docs/source/examples.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Examples of usage"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import modules needed\n",
    "---------------------"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition to the modules of `fredeco`, `matplotlib` is imported because it will be used to quickly plot data retrieved from FRED® API. My API key is not provided, for the illustrations below. It is saved in the object `my_fred_key`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fredeco.fredData import fred_series, fred_multi_series, explore\n",
    "from fredeco.fredSearch import fred_list_series, fred_info_series, fred_search, fred_vintagedates\n",
    "import matplotlib"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Retrieve data\n",
    "--------------"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below, data related to the US GDP are retrieved. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dates</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>21380.976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>21060.474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01</th>\n",
       "      <td>23315.081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
       "      <td>25462.722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  GDP\n",
       "Dates                \n",
       "2019-01-01  21380.976\n",
       "2020-01-01  21060.474\n",
       "2021-01-01  23315.081\n",
       "2022-01-01  25462.722\n",
       "2023-01-01        NaN"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_one=fred_series(series='GDP',fred_api=my_fred_key)\n",
    "df_one.tail(5)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below data related to several indicators are retrieved."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GDP</th>\n",
       "      <th>GDPCA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dates</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>21380.976</td>\n",
       "      <td>19036.052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>21060.474</td>\n",
       "      <td>18509.143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01</th>\n",
       "      <td>23315.081</td>\n",
       "      <td>19609.812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
       "      <td>25462.722</td>\n",
       "      <td>20014.128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  GDP      GDPCA\n",
       "Dates                           \n",
       "2019-01-01  21380.976  19036.052\n",
       "2020-01-01  21060.474  18509.143\n",
       "2021-01-01  23315.081  19609.812\n",
       "2022-01-01  25462.722  20014.128\n",
       "2023-01-01        NaN        NaN"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_multi=fred_multi_series(series=['GDP','GDPCA'],fred_api=my_fred_key)\n",
    "df_multi.tail(5)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Search for information\n",
    "----------------------"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If a user want to look for indicators related to price index, ``fredeco`` allows to search for them as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>id</th>\n",
       "      <th>realtime_start</th>\n",
       "      <th>realtime_end</th>\n",
       "      <th>title</th>\n",
       "      <th>observation_start</th>\n",
       "      <th>observation_end</th>\n",
       "      <th>frequency</th>\n",
       "      <th>frequency_short</th>\n",
       "      <th>units</th>\n",
       "      <th>units_short</th>\n",
       "      <th>seasonal_adjustment</th>\n",
       "      <th>seasonal_adjustment_short</th>\n",
       "      <th>last_updated</th>\n",
       "      <th>popularity</th>\n",
       "      <th>group_popularity</th>\n",
       "      <th>notes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CPIAUCSL</td>\n",
       "      <td>2023-07-11</td>\n",
       "      <td>2023-07-11</td>\n",
       "      <td>Consumer Price Index for All Urban Consumers: ...</td>\n",
       "      <td>1947-01-01</td>\n",
       "      <td>2023-05-01</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>M</td>\n",
       "      <td>Index 1982-1984=100</td>\n",
       "      <td>Index 1982-1984=100</td>\n",
       "      <td>Seasonally Adjusted</td>\n",
       "      <td>SA</td>\n",
       "      <td>2023-06-13 07:44:03-05</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>The Consumer Price Index for All Urban Consume...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CPIAUCNS</td>\n",
       "      <td>2023-07-11</td>\n",
       "      <td>2023-07-11</td>\n",
       "      <td>Consumer Price Index for All Urban Consumers: ...</td>\n",
       "      <td>1913-01-01</td>\n",
       "      <td>2023-05-01</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>M</td>\n",
       "      <td>Index 1982-1984=100</td>\n",
       "      <td>Index 1982-1984=100</td>\n",
       "      <td>Not Seasonally Adjusted</td>\n",
       "      <td>NSA</td>\n",
       "      <td>2023-06-13 07:44:06-05</td>\n",
       "      <td>71</td>\n",
       "      <td>94</td>\n",
       "      <td>Handbook of Methods (https://www.bls.gov/opub/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CSUSHPINSA</td>\n",
       "      <td>2023-07-11</td>\n",
       "      <td>2023-07-11</td>\n",
       "      <td>S&amp;P/Case-Shiller U.S. National Home Price Index</td>\n",
       "      <td>1987-01-01</td>\n",
       "      <td>2023-04-01</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>M</td>\n",
       "      <td>Index Jan 2000=100</td>\n",
       "      <td>Index Jan 2000=100</td>\n",
       "      <td>Not Seasonally Adjusted</td>\n",
       "      <td>NSA</td>\n",
       "      <td>2023-06-27 08:13:02-05</td>\n",
       "      <td>90</td>\n",
       "      <td>92</td>\n",
       "      <td>For more information regarding the index, plea...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CUUS0000SA0</td>\n",
       "      <td>2023-07-11</td>\n",
       "      <td>2023-07-11</td>\n",
       "      <td>Consumer Price Index for All Urban Consumers: ...</td>\n",
       "      <td>1984-01-01</td>\n",
       "      <td>2022-07-01</td>\n",
       "      <td>Semiannual</td>\n",
       "      <td>SA</td>\n",
       "      <td>Index 1982-1984=100</td>\n",
       "      <td>Index 1982-1984=100</td>\n",
       "      <td>Not Seasonally Adjusted</td>\n",
       "      <td>NSA</td>\n",
       "      <td>2023-01-12 07:38:17-06</td>\n",
       "      <td>43</td>\n",
       "      <td>94</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CSUSHPISA</td>\n",
       "      <td>2023-07-11</td>\n",
       "      <td>2023-07-11</td>\n",
       "      <td>S&amp;P/Case-Shiller U.S. National Home Price Index</td>\n",
       "      <td>1987-01-01</td>\n",
       "      <td>2023-04-01</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>M</td>\n",
       "      <td>Index Jan 2000=100</td>\n",
       "      <td>Index Jan 2000=100</td>\n",
       "      <td>Seasonally Adjusted</td>\n",
       "      <td>SA</td>\n",
       "      <td>2023-06-27 08:13:03-05</td>\n",
       "      <td>80</td>\n",
       "      <td>92</td>\n",
       "      <td>For more information regarding the index, plea...</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>995</th>\n",
       "      <td>CUUS0000SAF113</td>\n",
       "      <td>2023-07-11</td>\n",
       "      <td>2023-07-11</td>\n",
       "      <td>Consumer Price Index for All Urban Consumers: ...</td>\n",
       "      <td>1984-01-01</td>\n",
       "      <td>2022-07-01</td>\n",
       "      <td>Semiannual</td>\n",
       "      <td>SA</td>\n",
       "      <td>Index 1982-1984=100</td>\n",
       "      <td>Index 1982-1984=100</td>\n",
       "      <td>Not Seasonally Adjusted</td>\n",
       "      <td>NSA</td>\n",
       "      <td>2023-01-12 07:37:52-06</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>CUUS0000SAF116</td>\n",
       "      <td>2023-07-11</td>\n",
       "      <td>2023-07-11</td>\n",
       "      <td>Consumer Price Index for All Urban Consumers: ...</td>\n",
       "      <td>1984-01-01</td>\n",
       "      <td>2022-07-01</td>\n",
       "      <td>Semiannual</td>\n",
       "      <td>SA</td>\n",
       "      <td>Index 1982-1984=100</td>\n",
       "      <td>Index 1982-1984=100</td>\n",
       "      <td>Not Seasonally Adjusted</td>\n",
       "      <td>NSA</td>\n",
       "      <td>2023-01-12 07:37:58-06</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>RTFPNAJPA632NRUG</td>\n",
       "      <td>2023-07-11</td>\n",
       "      <td>2023-07-11</td>\n",
       "      <td>Total Factor Productivity at Constant National...</td>\n",
       "      <td>1954-01-01</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>Annual</td>\n",
       "      <td>A</td>\n",
       "      <td>Index 2017=1</td>\n",
       "      <td>Index 2017=1</td>\n",
       "      <td>Not Seasonally Adjusted</td>\n",
       "      <td>NSA</td>\n",
       "      <td>2023-02-27 14:32:04-06</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>Source ID: rtfpna\\n\\nWhen using these data in ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>CPALTT01CHM657N</td>\n",
       "      <td>2023-07-11</td>\n",
       "      <td>2023-07-11</td>\n",
       "      <td>Consumer Price Index: All Items: Total: Total ...</td>\n",
       "      <td>1960-01-01</td>\n",
       "      <td>2023-05-01</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>M</td>\n",
       "      <td>Growth rate previous period</td>\n",
