       "2022-12-01                            -0.376290   \n",
       "2022-11-01                             0.818770   \n",
       "2022-10-01                             1.237495   \n",
       "2022-09-01                            -0.977496   \n",
       "2022-08-01                             1.755737   \n",
       "2022-07-01                            -0.358137   \n",
       "2022-06-01                            -0.602736   \n",
       "2022-05-01                            -0.286598   \n",
       "2022-04-01                            -0.096165   \n",
       "2022-03-01                            -1.936705   \n",
       "2022-02-01                            -2.587762   \n",
       "2022-01-01                            -5.578220   \n",
       "\n",
       "            Industrial Production Index: Chile  \\\n",
       "2023-01-01                            0.469939   \n",
       "2022-12-01                           -1.207549   \n",
       "2022-11-01                           -5.020079   \n",
       "2022-10-01                           -4.239197   \n",
       "2022-09-01                           -1.426611   \n",
       "2022-08-01                           -5.057788   \n",
       "2022-07-01                           -5.082168   \n",
       "2022-06-01                           -1.543413   \n",
       "2022-05-01                            1.595362   \n",
       "2022-04-01                           -3.511445   \n",
       "2022-03-01                            0.926233   \n",
       "2022-02-01                           -3.192266   \n",
       "2022-01-01                           -1.179176   \n",
       "\n",
       "            Industrial Production Index: Colombia  \\\n",
       "2023-01-01                               0.796217   \n",
       "2022-12-01                               0.670779   \n",
       "2022-11-01                               2.370211   \n",
       "2022-10-01                               3.307630   \n",
       "2022-09-01                               4.399554   \n",
       "2022-08-01                               7.005781   \n",
       "2022-07-01                               4.198423   \n",
       "2022-06-01                               8.824992   \n",
       "2022-05-01                              29.857277   \n",
       "2022-04-01                               9.144909   \n",
       "2022-03-01                               8.220472   \n",
       "2022-02-01                               7.539397   \n",
       "2022-01-01                              10.221646   \n",
       "\n",
       "            Industrial Production Index: Mexico  \\\n",
       "2023-01-01                             2.800173   \n",
       "2022-12-01                             2.984193   \n",
       "2022-11-01                             3.475831   \n",
       "2022-10-01                             3.221673   \n",
       "2022-09-01                             3.892250   \n",
       "2022-08-01                             3.886185   \n",
       "2022-07-01                             2.820257   \n",
       "2022-06-01                             3.677589   \n",
       "2022-05-01                             3.590143   \n",
       "2022-04-01                             2.509069   \n",
       "2022-03-01                             2.623822   \n",
       "2022-02-01                             2.468984   \n",
       "2022-01-01                             3.960747   \n",
       "\n",
       "            Industrial Production Index: Peru  \\\n",
       "2023-01-01                           1.077854   \n",
       "2022-12-01                          -4.649946   \n",
       "2022-11-01                          -2.004373   \n",
       "2022-10-01                           0.151988   \n",
       "2022-09-01                           0.421167   \n",
       "2022-08-01                           0.539114   \n",
       "2022-07-01                           2.361836   \n",
       "2022-06-01                           5.479845   \n",
       "2022-05-01                           1.748772   \n",
       "2022-04-01                           3.239319   \n",
       "2022-03-01                           4.229140   \n",
       "2022-02-01                           2.673956   \n",
       "2022-01-01                          -1.575848   \n",
       "\n",
       "            Industrial Production Index: Uruguay  \\\n",
       "2023-01-01                              0.575253   \n",
       "2022-12-01                              1.146046   \n",
       "2022-11-01                             -5.943554   \n",
       "2022-10-01                             -4.218937   \n",
       "2022-09-01                              2.722993   \n",
       "2022-08-01                              9.306036   \n",
       "2022-07-01                             -1.040082   \n",
       "2022-06-01                              3.612164   \n",
       "2022-05-01                             11.970859   \n",
       "2022-04-01                              3.908415   \n",
       "2022-03-01                              5.494428   \n",
