OECD.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![This is an image](Quant-Trading.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\">\n",
    "Please visit our website <a href=\"https://www.quant-trading.co\" target=\"_blank\">quant-trading.co</a> for more tools on quantitative finance and data science.\n",
    "</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install pandas-datareader\n",
    "# !pip install requests\n",
    "# !pip install io"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **¿How to download data from the OECD database?**\n",
    "\n",
    "\n",
    "<font size=\"3\"> The OECD provides access to datasets in the catalogue of OECD databases through a RESTful application programming interface (API) based on the SDMX-ML standard.  This allows a developer to easily call the API using simple RESTful URL programmatically. Fortunately we can use the pandas_datareader library for that purpose as we have shown previously. However, the OECD changed their way to acces the data and the pandas_datareader in not working as it used to be. Here, we still show the old way to use it but it is a little bit slower.<br><br>\n",
    "\n",
    "   \n",
    "<font size=\"3\">\n",
    "We will of course show how to download data directly using the requests library. This is the new way to do that since July 2024. We will explain how to organize the query to get the data.     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import pandas as pd\n",
    "import pandas_datareader.data as web  #Pandas Datareader\n",
    "import datetime\n",
    "import requests\n",
    "import io\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Download the data using a query**\n",
    "\n",
    "\n",
    "<font size=\"3\">\n",
    "The OECD API has some instructions on how to download data directly in the following link https://data.oecd.org/api/sdmx-ml-documentation/ . With this information we can use the pandas \"read_csv()\" function to read the data. In this particular example we will write some specific text within {sdmx_query}. The text will be \"MEI_FIN\" , which mean that we are using the MEI_FIN database to download the information contained there. This database is heavy, so it might take sometime to download this data. <BR>\n",
    "    \n",
    "<font size=\"3\">Below we show how to do that:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATAFLOW</th>\n",
       "      <th>REF_AREA</th>\n",
       "      <th>FREQ</th>\n",
       "      <th>MEASURE</th>\n",
       "      <th>UNIT_MEASURE</th>\n",
       "      <th>ACTIVITY</th>\n",
       "      <th>ADJUSTMENT</th>\n",
       "      <th>TRANSFORMATION</th>\n",
       "      <th>TIME_HORIZ</th>\n",
       "      <th>METHODOLOGY</th>\n",
       "      <th>TIME_PERIOD</th>\n",
       "      <th>OBS_VALUE</th>\n",
       "      <th>OBS_STATUS</th>\n",
       "      <th>UNIT_MULT</th>\n",
       "      <th>DECIMALS</th>\n",
       "      <th>BASE_PER</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)</td>\n",
       "      <td>GRC</td>\n",
       "      <td>A</td>\n",
       "      <td>IRSTCI</td>\n",
       "      <td>PA</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>N</td>\n",
       "      <td>1980</td>\n",
       "      <td>15.3000</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)</td>\n",
       "      <td>GRC</td>\n",
       "      <td>A</td>\n",
       "      <td>IRSTCI</td>\n",
       "      <td>PA</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>N</td>\n",
       "      <td>1981</td>\n",
       "      <td>15.1000</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)</td>\n",
       "      <td>GRC</td>\n",
       "      <td>A</td>\n",
       "      <td>IRSTCI</td>\n",
       "      <td>PA</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>N</td>\n",
       "      <td>1982</td>\n",
       "      <td>15.0000</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)</td>\n",
       "      <td>GRC</td>\n",
       "      <td>A</td>\n",
       "      <td>IRSTCI</td>\n",
       "      <td>PA</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>N</td>\n",
       "      <td>1983</td>\n",
       "      <td>12.5000</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)</td>\n",
       "      <td>GRC</td>\n",
       "      <td>A</td>\n",
       "      <td>IRSTCI</td>\n",
       "      <td>PA</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>N</td>\n",
       "      <td>1984</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199930</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)</td>\n",
       "      <td>EA19</td>\n",
       "      <td>M</td>\n",
       "      <td>CCRE</td>\n",
       "      <td>IX</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>N</td>\n",
       "      <td>2024-03</td>\n",
       "      <td>105.2679</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199931</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)</td>\n",
       "      <td>EA19</td>\n",
       "      <td>M</td>\n",
       "      <td>CCRE</td>\n",
       "      <td>IX</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>N</td>\n",
       "      <td>2024-04</td>\n",
       "      <td>104.9788</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199932</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)</td>\n",
       "      <td>EA19</td>\n",
       "      <td>M</td>\n",
       "      <td>CCRE</td>\n",
       "      <td>IX</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>N</td>\n",
       "      <td>2024-05</td>\n",
       "      <td>105.1068</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199933</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)</td>\n",
       "      <td>EA19</td>\n",
       "      <td>M</td>\n",
       "      <td>CCRE</td>\n",
       "      <td>IX</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>N</td>\n",
       "      <td>2024-06</td>\n",
       "      <td>104.7814</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199934</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)</td>\n",
       "      <td>EA19</td>\n",
       "      <td>M</td>\n",
       "      <td>CCRE</td>\n",
       "      <td>IX</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>N</td>\n",
       "      <td>2024-07</td>\n",
       "      <td>105.3012</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>199935 rows × 16 columns</p>\n",
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      ],
      "text/plain": [
       "                                      DATAFLOW REF_AREA FREQ MEASURE  \\\n",
       "0       OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)      GRC    A  IRSTCI   \n",
       "1       OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)      GRC    A  IRSTCI   \n",
       "2       OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)      GRC    A  IRSTCI   \n",
       "3       OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)      GRC    A  IRSTCI   \n",
       "4       OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)      GRC    A  IRSTCI   \n",
       "...                                        ...      ...  ...     ...   \n",