       "      <td>Growth rate previous period</td>\n",
       "      <td>Not Seasonally Adjusted</td>\n",
       "      <td>NSA</td>\n",
       "      <td>2023-06-23 12:37:05-05</td>\n",
       "      <td>17</td>\n",
       "      <td>26</td>\n",
       "      <td>OECD descriptor ID: CPALTT01\\nOECD unit ID: GP...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>CHECPIALLMINMEI</td>\n",
       "      <td>2023-07-11</td>\n",
       "      <td>2023-07-11</td>\n",
       "      <td>Consumer Price Index: All Items: Total: Total ...</td>\n",
       "      <td>1960-01-01</td>\n",
       "      <td>2023-05-01</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>M</td>\n",
       "      <td>Index 2015=100</td>\n",
       "      <td>Index 2015=100</td>\n",
       "      <td>Not Seasonally Adjusted</td>\n",
       "      <td>NSA</td>\n",
       "      <td>2023-06-23 12:37:06-05</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>Copyright, 2016, OECD. Reprinted with permissi...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   id realtime_start realtime_end  \\\n",
       "0            CPIAUCSL     2023-07-11   2023-07-11   \n",
       "1            CPIAUCNS     2023-07-11   2023-07-11   \n",
       "2          CSUSHPINSA     2023-07-11   2023-07-11   \n",
       "3         CUUS0000SA0     2023-07-11   2023-07-11   \n",
       "4           CSUSHPISA     2023-07-11   2023-07-11   \n",
       "..                ...            ...          ...   \n",
       "995    CUUS0000SAF113     2023-07-11   2023-07-11   \n",
       "996    CUUS0000SAF116     2023-07-11   2023-07-11   \n",
       "997  RTFPNAJPA632NRUG     2023-07-11   2023-07-11   \n",
       "998   CPALTT01CHM657N     2023-07-11   2023-07-11   \n",
       "999   CHECPIALLMINMEI     2023-07-11   2023-07-11   \n",
       "\n",
       "                                                 title observation_start  \\\n",
       "0    Consumer Price Index for All Urban Consumers: ...        1947-01-01   \n",
       "1    Consumer Price Index for All Urban Consumers: ...        1913-01-01   \n",
       "2      S&P/Case-Shiller U.S. National Home Price Index        1987-01-01   \n",
       "3    Consumer Price Index for All Urban Consumers: ...        1984-01-01   \n",
       "4      S&P/Case-Shiller U.S. National Home Price Index        1987-01-01   \n",
       "..                                                 ...               ...   \n",
       "995  Consumer Price Index for All Urban Consumers: ...        1984-01-01   \n",
       "996  Consumer Price Index for All Urban Consumers: ...        1984-01-01   \n",
       "997  Total Factor Productivity at Constant National...        1954-01-01   \n",
       "998  Consumer Price Index: All Items: Total: Total ...        1960-01-01   \n",
       "999  Consumer Price Index: All Items: Total: Total ...        1960-01-01   \n",
       "\n",
       "    observation_end   frequency frequency_short                        units  \\\n",
       "0        2023-05-01     Monthly               M          Index 1982-1984=100   \n",
       "1        2023-05-01     Monthly               M          Index 1982-1984=100   \n",
       "2        2023-04-01     Monthly               M           Index Jan 2000=100   \n",
       "3        2022-07-01  Semiannual              SA          Index 1982-1984=100   \n",
       "4        2023-04-01     Monthly               M           Index Jan 2000=100   \n",
       "..              ...         ...             ...                          ...   \n",
       "995      2022-07-01  Semiannual              SA          Index 1982-1984=100   \n",
       "996      2022-07-01  Semiannual              SA          Index 1982-1984=100   \n",
       "997      2019-01-01      Annual               A                 Index 2017=1   \n",
       "998      2023-05-01     Monthly               M  Growth rate previous period   \n",
       "999      2023-05-01     Monthly               M               Index 2015=100   \n",
       "\n",
       "                     units_short      seasonal_adjustment  \\\n",
       "0            Index 1982-1984=100      Seasonally Adjusted   \n",
       "1            Index 1982-1984=100  Not Seasonally Adjusted   \n",
       "2             Index Jan 2000=100  Not Seasonally Adjusted   \n",
       "3            Index 1982-1984=100  Not Seasonally Adjusted   \n",
       "4             Index Jan 2000=100      Seasonally Adjusted   \n",
       "..                           ...                      ...   \n",
       "995          Index 1982-1984=100  Not Seasonally Adjusted   \n",
       "996          Index 1982-1984=100  Not Seasonally Adjusted   \n",
       "997                 Index 2017=1  Not Seasonally Adjusted   \n",
       "998  Growth rate previous period  Not Seasonally Adjusted   \n",
       "999               Index 2015=100  Not Seasonally Adjusted   \n",
       "\n",
       "    seasonal_adjustment_short            last_updated  popularity  \\\n",
       "0                          SA  2023-06-13 07:44:03-05          94   \n",
       "1                         NSA  2023-06-13 07:44:06-05          71   \n",
       "2                         NSA  2023-06-27 08:13:02-05          90   \n",
       "3                         NSA  2023-01-12 07:38:17-06          43   \n",
       "4                          SA  2023-06-27 08:13:03-05          80   \n",
       "..                        ...                     ...         ...   \n",
       "995                       NSA  2023-01-12 07:37:52-06           1   \n",
       "996                       NSA  2023-01-12 07:37:58-06           1   \n",
       "997                       NSA  2023-02-27 14:32:04-06          26   \n",
       "998                       NSA  2023-06-23 12:37:05-05          17   \n",
       "999                       NSA  2023-06-23 12:37:06-05          12   \n",
       "\n",
       "     group_popularity                                              notes  \n",
       "0                  94  The Consumer Price Index for All Urban Consume...  \n",
       "1                  94  Handbook of Methods (https://www.bls.gov/opub/...  \n",
       "2                  92  For more information regarding the index, plea...  \n",
       "3                  94                                                NaN  \n",
       "4                  92  For more information regarding the index, plea...  \n",
       "..                ...                                                ...  \n",
       "995                27                                                NaN  \n",
       "996                27                                                NaN  \n",
       "997                26  Source ID: rtfpna\\n\\nWhen using these data in ...  \n",
       "998                26  OECD descriptor ID: CPALTT01\\nOECD unit ID: GP...  \n",
       "999                26  Copyright, 2016, OECD. Reprinted with permissi...  \n",
       "\n",
       "[1000 rows x 16 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fred_search(text='price index',fred_api=my_fred_key)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To request information about an economic indicator, such as `GDPCA`, the package `fredeco` can be used as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'realtime_start': '2023-07-11',\n",
       " 'realtime_end': '2023-07-11',\n",
       " 'seriess': [{'id': 'GDPCA',\n",
       "   'realtime_start': '2023-07-11',\n",
       "   'realtime_end': '2023-07-11',\n",
       "   'title': 'Real Gross Domestic Product',\n",
       "   'observation_start': '1929-01-01',\n",
       "   'observation_end': '2022-01-01',\n",
       "   'frequency': 'Annual',\n",
       "   'frequency_short': 'A',\n",
       "   'units': 'Billions of Chained 2012 Dollars',\n",
       "   'units_short': 'Bil. of Chn. 2012 $',\n",
       "   'seasonal_adjustment': 'Not Seasonally Adjusted',\n",
       "   'seasonal_adjustment_short': 'NSA',\n",
       "   'last_updated': '2023-03-30 07:54:04-05',\n",
       "   'popularity': 52,\n",
       "   'notes': 'BEA Account Code: A191RX\\n\\nA Guide to the National Income and Product Accounts of the United States (http://www.bea.gov/national/pdf/nipaguid.pdf) (NIPA)'}]}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "fred_info_series(fred_api=my_fred_key,series='GDPCA')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These information are essential to know, to use for analysis. For example, the last information provided, the *notes*, indicate the source of data. The source should be provided, when using data retrieved from FRED® API."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To search for the list of economic indicators in a category, such as the category ID 125, one can proceed as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>id</th>\n",
       "      <th>realtime_start</th>\n",
       "      <th>realtime_end</th>\n",
       "      <th>title</th>\n",
       "      <th>observation_start</th>\n",
       "      <th>observation_end</th>\n",
       "      <th>frequency</th>\n",
       "      <th>frequency_short</th>\n",
       "      <th>units</th>\n",
       "      <th>units_short</th>\n",
       "      <th>seasonal_adjustment</th>\n",
       "      <th>seasonal_adjustment_short</th>\n",
       "      <th>last_updated</th>\n",
       "      <th>popularity</th>\n",
       "      <th>group_popularity</th>\n",
       "      <th>notes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AITGCBN</td>\n",
       "      <td>2023-07-11</td>\n",
       "      <td>2023-07-11</td>\n",