       "2022-02-01                             11.452309   \n",
       "2022-01-01                             10.051679   \n",
       "\n",
       "            Industrial Production Index: Latin America  \n",
       "2023-01-01                                    1.748897  \n",
       "2022-12-01                                   -0.590322  \n",
       "2022-11-01                                   -0.780053  \n",
       "2022-10-01                                    0.396615  \n",
       "2022-09-01                                    1.871829  \n",
       "2022-08-01                                    3.567913  \n",
       "2022-07-01                                    1.188877  \n",
       "2022-06-01                                    3.814703  \n",
       "2022-05-01                                    8.597759  \n",
       "2022-04-01                                    2.823974  \n",
       "2022-03-01                                    3.282607  \n",
       "2022-02-01                                    3.831258  \n",
       "2022-01-01                                    2.093188  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0012aaac",
   "metadata": {
    "id": "0012aaac"
   },
   "source": [
    "### Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "eddeaea0",
   "metadata": {
    "id": "eddeaea0"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"1002\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  const force = true;\n",
       "\n",
       "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  const JS_MIME_TYPE = 'application/javascript';\n",
       "  const HTML_MIME_TYPE = 'text/html';\n",
       "  const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  const CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    const script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    const cell = handle.cell;\n",
       "\n",
       "    const id = cell.output_area._bokeh_element_id;\n",
       "    const server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id != null && id in Bokeh.index) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd_clean, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            const id = msg.content.text.trim();\n",
       "            if (id in Bokeh.index) {\n",
       "              Bokeh.index[id].model.document.clear();\n",
       "              delete Bokeh.index[id];\n",
       "            }\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd_destroy);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    const output_area = handle.output_area;\n",
       "    const output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      const bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      const script_attrs = bk_div.children[0].attributes;\n",
       "      for (let i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      const toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[toinsert.length - 1]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    const events = require('base/js/events');\n",
       "    const OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  const NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    const el = document.getElementById(\"1002\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
       "        if (callback != null)\n",
       "          callback();\n",
       "      });\n",
       "    } finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(css_urls, js_urls, callback) {\n",
       "    if (css_urls == null) css_urls = [];\n",
       "    if (js_urls == null) js_urls = [];\n",
       "\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
       "\n",
       "    function on_load() {\n",
       "      root._bokeh_is_loading--;\n",
       "      if (root._bokeh_is_loading === 0) {\n",
       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
       "        run_callbacks()\n",
       "      }\n",
       "    }\n",
       "\n",
       "    function on_error(url) {\n",
       "      console.error(\"failed to load \" + url);\n",
       "    }\n",
       "\n",
       "    for (let i = 0; i < css_urls.length; i++) {\n",
       "      const url = css_urls[i];\n",
       "      const element = document.createElement(\"link\");\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error.bind(null, url);\n",
       "      element.rel = \"stylesheet\";\n",
       "      element.type = \"text/css\";\n",
       "      element.href = url;\n",
       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
       "      document.body.appendChild(element);\n",
       "    }\n",
       "\n",
       "    for (let i = 0; i < js_urls.length; i++) {\n",
       "      const url = js_urls[i];\n",
       "      const element = document.createElement('script');\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error.bind(null, url);\n",
       "      element.async = false;\n",
       "      element.src = url;\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "  };\n",
       "\n",
       "  function inject_raw_css(css) {\n",