       "199930  OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)     EA19    M    CCRE   \n",
       "199931  OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)     EA19    M    CCRE   \n",
       "199932  OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)     EA19    M    CCRE   \n",
       "199933  OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)     EA19    M    CCRE   \n",
       "199934  OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)     EA19    M    CCRE   \n",
       "\n",
       "       UNIT_MEASURE ACTIVITY ADJUSTMENT TRANSFORMATION TIME_HORIZ METHODOLOGY  \\\n",
       "0                PA       _Z         _Z             _Z         _Z           N   \n",
       "1                PA       _Z         _Z             _Z         _Z           N   \n",
       "2                PA       _Z         _Z             _Z         _Z           N   \n",
       "3                PA       _Z         _Z             _Z         _Z           N   \n",
       "4                PA       _Z         _Z             _Z         _Z           N   \n",
       "...             ...      ...        ...            ...        ...         ...   \n",
       "199930           IX       _Z         _Z             _Z         _Z           N   \n",
       "199931           IX       _Z         _Z             _Z         _Z           N   \n",
       "199932           IX       _Z         _Z             _Z         _Z           N   \n",
       "199933           IX       _Z         _Z             _Z         _Z           N   \n",
       "199934           IX       _Z         _Z             _Z         _Z           N   \n",
       "\n",
       "       TIME_PERIOD  OBS_VALUE OBS_STATUS  UNIT_MULT  DECIMALS  BASE_PER  \n",
       "0             1980    15.3000          A          0         2       NaN  \n",
       "1             1981    15.1000          A          0         2       NaN  \n",
       "2             1982    15.0000          A          0         2       NaN  \n",
       "3             1983    12.5000          A          0         2       NaN  \n",
       "4             1984    16.0000          A          0         2       NaN  \n",
       "...            ...        ...        ...        ...       ...       ...  \n",
       "199930     2024-03   105.2679          A          0         2    2015.0  \n",
       "199931     2024-04   104.9788          A          0         2    2015.0  \n",
       "199932     2024-05   105.1068          A          0         2    2015.0  \n",
       "199933     2024-06   104.7814          A          0         2    2015.0  \n",
       "199934     2024-07   105.3012          A          0         2    2015.0  \n",
       "\n",
       "[199935 rows x 16 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "def get_from_oecd(sdmx_query):\n",
    "    return pd.read_csv(f\"https://stats.oecd.org/SDMX-JSON/data/{sdmx_query}?contentType=csv\")\n",
    "\n",
    "\n",
    "df = get_from_oecd(\"MEI_FIN\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Apply a filter to select the country or countries**\n",
    "\n",
    "\n",
    "<font size=\"3\">\n",
    "Here we are using the function unique, applied to the column \"REF_AREA\". In this way, we are able to visualize all the countries in the DataFrame. We will select \"AUS\" which stands for Australia<BR>\n",
    "    \n",
    "<font size=\"3\">Below we show how to do that:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['GRC', 'COL', 'BRA', 'NOR', 'DEU', 'SWE', 'ITA', 'FIN', 'LUX',\n",
       "       'HUN', 'FRA', 'AUT', 'AUS', 'IDN', 'SVK', 'ISR', 'NZL', 'ISL',\n",
       "       'CRI', 'USA', 'POL', 'PRT', 'LVA', 'MEX', 'ZAF', 'IRL', 'CHN',\n",
       "       'EA19', 'RUS', 'IND', 'GBR', 'ESP', 'ROU', 'NLD', 'JPN', 'EST',\n",
       "       'HRV', 'BEL', 'BGR', 'CHE', 'TUR', 'CZE', 'CHL', 'CAN', 'SAU',\n",
       "       'DNK', 'SDR', 'KOR', 'ARG', 'LTU', 'SVN'], dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['REF_AREA'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATAFLOW</th>\n",
       "      <th>REF_AREA</th>\n",
       "      <th>FREQ</th>\n",
       "      <th>MEASURE</th>\n",
       "      <th>UNIT_MEASURE</th>\n",
       "      <th>ACTIVITY</th>\n",
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       "      <th>TIME_HORIZ</th>\n",
       "      <th>METHODOLOGY</th>\n",
       "      <th>TIME_PERIOD</th>\n",
       "      <th>OBS_VALUE</th>\n",
       "      <th>OBS_STATUS</th>\n",
       "      <th>UNIT_MULT</th>\n",
       "      <th>DECIMALS</th>\n",
       "      <th>BASE_PER</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2277</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)</td>\n",
       "      <td>AUS</td>\n",
       "      <td>A</td>\n",
       "      <td>CCULC</td>\n",
       "      <td>IX</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>N</td>\n",
       "      <td>1970</td>\n",
       "      <td>96.676960</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2278</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)</td>\n",
       "      <td>AUS</td>\n",
       "      <td>A</td>\n",
       "      <td>CCULC</td>\n",
       "      <td>IX</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>N</td>\n",
       "      <td>1971</td>\n",
       "      <td>99.640120</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2279</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)</td>\n",
       "      <td>AUS</td>\n",
       "      <td>A</td>\n",
       "      <td>CCULC</td>\n",
       "      <td>IX</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>N</td>\n",
       "      <td>1972</td>\n",
       "      <td>100.591900</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2280</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)</td>\n",
       "      <td>AUS</td>\n",
       "      <td>A</td>\n",
       "      <td>CCULC</td>\n",
       "      <td>IX</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>N</td>\n",
       "      <td>1973</td>\n",
       "      <td>115.722400</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2281</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)</td>\n",
       "      <td>AUS</td>\n",
       "      <td>A</td>\n",
       "      <td>CCULC</td>\n",
       "      <td>IX</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>N</td>\n",
       "      <td>1974</td>\n",
       "      <td>129.865800</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191448</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)</td>\n",
       "      <td>AUS</td>\n",
       "      <td>A</td>\n",
       "      <td>CC</td>\n",
       "      <td>XDC_USD</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>N</td>\n",
       "      <td>2019</td>\n",
       "      <td>1.438669</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191449</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)</td>\n",
       "      <td>AUS</td>\n",
       "      <td>A</td>\n",
       "      <td>CC</td>\n",
       "      <td>XDC_USD</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>N</td>\n",
       "      <td>2020</td>\n",