       "      <td>Advance U.S. International Trade in Goods: Bal...</td>\n",
       "      <td>2023-05-01</td>\n",
       "      <td>2023-05-01</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>M</td>\n",
       "      <td>Millions of Dollars</td>\n",
       "      <td>Mil. of $</td>\n",
       "      <td>Not Seasonally Adjusted</td>\n",
       "      <td>NSA</td>\n",
       "      <td>2023-06-28 07:31:03-05</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>This advance estimate represents the current m...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AITGCBS</td>\n",
       "      <td>2023-07-11</td>\n",
       "      <td>2023-07-11</td>\n",
       "      <td>Advance U.S. International Trade in Goods: Bal...</td>\n",
       "      <td>2023-05-01</td>\n",
       "      <td>2023-05-01</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>M</td>\n",
       "      <td>Millions of Dollars</td>\n",
       "      <td>Mil. of $</td>\n",
       "      <td>Seasonally Adjusted</td>\n",
       "      <td>SA</td>\n",
       "      <td>2023-06-28 07:31:01-05</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>This advance estimate represents the current m...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BOPBCA</td>\n",
       "      <td>2023-07-11</td>\n",
       "      <td>2023-07-11</td>\n",
       "      <td>Balance on Current Account (DISCONTINUED)</td>\n",
       "      <td>1960-01-01</td>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>Quarterly</td>\n",
       "      <td>Q</td>\n",
       "      <td>Billions of Dollars</td>\n",
       "      <td>Bil. of $</td>\n",
       "      <td>Seasonally Adjusted</td>\n",
       "      <td>SA</td>\n",
       "      <td>2014-06-18 08:41:28-05</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>This series has been discontinued as a result ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BOPBCAA</td>\n",
       "      <td>2023-07-11</td>\n",
       "      <td>2023-07-11</td>\n",
       "      <td>Balance on Current Account (DISCONTINUED)</td>\n",
       "      <td>1960-01-01</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>Annual</td>\n",
       "      <td>A</td>\n",
       "      <td>Billions of Dollars</td>\n",
       "      <td>Bil. of $</td>\n",
       "      <td>Not Seasonally Adjusted</td>\n",
       "      <td>NSA</td>\n",
       "      <td>2014-06-18 08:41:28-05</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>This series has been discontinued as a result ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BOPBCAN</td>\n",
       "      <td>2023-07-11</td>\n",
       "      <td>2023-07-11</td>\n",
       "      <td>Balance on Current Account (DISCONTINUED)</td>\n",
       "      <td>1960-01-01</td>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>Quarterly</td>\n",
       "      <td>Q</td>\n",
       "      <td>Billions of Dollars</td>\n",
       "      <td>Bil. of $</td>\n",
       "      <td>Not Seasonally Adjusted</td>\n",
       "      <td>NSA</td>\n",
       "      <td>2014-06-18 08:41:28-05</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>This series has been discontinued as a result ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id realtime_start realtime_end  \\\n",
       "0  AITGCBN     2023-07-11   2023-07-11   \n",
       "1  AITGCBS     2023-07-11   2023-07-11   \n",
       "2   BOPBCA     2023-07-11   2023-07-11   \n",
       "3  BOPBCAA     2023-07-11   2023-07-11   \n",
       "4  BOPBCAN     2023-07-11   2023-07-11   \n",
       "\n",
       "                                               title observation_start  \\\n",
       "0  Advance U.S. International Trade in Goods: Bal...        2023-05-01   \n",
       "1  Advance U.S. International Trade in Goods: Bal...        2023-05-01   \n",
       "2          Balance on Current Account (DISCONTINUED)        1960-01-01   \n",
       "3          Balance on Current Account (DISCONTINUED)        1960-01-01   \n",
       "4          Balance on Current Account (DISCONTINUED)        1960-01-01   \n",
       "\n",
       "  observation_end  frequency frequency_short                units units_short  \\\n",
       "0      2023-05-01    Monthly               M  Millions of Dollars   Mil. of $   \n",
       "1      2023-05-01    Monthly               M  Millions of Dollars   Mil. of $   \n",
       "2      2014-01-01  Quarterly               Q  Billions of Dollars   Bil. of $   \n",
       "3      2013-01-01     Annual               A  Billions of Dollars   Bil. of $   \n",
       "4      2014-01-01  Quarterly               Q  Billions of Dollars   Bil. of $   \n",
       "\n",
       "       seasonal_adjustment seasonal_adjustment_short            last_updated  \\\n",
       "0  Not Seasonally Adjusted                       NSA  2023-06-28 07:31:03-05   \n",
       "1      Seasonally Adjusted                        SA  2023-06-28 07:31:01-05   \n",
       "2      Seasonally Adjusted                        SA  2014-06-18 08:41:28-05   \n",
       "3  Not Seasonally Adjusted                       NSA  2014-06-18 08:41:28-05   \n",
       "4  Not Seasonally Adjusted                       NSA  2014-06-18 08:41:28-05   \n",
       "\n",
       "   popularity  group_popularity  \\\n",
       "0           2                23   \n",
       "1          23                23   \n",
       "2           7                 8   \n",
       "3           2                 8   \n",
       "4           1                 8   \n",
       "\n",
       "                                               notes  \n",
       "0  This advance estimate represents the current m...  \n",
       "1  This advance estimate represents the current m...  \n",
       "2  This series has been discontinued as a result ...  \n",
       "3  This series has been discontinued as a result ...  \n",
       "4  This series has been discontinued as a result ...  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_list=fred_list_series(fred_api=my_fred_key,category_id=125)\n",
    "df_list.head(5)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code below allows to retrieve a list of the dates of the releases of an indicator. The illustration below considers the indicator ID `FEDFUNDS`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1996-12-03',\n",
       " '1997-01-07',\n",
       " '1997-02-04',\n",
       " '1997-03-04',\n",
       " '1997-04-08',\n",
       " '1997-05-06',\n",
       " '1997-06-03',\n",
       " '1997-07-08',\n",
       " '1997-08-05',\n",
       " '1997-09-02',\n",
       " '1997-10-07',\n",
       " '1997-11-04',\n",
       " '1997-12-02',\n",
       " '1997-12-10',\n",
       " '1998-01-06',\n",
       " '1998-02-03',\n",
       " '1998-03-03',\n",
       " '1998-04-07',\n",
       " '1998-05-05',\n",
       " '1998-06-02',\n",
       " '1998-07-07',\n",
       " '1998-08-04',\n",
       " '1998-09-08',\n",
       " '1998-10-06',\n",
       " '1998-11-03',\n",
       " '1998-12-08',\n",
       " '1999-01-05',\n",
       " '1999-02-02',\n",
       " '1999-03-02',\n",
       " '1999-04-06',\n",
       " '1999-05-04',\n",
       " '1999-06-08',\n",
       " '1999-07-06',\n",
       " '1999-08-03',\n",
       " '1999-09-07',\n",
       " '1999-10-05',\n",
       " '1999-11-02',\n",
       " '1999-12-07',\n",
       " '2000-01-04',\n",
       " '2000-02-08',\n",
       " '2000-03-07',\n",
       " '2000-04-04',\n",
       " '2000-05-02',\n",
       " '2000-06-06',\n",
       " '2000-07-05',\n",
       " '2000-08-08',\n",
       " '2000-09-05',\n",
       " '2000-10-03',\n",
       " '2000-11-07',\n",
       " '2000-12-05',\n",
       " '2001-01-02',\n",
       " '2001-02-06',\n",
       " '2001-03-06',\n",
       " '2001-04-03',\n",
       " '2001-05-08',\n",
       " '2001-06-05',\n",
       " '2001-07-03',\n",
       " '2001-08-07',\n",
       " '2001-09-04',\n",
       " '2001-10-02',\n",
       " '2001-11-06',\n",
       " '2001-12-04',\n",
       " '2002-01-08',\n",
       " '2002-02-04',\n",
       " '2002-03-04',\n",
       " '2002-04-01',\n",
       " '2002-05-06',\n",
       " '2002-06-03',\n",
       " '2002-07-01',\n",
       " '2002-08-05',\n",
       " '2002-09-03',\n",
       " '2002-10-07',\n",
       " '2002-11-04',\n",
       " '2002-12-02',\n",
       " '2003-01-06',\n",
       " '2003-02-03',\n",
       " '2003-03-03',\n",
       " '2003-04-07',\n",
       " '2003-05-05',\n",
       " '2003-06-02',\n",
       " '2003-07-07',\n",
       " '2003-08-04',\n",
       " '2003-09-02',\n",
       " '2003-10-06',\n",
       " '2003-11-03',\n",
       " '2003-12-01',\n",
       " '2004-01-05',\n",
       " '2004-02-02',\n",
       " '2004-03-01',\n",
       " '2004-04-05',\n",
       " '2004-05-03',\n",
       " '2004-06-07',\n",
       " '2004-07-06',\n",
       " '2004-08-02',\n",
       " '2004-09-07',\n",
       " '2004-10-04',\n",
       " '2004-11-01',\n",
       " '2004-12-06',\n",
       " '2005-01-03',\n",
       " '2005-02-07',\n",
       " '2005-03-07',\n",
       " '2005-04-04',\n",
       " '2005-05-02',\n",
       " '2005-06-06',\n",
       " '2005-07-05',\n",
       " '2005-08-01',\n",
       " '2005-09-06',\n",
       " '2005-10-03',\n",
       " '2005-11-07',\n",
       " '2005-12-05',\n",
       " '2006-01-03',\n",
       " '2006-02-06',\n",
       " '2006-03-06',\n",
       " '2006-04-03',\n",
       " '2006-05-01',\n",
       " '2006-06-05',\n",
       " '2006-07-03',\n",
       " '2006-08-07',\n",
       " '2006-09-05',\n",
       " '2006-10-02',\n",
       " '2006-11-06',\n",
       " '2006-12-04',\n",
       " '2007-01-03',\n",