       "    const element = document.createElement(\"style\");\n",
       "    element.appendChild(document.createTextNode(css));\n",
       "    document.body.appendChild(element);\n",
       "  }\n",
       "\n",
       "  \n",
       "  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.2.min.js\"];\n",
       "  const css_urls = [];\n",
       "  \n",
       "\n",
       "  const inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "    \n",
       "    \n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if (root.Bokeh !== undefined || force === true) {\n",
       "      \n",
       "    for (let i = 0; i < inline_js.length; i++) {\n",
       "      inline_js[i].call(root, root.Bokeh);\n",
       "    }\n",
       "    if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      const cell = $(document.getElementById(\"1002\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(css_urls, js_urls, function() {\n",
       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"1002\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  \n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.2.min.js\"];\n  const css_urls = [];\n  \n\n  const inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    function(Bokeh) {\n    \n    \n    }\n  ];\n\n  function run_inline_js() {\n    \n    if (root.Bokeh !== undefined || force === true) {\n      \n    for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\n    if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"1002\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Allows bokeh to display visuals in Jupyter Notebook\n",
    "output_notebook()\n",
    "\n",
    "def create_chart(dtf, chart_type, chart_style):\n",
    "    source = ColumnDataSource(dtf)\n",
    "    p = figure(x_axis_type=\"datetime\", title=chart_style['title'], width=450, height=450)\n",
    "    p.title.text_font_size = str(chart_style['title_params']['fontsize']) + \"pt\"\n",
    "    p.title.align = \"center\"\n",
    "    p.title.text_font_style = chart_style['title_params']['fontweight']\n",
    "    p.xaxis.axis_label = chart_style['xlabel']\n",
    "    p.yaxis.axis_label = chart_style['ylabel']\n",
    "    p.toolbar.autohide = True\n",
    "    \n",
    "    bar_width = 0.9 * 30 * 24 * 60 * 60 * 1000 / len(dtf.columns[:])\n",
    "    \n",
    "    colors = bokeh.palettes.d3['Category20'][20]\n",
    "\n",
    "    for i, value_column in enumerate(dtf.columns[:]):\n",
    "        if chart_type == 'line':\n",
    "            TOOLTIPS = [\n",
    "            (\"Value\", \"$y{0.0}\")\n",
    "                ]\n",
    "\n",
    "            p.line(x='index', y=value_column, source=source, legend_label=value_column, color = colors[i])          \n",
    "            p.circle(x='index', y=value_column, source=source, legend_label=value_column, color = colors[i])\n",
    "            # agregar herramienta de hover\n",
    "            hover = HoverTool(tooltips=TOOLTIPS)\n",
    "            p.add_tools(hover)\n",
    "            \n",
    "        elif chart_type == 'bar':\n",
    "\n",
    "            formatter = CustomJSHover(args=dict(source=source),\n",
    "                                    code=\"compute percentage with JavaScript based on source, data_x, and data_y\")\n",
    "            TOOLTIPS = [\n",
    "            (\"Value\", \"@height{0.0}\")\n",
    "                ]\n",
    "\n",
    "            #  The `{%}` is needed just to trigger the formatter.\n",
    "            # HoverTool(tooltips=TOOLTIPS, formatters={'@y':formatter})\n",
    "\n",
    "\n",
    "            p.vbar(x=dodge('index', i * bar_width, range=p.x_range), top=value_column, source=source, width=bar_width, legend_label=value_column, color = colors[i])\n",
    "                        # agregar herramienta de hover\n",
    "\n",
    "            hover = p.select(dict(type=HoverTool))\n",
    "            hover.tooltips = [(\"Value\", \"@height\")]\n",
    "            # p.add_tools(hover)\n",
    "            \n",
    "  \n",
    "    p.legend.label_text_font_size = \"8pt\"\n",
    "    p.legend.click_policy = \"mute\"\n",
    "    \n",
    "    # Legend glyph (marker) properties\n",
    "    p.legend.glyph_width = 8\n",
    "    p.legend.glyph_height = 8\n",
    "    \n",
    "    citation = Label(x=300, y=-50, x_units='screen', y_units='screen',\n",
    "                 text='Source: CEIC Data', render_mode='css',\n",
    "                 text_font_size='8pt', border_line_alpha=1.0,\n",
    "                 background_fill_color=None, background_fill_alpha=1.0)\n",
    "    \n",
    "    p.add_layout(citation)\n",
    "    \n",
    "    # Legend background and border properties\n",
    "#     p.legend.background_fill_color = \"lightgray\"\n",
    "    \n",
    "    start_picker = DatePicker(title=\"Start Date\", value=pd.to_datetime(dtf.index.min()).strftime('%Y-%m-%d'), width=150)\n",
    "    end_picker = DatePicker(title=\"End Date\", value=pd.to_datetime(dtf.index.max()).strftime('%Y-%m-%d'), width=150)\n",
    "\n",
    "    \n",