       "      <td>1.453532</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191450</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)</td>\n",
       "      <td>AUS</td>\n",
       "      <td>A</td>\n",
       "      <td>CC</td>\n",
       "      <td>XDC_USD</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>N</td>\n",
       "      <td>2021</td>\n",
       "      <td>1.331484</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191451</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)</td>\n",
       "      <td>AUS</td>\n",
       "      <td>A</td>\n",
       "      <td>CC</td>\n",
       "      <td>XDC_USD</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>N</td>\n",
       "      <td>2022</td>\n",
       "      <td>1.441797</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191452</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)</td>\n",
       "      <td>AUS</td>\n",
       "      <td>A</td>\n",
       "      <td>CC</td>\n",
       "      <td>XDC_USD</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>N</td>\n",
       "      <td>2023</td>\n",
       "      <td>1.505082</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5917 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      DATAFLOW REF_AREA FREQ MEASURE  \\\n",
       "2277    OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)      AUS    A   CCULC   \n",
       "2278    OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)      AUS    A   CCULC   \n",
       "2279    OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)      AUS    A   CCULC   \n",
       "2280    OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)      AUS    A   CCULC   \n",
       "2281    OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)      AUS    A   CCULC   \n",
       "...                                        ...      ...  ...     ...   \n",
       "191448  OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)      AUS    A      CC   \n",
       "191449  OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)      AUS    A      CC   \n",
       "191450  OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)      AUS    A      CC   \n",
       "191451  OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)      AUS    A      CC   \n",
       "191452  OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)      AUS    A      CC   \n",
       "\n",
       "       UNIT_MEASURE ACTIVITY ADJUSTMENT TRANSFORMATION TIME_HORIZ METHODOLOGY  \\\n",
       "2277             IX       _Z         _Z             _Z         _Z           N   \n",
       "2278             IX       _Z         _Z             _Z         _Z           N   \n",
       "2279             IX       _Z         _Z             _Z         _Z           N   \n",
       "2280             IX       _Z         _Z             _Z         _Z           N   \n",
       "2281             IX       _Z         _Z             _Z         _Z           N   \n",
       "...             ...      ...        ...            ...        ...         ...   \n",
       "191448      XDC_USD       _Z         _Z             _Z         _Z           N   \n",
       "191449      XDC_USD       _Z         _Z             _Z         _Z           N   \n",
       "191450      XDC_USD       _Z         _Z             _Z         _Z           N   \n",
       "191451      XDC_USD       _Z         _Z             _Z         _Z           N   \n",
       "191452      XDC_USD       _Z         _Z             _Z         _Z           N   \n",
       "\n",
       "       TIME_PERIOD   OBS_VALUE OBS_STATUS  UNIT_MULT  DECIMALS  BASE_PER  \n",
       "2277          1970   96.676960          A          0         2    2015.0  \n",
       "2278          1971   99.640120          A          0         2    2015.0  \n",
       "2279          1972  100.591900          A          0         2    2015.0  \n",
       "2280          1973  115.722400          A          0         2    2015.0  \n",
       "2281          1974  129.865800          A          0         2    2015.0  \n",
       "...            ...         ...        ...        ...       ...       ...  \n",
       "191448        2019    1.438669          A          0         2       NaN  \n",
       "191449        2020    1.453532          A          0         2       NaN  \n",
       "191450        2021    1.331484          A          0         2       NaN  \n",
       "191451        2022    1.441797          A          0         2       NaN  \n",
       "191452        2023    1.505082          A          0         2       NaN  \n",
       "\n",
       "[5917 rows x 16 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.loc[df['REF_AREA'] ==  'AUS']\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Apply a filter to select the indicator**\n",
    "\n",
    "\n",
    "<font size=\"3\">\n",
    "Here we are using the function unique, applied to the column \"MEASURE\". In this way, we are able to visualize all the indicators in the DataFrame. We will select \"IRLT\" which stands for Long Term Interest Rates<BR>\n",
    "    \n",
    "<font size=\"3\">Below we show how to do that:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['CCULC', 'CCRE', 'IRLT', 'IR3TIB', 'IRSTCI', 'CC', 'SHARE'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['MEASURE'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATAFLOW</th>\n",
       "      <th>REF_AREA</th>\n",
       "      <th>FREQ</th>\n",
       "      <th>MEASURE</th>\n",
       "      <th>UNIT_MEASURE</th>\n",
       "      <th>ACTIVITY</th>\n",
       "      <th>ADJUSTMENT</th>\n",
       "      <th>TRANSFORMATION</th>\n",
       "      <th>TIME_HORIZ</th>\n",
       "      <th>METHODOLOGY</th>\n",
       "      <th>TIME_PERIOD</th>\n",
       "      <th>OBS_VALUE</th>\n",
       "      <th>OBS_STATUS</th>\n",
       "      <th>UNIT_MULT</th>\n",
       "      <th>DECIMALS</th>\n",
       "      <th>BASE_PER</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19839</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)</td>\n",
       "      <td>AUS</td>\n",
       "      <td>A</td>\n",
       "      <td>IRLT</td>\n",
       "      <td>PA</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>N</td>\n",
       "      <td>1970</td>\n",
       "      <td>6.646667</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19840</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)</td>\n",
       "      <td>AUS</td>\n",
       "      <td>A</td>\n",
       "      <td>IRLT</td>\n",
       "      <td>PA</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>N</td>\n",
       "      <td>1971</td>\n",
       "      <td>6.713333</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19841</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)</td>\n",
       "      <td>AUS</td>\n",
       "      <td>A</td>\n",
       "      <td>IRLT</td>\n",
       "      <td>PA</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>N</td>\n",
       "      <td>1972</td>\n",
       "      <td>5.831666</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19842</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)</td>\n",
       "      <td>AUS</td>\n",
       "      <td>A</td>\n",