       " '2007-02-05',\n",
       " '2007-03-05',\n",
       " '2007-04-02',\n",
       " '2007-05-07',\n",
       " '2007-06-04',\n",
       " '2007-07-02',\n",
       " '2007-08-06',\n",
       " '2007-09-04',\n",
       " '2007-10-01',\n",
       " '2007-11-05',\n",
       " '2007-12-03',\n",
       " '2008-01-07',\n",
       " '2008-02-04',\n",
       " '2008-03-03',\n",
       " '2008-04-07',\n",
       " '2008-05-05',\n",
       " '2008-06-02',\n",
       " '2008-07-07',\n",
       " '2008-08-04',\n",
       " '2008-09-02',\n",
       " '2008-10-06',\n",
       " '2008-11-03',\n",
       " '2008-12-01',\n",
       " '2009-01-05',\n",
       " '2009-02-02',\n",
       " '2009-03-02',\n",
       " '2009-04-06',\n",
       " '2009-05-04',\n",
       " '2009-06-01',\n",
       " '2009-07-06',\n",
       " '2009-08-03',\n",
       " '2009-09-08',\n",
       " '2009-10-05',\n",
       " '2009-11-02',\n",
       " '2009-12-07',\n",
       " '2010-01-04',\n",
       " '2010-02-01',\n",
       " '2010-03-01',\n",
       " '2010-04-05',\n",
       " '2010-05-03',\n",
       " '2010-06-07',\n",
       " '2010-07-06',\n",
       " '2010-08-02',\n",
       " '2010-09-07',\n",
       " '2010-10-04',\n",
       " '2010-11-01',\n",
       " '2010-12-06',\n",
       " '2011-01-03',\n",
       " '2011-01-10',\n",
       " '2011-01-11',\n",
       " '2011-02-07',\n",
       " '2011-03-07',\n",
       " '2011-04-04',\n",
       " '2011-05-02',\n",
       " '2011-06-06',\n",
       " '2011-07-05',\n",
       " '2011-08-01',\n",
       " '2011-09-06',\n",
       " '2011-10-11',\n",
       " '2011-11-07',\n",
       " '2011-12-05',\n",
       " '2012-01-09',\n",
       " '2012-02-06',\n",
       " '2012-03-05',\n",
       " '2012-04-02',\n",
       " '2012-05-07',\n",
       " '2012-06-04',\n",
       " '2012-07-02',\n",
       " '2012-08-06',\n",
       " '2012-09-04',\n",
       " '2012-10-01',\n",
       " '2012-11-05',\n",
       " '2012-12-10',\n",
       " '2013-01-07',\n",
       " '2013-02-04',\n",
       " '2013-03-04',\n",
       " '2013-04-04',\n",
       " '2013-05-06',\n",
       " '2013-06-10',\n",
       " '2013-07-08',\n",
       " '2013-08-12',\n",
       " '2013-09-03',\n",
       " '2013-10-07',\n",
       " '2013-11-04',\n",
       " '2013-12-02',\n",
       " '2014-01-06',\n",
       " '2014-02-03',\n",
       " '2014-03-04',\n",
       " '2014-04-07',\n",
       " '2014-05-05',\n",
       " '2014-06-02',\n",
       " '2014-07-10',\n",
       " '2014-08-04',\n",
       " '2014-09-02',\n",
       " '2014-10-06',\n",
       " '2014-11-03',\n",
       " '2014-12-01',\n",
       " '2015-01-05',\n",
       " '2015-02-02',\n",
       " '2015-03-02',\n",
       " '2015-04-06',\n",
       " '2015-05-04',\n",
       " '2015-06-01',\n",
       " '2015-07-01',\n",
       " '2015-08-03',\n",
       " '2015-09-14',\n",
       " '2015-10-05',\n",
       " '2015-11-02',\n",
       " '2015-12-07',\n",
       " '2016-01-04',\n",
       " '2016-02-01',\n",
       " '2016-03-07',\n",
       " '2016-04-04',\n",
       " '2016-05-02',\n",
       " '2016-06-06',\n",
       " '2016-07-05',\n",
       " '2016-08-01',\n",
       " '2016-09-06',\n",
       " '2016-10-03',\n",
       " '2016-11-03',\n",
       " '2016-12-01',\n",
       " '2017-01-03',\n",
       " '2017-02-01',\n",
       " '2017-03-01',\n",
       " '2017-04-03',\n",
       " '2017-05-01',\n",
       " '2017-06-01',\n",
       " '2017-07-03',\n",
       " '2017-08-01',\n",
       " '2017-09-01',\n",
       " '2017-10-02',\n",
       " '2017-11-01',\n",
       " '2017-12-01',\n",
       " '2018-01-02',\n",
       " '2018-02-01',\n",
       " '2018-03-01',\n",
       " '2018-04-02',\n",
       " '2018-05-01',\n",
       " '2018-06-01',\n",
       " '2018-07-02',\n",
       " '2018-08-01',\n",
       " '2018-09-04',\n",
       " '2018-10-01',\n",
       " '2018-11-01',\n",
       " '2018-12-03',\n",
       " '2019-01-02',\n",
       " '2019-02-01',\n",
       " '2019-03-01',\n",
       " '2019-04-01',\n",
       " '2019-05-01',\n",
       " '2019-06-03',\n",
       " '2019-07-01',\n",
       " '2019-08-01',\n",
       " '2019-09-03',\n",
       " '2019-10-01',\n",
       " '2019-11-01',\n",
       " '2019-12-02',\n",
       " '2020-01-02',\n",
       " '2020-02-03',\n",
       " '2020-03-02',\n",
       " '2020-04-01',\n",
       " '2020-05-01',\n",
       " '2020-06-01',\n",
       " '2020-07-01',\n",
       " '2020-07-21',\n",
       " '2020-08-03',\n",
       " '2020-08-05',\n",
       " '2020-09-01',\n",
       " '2020-10-01',\n",
       " '2020-10-13',\n",
       " '2020-11-02',\n",
       " '2020-12-01',\n",
       " '2021-01-04',\n",
       " '2021-02-01',\n",
       " '2021-03-01',\n",
       " '2021-04-01',\n",
       " '2021-05-03',\n",
       " '2021-06-01',\n",
       " '2021-07-01',\n",
       " '2021-08-02',\n",
       " '2021-09-01',\n",
       " '2021-10-01',\n",
       " '2021-11-01',\n",
       " '2021-12-01',\n",
       " '2022-01-03',\n",
       " '2022-02-01',\n",
       " '2022-03-01',\n",
       " '2022-04-01',\n",
       " '2022-05-02',\n",
       " '2022-06-01',\n",
       " '2022-07-01',\n",
       " '2022-08-01',\n",
       " '2022-09-01',\n",
       " '2022-10-03',\n",
       " '2022-11-01',\n",
       " '2022-12-01',\n",
       " '2023-01-03',\n",
       " '2023-02-01',\n",
       " '2023-03-01',\n",
       " '2023-04-03',\n",
       " '2023-05-01',\n",
       " '2023-06-01',\n",
       " '2023-07-03']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fred_vintagedates(fred_api=my_fred_key, series='FEDFUNDS')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Explore data\n",
    "-------------"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After retrieving data, one can use a `fredeco` function to get some statistics and other informations about them. This is possible for one or several indicators, as illustrated below. We use the `df_one` and `df_multi`, the objects where have been stored data retrieved previously."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Units</th>\n",
       "      <td>Billions of Dollars</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N</th>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>6873.642447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Median</th>\n",
       "      <td>4188.2975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Std</th>\n",
       "      <td>7055.960951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Min</th>\n",
       "      <td>249.616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Max</th>\n",
       "      <td>25462.722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CV</th>\n",
       "      <td>1.019748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25% quantile</th>\n",
       "      <td>795.633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50% quantile</th>\n",
       "      <td>4188.2975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75% quantile</th>\n",
       "      <td>11646.6365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>skewness</th>\n",
       "      <td>0.916755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kurtosis</th>\n",
       "      <td>-0.34893</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              GDP\n",
       "Units         Billions of Dollars\n",
       "N                              76\n",
       "Mean                  6873.642447\n",
       "Median                  4188.2975\n",
       "Std                   7055.960951\n",
       "Min                       249.616\n",
       "Max                     25462.722\n",
       "CV                       1.019748\n",
       "25% quantile              795.633\n",
       "50% quantile            4188.2975\n",
       "75% quantile           11646.6365\n",
       "skewness                 0.916755\n",
       "kurtosis                 -0.34893"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "explore(fred_api=my_fred_key,data=df_one)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>GDP</th>\n",
       "      <th>GDPCA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Units</th>\n",
       "      <td>Billions of Dollars</td>\n",
       "      <td>Billions of Chained 2012 Dollars</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N</th>\n",
       "      <td>76</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>6873.642447</td>\n",
       "      <td>9018.846338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Median</th>\n",
       "      <td>4188.2975</td>\n",
       "      <td>7637.704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Std</th>\n",
       "      <td>7055.960951</td>\n",
       "      <td>5516.176475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Min</th>\n",
       "      <td>249.616</td>\n",
       "      <td>2036.204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Max</th>\n",
       "      <td>25462.722</td>\n",
       "      <td>20014.128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CV</th>\n",
       "      <td>1.019748</td>\n",
       "      <td>0.607643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25% quantile</th>\n",
       "      <td>795.633</td>\n",
       "      <td>4173.424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50% quantile</th>\n",
       "      <td>4188.2975</td>\n",
       "      <td>7637.704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75% quantile</th>\n",
       "      <td>11646.6365</td>\n",
       "      <td>13865.519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>skewness</th>\n",