    "    callback = CustomJS(args=dict(source=source, start_picker=start_picker, end_picker=end_picker), code=\"\"\"\n",
    "        var start_date = new Date(start_picker.value);\n",
    "        var end_date = new Date(end_picker.value);\n",
    "        var data = source.data;\n",
    "        \n",
    "        var new_data = {'index': []};\n",
    "        \n",
    "        for (var key in data) {\n",
    "            if (key != 'index') {\n",
    "                new_data[key] = [];\n",
    "            }\n",
    "        }\n",
    "        \n",
    "        for (var i = 0; i < data['index'].length; i++) {\n",
    "            var date = new Date(data['index'][i]);\n",
    "            \n",
    "            if (date >= start_date && date <= end_date) {\n",
    "                new_data['index'].push(data['index'][i]);\n",
    "                for (var key in new_data) {\n",
    "                    if (key != 'index') {\n",
    "                        new_data[key].push(data[key][i]);\n",
    "                    }\n",
    "                }\n",
    "            }\n",
    "        }\n",
    "        \n",
    "        source.data = new_data;\n",
    "        source.change.emit();\n",
    "    \"\"\")\n",
    "    \n",
    "    start_picker.js_on_change(\"value\", callback)\n",
    "    end_picker.js_on_change(\"value\", callback)\n",
    "\n",
    "    return p, (Row(start_picker, end_picker),)\n",
    "\n",
    "\n",
    "def plot_dataframes(dataframes, chart_types, chart_styles):\n",
    "    rows = []\n",
    "    for i, (df, chart_type, chart_style) in enumerate(zip(dataframes, chart_types, chart_styles)):\n",
    "        p, widgets = create_chart(df, chart_type, chart_style)\n",
    "        rows.append(column([column(*widgets), p]))\n",
    "\n",
    "    grid = gridplot([rows[0:2], rows[2:]])\n",
    "    show(grid)\n",
    "\n",
    "\n",
    "# # Keep your existing dtf_list, chart_types, and chart_styles\n",
    "# plot_dataframes(dtf_list, chart_types, chart_styles)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f7b5c8e",
   "metadata": {
    "id": "7f7b5c8e"
   },
   "source": [
    "### Dataframes\n",
    "> We have to slice the big dataframe into smaller datasets to plot each one of them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fc807405",
   "metadata": {
    "id": "fc807405"
   },
   "outputs": [],
   "source": [
    "dtf1 = data.loc[:,['Industrial Production Index: Latin America Weighted Average'\n",
    "                   ,'Industrial Production Index: Latin America']].copy()\n",
    "\n",
    "dtf2 = data.loc[:,['Industrial Production Index: Argentina',\n",
    "                   'Industrial Production Index: Brazil',\n",
    "                   'Industrial Production Index: Chile',\n",
    "                   'Industrial Production Index: Colombia',\n",
    "                   'Industrial Production Index: Mexico',\n",
    "                   'Industrial Production Index: Peru',\n",
    "                   'Industrial Production Index: Uruguay']].copy()\n",
    "\n",
    "dtf3 = data.loc[:,['Industrial Production Index: Argentina',\n",
    "                   'Industrial Production Index: Brazil',\n",
    "                   'Industrial Production Index: Colombia',\n",
    "                   'Industrial Production Index: Mexico',]].copy()\n",
    "\n",
    "dtf4 = data.loc[:,['Industrial Production Index: Peru',\n",
    "                   'Industrial Production Index: Chile',\n",
    "                   'Industrial Production Index: Uruguay']].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e267dd9c",
   "metadata": {
    "id": "e267dd9c"
   },
   "source": [
    "### Styles\n",
    "> The style of each chart is defined separately "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0981ffa2",
   "metadata": {
    "id": "0981ffa2"
   },
   "outputs": [],
   "source": [
    "dtf_list = [dtf1,dtf2,dtf3,dtf4]\n",
    "chart_types = ['bar','bar','line','line']  \n",
    "chart_styles = [\n",
    "    {\n",
    "        \"title\": \"Industrial Production Index: Latin America\",\n",
    "        \"title_params\": {\n",
    "            \"fontsize\": 10,\n",
    "            \"fontweight\": \"bold\",\n",
    "            \"pad\" : 25\n",
    "        },\n",
    "        \"xlabel\": \"Month\",\n",
    "        \"ylabel\": \"%\"\n",
    "    },\n",
    "    {\n",
    "        \"title\": \"Industrial Production Index: Latin America\",\n",
    "        \"title_params\": {\n",
    "            \"fontsize\": 10,\n",
    "            \"fontweight\": \"bold\",\n",
    "            \"pad\" : 25\n",
    "        },\n",
    "        \"xlabel\": \"Month\",\n",
    "        \"ylabel\": \"%\"\n",
    "    },\n",
    "    {\n",
    "        \"title\": \"Moderate Slowdown\",\n",
    "        \"title_params\": {\n",
    "            \"fontsize\": 10,\n",
    "            \"fontweight\": \"bold\",\n",
    "            \"pad\" : 25\n",
    "        },\n",
    "        \"xlabel\": \"Month\",\n",
    "        \"ylabel\": \"%\",\n",
    "    },\n",
    "    {\n",
    "        \"title\": \"More Pronounced Slowdown\",\n",