       "      <td>IRLT</td>\n",
       "      <td>PA</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>N</td>\n",
       "      <td>1973</td>\n",
       "      <td>6.933333</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19843</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)</td>\n",
       "      <td>AUS</td>\n",
       "      <td>A</td>\n",
       "      <td>IRLT</td>\n",
       "      <td>PA</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>N</td>\n",
       "      <td>1974</td>\n",
       "      <td>9.036667</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98461</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)</td>\n",
       "      <td>AUS</td>\n",
       "      <td>Q</td>\n",
       "      <td>IRLT</td>\n",
       "      <td>PA</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>N</td>\n",
       "      <td>2023-Q2</td>\n",
       "      <td>3.586333</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98462</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)</td>\n",
       "      <td>AUS</td>\n",
       "      <td>Q</td>\n",
       "      <td>IRLT</td>\n",
       "      <td>PA</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>N</td>\n",
       "      <td>2023-Q3</td>\n",
       "      <td>4.125000</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98463</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)</td>\n",
       "      <td>AUS</td>\n",
       "      <td>Q</td>\n",
       "      <td>IRLT</td>\n",
       "      <td>PA</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>N</td>\n",
       "      <td>2023-Q4</td>\n",
       "      <td>4.463666</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98464</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)</td>\n",
       "      <td>AUS</td>\n",
       "      <td>Q</td>\n",
       "      <td>IRLT</td>\n",
       "      <td>PA</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>N</td>\n",
       "      <td>2024-Q1</td>\n",
       "      <td>4.111333</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98465</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)</td>\n",
       "      <td>AUS</td>\n",
       "      <td>Q</td>\n",
       "      <td>IRLT</td>\n",
       "      <td>PA</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>N</td>\n",
       "      <td>2024-Q2</td>\n",
       "      <td>4.278333</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>935 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     DATAFLOW REF_AREA FREQ MEASURE  \\\n",
       "19839  OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)      AUS    A    IRLT   \n",
       "19840  OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)      AUS    A    IRLT   \n",
       "19841  OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)      AUS    A    IRLT   \n",
       "19842  OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)      AUS    A    IRLT   \n",
       "19843  OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)      AUS    A    IRLT   \n",
       "...                                       ...      ...  ...     ...   \n",
       "98461  OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)      AUS    Q    IRLT   \n",
       "98462  OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)      AUS    Q    IRLT   \n",
       "98463  OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)      AUS    Q    IRLT   \n",
       "98464  OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)      AUS    Q    IRLT   \n",
       "98465  OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)      AUS    Q    IRLT   \n",
       "\n",
       "      UNIT_MEASURE ACTIVITY ADJUSTMENT TRANSFORMATION TIME_HORIZ METHODOLOGY  \\\n",
       "19839           PA       _Z         _Z             _Z         _Z           N   \n",
       "19840           PA       _Z         _Z             _Z         _Z           N   \n",
       "19841           PA       _Z         _Z             _Z         _Z           N   \n",
       "19842           PA       _Z         _Z             _Z         _Z           N   \n",
       "19843           PA       _Z         _Z             _Z         _Z           N   \n",
       "...            ...      ...        ...            ...        ...         ...   \n",
       "98461           PA       _Z         _Z             _Z         _Z           N   \n",
       "98462           PA       _Z         _Z             _Z         _Z           N   \n",
       "98463           PA       _Z         _Z             _Z         _Z           N   \n",
       "98464           PA       _Z         _Z             _Z         _Z           N   \n",
       "98465           PA       _Z         _Z             _Z         _Z           N   \n",
       "\n",
       "      TIME_PERIOD  OBS_VALUE OBS_STATUS  UNIT_MULT  DECIMALS  BASE_PER  \n",
       "19839        1970   6.646667          A          0         2       NaN  \n",
       "19840        1971   6.713333          A          0         2       NaN  \n",
       "19841        1972   5.831666          A          0         2       NaN  \n",
       "19842        1973   6.933333          A          0         2       NaN  \n",
       "19843        1974   9.036667          A          0         2       NaN  \n",
       "...           ...        ...        ...        ...       ...       ...  \n",
       "98461     2023-Q2   3.586333          A          0         2       NaN  \n",
       "98462     2023-Q3   4.125000          A          0         2       NaN  \n",
       "98463     2023-Q4   4.463666          A          0         2       NaN  \n",
       "98464     2024-Q1   4.111333          A          0         2       NaN  \n",
       "98465     2024-Q2   4.278333          A          0         2       NaN  \n",
       "\n",
       "[935 rows x 16 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.loc[df['MEASURE'] ==  'IRLT']\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Apply a filter to select the frequency**\n",
    "\n",
    "\n",
    "<font size=\"3\">\n",
    "Here we are using the function unique, applied to the column \"FREQ\". In this way, we are able to visualize all the frequancies in the DataFrame. We will select \"M\" which stands for Monthly<BR>\n",
    "    \n",
    "<font size=\"3\">Below we show how to do that:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['A', 'M', 'Q'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['FREQ'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATAFLOW</th>\n",
       "      <th>REF_AREA</th>\n",
       "      <th>FREQ</th>\n",
       "      <th>MEASURE</th>\n",
       "      <th>UNIT_MEASURE</th>\n",
       "      <th>ACTIVITY</th>\n",
       "      <th>ADJUSTMENT</th>\n",
       "      <th>TRANSFORMATION</th>\n",
       "      <th>TIME_HORIZ</th>\n",
       "      <th>METHODOLOGY</th>\n",
       "      <th>TIME_PERIOD</th>\n",
       "      <th>OBS_VALUE</th>\n",
       "      <th>OBS_STATUS</th>\n",
       "      <th>UNIT_MULT</th>\n",
       "      <th>DECIMALS</th>\n",
       "      <th>BASE_PER</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31083</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)</td>\n",