       "      <td>0.916755</td>\n",
       "      <td>0.449427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kurtosis</th>\n",
       "      <td>-0.34893</td>\n",
       "      <td>-1.146103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              GDP                             GDPCA\n",
       "Units         Billions of Dollars  Billions of Chained 2012 Dollars\n",
       "N                              76                                77\n",
       "Mean                  6873.642447                       9018.846338\n",
       "Median                  4188.2975                          7637.704\n",
       "Std                   7055.960951                       5516.176475\n",
       "Min                       249.616                          2036.204\n",
       "Max                     25462.722                         20014.128\n",
       "CV                       1.019748                          0.607643\n",
       "25% quantile              795.633                          4173.424\n",
       "50% quantile            4188.2975                          7637.704\n",
       "75% quantile           11646.6365                         13865.519\n",
       "skewness                 0.916755                          0.449427\n",
       "kurtosis                 -0.34893                         -1.146103"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "explore(fred_api=my_fred_key,data=df_multi)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To explore data retrieved, one can quickly plot them, using the package `matplotlib`. That's why it has been called with `import matplotlib` previously."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Dates'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_one.tail(20).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Dates'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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epFkzy0d0L1q0iNDQULy9vS1uQbVq1QqAlJQUOnXqdNnjGo1GFixYgF6vx87OzmL73Llz6d+//1VrE0IIYR1/7T9Fij4XFwdbHuzZ1NrllImEnLLS6cp8y8iavL29ueWWW/jkk08YP378ZcflXMmHH36IjY0NgwcPttgeEBBAixYXPw+hY8eOhIaG8t577zF06NCLxuVkZWXh6enJihUryM3NJSEhAVvbC/8HkJSUxOjRo83thBBCVD+fb9B6cYZ2C8DTxcHK1ZSN3K6qhT777DNKS0vp2rUrixYtYu/evaSmpvLdd9+RkpJiETByc3PR6/UcOXKEDRs28Nhjj/H666/zxhtvEBwcXKbz6XQ65s2bx759+7jhhhtYsWIFBw8eZNeuXbzxxhvceeedgDbgOCoqirCwMNq1a2d+3XfffXh6evL9999Xyu9DCCHE9Uk6ls3GA6extdExpk/1W4jzciTk1EItWrQgISGBiIgIpk6dSlhYGF27duXjjz/mueee47XXXjO3nTZtGo0aNSI4OJgRI0aQnZ3NmjVrmDJlSrnO2b17d7Zv305wcDCPPvoobdq04Y477iA5OZkPPviAjIwMli9fzpAhQy76WRsbG+666y6+/vrr6752IYQQFe/zDdpCnLd3aEST+mUfQmFtOlWeUaO1TE5ODh4eHmRnZ+Pu7m6xr6ioiLS0NJo1a4aTU/VbWbUukc9CCCGs58iZAm58Zy0mBSueuoFQf/er/1Alu9L3979JT44QQgghLuurvw5iUnBDywbVIuCUh4QcIYQQQlzSmfwSFm0/AsDYarwQ5+VIyBFCCCHEJX0bd5gig4l2jd3p1cLb2uWUm4QcIYQQQlyksMTIgrhDADzWt0WNfCp9uULOzJkz6datG25ubvj4+DB48OCLHjp3nlKKQYMGodPp+OWXXyz2paenExUVhYuLCz4+PkyaNInS0lKLNuvWraNz5844OjoSHBx8yfWNPv30U4KCgnBycqJHjx5s3bq1PJdTJnV4XHa1IZ+BEEJUvZ/ij3Amv4Qm9Z25tV31XojzcsoVctavX090dDSbN28mJiYGg8HAgAEDyM/Pv6jtBx98cMnUZzQaiYqKoqSkhE2bNrFgwQLmz5/PtGnTzG3S0tKIioqiX79+JCYmMmHCBB555BFWrVplbrNo0SImTpzI9OnT2bFjB2FhYURGRpKZmVmeS7qs88+SKSkpqZDjiWtXUFAAgL29vZUrEUKIusFoUnz5l7YQ56M3NMfOtmbe+LmuKeQnT57Ex8eH9evX07dvX/P2xMREbrvtNrZv306jRo1YunSp+em5f/zxB7fddhvHjx/H19cXgDlz5jBlyhROnjyJg4MDU6ZMYfny5SQlJZmPef/995OVlcXKlSsB6NGjB926deOTTz4BwGQyERAQwPjx43n++efLVP+VpqAppUhPT8dgMODv73/J1bVF5VJKUVBQQGZmJp6enjRq1MjaJQkhRJ2wfNcJon/YgaeLPZuevxkXh+q1QEJZp5BfV9XZ2dmAtmr1eQUFBTzwwAN8+umn+Pld3L0VFxdH+/btzQEHIDIykieeeILk5GQ6depEXFwcERERFj8XGRnJhAkTAK13JT4+nqlTp5r329jYEBERQVxc3GXrLS4utlicMicn57JtdTodjRo1Ii0tjcOHD1+2nah8np6el/yzJIQQouIppcxLODwUHlTtAk55XHPlJpOJCRMm0Lt3b9q1a2fe/swzz9CrVy/zo/z/S6/XWwQcwPxer9dfsU1OTg6FhYWcPXsWo9F4yTYpKSmXrXnmzJnMmDGjzNfo4OBAy5Yt5ZaVFdnb21ssQyGEEKJybT54hl1Hs3G0s2FkeM1YiPNyrjnkREdHk5SUxN9//23e9ttvvxEbG0tCQkKFFFfRpk6dysSJE83vc3JyCAgIuOLP2NjYyFN2hRBC1Bnne3Hu7doEb1dHK1dzfa5poMm4ceNYtmwZa9eupUmTJubtsbGx/PPPP3h6emJnZ4ednZahhgwZwk033QSAn58fGRkZFsc7//78LYnLtXF3d8fZ2ZkGDRpga2t7yTZXuq3h6OiIu7u7xUsIIYQQmhR9DutST2Kjg0f6NLd2OdetXCFHKcW4ceNYunQpsbGxNGtmuRLp888/z65du0hMTDS/AN5//33mzZsHQHh4OLt377aYBRUTE4O7uzuhoaHmNmvWrLE4dkxMDOHh4YB2G6lLly4WbUwmE2vWrDG3EUIIIUT5fHFuIc5B7RoR1KCelau5fuW6XRUdHc0PP/zAr7/+ipubm3kMjYeHB87Ozvj5+V2yJyUwMNAciAYMGEBoaCgjRoxg1qxZ6PV6XnrpJaKjo3F01LrFxo4dyyeffMLkyZN5+OGHiY2NZfHixSxfvtx8zIkTJzJy5Ei6du1K9+7d+eCDD8jPz2f06NHX/MsQQggh6qrjWYX8lngcgMf61vxeHChnyJk9ezaA+dbTefPmzWPUqFFlOoatrS3Lli3jiSeeIDw8nHr16jFy5EheffVVc5tmzZqxfPlynnnmGT788EOaNGnCV199RWRkpLnN0KFDOXnyJNOmTUOv19OxY0dWrlx50WBkIYQQQlzd3L/TKDUpejb3IizA09rlVIjrek5OTVfWefZCCCFEbZZdaKDXzDXklxiZN7ob/UJ8rF3SFZX1+1uecCeEEELUcd9vOUx+iZEQXzduatXQ2uVUGAk5QgghRB1WZDAyb+MhQBuLUxMX4rwcCTlCCCFEHfZLwjFO5hbTyMOJ28P8rV1OhZKQI4QQQtRRJpPii7+0aeNj+jTDwa52xYLadTVCCCGEKLPVezM4eDIfNyc77u8eaO1yKpyEHCGEEKKO+vzcw/8e7NkUV8eauxDn5UjIEUIIIeqg7YfOEH/4LA62NozuFWTtciqFhBwhhBCiDjrfi3N358b4uNfOhagl5AghhBB1zIHMPGL2aItcP3JD7VjC4VIk5AghhBB1zJfnenFuCfUl2MfVytVUHgk5QgghRB1yKq+YpQnHAHi8lizEeTkScoQQQog65PvN6ZQYTXQM8KRrkJe1y6lUEnKEEEKIOqK41Mi3mw8D8HCfZlaupvJJyBFCCCHqiN93nuBUXjF+7k4Maudn7XIqnYQcIYQQog5QSjH37zQAHurVFHvb2h8Bav8VCiGEEIItaWfYcyIHJ3sbhnWrfUs4XIqEHCGEEKIOON+Lc3fnJtSv52DlaqqGhBwhhBCilks/XUDMXu3hfw/3DrJuMVVIQo4QQghRy83fdAil4MZWDQn2cbN2OVVGQo4QQghRi+UWGVi8/QhQN6aN/5uEHCGEEKIWW7z9KHnFpQT7uNK3ZQNrl1OlJOQIIYQQtZTRpJi/SRtwPLp3EDqdzsoVVS0JOUIIIUQttXpvBkfOFOLhbM/dnZpYu5wqJyFHCCGEqKXOTxt/oEcgzg62Vq6m6knIEUIIIWqhpGPZbEk7g52NjofCm1q7HKuQkCOEEELUQvM2HgLg1vaNaOThbN1irERCjhBCCFHLZOYW8fvO40Ddmzb+bxJyhBBCiFrm+83plBhNdA70pGOAp7XLsRoJOUIIIUQtUmQw8t3mw0Dd7sUBCTlCCCFErfLbzuOczi/B38OJgW39rF2OVUnIEUIIIWoJpZR52vhDvYKws63bX/N1++qFEEKIWiTu4GlS9Lk429syrFugtcuxOgk5QgghRC0x9+9DANzTpQkeLvbWLaYakJAjhBBC1AKHTuWzJiUDgFG9g6xbTDUhIUcIIYSoBeZvOoRS0C+kIS0aulq7nGpBQo4QQghRw+UUGViy/Qgg08b/TUKOEEIIUcMt3naE/BIjLX1c6RPcwNrlVBsScoQQQogarNRoMq9T9XCfZuh0OusWVI1IyBFCCCFqsNV7MziWVUh9F3vu6tTY2uVUK+UKOTNnzqRbt264ubnh4+PD4MGDSU1NNe8/c+YM48ePJyQkBGdnZwIDA3nqqafIzs62OE56ejpRUVG4uLjg4+PDpEmTKC0ttWizbt06OnfujKOjI8HBwcyfP/+iej799FOCgoJwcnKiR48ebN26tTyXI4QQQtR456eNP9AjECd7W+sWU82UK+SsX7+e6OhoNm/eTExMDAaDgQEDBpCfnw/A8ePHOX78OO+++y5JSUnMnz+flStXMmbMGPMxjEYjUVFRlJSUsGnTJhYsWMD8+fOZNm2auU1aWhpRUVH069ePxMREJkyYwCOPPMKqVavMbRYtWsTEiROZPn06O3bsICwsjMjISDIzM6/3dyKEEELUCLuPZrP10BnsbHQ8FB5k7XKqH3UdMjMzFaDWr19/2TaLFy9WDg4OymAwKKWUWrFihbKxsVF6vd7cZvbs2crd3V0VFxcrpZSaPHmyatu2rcVxhg4dqiIjI83vu3fvrqKjo83vjUaj8vf3VzNnzixz/dnZ2QpQ2dnZZf4ZIYQQorp4ZmGCajplmXr6xx3WLqVKlfX7+7rG5Jy/DeXl5XXFNu7u7tjZ2QEQFxdH+/bt8fX1NbeJjIwkJyeH5ORkc5uIiAiL40RGRhIXFwdASUkJ8fHxFm1sbGyIiIgwtxFCCCFqs8ycIn7fdRyQaeOXY3etP2gymZgwYQK9e/emXbt2l2xz6tQpXnvtNR577DHzNr1ebxFwAPN7vV5/xTY5OTkUFhZy9uxZjEbjJdukpKRctubi4mKKi4vN73NycspwpUIIIUT18+3mwxiMiq5N69Ohiae1y6mWrrknJzo6mqSkJBYuXHjJ/Tk5OURFRREaGsorr7xyraepUDNnzsTDw8P8CggIsHZJQgghRLkVGYx8vyUdkF6cK7mmkDNu3DiWLVvG2rVradKkyUX7c3NzGThwIG5ubixduhR7+wuLhPn5+ZGRkWHR/vx7Pz+/K7Zxd3fH2dmZBg0aYGtre8k2549xKVOnTiU7O9v8OnLkSPkuXAghhKgGfk08xpn8Ehp7OjMg1PfqP1BHlSvkKKUYN24cS5cuJTY2lmbNLk6POTk5DBgwAAcHB3777TecnJws9oeHh7N7926LWVAxMTG4u7sTGhpqbrNmzRqLn4uJiSE8PBwABwcHunTpYtHGZDKxZs0ac5tLcXR0xN3d3eIlhBBC1CRKKfO08ZG9mmJnK4+8u5xy/Waio6P57rvv+OGHH3Bzc0Ov16PX6yksLAQuBJz8/Hy+/vprcnJyzG2MRiMAAwYMIDQ0lBEjRrBz505WrVrFSy+9RHR0NI6OjgCMHTuWgwcPMnnyZFJSUvjss89YvHgxzzzzjLmWiRMn8uWXX7JgwQL27t3LE088QX5+PqNHj66o340QQghR7Wz65zSpGbm4ONgytFugtcup3sozZQu45GvevHlKKaXWrl172TZpaWnm4xw6dEgNGjRIOTs7qwYNGqhnn33WPMX8vLVr16qOHTsqBwcH1bx5c/M5/u3jjz9WgYGBysHBQXXv3l1t3ry5PJcjU8iFEELUOA/P26qaTlmmpv2y29qlWE1Zv791SilljXBVHeTk5ODh4WGe5i6EEEJUZ2mn8un37joA1j53E80a1LNuQVZS1u9vuZEnhBBC1BDzN6YB0L+1T50NOOUhIUcIIYSoAbILDSyJPwrItPGykpAjhBBC1ACLtqVTUGIkxNeNXi28rV1OjSAhRwghhKjmSo0mFmw6DMDDfYLQ6XRWrqhmkJAjhBBCVHN/7sngWFYhXvUcuLNjY2uXU2NIyBFCCCGqubl/awOOh/cIxMne1srV1BwScoQQQohqbOeRLLYfPou9rY4RPZtau5waRUKOEEIIUY3NOzdt/PYO/vi4O12ltfg3O2sXIIQQQtQESil+ij/KqbwSbgn1JdjHtdLPqc8uYtmuEwCM7i3TxstLQo4QQghxFQajiWm/JvHj1iMAvL0yhZY+rgxq58fAdo1o08itUmY8fbv5EKUmRfcgL9o38ajw49d2EnKEEEKIK8gpMhD9/Q7+2n8KnQ66BXmRkH6W/Zl57I89wEexBwjydiGynR+D2jUirIlHhQSewhIjP2xJB7Rp46L8JOQIIYQQl3HkTAEPz9/G/sw8nO1t+XhYJyJCfckuNLBmbwZ/JOlZv+8kh04X8Pn6g3y+/iCNPZ2JbOvHoPZ+dAmsj43NtQWeXxKPcbbAQJP6ztwS6lfBV1Y3yAKdskCnEEKIS0g8ksUjC7ZzKq8YHzdH5o7qRrvGF98yyi8uZW1qJn8k6VmbkklBidG8r6GbI5FtfRnUrhE9mnlhZ1u2+T5KKQa8v4H9mXm8FNWGR25oXmHXVRuU9ftbQo6EHCGEEP+xMukEExYlUmQw0drPjbmjuuHv6XzVnysyGFm/7yQrk/Ss3ptBblGpeV99F3tuCdUCT+/gBjjYXT7w/LX/JCO+3ko9B1viXuiPu5N9hVxXbVHW72+5XSWEEEKco5Tiy78OMvOPFJSCm0Ia8skDnXF1LNvXpZO9LZFt/Yhs60dJqYmN/5xi5W49f+7Rc7bAwOLtR1m8/ShujnZEhPoysJ0fN7ZqeNED/s4//O/ergEScK6D9ORIT44QQgi09aGm/ZZsHuw7omdTpt8eWuZbTFc79ta0M/yRpGdlsp6TucXmfS4OtvQL8WFgOz/6tfYhI6eI/u+tR6eDtc/eRFCDetd9/tpGbleVgYQcIYQQALlFBqJ/SGDDvpPodPBSVCgP966chTBNJsWO9LNa4EnScyyr0LzPwc4GX3dHjpwpJKKNL1+N7Frh568N5HaVEEIIUQbHsgoZM38bKfpcnO1t+fD+jgxoW3mzmWxsdHQN8qJrkBcvRbVh19Hsc4HnBIdOF3DkjBZ6ZNr49ZOQI4QQos7afTSbhxds42RuMQ3dHPl6ZFc6NPGssvPrdDrCAjwJC/BkysAQUvS5/JmcgbuzHeHNvausjtpKQo4QQog66c9kPU8vTKTQYKS1nxtfj+pG4zLMoKosOp2ONo3cadNIhk9UFAk5Qggh6hSlFF//ncYbK/aiFPRt1ZBPH+iEm8xiqnUk5AghhKgzSo0mZvy+h283HwbggR6BvHpH2wqZQSWqHwk5Qggh6oS84lLG/bCDdanaDKoXBrXhkRuaVcoMKlE9SMgRQghR653ILmT0PG0GlZO9DR8M7cTAdrIeVG0nIUcIIUStlnQsmzELtpGRU0wDV20GVViAp7XLElVAQo4QQohaa/WeDJ5amEBBiZFWvq7MHdWNJvVdrF2WqCIScoQQQtRK8zam8dqyPZgU3NCyAZ8O7yzrQNUxEnKEEELUKkaT4rVle5i/6RAAw7oH8Oqd7bCXGVR1joQcIYQQtUZ+cSnjf0wgNiUTgKmDWvNY3+Yyg6qOkpAjhBCiVtBnF/Hw/G3sOZGDo50NHwztyKD2jaxdlrAiCTlCCCFqvCNnChj6eRzHs4to4OrAlw91pVNgfWuXJaxMQo4QQogaLTO3iBFfb+F4dhHNG9ZjwejuBHjJDCohIUcIIUQNll1g4KGvt3LodAFN6jvzwyM98fNwsnZZopqQoeZCCCFqpPziUkbP30qKPpeGbo58/0gPCTjCgoQcIYQQNU5xqZGx38WzIz0LD2d7vhvTg6be9axdlqhmJOQIIYSoUUqNJp7+MZG/9p/CxcGW+aO7EeLnZu2yRDUkIUcIIUSNYTIpnv95NyuT9TjY2sgsKnFFEnKEEELUCEopXlu+h5/ij2Jro+PjBzrRO7iBtcsS1ZiEHCGEEDXCh2v2M2/jIQBmDelAZFs/6xYkqj0JOUIIIaq9uX+n8cHq/QC8cnsoQ7o0sXJFoiYoV8iZOXMm3bp1w83NDR8fHwYPHkxqaqpFm6KiIqKjo/H29sbV1ZUhQ4aQkZFh0SY9PZ2oqChcXFzw8fFh0qRJlJaWWrRZt24dnTt3xtHRkeDgYObPn39RPZ9++ilBQUE4OTnRo0cPtm7dWp7LEUIIUQMs2X6EV5ftAWDiLa0Y1buZlSsSNUW5Qs769euJjo5m8+bNxMTEYDAYGDBgAPn5+eY2zzzzDL///jtLlixh/fr1HD9+nLvvvtu832g0EhUVRUlJCZs2bWLBggXMnz+fadOmmdukpaURFRVFv379SExMZMKECTzyyCOsWrXK3GbRokVMnDiR6dOns2PHDsLCwoiMjCQzM/N6fh9CCCGqkZVJJ5jyf7sAeKRPM8bfHGzlikSNoq5DZmamAtT69euVUkplZWUpe3t7tWTJEnObvXv3KkDFxcUppZRasWKFsrGxUXq93txm9uzZyt3dXRUXFyullJo8ebJq27atxbmGDh2qIiMjze+7d++uoqOjze+NRqPy9/dXM2fOLHP92dnZClDZ2dnluGohhBBV4a99J1XLF1aoplOWqUlLEpXJZLJ2SaK88k9XymHL+v19XWNysrOzAfDy8gIgPj4eg8FARESEuU3r1q0JDAwkLi4OgLi4ONq3b4+vr6+5TWRkJDk5OSQnJ5vb/PsY59ucP0ZJSQnx8fEWbWxsbIiIiDC3uZTi4mJycnIsXkIIIaqf+MNneezb7ZQYTdza3o+Zd3dAp9NZuyxRFsZSSFkO394N/wuFgjNWK+WaQ47JZGLChAn07t2bdu3aAaDX63FwcMDT09Oira+vL3q93tzm3wHn/P7z+67UJicnh8LCQk6dOoXRaLxkm/PHuJSZM2fi4eFhfgUEBJT/woUQQlSqvSdyGD1vKwUlRm5o2YD3h3bE1kYCTrWXq4f1s+DDDrDwAfhnDZQWwsF1VivpmhfojI6OJikpib///rsi66lUU6dOZeLEieb3OTk5EnSEEKIaOXQqnxFfbyWnqJQuTevz+YguONrZWrsscTlKQdoG2P611ntjOjeJyNkLOj0IXUeDV3OrlXdNIWfcuHEsW7aMDRs20KTJhWl8fn5+lJSUkJWVZdGbk5GRgZ+fn7nNf2dBnZ999e82/52RlZGRgbu7O87Oztja2mJra3vJNuePcSmOjo44OjqW/4KFEEJUuhPZhQz/agun8opp08iduaO64eJwzf8vLipTwRnY+SNsnwunD1zYHtADuo6B0DvB3vqLpZbrdpVSinHjxrF06VJiY2Np1sxyGl+XLl2wt7dnzZo15m2pqamkp6cTHh4OQHh4OLt377aYBRUTE4O7uzuhoaHmNv8+xvk254/h4OBAly5dLNqYTCbWrFljbiOEEKLmOJ1XzINfbeFYViHNGtTjm4e74+Fsb+2yxL8pBUe3wy9Pwv/awKoXtIDj4KoFm7EbYcyfEDa0WgQcKGdPTnR0ND/88AO//vorbm5u5vEvHh4eODs74+HhwZgxY5g4cSJeXl64u7szfvx4wsPD6dmzJwADBgwgNDSUESNGMGvWLPR6PS+99BLR0dHmXpaxY8fyySefMHnyZB5++GFiY2NZvHgxy5cvN9cyceJERo4cSdeuXenevTsffPAB+fn5jB49uqJ+N0IIIapAbpGBUfO28c/JfBp5OPHtmO40dJNe92qjJB92L4FtX4N+14Xtvu2g68PQ4T5wrKYLpJZnyhZwyde8efPMbQoLC9WTTz6p6tevr1xcXNRdd92lTpw4YXGcQ4cOqUGDBilnZ2fVoEED9eyzzyqDwWDRZu3atapjx47KwcFBNW/e3OIc53388ccqMDBQOTg4qO7du6vNmzeX53JkCrkQQlhZYUmpunfOJtV0yjLV6dU/1f6MXGuXJM7L2KPU8ueUerOJUtPdtderDZX6v8eUSt+ilBWn9Jf1+1unlFLWi1jWlZOTg4eHB9nZ2bi7u1u7HCGEqFMMRhOPfxtPbEombo52/PhYT9o19rB2WXVbaTHs/V0ba3N444XtXs21XpuOw8HFy3r1nVPW728Z0SWEEKLKGU2KiYt3EpuSiaOdDV+P6iYBx5rOHoL4+bDjWyg4pW3T2ULIIOg2BprdBDY1b7lLCTlCCCGqlFKKl39N4vedx7Gz0TFnRBe6N7N+70CdYzLC/j+1sTYHVqONQAHcGkGXUdD5IXD3t2aF101CjhBCiCo1a1UqP2xJR6eD94d2pF+Ij7VLqnsSvoN1b0H2kQvbmvfTem1aDQLb2hEPasdVCCGEqBFmr/uH2ev+AeDNu9pze1jN7imokRK+h1+jtX93rq+Ns+n6MHi3sG5dlUBCjhBCiCrx/ZbDvL0yBYCpg1ozrHuglSuqg/bHwG/jtX/vGQ39p1WbZ9pUBgk5QgghKt1vO4/z0i9JAET3a8HjN9a+XoNq72g8LH4IlBHChkHkG1DLFz2VkCOEEKLSKKX4YsNB3l6ZglIwomdTnhsQYu2y6p5TB+CHe8FQAC36wx0f1/qAAxJyhBBCVJL84lIm/98ulu86AcDQrgHMuKMtujrw5Vqt5GbAd3dDwWnw7wT3fQO2dWPJDAk5QgghKtyhU/k8/m08qRm52NnomH57KA/2bCoBp6oV5cD390DWYajfDB5YAo6u1q6qykjIEUIIUaFiUzJ4emEiuUWlNHRzZPbwznQNkufgVLnSElj0oLbeVL2GMOJncG1o7aqqlIQcIYQQFcJkUnwce4AP1uxDKejStD6fDe+Mr3vtnb1TbZlM8OuTkLZeWyV8+BJtaYY6RkKOEEKI65ZTZGDiokRW780E4MGegUy7rS0OdjVvKYBaIeZlbeVwGzttDI5/J2tXZBUScoQQQlyXfRm5PP5tPGmn8nGws+H1we24r2uAtcuquzZ9AnGfaP9+52cQ3N+69ViRhBwhhBDXbMXuEzy3ZCcFJUb8PZyYM6ILHZp4Wrusumv3T/Dni9q/3/IqhA21bj1WJiFHCCFEuRlNindWpTJnvbZEQ68W3nw8rBPero5WrqwO+2ctLB2r/XvPJ6HXU9atpxqQkCOEEKJczuaXMP7HBP4+cAqAx/o2Z3JkCHa2Mv7Gak7s1GZSmQzQ9m4YUPufZlwWEnKEEEKUWdKxbB7/Np5jWYU429sy654OdWuRTaWqX3g4ewi+uwdK8iDoBrhrDthI4AQJOUIIIcro5x1HmfrzbopLTTT1duHzEV1o7edu7bKqRvZR2Pol7FgAOhvo8wx0e9T6i1vmn4Jv74b8TPBtD/d/D3Zyy/A8CTlCCCGuyGA08fqyPSyIOwxAv5CGfDC0Ex4utXxpAKUgfTNsmQN7f9cWtjzvz5dg82y4aaq22KWtFb5OS/Lh+3vhzD/gEQgP/gROHlVfRzUmIUcIIcRlZeYWEf39DrYdOgvAUzcHMyGiFTY21eyWTUUqLYak/9PCzYmdF7Y36ws9xkLBGVg3E3KOwW/jYNPH0P9laH1b1d3KMhpgySg4vgOcvbSnGbv5Vc25axAJOUIIIS4p/vBZnvw+noycYtwc7fjf0I7cEupr7bIqT64ets/VXvkntW12TtDhPi3c+La90Lb9vbDtS/jrPTiVqg36bdINIl6BoD6VW6dS8PvTsP9PsHOGBxZDg5aVe84aSqeUUtYuwlpycnLw8PAgOzsbd/c6cl9ZCCGuQinFD1vTeeW3ZAxGRbCPK5+P6EKLhrV0Ycdj8bB5DiQv1WYnAbg3hm6PQJdR4HKFdbeKsmHjR7D5MzAUaNuCI6D/dGjUoXLqXfOqFq50tnD/DxAysHLOU42V9ftbQo6EHCGEMCsyGJn+azKLth8BYFA7P965NwxXx1rW8W80wJ5fYcvncHTrhe0BPaHnWO3Wk205xhzl6mHDOxA/H0yl2rZ298DNL1bsmlFbv4QVz2n/fscn0HlExR27BpGQUwYScoQQ4oLjWYU88V08O49mY6OD5yJDeOLGFuiq25Tp65F/Sgsi276G3OPaNht7aDdECzfXu8bT6X9g7RvamB7Q1o7qMhr6TgK367zVl/yLNg4HBf1eghsnXd/xajAJOWUgIUcIITRx/5xm3A87OJ1fgqeLPR/d34m+rRpau6yKo9+tDSTetQSMxdq2ej7QbYwWQq43gPzXiZ2wegb8s0Z7b18Pwp+EXuOvbQbUoY3w7V1a7V3HQNR71e95PVVIQk4ZSMgRQtR1BqOJuX+nMWtVKkaTIrSRO5+P6EKAl4u1S7t+JiOkrtDG2xz++8L2Rh2h5xPQ9q7Kf6ZM2gZY/Yo27ge0mVA3PKuN9ynrM3YykmHuICjOhja3w70LwMa20kquCSTklIGEHCFEXVVSauL/dhzl07UHOHq2EIC7OjXmzbva4+xQw79AC8/Cjm+18SvZ6do2nS2E3gE9noCA7lXbC6KU9pydNa/C6f3aNvcm0O/cM3auFFiyjsDXA7Rba4HhMGIp2DtXTd3VmIScMpCQI4Soa4pLjSzZfpTZ6/7hWJYWbhq4OvDMLa14oHtgzR5/c2q/9oC+nT9emOnk7KXNkOo2BjyaWLU8jKWw8wdYO/PCeKCGraH/NAi59eLgVXAG5g7Upqg3bAMP/wHO9au+7mpIQk4ZSMgRQtQVxaVGFm87wmfr/uFEdhEADd0cGXtjCx7oHlize2+yj8G6NyHxB1AmbZtPqPZsmw73Vb+eD0Oh1sv013tQlKVta9L93DN2el9o881gOLJZm84+5k/rh7RqREJOGUjIEULUdkUGI4u2HWH2un/Q52jhxtddCzfDugfiZF+Dw03hWfj7fW0aeKl2bbQaCOHR2kKV1b1XqjALNn0EcZ9BqdarRvAtcPNLsH4WpC7XBik/vAp82li11OpGQk4ZSMgRQtRWRQYjP2xJZ876f8jM1WYT+bk78WS/FtzXNaBmhxtDIWz94lxPSLa2LbAX3DJDG29T0+TqYf3bEL/Acn0sW0d46Bdo2stqpVVXZf3+rmVPdxJCiLqtsMTI91sO8/mGg5w8F278PZx4ol8w93VtgqNdDQ43JqM23mbtm9q6UaDdluo/HVpFVv+em8tx84Pb3ofwcRD7OiT/rK10fs/XEnCuk/TkSE+OEKIWKCgp5bvNh/liw0FO5ZUA0NjTmeh+wQzp0rhmhxulYN9K7bkzJ/dq29ybaE8T7jC09k2nzkwBY0nlLQtRC0hPjhBC1AH5xaV8u/kwX244yOl8Ldw0qe/MuH7B3N25CQ52Nlau8Dqlb4HV0yE9Tnvv5Kk9Z6b7Y2V/zkxN49Pa2hXUGhJyhBCiBsorLmXBpkN89ddBzhZoi0o29XYhul8wd3VqjL1tDQ83J1O158qkLNPe2zlps6X6TJBp1KLMJOQIIUQNkltk0MLN32lknQs3Qd4ujLu5JYM7+mNX08NNznFYNxMSvtOmg+tsoONwuGkqeDS2dnWihpGQI4QQNUBOkYH5Gw/x9d9pZBdq4aZ5g3qM7x/M7R1qQbgpzIKNH2gP8zs/HTwkSntQnty+EddIQo4QQlRj2YUG5v6dxtyNaeQWlQLQomE9nurfkts6+GNrU0NnFJ1nKIJtX8KGdy88GC+gpzYdPLCnVUsTNZ+EHCGEqIaUUvzfjmO8+nsyOefCTUsfV8b3b0lU+0Y1P9yYjLBrEcS+ATlHtW0NW2vTwUMG1dzp4KJakZAjhBDVjD67iKk/72Jt6kkAQnzdeKp/Swa188OmpocbpWD/n9rK3Jl7tG1u/tDvBW2xSlv5WhIVp9w3cTds2MDtt9+Ov78/Op2OX375xWJ/Xl4e48aNo0mTJjg7OxMaGsqcOXMs2hQVFREdHY23tzeurq4MGTKEjIwMizbp6elERUXh4uKCj48PkyZNorS01KLNunXr6Ny5M46OjgQHBzN//vzyXo4QQlQbSikWbz/CLe+vZ23qSRxsbZgysDXLn+pDVIdGNT/gHN0O82+DH+7TAo6TB0TMgKd2QOcREnBEhSv3n6j8/HzCwsJ4+OGHufvuuy/aP3HiRGJjY/nuu+8ICgrizz//5Mknn8Tf35877rgDgGeeeYbly5ezZMkSPDw8GDduHHfffTcbN24EwGg0EhUVhZ+fH5s2beLEiRM89NBD2Nvb8+abbwKQlpZGVFQUY8eO5fvvv2fNmjU88sgjNGrUiMjIyOv5nQghRJU7nlXI8z/vZsM+rfemY4An79zTgZa+blau7DqZTJC2DrZ+pa3FBNpyBT0ehz7PgIuXVcsTtdt1PfFYp9OxdOlSBg8ebN7Wrl07hg4dyssvv2ze1qVLFwYNGsTrr79OdnY2DRs25IcffuCee+4BICUlhTZt2hAXF0fPnj35448/uO222zh+/Di+vr4AzJkzhylTpnDy5EkcHByYMmUKy5cvJykpyXye+++/n6ysLFauXFmm+uWJx0IIa1NKsWjbEV5fvpe84lIc7Gx4bkArxvRpXrPH3eRlatPAdyyAs4e0bTobCHsA+k2VFbXFdSnr93eFzzns1asXv/32G8eOHUMpxdq1a9m3bx8DBgwAID4+HoPBQEREhPlnWrduTWBgIHFx2hMt4+LiaN++vTngAERGRpKTk0NycrK5zb+Pcb7N+WNcSnFxMTk5ORYvIYSwlmNZhTw0dyvP/7ybvOJSOgd6suKpG3isb4uaGXBMJji4DhaPhP+FwpoZWsBx9NCeUPzkZhj8qQQcUWUq/Aboxx9/zGOPPUaTJk2ws7PDxsaGL7/8kr59+wKg1+txcHDA09PT4ud8fX3R6/XmNv8OOOf3n993pTY5OTkUFhbi7Ox8UW0zZ85kxowZFXKdQghxrZRS/Lj1CG+u0HpvHO1smBQZwujezWpmuMk7CYnfa702Zw5e2N64K3QdDW3vBgcX69Un6qxKCTmbN2/mt99+o2nTpmzYsIHo6Gj8/f0v6nmpalOnTmXixInm9zk5OQQEBFixIiFEXXPkTAFTf97N3wdOAdC1aX1m3dOB5g1drVxZOSkFaRsgfh7sXQYm7QGFOLpDh/ugyyjwa2/VEoWo0JBTWFjICy+8wNKlS4mKigKgQ4cOJCYm8u677xIREYGfnx8lJSVkZWVZ9OZkZGTg5+cHgJ+fH1u3brU49vnZV/9u898ZWRkZGbi7u1+yFwfA0dERR0fHCrlWIYQoD5NJ8f3WdN5asZf8EiNO9jZMimzNqF5BNav3Jv+01msTPx/O/HNhu39nrdem3RBwqGe18oT4twoNOQaDAYPBgI2N5VAfW1tbTCYToA1Ctre3Z82aNQwZMgSA1NRU0tPTCQ8PByA8PJw33niDzMxMfHx8AIiJicHd3Z3Q0FBzmxUrVlicJyYmxnwMIYSoLo6cKWDyT7uIO3gagO5BXsy6pwNBDWpIGFAKDv19rtfmdzBqq53j4AYd7tV6bRqFWbVEIS6l3CEnLy+PAwcOmN+npaWRmJiIl5cXgYGB3HjjjUyaNAlnZ2eaNm3K+vXr+eabb/jf//4HgIeHB2PGjGHixIl4eXnh7u7O+PHjCQ8Pp2dP7RHeAwYMIDQ0lBEjRjBr1iz0ej0vvfQS0dHR5p6YsWPH8sknnzB58mQefvhhYmNjWbx4McuXL6+I34sQQlw3k0nx3ZbDvPVHCgUlRpztbZkyMISHwoNqxjNvCs5A4g9ar83p/Re2N+p4rtfmHnCsYbfZRN2iymnt2rUKuOg1cuRIpZRSJ06cUKNGjVL+/v7KyclJhYSEqPfee0+ZTCbzMQoLC9WTTz6p6tevr1xcXNRdd92lTpw4YXGeQ4cOqUGDBilnZ2fVoEED9eyzzyqDwXBRLR07dlQODg6qefPmat68eeW6luzsbAWo7Ozs8v4ahBDiig6dylP3zdmkmk5ZpppOWabum7NJHTqVZ+2yrs5kUirtb6V+GqPUqw2Umu6uvd7wV+q3p5Q6tsPaFQpR5u/v63pOTk0nz8kRQlQ0k0nxTdwh3l6ZSqHBiIuDLc8Pas2DPZpW796bgjOwc6HWa3Mq9cJ2vw5ar037e8Gxhj+YUNQaZf3+lmdoCyFEBTl0Kp/JP+1i66EzAIQ39+btIR0I9K6m06cLzsA/sZC6QpshZSzWttvXg/ZDoMtoaNzZujUKcR0k5AghxHUymhTzNx3inVUpFBlMuDjYMvXWNgzvHli9em+Ugsy9sH8V7PsTjmwGZbqw37c9dB0F7e8DJ+ndFjWfhBwhhLgOB0/mMfmnXWw/fBaAXi203psAr2rSe2MohLS/LgSb7HTL/Q3bQKsB0OZOrddGV41CmRDXSUKOEEJcA5NJMXdjGu+sSqW41EQ9B1tejAplWPcAdNYOCllHLoSatA1QWnhhn50TBN0ArSKh5QCo39R6dQpRySTkCCFEOemzi5i4OJFN/2jPvbmhZQNm3t2eJvWt1HtjLIWj284Fm1WQucdyv3sTrbemZSQ06ytLLIg6Q0KOEEKUw8okPc//vIusAgPO9ra8fJuVem8KzsCB1VqoObAairIu7NPZQJPuWrBpNRB8QuU2lKiTJOQIIUQZFJSU8tqyvfy4VRvT0r6xBx/e37Hq1pxSCjKSL9yGOrrVctCwc30IjtB6a4L7g4tX1dQlRDUmIUcIIa4i6Vg2Ty1M4ODJfHQ6eKxvc569JQQHO5ur//D1MJm0Kd4py2B/DOQctdzv0/ZCb03jrmAr/0kX4t/kb4QQQlyGyaT4+u80Zq1KwWBU+Lo78v59HekV3KByT1xaArsXw8aPLB/MZ+cMzW/UBgy3HACeAZVbhxA1nIQcIYS4hMycIp5dspO/9p8CYECoL28P6UD9eg6Vd9KiHNixAOI+g9zj2jZHd+1pwyGDIKgP2DtX3vmFqGUk5AghxH+s3pPB5P/bxZn8EpzsbZh2W9vKHVyclwmbZ8O2r6E4W9vm6gfhT2pPHZYH8wlxTSTkCCHEOUUGI28s38u3mw8DENrInY+GdSTYp5LWbDr9D2z6WFvp+/ySCt4toffT0OE+sHOsnPMKUUdIyBFCCGDviRye+jGB/Zl5ADx6QzOeiwzB0c624k92bAds/AD2/AacWyO5STfoPQFCbgWbSh7QLEQdISFHCFGnKaWtOzXzjxRKSk00dHPkvXvD6NuqYUWfSJsptfED7SnE57WM1HpumvaSZ9kIUcEk5Agh6qyTucVM+mkn61JPAtC/tQ+z7umAt2sF3iYylsKeX7Rwo9+tbbOxg3b3QO+nwLdtxZ1LCGFBQo4Qok5am5LJpJ92ciqvBEc7G16KasODPZtW3ODikgJI/F4bc5OljfHB3gU6j4TwaJn+LUQVkJAjhKhTigxG3vojhfmbDgHQ2s+Nj4Z1opVvBQ0uLjgDW7+ErZ9Dgba2FS7e0GMsdHtEnkQsRBWSkCOEqDNS9bk8vTCBFH0uAKN7BzFlYGuc7CtgcHHWEYj7VHvOjaFA2+YZCL2ego7DZVFMIaxAQo4QotZTSvHt5sO8sXwvxaUmGrg68M69YfQL8bn+g2ckw8YPYfdPoIzaNt/20GcChA6WpRaEsCL52yeEqNVO5