    "        \"title_params\": {\n",
    "            \"fontsize\": 10,\n",
    "            \"fontweight\": \"bold\",\n",
    "            \"pad\" : 25\n",
    "        },\n",
    "        \"xlabel\": \"Month\",\n",
    "        \"ylabel\": \"%\",\n",
    "    }\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1f1a223",
   "metadata": {
    "id": "c1f1a223"
   },
   "source": [
    "# Code execution"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb9a784a",
   "metadata": {
    "id": "cb9a784a"
   },
   "source": [
    "### Industrial production in Latin America already cooling after the 2021 revival"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "261daf8d",
   "metadata": {
    "id": "261daf8d"
   },
   "source": [
    "Industrial activity in Latin America is starting to slow down, according to data from CEIC's Latin America Economic Activity Tracker. The industrial production of seven Latin American countries (Argentina, Brazil, Chile, Colombia, Peru, Mexico, and Uruguay) grew at a weighted average of 1% y/y in November 2022, the third deceleration in a row following a recent peak of 5.1% y/y in May 2022.\n",
    "\n",
    "The positive change figure for the region is driven by the industrial production in Argentina, Brazil, Colombia and Mexico, which grew at positive rates in November 2022. On the other hand, in Chile, Peru and Uruguay industrial production is already lower than 2021. Brazil is a specific case, recording four straight months of positive growth rate as of November after decreases on an annual basis for 11 consecutive months between August 2021 and July 2022. Industrial Production in Brazil was affected by the severe drought observed in H2 2021, that increased energy costs for producers. In addition, rising inflation affected households' purchasing power, further discouraging the production of industrial goods.\n",
    "\n",
    "Latin America's industrial sector is faced with different challenges stemming from the current macroeconomic environment. Higher consumer prices are negatively impacting household demand for consumer goods, mainly manufactured ones. Further, high input prices and high interest rates are putting the brakes on industrial activity. Finally, Latin America is already starting to feel the lower external demand as a consequence of the global slowdown, decreasing the export revenues from the key trade partners, which could lead to further slowdown in the coming months, until demand for industrial goods strengthens again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "44dc5d7a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "collapsed": true,
    "id": "44dc5d7a",
    "outputId": "9e900bb2-7c58-488e-f826-7ec76f61be6f"
   },
   "outputs": [
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Mexico\"}},\"id\":\"1780\",\"type\":\"Line\"},{\"attributes\":{\"months\":[0,4,8]},\"id\":\"1214\",\"type\":\"MonthsTicker\"},{\"attributes\":{},\"id\":\"1007\",\"type\":\"DataRange1d\"},{\"attributes\":{\"label\":{\"value\":\"Industrial Production Index: Mexico\"},\"renderers\":[{\"id\":\"1783\"},{\"id\":\"1812\"}]},\"id\":\"1807\",\"type\":\"LegendItem\"},{\"attributes\":{\"axis_label\":\"Month\",\"coordinates\":null,\"formatter\":{\"id\":\"1051\"},\"group\":null,\"major_label_policy\":{\"id\":\"1052\"},\"ticker\":{\"id\":\"1016\"}},\"id\":\"1015\",\"type\":\"DatetimeAxis\"},{\"attributes\":{\"fill_color\":{\"value\":\"#ffbb78\"},\"hatch_color\":{\"value\":\"#ffbb78\"},\"line_color\":{\"value\":\"#ffbb78\"},\"x\":{\"field\":\"index\"},\"y\":{\"field\":\"Industrial Production Index: Mexico\"}},\"id\":\"1809\",\"type\":\"Circle\"},{\"attributes\":{\"fill_color\":{\"value\":\"#1f77b4\"},\"hatch_color\":{\"value\":\"#1f77b4\"},\"line_color\":{\"value\":\"#1f77b4\"},\"top\":{\"field\":\"Industrial Production Index: Latin America Weighted Average\"},\"width\":{\"value\":1166400000.0},\"x\":{\"field\":\"index\",\"transform\":{\"id\":\"1039\"}}},\"id\":\"1041\",\"type\":\"VBar\"}],\"root_ids\":[\"2160\"]},\"title\":\"Bokeh Application\",\"version\":\"2.4.2\"}};\n",
       "  const render_items = [{\"docid\":\"970e2b85-80e5-45b3-968c-3643b1926d69\",\"root_ids\":[\"2160\"],\"roots\":{\"2160\":\"37fa9205-3a8a-4210-aef4-3f58fd5e2651\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    let attempts = 0;\n",
       "    const timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "2160"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_dataframes(dtf_list, chart_types, chart_styles)"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
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   "cell_type": "markdown",
   "id": "b9be2132",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
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   "id": "3ec2ba0a",
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   "source": [
    "*Note that the charts can be updated interactively and downloaded in .png "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e14a7740",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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