       "      <td>AUS</td>\n",
       "      <td>M</td>\n",
       "      <td>IRLT</td>\n",
       "      <td>PA</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>N</td>\n",
       "      <td>1969-07</td>\n",
       "      <td>5.800</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31084</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)</td>\n",
       "      <td>AUS</td>\n",
       "      <td>M</td>\n",
       "      <td>IRLT</td>\n",
       "      <td>PA</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>N</td>\n",
       "      <td>1969-08</td>\n",
       "      <td>5.790</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31085</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)</td>\n",
       "      <td>AUS</td>\n",
       "      <td>M</td>\n",
       "      <td>IRLT</td>\n",
       "      <td>PA</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>N</td>\n",
       "      <td>1969-09</td>\n",
       "      <td>5.810</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31086</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)</td>\n",
       "      <td>AUS</td>\n",
       "      <td>M</td>\n",
       "      <td>IRLT</td>\n",
       "      <td>PA</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>N</td>\n",
       "      <td>1969-10</td>\n",
       "      <td>5.830</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31087</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)</td>\n",
       "      <td>AUS</td>\n",
       "      <td>M</td>\n",
       "      <td>IRLT</td>\n",
       "      <td>PA</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>N</td>\n",
       "      <td>1969-11</td>\n",
       "      <td>5.850</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31739</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)</td>\n",
       "      <td>AUS</td>\n",
       "      <td>M</td>\n",
       "      <td>IRLT</td>\n",
       "      <td>PA</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>N</td>\n",
       "      <td>2024-03</td>\n",
       "      <td>4.045</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31740</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)</td>\n",
       "      <td>AUS</td>\n",
       "      <td>M</td>\n",
       "      <td>IRLT</td>\n",
       "      <td>PA</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>N</td>\n",
       "      <td>2024-04</td>\n",
       "      <td>4.268</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31741</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)</td>\n",
       "      <td>AUS</td>\n",
       "      <td>M</td>\n",
       "      <td>IRLT</td>\n",
       "      <td>PA</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>N</td>\n",
       "      <td>2024-05</td>\n",
       "      <td>4.325</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31742</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)</td>\n",
       "      <td>AUS</td>\n",
       "      <td>M</td>\n",
       "      <td>IRLT</td>\n",
       "      <td>PA</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>N</td>\n",
       "      <td>2024-06</td>\n",
       "      <td>4.242</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31743</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)</td>\n",
       "      <td>AUS</td>\n",
       "      <td>M</td>\n",
       "      <td>IRLT</td>\n",
       "      <td>PA</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>N</td>\n",
       "      <td>2024-07</td>\n",
       "      <td>4.326</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>661 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     DATAFLOW REF_AREA FREQ MEASURE  \\\n",
       "31083  OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)      AUS    M    IRLT   \n",
       "31084  OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)      AUS    M    IRLT   \n",
       "31085  OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)      AUS    M    IRLT   \n",
       "31086  OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)      AUS    M    IRLT   \n",
       "31087  OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)      AUS    M    IRLT   \n",
       "...                                       ...      ...  ...     ...   \n",
       "31739  OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)      AUS    M    IRLT   \n",
       "31740  OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)      AUS    M    IRLT   \n",
       "31741  OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)      AUS    M    IRLT   \n",
       "31742  OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)      AUS    M    IRLT   \n",
       "31743  OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)      AUS    M    IRLT   \n",
       "\n",
       "      UNIT_MEASURE ACTIVITY ADJUSTMENT TRANSFORMATION TIME_HORIZ METHODOLOGY  \\\n",
       "31083           PA       _Z         _Z             _Z         _Z           N   \n",
       "31084           PA       _Z         _Z             _Z         _Z           N   \n",
       "31085           PA       _Z         _Z             _Z         _Z           N   \n",
       "31086           PA       _Z         _Z             _Z         _Z           N   \n",
       "31087           PA       _Z         _Z             _Z         _Z           N   \n",
       "...            ...      ...        ...            ...        ...         ...   \n",
       "31739           PA       _Z         _Z             _Z         _Z           N   \n",
       "31740           PA       _Z         _Z             _Z         _Z           N   \n",
       "31741           PA       _Z         _Z             _Z         _Z           N   \n",
       "31742           PA       _Z         _Z             _Z         _Z           N   \n",
       "31743           PA       _Z         _Z             _Z         _Z           N   \n",
       "\n",
       "      TIME_PERIOD  OBS_VALUE OBS_STATUS  UNIT_MULT  DECIMALS  BASE_PER  \n",
       "31083     1969-07      5.800          A          0         2       NaN  \n",
       "31084     1969-08      5.790          A          0         2       NaN  \n",
       "31085     1969-09      5.810          A          0         2       NaN  \n",
       "31086     1969-10      5.830          A          0         2       NaN  \n",
       "31087     1969-11      5.850          A          0         2       NaN  \n",
       "...           ...        ...        ...        ...       ...       ...  \n",
       "31739     2024-03      4.045          A          0         2       NaN  \n",
       "31740     2024-04      4.268          A          0         2       NaN  \n",
       "31741     2024-05      4.325          A          0         2       NaN  \n",
       "31742     2024-06      4.242          A          0         2       NaN  \n",
       "31743     2024-07      4.326          A          0         2       NaN  \n",
       "\n",
       "[661 rows x 16 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.loc[df['FREQ'] ==  'M']\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Plot the information**\n",
    "\n",
    "\n",
    "<font size=\"3\">\n",