xUz+addrEnJBOCmkIa8c08YDd2uY3Cx0QD7/4Tt8+BAzIXtzfpq08Bb3CwzpYSoBiTkCCFqrQ37TvLskp2czC3Gwc6GqYNaM6pX0LUPLs5MgcTvYOdCyD95bqMOQu/QpoE37lJhtQshrp+EHCFErVNYYuS9P1P56u80AFr6uPLRsE60aXQNyyMU5UDyz5DwHRzddmF7vYYQNgy6jALvFhVTuBCiQknIEULUGkopViVn8NqyPRzLKgTgofCmvHBrm/INLlYKDm/Sgs2eXy4MJNbZQqtI6PSgtgq4rX3FX4QQosJIyBFC1AoHT+bxyu972LBPu43U2NOZV+9sS/82vmU/SM5xbR2pxO/hzMEL2xu00oJNh/vBrRzHE0JYlYQcIUSNVlBSysexB/jqr4MYjAoHWxsev7E5T94UjLNDGXpvSktg3x9ar82B1aBM2nYHV2h3N3Qaoa0rJQOJhahxJOQIIWokpRQrdut5ffkeTmQXAdAvpCHTb29LUIN6Vz9ARrIWbHYtuvDQPoDAXlqvTeid4OhaSdULIaqChBwhRI1zIDOX6b8ls/GAFk4CvJyZfltb+rfxufLMqcIsSPo/SPgWjidc2O7qBx0f0B7a1yC4cosXQlQZCTlCiBojr7iUj9bsZ+7faZSaFI52NjxxUwvG3tji8gOLTSY49JfWa7P3NyjVen2wsYOQQdrtqBb95aF9QtRC8rdaCFHtKaX4bedx3lyxl4ycYgAi2vgy/fZQArwus1xC1hHY+aMWbs4vkAnQsA10HgEdhkK9BlVQvRDCWiTkCCGqtVR9LtN+TWJL2hkAgrxdmH57W/q1/s+SDEqBfjek/qENJP737ShHd2g3RAs3/p1lELEQdYSEHCFEtZRTZOCDmP0siDuE0aRwsrdhXL9gHrmh+YVbU6XF2q2o1D8gdSXkHP3XEXQQ1Ee7HdXmdlkgU4g6SEKOEKJaUUqxNOEYb65I4VSedmtqUDs/XoxqQ5P6LpB/GpJXacHmn1goybvww3bO2rpRIYO0h/a5VsACnEKIGktCjhCi2thzPIdpvyax/fBZAJo3rMcrt4XS1ysLkr/Qgs3RrReeZQPazKiQgRByq7ZApr2zdYoXQlQ7EnKEEFaXXWDgfzGpfLv5MCYFbg7wWud8bnfciO2qZyyfPgzg1x5aDdJ6bBp1BBsbq9QthKjeJOQIIazGZFL8FH+Ut1emUJKfxa02O3mowV66lmzHJjHrQkNbBwi64dxtqIHgGWC1moUQNYeEHCGEVew+ms3HP6/GP2MdH9rE09MpBTuMkHuugbOXNq4mZJA2zsbRzar1CiFqHgk5QogqlXskie2/f04j/Vq+sDkC/17Iu0Erracm5FYI6A425Vg5XAgh/kNCjhCi8uWfgt0/kbPlW9zPJtEPwAaM2GBs0hOH0Citx8a7hbUrFULUIhJyhBCVw1AE+1bCzoWoAzHoTKW4AwZly1a7LviG309wr7uwdfGydqVCiFpKQo4QouIoBUe2asspJP8MRdkA6ICdpuYsNd6AZ/f7GXtrj8uvNSWEEBWk3PMuN2zYwO23346/vz86nY5ffvnlojZ79+7ljjvuwMPDg3r16tGtWzfS09PN+4uKioiOjsbb2xtXV1eGDBlCRkaGxTHS09OJiorCxcUFHx8fJk2aRGlpqUWbdevW0blzZxwdHQkODmb+/PnlvRwhREU4ewjWvQ0fdYK5AyB+HhRlc9auIZ+V3kFE8SwmerzPHY/PYMKdvSTgCCGqRLl7cvLz8wkLC+Phhx/m7rvvvmj/P//8Q58+fRgzZgwzZszA3d2d5ORknJyczG2eeeYZli9fzpIlS/Dw8GDcuHHcfffdbNy4EQCj0UhUVBR+fn5s2rSJEydO8NBDD2Fvb8+bb74JQFpaGlFRUYwdO5bvv/+eNWvW8Mgjj9CoUSMiIyOv9fchhCiromxI/gV2LoT0TRe229fjmP8tvHakI3/mBYPOhsf6tmBCREsJN0KIKqVTSqlr/mGdjqVLlzJ48GDztvvvvx97e3u+/fbbS/5MdnY2DRs25IcffuCee+4BICUlhTZt2hAXF0fPnj35448/uO222zh+/Di+vr4AzJkzhylTpnDy5EkcHByYMmUKy5cvJykpyeLcWVlZrFy5skz15+Tk4OHhQXZ2Nu7u7tf4WxCiDjGWaksp7PwRUldAadG5HTpofhO5re9h+r5m/JyUBUBLH1feuTeMjgGe1qpYCFELlfX7u0IfE2oymVi+fDmtWrUiMjISHx8fevToYXFLKz4+HoPBQEREhHlb69atCQwMJC4uDoC4uDjat29vDjgAkZGR5OTkkJycbG7z72Ocb3P+GJdSXFxMTk6OxUsIcRVKwYldsPIF+F8b+OFebbxNaRE0bA0RM+CZZJZ3nM1Nq3z5OSkLWxsd0f1asOypPhJwhBBWU6EDjzMzM8nLy+Ott97i9ddf5+2332blypXcfffdrF27lhtvvBG9Xo+DgwOenp4WP+vr64terwdAr9dbBJzz+8/vu1KbnJwcCgsLcXa+eP2amTNnMmPGjIq6XCFqt5wTsHuJdjsqM/nCdpcG0P5eCLsfGoVxKr+Eab8msWK39nczxNeNd+8No30TDysVLoQQmgoNOSaTtmjenXfeyTPPPANAx44d2bRpE3PmzOHGG2+syNOV29SpU5k4caL5fU5ODgEB8nh4IczyMuGftbBrERxce2EhTFtH7Tk2YcMguD/Y2qOU4vddJ5j+axJnCwzY2eh48qYWjLu5JQ52spaUEML6KjTkNGjQADs7O0JDQy22t2nThr///hsAPz8/SkpKyMrKsujNycjIwM/Pz9xm69atFsc4P/vq323+OyMrIyMDd3f3S/biADg6OuLo6HjtFyhEbZNzAg5vhEN/a/88tc9yf0BPrcem7WBwrm/enJlbxMu/JLEqWfs72KaRO+/c04F2jaX3RghRfVRoyHFwcKBbt26kpqZabN+3bx9NmzYFoEuXLtjb27NmzRqGDBkCQGpqKunp6YSHhwMQHh7OG2+8QWZmJj4+PgDExMTg7u5uDlDh4eGsWLHC4jwxMTHmYwghLiErHQ5thMN/a/88m3ZxG9920Po2CBsKXs0tdiml+DXxOK/8nkzWud6bcTcH8+RNwdJ7I4SodsodcvLy8jhw4ID5fVpaGomJiXh5eREYGMikSZMYOnQoffv2pV+/fqxcuZLff/+ddevWAeDh4cGYMWOYOHEiXl5euLu7M378eMLDw+nZsycAAwYMIDQ0lBEjRjBr1iz0ej0vvfQS0dHR5p6YsWPH8sknnzB58mQefvhhYmNjWbx4McuXL6+AX4sQtYBScOYgHN50rrdmI2SnW7bR2YBfe2jaB4J6Q2A4XOYJxBk5Rby4dDer92YC0NbfnXfuCSPUX2YmCiGqp3JPIV+3bh39+vW7aPvIkSPND+ObO3cuM2fO5OjRo4SEhDBjxgzuvPNOc9uioiKeffZZfvzxR4qLi4mMjOSzzz4z34oCOHz4ME888QTr1q2jXr16jBw5krfeegs7uwu5bN26dTzzzDPs2bOHJk2a8PLLLzNq1KgyX4tMIRe1ilJwav+FXprDmyD3uGUbnS34d9ICTdM+ENgDnK58i0kpxf/tOMarvyeTU1SKva2Op25uydibWmBvK703QoiqV9bv7+t6Tk5NJyFH1GgmE5zcey7QnAs1+ZmWbWzsoXGXc6GmNwT0AEfXMp9Cn13E1J93sTb1JADtG3vw7r1hhPi5VeSVCCFEuZT1+1vWrhK1n1LaLCGbGv60XaVAv/vCIOHDm6DwjGUbOydo0k0LNEG9tX+3v/RA/CufSrEk/iivLdtDblEpDrY2TLilJY/d0Bw76b0RQtQQEnKE9RgK4eA6OLIFSovBaACTQXuqrslwmfelV29nKrXcp4xgYwetBkLXh6F5P7CpQV/UxXmwezFs+xoykiz32btovTPnQ03jLmB3fTMIj2cV8vzPu9mwT+u9CQvw5N17OtDSV3pvhBA1i4QcUbUKzsC+VZCyTFsewFBQNec1lWrnTFmmzRjq+jB0HH7ZQbbVwqn9sO0rSPwBis89ndvOGYL6XBhT498RbO0r5HTFpUYWbDrER2sOkFdcioOdDc/e0ooxfZpJ740QokaSMTkyJqfyZaVDygotYBzepPWsnOcRAMER2jNYbOy0L2zzP+3B1u7cPy/1/t/tzr2/5DHsIS8D4hdoay6ZA4MTtL0buj0CjTuDTmed38+/GUth30rY9qXWy3WeV3Otzo4PWDyvpiIopVi26wRvr0zh6NlCADoFevLOPWEE+5R9/I4QQlQVGXhcBhJyKolS2m2VlOVasNHvttzv2w5aR2kvvw5VGy6K8yDpJ62H5N91NQrTQkS7e8DBperqOS/vJOxYAPHzIfvIuY067RZb90eg+c2Vcost/vAZXl++l4T0LAB83R15bkAId3dugq1NNQh9QghxCRJyykBCTgUylkJ6nBZsUpdrvTfn6WwgsNe5YHMr1A+yWplmSsHR7VrYSV4KxmJtu6OH1lvS9WFo2KoKatgGW7+EPb+AsUTb7uwFnR/SaqjftFJOffh0PrNWprJ89wkAXBxsebxvCx7t2wwXB7mLLYSo3iTklIGEnOtUUqCNq0lZDvv+gMKzF/bZOUOLm7Vg02og1PO2Xp1Xk38aEr+H7V/D2UMXtgfdoPXutI6qsHEvgPZ7S/pJCzf6XRe2N+4C3R6FtneBvVPFne9fsgsMfBy7nwVxhzAYFTY6uK9rABNvaYWPe+WcUwghKpqEnDKQkHMN8k9pY0ZSlmsBp7Towj5nL20Rx9ZR2gwma9z2uR4mExyM1WYx7Vt5YXFKVz/oMhI6jwSPxtd+/NP/wPa5kPAdFGVp22wdof09F8YFVZKSUhPfbT7MR7H7ySowAHBDywa8cGsb2jSSP/tCiJpFQk4ZSMgpozNpkLpCCzbpcRe+/AE8A7V1jlpHaYs52taSWx1ZR7TxMTu+ufCAPZ2tFuK6jYFmN5VtjIzJCAdWa702B2IubPdsqh2n04hKneGllGJVsp63/kjh0GltJlsrX1deuLUNN4X4VNp5hRCiMknIKQMJOVdQcEbrcdi5EDKTLff5dTgXbG7VBhFXh1lJlaW0RBs8ve1rbbmE87xanJuG/sClQ0rBGUj4Vvu5rMPnNuq0mWTdH9X+WckPJ9x5JIs3lu9l6yHtgYENXB15dkAr7u3SRKaECyFqNAk5ZSAh5xKOJ2rTl3f/dOFWlM4Wmva6EGw8A61aotVkpmi3m/47Db3dEK1XpnEXOLZDG8y8+6cLg5mdPKHTg1qb/6zqXRmOni3gnVWp/JqorVvlZG/Dozc05/EbW+DqWEt62oQQdZqEnDKQkHNOaTHs+VW7pXJ064Xtvu21L+bQO6v3Q/Oq2uWmobs1gtwTF943CtMGErcbUiXjk3KKDHy29h/mbkyjpNSETgd3dWrMpMgQGnmUf2kHIYSormTtKnF12ccgfp429iRfe4Q/NnYQOli7pRLQo3bfirpWjq7QZZQ2EPnf09BzT4CtgzY7qtuj0KRrlfz+DEYTC7em8/7q/ZzJ16ahhzf35sWoNrRrfOUVxoUQojaTkFPXKAWH/tJ6bVKWX3j6sFsjbYxJ55Hg5mvdGmsKnQ4CummvyDe1Z9407gKuDavk9Eop1uzNZOYfe/nnZD4AzRvW44VBbejfxgedBFQhRB0nIaeuKM7VBhFv+wpOplzY3rSP1mtT0c+CqWvqeUPIwCo7XdKxbN5Yvpe4g6cB8KrnwISIlgzrHoi9DCoWQghAQk7td3KfNpA48UcoydW22deDsKHaLRXfUOvWJ8rlRHYh767ax88JR1EKHOxsGN07iOh+wbg7SUgVQoh/k5BTG51f5HHrF5C2/sJ272At2HQcBk4yVqMmKSwxMmf9P3y+4R+KDNpziu4I82dSZAgBXjXsoYtCCFFFJOTUJvmntEUet82FnKPaNp0NtBqkLfLY7KZKWeRRVB6lFL/vOsFbK/ZyPFub0t+1aX1ejGpDp8CKXY1cCCFqGwk5NZ1ScCxeG0ic/LPlIo9dRmqDievqc21quN1Hs5nxezLbD2trgjX2dOaFW9twa3s/GVQshBBlICGnpjIUQdL/aeNtjidc2O7fGbo/VqmLPIrKdTK3mHdXpbI4/ghKaQ/ze/KmYB7r2xwn+8p9SrIQQtQmEnJqmtJibT2lv/4HudoTbbF1hHZ3n3s2Sxfr1ieuWUmpifmb0vh4zQFyi0sBuLOjP1MGtsbfUx7mJ4QQ5SUhp6YoLYHE72HDuxfG27j5a9O/Oz8E9RpYtz5xzZRSxKZk8vryvaSd0p53076xB9NvD6VrkDxpWgghrpWEnOrOWKqtlbRhFmSla9vcGsENz2rhxs7RuvWJ63IgM5dXl+1lwz7tidMNXB2ZPDCEezo3wcZGxt0IIcT1kJBTXRlLtfWR1r0FZ9O0bfV84IaJ2pIC9nL74mpyiwxs2HeKNSkZ7DmeQ/vGHvRv40Oflg2tvlBldoGBD9bs45u4wxhNCntbHQ/3aca4fsG4yfNuhBCiQkjIqW5MRm0dpHVvwen92jaXBtBnAnQdUyULPdZk6acLWL03gzUpGWxNO4PBeGH92RR9Lkvij2Jvq6NHM29ubu3Dza19CGpQr8rqM5oUP25N570/UzlbYAAgoo0vL0a1oVkV1iGEEHWBrEJeXVYhN5lg769auDm/7IJzfej9tDag2NHVuvVVU6VGEwlHsrRgszeTA5l5FvubN6xHRBtfOgZ4En/4LGv2ZnDodMFFbfq39qFfax+6BXlV2rIIcf+cZsbvyaTotSdPt/Rx5eXbQunbqmrWuhJCiNqirN/fEnKsHXKUgpRlsHYmZCZr25w8oNd46P44OFk5fFVDOUUGNuw7yZq9maxNzSTrXI8IgJ2Njm5BXvRv40P/Nr6X7B05eDKP2JRMYlMy2Zp2hlLThb8Cbk529G3VkP6tfbgpxAeveg7XXe+RMwW8uWIvfyTpAXB3smPiLa0Y3rOprDMlhBDXQEJOGVg15CilLb2w9k3Q79K2ObpDeDT0fEKWXfiPQ6fyWb0345LBxMPZnn4hDenfxpe+rRri4Vz2MS05RQb+2neK2JRM1qVmcjq/xLxPp4NOAZ7nbmv50qaRW7kewpdfXMrsdf/wxV8HKSk1YaOD4T2a8swtrSokPAkhRF0lIacMrBJylIIDq2HtGxce4ufgCj3GagHHRaYMg3YbKv7wWdakZLJmbwb/nMy32N/i3G2o/m186RzoiV0F9IgYTYqdR7NYm5LJmr2Z7DmRY7G/kYcT/Vr70L+1D71aNMDZ4dIP5jOZFL/uPMZbf6SQkVMMQK8W3ky7PZTWftIzJ4QQ10tCThlUachRCg6u1Xpujm7Tttm7aE8n7vUU1POu3PPXANmFBtbvO8mavRmsSz1JdqHlbagezb24ubUv/atosPCJ7ELWppwkNiWDvw+cMi+MCeBoZ0OvFt7c3MaXm1v70Pjcw/oSj2Qx4/dkEtKzAAjwcubFW0OJbOsrSzEIIUQFkZBTBlUWctI2aOEmPU57b+cE3R6B3hPAtW4POs0qKOGn+KOs3pvBtkNnMf7rNlR9F3v6hfhwcxsf+rZqiLsVp1YXGYzEHTxN7F5tLM+xrEKL/a393AjwciFmTwYALg62RPcLZkyfZrIUgxBCVDAJOWVQ6SHn8CYt3Bz6S3tv66gtmNlnArj5Vfz5apg/k/W8sDSJU3nF5m0tfVzp38aX/m186BxYH9tq+EA8pRT7MvJYk5LB2pRM4g+f5V/ZjLs7N2bKwNb4usvaYUIIURkk5JRBpYWcI1u1MTcH12nvbR2g80jtQX7u/hV3nhrqbH4Jr/yezK+J2tpbLRrW48GeTenf2pdA75r3HKCz+SWs33eS1IxcBoT60imwvrVLEkKIWq2s39/yMMCKVloCi0ZAnh5s7KDTCG0JBs8Aa1dWLfy798ZGB4/1bcGEiJY1+pZO/XoODO7U2NplCCGE+A8JORXNzgFunATHE6HvJKjf1NoVVQtZBSW88lsyv5zrvQn2ceWdezpIr4cQQohKIyGnMnR7xNoVVCu1sfdGCCFE9SchR1Sa//betGhYj3fvDZPeGyGEEFVCQo6oFP/tvXm0b3OeiWglvTdCCCGqjIQcUaGyCkqY8fseliYcA6T3RgghhPWU+1n4GzZs4Pbbb8ff3x+dTscvv/xy2bZjx45Fp9PxwQcfWGw/c+YMw4cPx93dHU9PT8aMGUNenuXq0bt27eKGG27AycmJgIAAZs2addHxlyxZQuvWrXFycqJ9+/asWLGivJcjKlDMngxueX8DSxOOYaODx29szvKnbpCAI4QQwirKHXLy8/MJCwvj008/vWK7pUuXsnnzZvz9L34uzPDhw0lOTiYmJoZly5axYcMGHnvsMfP+nJwcBgwYQNOmTYmPj+edd97hlVde4YsvvjC32bRpE8OGDWPMmDEkJCQwePBgBg8eTFJSUnkvSVynrIISnlmUyKPfbOdkbjEtGtbj/57oxdRBbeT2lBBCCOtR1wFQS5cuvWj70aNHVePGjVVSUpJq2rSpev/998379uzZowC1bds287Y//vhD6XQ6dezYMaWUUp999pmqX7++Ki4uNreZMmWKCgkJMb+/7777VFRUlMV5e/TooR5//PEy15+dna0AlZ2dXeafEZb+TNarrq/HqKZTlqlmzy9Tb67YowpLSq1dlhBCiFqsrN/f179083+YTCZGjBjBpEmTaNu27UX74+Li8PT0pGvXruZtERER2NjYsGXLFnObvn374uDgYG4TGRlJamoqZ8+eNbeJiIiwOHZkZCRxcXEVfUniEqT3RgghRHVX4QOP3377bezs7HjqqacuuV+v1+Pj42NZhJ0dXl5e6PV6c5tmzZpZtPH19TXvq1+/Pnq93rzt323OH+NSiouLKS6+sE5STk5O2S9MmK3ek8HUpbs5mSszp4QQQlRfFRpy4uPj+fDDD9mxYwc6XfVbWHHmzJnMmDHD2mXUWJeaOfXOvWF0loHFQgghqqEKvV31119/kZmZSWBgIHZ2dtjZ2XH48GGeffZZgoKCAPDz8yMzM9Pi50pLSzlz5gx+fn7mNhkZGRZtzr+/Wpvz+y9l6tSpZGdnm19Hjhy5ruutS1ZfZuaUBBwhhBDVVYWGnBEjRrBr1y4SExPNL39/fyZNmsSqVasACA8PJysri/j4ePPPxcbGYjKZ6NGjh7nNhg0bMBgM5jYxMTGEhIRQv359c5s1a9ZYnD8mJobw8PDL1ufo6Ii7u7vFS1zZ+bE3j/xr7M1PMvZGCCFEDVDu21V5eXkcOHDA/D4tLY3ExES8vLwIDAzE29vbor29vT1+fn6EhIQA0KZNGwYOHMijjz7KnDlzMBgMjBs3jvvvv9883fyBBx5gxowZjBkzhilTppCUlMSHH37I+++/bz7u008/zY033sh7771HVFQUCxcuZPv27RbTzMX1Wb/vJM8t2Sljb4QQQtRM5Z22tXbtWgVc9Bo5cuQl2/93CrlSSp0+fVoNGzZMubq6Knd3dzV69GiVm5tr0Wbnzp2qT58