    "We can plot the information very easily using the function plot from the library matplotlib.pyplot. <BR>\n",
    "    \n",
    "<font size=\"3\">Below we show how to do that:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2370f545f40>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df['OBS_VALUE'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Use the new standard way to download the data**\n",
    "\n",
    "\n",
    "<font size=\"3\">\n",
    "The OECD updated the way to query their data. The put a guide in the following  <a href=\"https://gitlab.algobank.oecd.org/public-documentation/dotstat-migration/-/raw/main/OECD_Data_API_documentation.pdf\" target=\"_blank\">link.</a><BR>\n",
    "    \n",
    "<font size=\"3\">In any case, we will explain how to perform the queries easily. You will need some particular information like the database, the country or countries codes, the frequency, the indicator, the unit of measurement or the start period. Most of them are self explanatory or we have mentioned them earlier in the notebook. Let's watch an example for the long term interest rates for USA, JAPAN and GERMANY, on a monthly basis, for a per annum measure and a start period of January 1955"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATAFLOW</th>\n",
       "      <th>REF_AREA</th>\n",
       "      <th>FREQ</th>\n",
       "      <th>MEASURE</th>\n",
       "      <th>UNIT_MEASURE</th>\n",
       "      <th>ACTIVITY</th>\n",
       "      <th>ADJUSTMENT</th>\n",
       "      <th>TRANSFORMATION</th>\n",
       "      <th>TIME_HORIZ</th>\n",
       "      <th>METHODOLOGY</th>\n",
       "      <th>TIME_PERIOD</th>\n",
       "      <th>OBS_VALUE</th>\n",
       "      <th>OBS_STATUS</th>\n",
       "      <th>UNIT_MULT</th>\n",
       "      <th>DECIMALS</th>\n",
       "      <th>BASE_PER</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)</td>\n",
       "      <td>USA</td>\n",
       "      <td>M</td>\n",
       "      <td>IRLT</td>\n",
       "      <td>PA</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>N</td>\n",
       "      <td>2022-06</td>\n",
       "      <td>3.140</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)</td>\n",
       "      <td>USA</td>\n",
       "      <td>M</td>\n",
       "      <td>IRLT</td>\n",
       "      <td>PA</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>N</td>\n",
       "      <td>2022-07</td>\n",
       "      <td>2.900</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)</td>\n",
       "      <td>JPN</td>\n",
       "      <td>M</td>\n",
       "      <td>IRLT</td>\n",
       "      <td>PA</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>N</td>\n",
       "      <td>1989-03</td>\n",
       "      <td>5.147</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)</td>\n",
       "      <td>JPN</td>\n",
       "      <td>M</td>\n",
       "      <td>IRLT</td>\n",
       "      <td>PA</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>N</td>\n",
       "      <td>1989-04</td>\n",
       "      <td>5.221</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)</td>\n",
       "      <td>JPN</td>\n",
       "      <td>M</td>\n",
       "      <td>IRLT</td>\n",
       "      <td>PA</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>N</td>\n",
       "      <td>1989-05</td>\n",
       "      <td>5.174</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2075</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)</td>\n",
       "      <td>USA</td>\n",
       "      <td>M</td>\n",
       "      <td>IRLT</td>\n",
       "      <td>PA</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>N</td>\n",
       "      <td>2022-01</td>\n",
       "      <td>1.760</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2076</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)</td>\n",
       "      <td>USA</td>\n",
       "      <td>M</td>\n",
       "      <td>IRLT</td>\n",
       "      <td>PA</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>N</td>\n",
       "      <td>2022-02</td>\n",
       "      <td>1.930</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2077</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)</td>\n",
       "      <td>USA</td>\n",
       "      <td>M</td>\n",
       "      <td>IRLT</td>\n",
       "      <td>PA</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>N</td>\n",
       "      <td>2022-03</td>\n",
       "      <td>2.130</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2078</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)</td>\n",
       "      <td>USA</td>\n",
       "      <td>M</td>\n",
       "      <td>IRLT</td>\n",
       "      <td>PA</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>N</td>\n",
       "      <td>2022-04</td>\n",
       "      <td>2.750</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2079</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)</td>\n",
       "      <td>USA</td>\n",
       "      <td>M</td>\n",
       "      <td>IRLT</td>\n",
       "      <td>PA</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>_Z</td>\n",
       "      <td>N</td>\n",
       "      <td>2022-05</td>\n",
       "      <td>2.900</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2080 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    DATAFLOW REF_AREA FREQ MEASURE  \\\n",
       "0     OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)      USA    M    IRLT   \n",
       "1     OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)      USA    M    IRLT   \n",
       "2     OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)      JPN    M    IRLT   \n",
       "3     OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)      JPN    M    IRLT   \n",
       "4     OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)      JPN    M    IRLT   \n",
       "...                                      ...      ...  ...     ...   \n",
       "2075  OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)      USA    M    IRLT   \n",
       "2076  OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)      USA    M    IRLT   \n",
       "2077  OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)      USA    M    IRLT   \n",
       "2078  OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)      USA    M    IRLT   \n",
       "2079  OECD.SDD.STES:DSD_STES@DF_FINMARK(4.0)      USA    M    IRLT   \n",
       "\n",
       "     UNIT_MEASURE ACTIVITY ADJUSTMENT TRANSFORMATION TIME_HORIZ METHODOLOGY  \\\n",
       "0              PA       _Z         _Z             _Z         _Z           N   \n",
       "1              PA       _Z         _Z             _Z         _Z           N   \n",
       "2              PA       _Z         _Z             _Z         _Z           N   \n",
       "3              PA       _Z         _Z             _Z         _Z           N   \n",
       "4              PA       _Z         _Z             _Z         _Z           N   \n",
       "...           ...      ...        ...            ...        ...         ...   \n",
       "2075           PA       _Z         _Z             _Z         _Z           N   \n",
       "2076           PA       _Z         _Z             _Z         _Z           N   \n",
       "2077           PA       _Z         _Z             _Z         _Z           N   \n",
       "2078           PA       _Z         _Z             _Z         _Z           N   \n",
       "2079           PA       _Z         _Z             _Z         _Z           N   \n",
       "\n",
       "     TIME_PERIOD  OBS_VALUE OBS_STATUS  UNIT_MULT  DECIMALS  BASE_PER  \n",
       "0        2022-06      3.140          A          0         2       NaN  \n",
       "1        2022-07      2.900          A          0         2       NaN  \n",
       "2        1989-03      5.147          A          0         2       NaN  \n",
       "3        1989-04      5.221          A          0         2       NaN  \n",
       "4        1989-05      5.174          A          0         2       NaN  \n",
       "...          ...        ...        ...        ...       ...       ...  \n",
       "2075     2022-01      1.760          A          0         2       NaN  \n",
       "2076     2022-02      1.930          A          0         2       NaN  \n",
       "2077     2022-03      2.130          A          0         2       NaN  \n",
       "2078     2022-04      2.750          A          0         2       NaN  \n",
       "2079     2022-05      2.900          A          0         2       NaN  \n",
       "\n",
       "[2080 rows x 16 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "database = '@DF_FINMARK,4.0'\n",
    "country_code = 'USA+JPN+DEU'\n",
    "frequency = 'M'\n",
    "indicator = 'IRLT'\n",
    "unit_of_measure = 'PA'\n",
    "start_period = '1955-01'\n",
    "\n",
    "\n",
    "query_text = database+\"/\"+country_code+\".\"+frequency+\".\"+indicator+\".\"+unit_of_measure+\".....?startPeriod=\"+start_period+\"&dimensionAtObservation=AllDimensions\"\n",
    "\n",
    "\n",
    "url = f\"https://sdmx.oecd.org/public/rest/data/OECD.SDD.STES,DSD_STES{query_text}\"\n",
    "headers = { 'User-Agent': 'Mozilla/5.0','Accept':  'application/vnd.sdmx.data+csv; charset=utf-8' } \n",
    "\n",
    "download = requests.get(url = url,headers = headers)\n",
    "df2 = pd.read_csv(io.StringIO(download.text))\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Plot the information**\n",
    "\n",
    "\n",
    "<font size=\"3\">\n",
    "We can plot the information very easily using the function plot from the library matplotlib.pyplot. We did some other formatting to the chart to make it nices. If you would like to know how to make these kind of plots, please take a look at our course <a href=\"https://quant-trading.co/cursos/data-science-for-trading/\" target=\"_blank\">\"Introductory Data Science for Investing and Trading\".</a><BR>\n",
    "    \n",
    "<font size=\"3\">Below we show how to do that:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2.sort_values(by='TIME_PERIOD',inplace=True)\n",
    "df_USA = df2.loc[df2['REF_AREA'] ==  'USA']\n",
    "df_JPN = df2.loc[df2['REF_AREA'] ==  'JPN']\n",
    "df_GER = df2.loc[df2['REF_AREA'] ==  'DEU']\n",
    "\n",
    "#----------------------------------------------------CREATE CHART----------------------------------------------------------#\n",
    "import matplotlib.ticker as ticker\n",
    "import matplotlib.font_manager as font_manager\n",
    "import matplotlib.ticker as ticker\n",
    "from matplotlib.ticker import FormatStrFormatter\n",
    "\n",
    "\n",
    "font = font_manager.FontProperties(family='Impact',weight='bold', style='normal', size=12)\n",
    "tick_spacing = 50\n",
    "i = 0\n",
    "\n",
    "fig = plt.figure()\n",
    "\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "\n",
    "ax.plot(df_USA['TIME_PERIOD'],df_USA['OBS_VALUE'],marker='o', linestyle='solid',linewidth=3,color='darkblue',label='USA',alpha=0.99,markersize=1)\n",
    "ax.plot(df_JPN['TIME_PERIOD'],df_JPN['OBS_VALUE'],marker='o', linestyle='solid',linewidth=3,color='royalblue',label='JPN',alpha=0.99,markersize=1)\n",
    "ax.plot(df_GER['TIME_PERIOD'],df_GER['OBS_VALUE'],marker='o', linestyle='solid',linewidth=3,color='dodgerblue',label='GER',alpha=0.99,markersize=1)\n",
    "\n",
    "ax.xaxis.set_major_locator(ticker.MultipleLocator(tick_spacing))\n",
    "ax.legend(frameon=False,prop=font,loc=1, bbox_to_anchor=(0.85, 1.02),ncol=10,markerscale=5)\n",
    "plt.xticks(rotation=90)\n",
    "plt.yticks(fontname = \"Tahoma\",fontsize=15)\n",
    "plt.xticks(fontname = \"Tahoma\",fontsize=15)\n",
    "ax.axis([None, None, None, None])\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%2.2f%%'))\n",
    "plt.title(\"Long Term Interest Rates\", size=25, family='Arial')\n",
    "plt.box(on=None)\n",
    "ax.set_facecolor('white') \n",
    "\n",
    "plt.subplots_adjust(bottom=0.1, right=1.8, top=1.0)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Use the new standard way to download the data**\n",
    "\n",
    "\n",
    "<font size=\"3\">\n",
    "A good thing is that we can search for all that information of the parameters in the OECD web page in the following <a href=\"https://data-explorer.oecd.org/\" target=\"_blank\">link.</a><BR>\n",
    "    \n",
    "<font size=\"3\">Now we are showing an example on how to download data of the Composite Leading Indicators for a group of countries. Please try going to the webpage and getting the information to put in the parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATAFLOW</th>\n",
       "      <th>REF_AREA</th>\n",
       "      <th>FREQ</th>\n",
       "      <th>MEASURE</th>\n",
       "      <th>UNIT_MEASURE</th>\n",
       "      <th>ACTIVITY</th>\n",
       "      <th>ADJUSTMENT</th>\n",
       "      <th>TRANSFORMATION</th>\n",
       "      <th>TIME_HORIZ</th>\n",
       "      <th>METHODOLOGY</th>\n",
       "      <th>TIME_PERIOD</th>\n",
       "      <th>OBS_VALUE</th>\n",
       "      <th>OBS_STATUS</th>\n",
       "      <th>UNIT_MULT</th>\n",
       "      <th>DECIMALS</th>\n",
       "      <th>BASE_PER</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_CLI(4.1)</td>\n",
       "      <td>AUS</td>\n",
       "      <td>M</td>\n",
       "      <td>LI</td>\n",
       "      <td>IX</td>\n",
       "      <td>_Z</td>\n",
       "      <td>AA</td>\n",
       "      <td>IX</td>\n",
       "      <td>_Z</td>\n",
       "      <td>H</td>\n",
       "      <td>2024-07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_CLI(4.1)</td>\n",
       "      <td>CAN</td>\n",
       "      <td>M</td>\n",
       "      <td>LI</td>\n",
       "      <td>IX</td>\n",
       "      <td>_Z</td>\n",
       "      <td>AA</td>\n",
       "      <td>IX</td>\n",
       "      <td>_Z</td>\n",
       "      <td>H</td>\n",
       "      <td>2023-01</td>\n",
       "      <td>98.53239</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_CLI(4.1)</td>\n",
       "      <td>AUS</td>\n",
       "      <td>M</td>\n",
       "      <td>LI</td>\n",
       "      <td>IX</td>\n",
       "      <td>_Z</td>\n",
       "      <td>AA</td>\n",
       "      <td>IX</td>\n",
       "      <td>_Z</td>\n",
       "      <td>H</td>\n",
       "      <td>2023-03</td>\n",