+ytHRUTVu3Fi99dZbFx178eLFqlWrVsrBwUG1bdtWLV++vFzXIlPIL81oNKmPVu9TQc8vU02nLFM3v7tWxR8+Y+2yhBBCCKVU2b+/dUopZcWMZVU5OTl4eHiQnZ0tt67OySkyMHFRIqv3auOmhnUPZPrtodJ7I4QQotoo6/e3rF0lzFL1uYz9Lp60U/k42Nnw+uB23Nc1wNplCSGEENdEQo4A4Pedx5n80y4KDUYaezoz58EutG/iYe2yhBBCiGsmIaeOMxhNvPVHCl//nQZAn+AGfDSsE171HK7yk0IIIUT1JiGnDjuZW8y4H3awJe0MAE/e1IJnB4Rga1P9HuQohBBClJeEnDpqR/pZnvgunoycYlwd7Xj33jAGtrv8gxSFEEKImkZCTh2jlOK7Lem8+nsyBqMi2MeVOQ92IdjH1dqlCSGEEBVKQk4dUmQw8tIvSfwUfxSAW9v7MeueMFwd5Y+BEEKI2ke+3eqII2cKeOL7eJKO5WCjgykDW/NY3+bVciFVIYQQoiJIyKkDNuw7yVMLE8gqMOBVz4FPhnWiV3ADa5clhBBCVCoJObWYyaSYvf4f3v0zFaUgrIkHsx/sgr+ns7VLE0IIISqdhJxaKqfIwLOLdxKzJwOAYd0DmH57W1meQQghRJ0hIacW2p+Ry+PfxnPwVD4Otja8emdb7u8eaO2yhBBCiColIaeWWb7rBJN+2klBiRF/DydmP9iFsABPa5clhBBCVDkJObVEqdHE2ytT+PIvbXmG3sHefHR/J7xdHa1cmRBCCGEdEnJqgVN52vIMmw9qyzOMvbEFzw1ohZ2tjZUrE0IIIaxHQk4Nl5B+lie/38GJ7CLqOdjy7r1hDGrfyNplCSGEEFYnIaeGUkrx49YjvPJbMiVGEy0a1uPzEV0I9nGzdmlCCCFEtSAhpwYqKTUx/bckftx6BICBbf149z5ZnkEIIYT4N/lWrGHO5Jcw9rt4tqadwUYHkyJbM/ZGWZ5BCCGE+C8JOTXIvoxcHlmwnfQzBbg52vHRA53oF+Jj7bKEEEKIaklCTg2xNiWT8T8mkFdcSqCXC1+P7EpLXxl/I4QQQlyOhJxqTinF13+n8eaKvZgU9GjmxZwHu1C/noO1SxNCCCGqNQk51VhJqYmXftnN4u1HAW39qRl3tMPBTp5/I4QQQlyNhJxq6nReMU98t4Oth7QBxi9FhTK6d5AMMBZCCCHKSEJONZSqz2XMgm0cPVuIm6MdHz/QiZtkgLEQQghRLhJyqpnYlAzG/5BAfomRpt7aAGN5wJ8QQghRfhJyqgmlFF/+dZCZf6SgFPRs7sXs4TLAWAghhLhWEnKqgeJSIy8tTWJJ/PkBxoG8emdb7GWBTSGEEOKaScixstN5xYz9Lp5th85io4OXbwtlVC8ZYCyEEEJcLwk5VmQxwNjJjk8e6MyNrRpauywhhBCiVpCQYyVr9mbw1I/aAOMgbxe+GtmNYB9Xa5clhBBC1BoScqqYUoovNhzkrZXaAOPw5t7MfrAzni4ywFgIIYSoSBJyqlBxqZEXlybx07kBxsN7BPLKHTLAWAghhKgMEnKqyKm8YsZ+G8/2w9oA4+m3t+Wh8KYywFgIIYSoJBJyqsDeEzk8smA7x7K0AcafPtCZvjLAWAghhKhUEnIqWcyeDCYslAHGQgghRFWTkFNJlFJ8vuEgb58bYNyrhTefDZcBxkIIIURVkZBTCYpLjUz9eTc/7zgGwIM9A5l+uwwwFkIIIaqShJwKVlBSyoivtxJ/+Cy2Njqm3x7KQ+FB1i5LCCGEqHMk5FQwZ3tbWjSsx/6MXD4d3pkbWsoAYyGEEMIayn3/ZMOGDdx+++34+/uj0+n45ZdfzPsMBgNTpkyhffv21KtXD39/fx566CGOHz9ucYwzZ84wfPhw3N3d8fT0ZMyYMeTl5Vm02bVrFzfccANOTk4EBAQwa9asi2pZsmQJrVu3xsnJifbt27NixYryXk6F0+l0vD64PcvG3yABRwghhLCicoec/Px8wsLC+PTTTy/aV1BQwI4dO3j55ZfZsWMHP//8M6mpqdxxxx0W7YYPH05ycjIxMTEsW7aMDRs28Nhjj5n35+TkMGDAAJo2bUp8fDzvvPMOr7zyCl988YW5zaZNmxg2bBhjxowhISGBwYMHM3jwYJKSksp7SRXOwc6GQG8Xa5chhBBC1Gk6pZS65h/W6Vi6dCmDBw++bJtt27bRvXt3Dh8+TGBgIHv37iU0NJRt27bRtWtXAFauXMmtt97K0aNH8ff3Z/bs2bz44ovo9XocHLTZSM8//zy//PILKSkpAAwdOpT8/HyWLVtmPlfPnj3p2LEjc+bMKVP9OTk5eHh4kJ2djbu7+zX+FoQQQghRlcr6/V3p032ys7PR6XR4enoCEBcXh6enpzngAERERGBjY8OWLVvMbfr27WsOOACRkZGkpqZy9uxZc5uIiAiLc0VGRhIXF3fZWoqLi8nJybF4CSGEEKJ2qtSQU1RUxJQpUxg2bJg5aen1enx8fCza2dnZ4eXlhV6vN7fx9fW1aHP+/dXanN9/KTNnzsTDw8P8CggIuL4LFEIIIUS1VWkhx2AwcN9996GUYvbs2ZV1mnKZOnUq2dnZ5teRI0esXZIQQgghKkmlTCE/H3AOHz5MbGysxf0yPz8/MjMzLdqXlpZy5swZ/Pz8zG0yMjIs2px/f7U25/dfiqOjI46Ojtd+YUIIIYSoMSq8J+d8wNm/fz+rV6/G29vbYn94eDhZWVnEx8ebt8XGxmIymejRo4e5zYYNGzAYDOY2MTExhISEUL9+fXObNWvWWBw7JiaG8PDwir4kIYQQQtRA5Q45eXl5JCYmkpiYCEBaWhqJiYmkp6djMBi455572L59O99//z1GoxG9Xo9er6ekpASANm3aMHDgQB599FG2bt3Kxo0bGTduHPfffz/+/v4APPDAAzg4ODBmzBiSk5NZtGgRH374IRMnTjTX8fTTT7Ny5Uree+89UlJSeOWVV9i+fTvjxo2rgF+LEEIIIWo8VU5r165VwEWvkSNHqrS0tEvuA9TatWvNxzh9+rQaNmyYcnV1Ve7u7mr06NEqNzfX4jw7d+5Uffr0UY6Ojqpx48bqrbfeuqiWxYsXq1atWikHBwfVtm1btXz58nJdS3Z2tgJUdnZ2eX8NQgghhLCSsn5/X9dzcmo6eU6OEEIIUfNUm+fkCCGEEEJYg4QcIYQQQtRKEnKEEEIIUStJyBFCCCFErVQpDwOsKc6PuZY1rIQQQoia4/z39tXmTtXpkJObmwsga1gJIYQQNVBubi4eHh6X3V+np5CbTCaOHz+Om5sbOp2uwo6bk5NDQEAAR44cqXNT0+Xa696119Xrhrp77XX1ukGuvbpcu1KK3Nxc/P39sbG5/MibOt2TY2NjQ5MmTSrt+O7u7lb/g2Atcu1179rr6nVD3b32unrdINdeHa79Sj0458nAYyGEEELUShJyhBBCCFErScipBI6OjkyfPh1HR0drl1Ll5Nrr3rXX1euGunvtdfW6Qa69pl17nR54LIQQQojaS3pyhBBCCFErScgRQgghRK0kIUcIIYQQtZKEHCGEEELUSjU65MycOZNu3brh5uaGj48PgwcPJjU11aJNUVER0dHReHt74+rqypAhQ8jIyLBok56eTlRUFC4uLvj4+DBp0iRKS0svec6NGzdiZ2dHx44dr1qfUopp06bRqFEjnJ2diYiIYP/+/RZt3njjDXr16oWLiwuenp5lvvZdu3Zxww034OTkREBAALNmzbLYn5yczJAhQwgKCkKn0/HBBx+U+djVWVV+5p9++ilt2rTB2dmZkJAQvvnmmzLV+OmnnxIUFISTkxM9evRg69atFvu/+OILbrrpJtzd3dHpdGRlZZXpuFer+cSJEzzwwAO0atUKGxsbJkyYUKbj1gQV9bk/9dRTdOnSBUdHx6v+HT5w4ABubm5l/nspn3vFqsrPfPHixXTs2BEXFxeaNm3KO++8U6YalyxZQuvWrXFycqJ9+/asWLHCYv/PP//MgAED8Pb2RqfTkZiYWKbjnjlzhuHDh+Pu7o6npydjxowhLy/P4rpHjRpF+/btsbOzY/DgwWU6bl1Uo0PO+vXriY6OZvPmzcTExGAwGBgwYAD5+fnmNs888wy///47S5YsYf369Rw/fpy7777bvN9oNBIVFUVJSQmbNm1iwYIFzJ8/n2nTpl10vqysLB566CH69+9fpvpmzZrFRx99xJw5c9iyZQv16tUjMjKSoqIic5uSkhLuvfdennjiiTJfd05ODgMGDKBp06bEx8fzzjvv8Morr/DFF1+Y2xQUFNC8eXPeeust/Pz8ynzs6q6qPvPZs2czdepUXnnlFZKTk5kxYwbR0dH8/vvvV6xv0aJFTJw4kenTp7Njxw7CwsKIjIwkMzPT3KagoICBAwfywgsvlPm6y1JzcXExDRs25KWXXiIsLKzMx64JKuJzP+/hhx9m6NChVzyfwWBg2LBh3HDDDWWqTz73ildVn/kff/zB8OHDGTt2LElJSXz22We8//77fPLJJ1esb9OmTQwbNowxY8aQkJDA4MGDGTx4MElJSeY2+fn59OnTh7fffrtc1z58+HCSk5OJiYlh2bJlbNiwgccee8y832g04uzszFNPPUVERES5jl3nqFokMzNTAWr9+vVKKaWysrKUvb29WrJkibnN3r17FaDi4uKUUkqtWLFC2djYKL1eb24ze/Zs5e7uroqLiy2OP3ToUPXSSy+p6dOnq7CwsCvWYjKZlJ+fn3rnnXfM27KyspSjo6P68ccfL2o/b9485eHhUabr/Oyzz1T9+vUt6psyZYoKCQm5ZPumTZuq999/v0zHrmkq6zMPDw9Xzz33nMW5Jk6cqHr37n3Ferp3766io6PN741Go/L391czZ868qO3atWsVoM6ePXvV6yzPn1OllLrxxhvV008/fdXj1lTX8rn/29X+Dk+ePFk9+OCDZf57KZ975ausz3zYsGHqnnvusdj20UcfqSZNmiiTyXTZeu677z4VFRVlsa1Hjx7q8ccfv6htWlqaAlRCQsKVLlEppdSePXsUoLZt22be9scffyidTqeOHTt2UfuRI0eqO++886rHratqdE/Of2VnZwPg5eUFQHx8PAaDwSLptm7dmsDAQOLi4gCIi4ujffv2+Pr6mttERkaSk5NDcnKyedu8efM4ePAg06dPL1MtaWlp6PV6i3N7eHjQo0cP87mvVVxcHH379sXBwcGi5tTUVM6ePXtdx65pKuszLy4uxsnJyeJczs7ObN26FYPBcMlaSkpKiI+Ptzi3jY0NERERFfKZl+XPaV1xLZ97WcXGxrJkyRI+/fTTMrWXz71qVNZnfrm/60ePHuXw4cOX/bm4uLiLelEiIyMr5DP39PSka9eu5m0RERHY2NiwZcuW6zp2XVRrQo7JZGLChAn07t2bdu3aAaDX63FwcLjonrqvry96vd7c5t//ATm///w+gP379/P888/z3XffYWdXtjVNz//spY59ft+1KkvNdUFlfuaRkZF89dVXxMfHo5Ri+/btfPXVVxgMBk6dOnXJek6dOoXRaJTPvJJd6+deFqdPn2bUqFHMnz+/zAsQyude+SrzM4+MjOTnn39mzZo1mEwm9u3bx3vvvQdo450u53KfTUV85j4+Phbb7Ozs8PLyqlOfeUWpNSEnOjqapKQkFi5cWKHHNRqNPPDAA8yYMYNWrVpdss3333+Pq6ur+fXXX39V2Pnbtm1rPu6gQYMq7Li1QWV95gAvv/wygwYNomfPntjb23PnnXcycuRIQPu/9L/++sviM//+++8r7NyDBg0yH7dt27YVdtzaojI/90cffZQHHniAvn37XnK/fO7WUdmf+bhx47jttttwcHCgZ8+e3H///YD2dz09Pd3iM3/zzTcr7Nxjx461OLaoeGXrlqjmxo0bZx6c1aRJE/N2Pz8/SkpKyMrKskj7GRkZ5sG4fn5+F82COD8638/Pj9zcXLZv305CQgLjxo0DtP+rUEphZ2fHn3/+yR133EGPHj3MP9+4cWPz/wFkZGTQqFEji2OXZWbWeStWrDDfHnF2djbX9d8ZBP+uuS6ozM8ctN/13Llz+fzzz82f4RdffIGbmxsNGzbE3d3dYqaEr68vjo6O2NraXvKzKc/n8tVXX1FYWAiAvb19mWuuC67ncy+L2NhYfvvtN959911AmyFpMpmws7Pjiy++YNiwYfK5V7HK/sx1Oh1vv/02b775Jnq9noYNG7JmzRoAmjdvTv369S0+8/O3yy733+HynPvVV1/lueees9jm5+dnMWAdoLS0lDNnztSZz7wi1eieHKUU48aNY+nSpcTGxtKsWTOL/V26dMHe3t78BxYgNTWV9PR0wsPDAQgPD2f37t0Wf6hiYmJwd3cnNDQUd3d3du/eTWJiovk1duxYQkJCSExMpEePHri5uREcHGx+OTs706xZM/z8/CzOnZOTw5YtW8znLoumTZuaj9u4cWNzzRs2bLAYGxITE0NISAj169cv3y+xhqmKz/zf7O3tadKkCba2tixcuJDbbrsNGxsbnJ2dLT5zNzc3HBwc6NKli8W5TSYTa9asKddn3rhxY/NxmzZtWu6aa6OK+NzLIi4uzuLv+quvvoqbmxuJiYncdddd8rlXoar6zM+ztbWlcePGODg48OOPPxIeHk7Dhg2xs7Oz+MzPh5zw8HCLc4P22ZTn3D4+PhbHPn/crKws4uPjze1iY2MxmUwW/zMtysiKg56v2xNPPKE8PDzUunXr1IkTJ8yvgoICc5uxY8eqwMBAFRsbq7Zv367Cw8NVeHi4eX9paalq166dGjBggEpMTFQrV65UDRs2VFOnTr3secsyu0oppd566y3l6empfv31V7Vr1y515513qmbNmqnCwkJzm8OHD6uEhAQ1Y8YM5erqqhISElRCQoLKzc297HGzsrKUr6+vGjFihEpKSlILFy5ULi4u6vPPPze3KS4uNh+rUaNG6rnnnlMJCQlq//79V627Oquqzzw1NVV9++23at++fWrLli1q6NChysvLS6WlpV2xvoULFypHR0c1f/58tWfPHvXYY48pT09Pi9kxJ06cUAkJCerLL79UgNqwYYNKSEhQp0+fvuxxy/rn9Pxn3qVLF/XAAw+ohIQElZycXNZfb7VVEZ+7Ukrt379fJSQkqMcff1y1atXK/Pu61Ewlpco+61E+94pXVZ/5yZMn1ezZs9XevXtVQkKCeuqpp5STk5PasmXLFevbuHGjsrOzU++++67au3evmj59urK3t1e7d+82tzl9+rRKSEhQy5cvV4BauHChSkhIUCdOnLjisQcOHKg6deqktmzZov7++2/VsmVLNWzYMIs2ycnJKiEhQd1+++3qpptuMl+XsFSjQw5wyde8efPMbQoLC9WTTz6p6tevr1xcXNRdd9110R+wQ4cOqUGDBilnZ2fVoEED9eyzzyqDwXDZ85Y15JhMJvXyyy8rX19f5ejoqPr3769SU1Mt2owcOfKS17B27dorHnvnzp2qT58+ytHRUTVu3Fi99dZbFvvPT1n87+vGG2+8at3VWVV95nv27FEdO3ZUzs7Oyt3dXd15550qJSWlTDV+/PHHKjAwUDk4OKju3burzZs3W+yfPn36Va/hUsry5/RSx23atGmZ6q7OKupzv/HGGy95nMuF1/I82kE+94pVVZ/5yZMnVc+ePVW9evWUi4uL6t+//0Wf3eUsXrxYtWrVSjk4OKi2bduq5cuXW+yfN2/eJc89ffr0Kx739OnTatiwYcrV1VW5u7ur0aNHX/Q/vk2bNr3ksYUlnVJKXVMXkBBCCCFENVajx+QIIYQQQlyOhBwhhBBC1EoScoQQQghRK0nIEUIIIUStJCFHCCGEELWShBwhhBBC1EoScoQQQghRK0nIEUIIIUStJCFHCFGtjBo1Cp1Oh06nw97eHl9fX2655Rbmzp2LyWQq83Hmz59vsXCjEKLukZAjhKh2Bg4cyIkTJzh06BB//PEH/fr14+mnn+a2226jtLTU2uUJIWoICTlCiGrH0dERPz8/GjduTOfOnXnhhRf49ddf+eOPP5g/fz4A//vf/2jfvj316tUjICCAJ598kry8PADWrVvH6NGjyc7ONvcKvfLKKwAUFxfz3HPP0bhxY+rVq0ePHj1Yt26d+dyHDx/m9ttvp379+tSrV4+2bduyYsWKKv4NCCEqgoQcIUSNcPPNNxMWFsbPP/8MgI2NDR999BHJycksWLCA2NhYJk+eDECvXr344IMPcHd358SJE5w4cYLnnnsOgHHjxhEXF8fChQvZtWsX9957LwMHDmT//v0AREdHU1xczIYNG9i9ezdvv/02rq6u1rloIcR1sbN2AUIIUVatW7dm165dAEyYMMG8PSgoiNdff52xY8fy2Wef4eDggIeHBzqdDj8/P3O79PR05s2bR3p6Ov7+/gA899xzrFy5knnz5vHmm2+Snp7OkCFDaN++PQDNmzevugsUQlQoCTlCiBpDKYVOpwNg9erVzJw5k5SUFHJycigtLaWoqIiCggJcXFwu+fO7d+/GaDTSqlUri+3FxcV4e3sD8NRTT/HEE0/w559/EhERwZAhQ+jQoUPlXpgQolLI7SohRI2xd+9emjVrxqFDh7jtttvo0KED//d//0d8fDyffvopACUlJZf9+by8PGxtbYmPjycxMdH82rt3Lx9++CEAjzzyCAcPHmTEiBHs3r2brl278vHHH1fJ9QkhKpaEHCFEjRAbG8vu3bsZMmQI8fHxmEwm3nvvPXr27EmrVq04fvy4RXsHBweMRqPFtk6dOmE0GsnMzCQ4ONji9e/bWgEBAYwdO5aff/6ZZ599li+//LJKrlEIUbHkdpUQotopLi5Gr9djNBrJyMhg5cqVzJw5k9tuu42HHnqIpKQkDAYDH3/8MbfffjsbN25kzpw5FscICgoiLy+PNWvWEBYWhouLC61atWL48OE89NBDvPfee3Tq1ImTJ0+yZs0aOnToQFRUFBMmTGDQoEG0atWKs2fPsnbtWtq0aWOl34QQ4rooIYSoRkaOHKkABSg7OzvVsGFDFRERoebOnauMRqO53f/+9z/VqFEj5ezsrCIjI9U333yjAHX27Flzm7Fjxypvb28FqOnTpyullCopKVHTpk1TQUFByt7eXjVq1EjdddddateuXUoppcaNG6datGihHB0dVcOGDdWIESPUqVOnqvJXIISoIDqllLJuzBJCCCGEqHgyJkcIIYQQtZKEHCGEEELUShJyhBBCCFErScgRQgghRK0kIUcIIYQQtZKEHCGEEELUShJyhBBCCFErScgRQgghRK0kIUcIIYQQtZKEHCGEEELUShJyhBBCCFErScgRQgghRK30/8qO2WC1Y5e7AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_multi.tail(20).plot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
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