       "      <td>99.32943</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_CLI(4.1)</td>\n",
       "      <td>AUS</td>\n",
       "      <td>M</td>\n",
       "      <td>LI</td>\n",
       "      <td>IX</td>\n",
       "      <td>_Z</td>\n",
       "      <td>AA</td>\n",
       "      <td>IX</td>\n",
       "      <td>_Z</td>\n",
       "      <td>H</td>\n",
       "      <td>2023-04</td>\n",
       "      <td>99.21564</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_CLI(4.1)</td>\n",
       "      <td>AUS</td>\n",
       "      <td>M</td>\n",
       "      <td>LI</td>\n",
       "      <td>IX</td>\n",
       "      <td>_Z</td>\n",
       "      <td>AA</td>\n",
       "      <td>IX</td>\n",
       "      <td>_Z</td>\n",
       "      <td>H</td>\n",
       "      <td>2023-05</td>\n",
       "      <td>99.11137</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_CLI(4.1)</td>\n",
       "      <td>JPN</td>\n",
       "      <td>M</td>\n",
       "      <td>LI</td>\n",
       "      <td>IX</td>\n",
       "      <td>_Z</td>\n",
       "      <td>AA</td>\n",
       "      <td>IX</td>\n",
       "      <td>_Z</td>\n",
       "      <td>H</td>\n",
       "      <td>2024-04</td>\n",
       "      <td>99.98034</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_CLI(4.1)</td>\n",
       "      <td>JPN</td>\n",
       "      <td>M</td>\n",
       "      <td>LI</td>\n",
       "      <td>IX</td>\n",
       "      <td>_Z</td>\n",
       "      <td>AA</td>\n",
       "      <td>IX</td>\n",
       "      <td>_Z</td>\n",
       "      <td>H</td>\n",
       "      <td>2024-05</td>\n",
       "      <td>100.01360</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_CLI(4.1)</td>\n",
       "      <td>JPN</td>\n",
       "      <td>M</td>\n",
       "      <td>LI</td>\n",
       "      <td>IX</td>\n",
       "      <td>_Z</td>\n",
       "      <td>AA</td>\n",
       "      <td>IX</td>\n",
       "      <td>_Z</td>\n",
       "      <td>H</td>\n",
       "      <td>2024-06</td>\n",
       "      <td>100.03710</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_CLI(4.1)</td>\n",
       "      <td>JPN</td>\n",
       "      <td>M</td>\n",
       "      <td>LI</td>\n",
       "      <td>IX</td>\n",
       "      <td>_Z</td>\n",
       "      <td>AA</td>\n",
       "      <td>IX</td>\n",
       "      <td>_Z</td>\n",
       "      <td>H</td>\n",
       "      <td>2024-07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>OECD.SDD.STES:DSD_STES@DF_CLI(4.1)</td>\n",
       "      <td>AUS</td>\n",
       "      <td>M</td>\n",
       "      <td>LI</td>\n",
       "      <td>IX</td>\n",
       "      <td>_Z</td>\n",
       "      <td>AA</td>\n",
       "      <td>IX</td>\n",
       "      <td>_Z</td>\n",
       "      <td>H</td>\n",
       "      <td>2024-06</td>\n",
       "      <td>99.40297</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>95 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              DATAFLOW REF_AREA FREQ MEASURE UNIT_MEASURE  \\\n",
       "0   OECD.SDD.STES:DSD_STES@DF_CLI(4.1)      AUS    M      LI           IX   \n",
       "1   OECD.SDD.STES:DSD_STES@DF_CLI(4.1)      CAN    M      LI           IX   \n",
       "2   OECD.SDD.STES:DSD_STES@DF_CLI(4.1)      AUS    M      LI           IX   \n",
       "3   OECD.SDD.STES:DSD_STES@DF_CLI(4.1)      AUS    M      LI           IX   \n",
       "4   OECD.SDD.STES:DSD_STES@DF_CLI(4.1)      AUS    M      LI           IX   \n",
       "..                                 ...      ...  ...     ...          ...   \n",
       "90  OECD.SDD.STES:DSD_STES@DF_CLI(4.1)      JPN    M      LI           IX   \n",
       "91  OECD.SDD.STES:DSD_STES@DF_CLI(4.1)      JPN    M      LI           IX   \n",
       "92  OECD.SDD.STES:DSD_STES@DF_CLI(4.1)      JPN    M      LI           IX   \n",
       "93  OECD.SDD.STES:DSD_STES@DF_CLI(4.1)      JPN    M      LI           IX   \n",
       "94  OECD.SDD.STES:DSD_STES@DF_CLI(4.1)      AUS    M      LI           IX   \n",
       "\n",
       "   ACTIVITY ADJUSTMENT TRANSFORMATION TIME_HORIZ METHODOLOGY TIME_PERIOD  \\\n",
       "0        _Z         AA             IX         _Z           H     2024-07   \n",
       "1        _Z         AA             IX         _Z           H     2023-01   \n",
       "2        _Z         AA             IX         _Z           H     2023-03   \n",
       "3        _Z         AA             IX         _Z           H     2023-04   \n",
       "4        _Z         AA             IX         _Z           H     2023-05   \n",
       "..      ...        ...            ...        ...         ...         ...   \n",
       "90       _Z         AA             IX         _Z           H     2024-04   \n",
       "91       _Z         AA             IX         _Z           H     2024-05   \n",
       "92       _Z         AA             IX         _Z           H     2024-06   \n",
       "93       _Z         AA             IX         _Z           H     2024-07   \n",
       "94       _Z         AA             IX         _Z           H     2024-06   \n",
       "\n",
       "    OBS_VALUE OBS_STATUS  UNIT_MULT  DECIMALS  BASE_PER  \n",
       "0         NaN          A          0         2       NaN  \n",
       "1    98.53239          A          0         2       NaN  \n",
       "2    99.32943          A          0         2       NaN  \n",
       "3    99.21564          A          0         2       NaN  \n",
       "4    99.11137          A          0         2       NaN  \n",
       "..        ...        ...        ...       ...       ...  \n",
       "90   99.98034          A          0         2       NaN  \n",
       "91  100.01360          A          0         2       NaN  \n",
       "92  100.03710          A          0         2       NaN  \n",
       "93        NaN          A          0         2       NaN  \n",
       "94   99.40297          A          0         2       NaN  \n",
       "\n",
       "[95 rows x 16 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "database = '@DF_CLI'\n",
    "country_code = 'JPN+DEU+FRA+CAN+AUS'\n",
    "frequency = 'M'\n",
    "indicator = 'LI..'\n",
    "unit_of_measure = 'AA...'\n",
    "start_period = '2023-01'\n",
    "\n",
    "\n",
    "query_text = database+\"/\"+country_code+\".\"+frequency+\".\"+indicator+\".\"+unit_of_measure+\"?startPeriod=\"+start_period+\"&dimensionAtObservation=AllDimensions\"\n",
    "\n",
    "\n",
    "url = f\"https://sdmx.oecd.org/public/rest/data/OECD.SDD.STES,DSD_STES{query_text}\"\n",
    "headers = { 'User-Agent': 'Mozilla/5.0','Accept':  'application/vnd.sdmx.data+csv; charset=utf-8' } \n",
    "\n",
    "download = requests.get(url = url,headers = headers)\n",
    "df3 = pd.read_csv(io.StringIO(download.text))\n",
    "df3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\">If this content is helpful and you want to make a donation please click on the button\n",
    "\n",
    "[![paypal](https://www.paypalobjects.com/en_US/i/btn/btn_donateCC_LG.gif)](https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&hosted_button_id=29CVY97MEQ9BY)"
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