diff --git a/PublicDataReader/__init__.py b/PublicDataReader/__init__.py
index d14dc9e..dcecf6b 100644
--- a/PublicDataReader/__init__.py
+++ b/PublicDataReader/__init__.py
@@ -20,6 +20,8 @@
     "Kamco",
     "Nts",
     "Kbland",
+    "Ecos",
+    "Fred",
     "code_bdong",
     "code_hdong",
     "code_hdong_bdong",
diff --git a/PublicDataReader/config/info.py b/PublicDataReader/config/info.py
index 1da32d5..2c6476c 100644
--- a/PublicDataReader/config/info.py
+++ b/PublicDataReader/config/info.py
@@ -1,4 +1,4 @@
-__version__ = "1.0.22"
+__version__ = "1.0.23"
 __author__ = "정우일(Wooil Jeong)"
 __contact__ = "wooil@kakao.com"
 __github__ = "https://github.com/WooilJeong/PublicDataReader"
diff --git a/PublicDataReader/data.py b/PublicDataReader/data.py
index b4dc284..ec22b06 100644
--- a/PublicDataReader/data.py
+++ b/PublicDataReader/data.py
@@ -28,5 +28,8 @@
 # KB부동산 API
 from PublicDataReader.kbland.kbland import Kbland
 
+# FRED API
+from PublicDataReader.fred.fred import Fred
+
 # 코드 데이터 조회
 from PublicDataReader.utils.code import code_bdong, code_hdong, code_hdong_bdong, get_vworld_data_api_info_by_dataframe, get_vworld_data_api_info_by_dict
diff --git a/PublicDataReader/fred/__init__.py b/PublicDataReader/fred/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/PublicDataReader/fred/fred.py b/PublicDataReader/fred/fred.py
new file mode 100644
index 0000000..2ac5d29
--- /dev/null
+++ b/PublicDataReader/fred/fred.py
@@ -0,0 +1,85 @@
+"""
+FRED API
+- https://fred.stlouisfed.org/docs/api/fred/
+"""
+import requests
+import pandas as pd
+
+requests.packages.urllib3.disable_warnings()
+
+class Fred:
+    
+    def __init__(self, api_key=None):
+        self.api_key = api_key
+        self.meta_data = {
+            
+            # Categories
+            "category": "https://api.stlouisfed.org/fred/category",
+            "category_children": "https://api.stlouisfed.org/fred/category/children",
+            "category_related": "https://api.stlouisfed.org/fred/category/related",
+            "category_series": "https://api.stlouisfed.org/fred/category/series",
+            "category_tags": "https://api.stlouisfed.org/fred/category/tags",
+            "category_related_tags": "https://api.stlouisfed.org/fred/category/related_tags",
+            
+            # Releases
+            "releases": "https://api.stlouisfed.org/fred/releases",
+            "releases_dates": "https://api.stlouisfed.org/fred/releases/dates",
+            "release": "https://api.stlouisfed.org/fred/release",
+            "release_dates": "https://api.stlouisfed.org/fred/release/dates",
+            "release_series": "https://api.stlouisfed.org/fred/release/series",
+            "release_sources": "https://api.stlouisfed.org/fred/release/sources",
+            "release_tags": "https://api.stlouisfed.org/fred/release/tags",
+            "release_related_tags": "https://api.stlouisfed.org/fred/release/related_tags",
+            "release_tables": "https://api.stlouisfed.org/fred/release/tables",
+            
+            # Series
+            "series": "https://api.stlouisfed.org/fred/series",
+            "series_categories": "https://api.stlouisfed.org/fred/series/categories",
+            "series_observations": "https://api.stlouisfed.org/fred/series/observations",
+            "series_release": "https://api.stlouisfed.org/fred/series/release",
+            "series_search": "https://api.stlouisfed.org/fred/series/search",
+            "series_search_tags": "https://api.stlouisfed.org/fred/series/search/tags",
+            "series_search_related_tags": "https://api.stlouisfed.org/fred/series/search/related_tags",
+            "series_tags": "https://api.stlouisfed.org/fred/series/tags",
+            "series_updates": "https://api.stlouisfed.org/fred/series/updates",
+            "series_vintagedates": "https://api.stlouisfed.org/fred/series/vintagedates",
+            
+            # Sources
+            "sources": "https://api.stlouisfed.org/fred/sources",
+            "source": "https://api.stlouisfed.org/fred/source",
+            "source_releases": "https://api.stlouisfed.org/fred/source/releases",
+            
+            # Tags
+            "tags": "https://api.stlouisfed.org/fred/tags",
+            "related_tags": "https://api.stlouisfed.org/fred/related_tags",
+            "tags_series": "https://api.stlouisfed.org/fred/tags/series",
+            
+            # Maps API
+            "maps_shape_files": "https://api.stlouisfed.org/geofred/shapes/file",
+            "maps_series_group_meta": "https://api.stlouisfed.org/geofred/series/group",
+            "maps_series_regional_data": "https://api.stlouisfed.org/geofred/series/data",
+            "regional_data": "https://api.stlouisfed.org/geofred/regional/data",
+            
+        }
+        
+    def get_data(self, api_name, **kwargs):
+        url = self.meta_data[api_name]
+        params = {
+            "api_key": self.api_key,
+            "file_type": "json",
+        }
+        params.update(kwargs)
+        res = requests.get(url, params=params)
+        data = res.json()
+
+        key_list = [
+            'categories', 'seriess', 'tags', 'releases',
+            'release_dates', 'sources', 'elements', 'observations', 'vintage_dates'
+        ]
+        for key in key_list:
+            if key in data:
+                if key != 'elements':
+                    return pd.DataFrame(data[key])
+                else:
+                    return pd.DataFrame(data[key]).T
+        return data
\ No newline at end of file
diff --git a/test/test_fred.ipynb b/test/test_fred.ipynb
new file mode 100644
index 0000000..7ee21ec
--- /dev/null
+++ b/test/test_fred.ipynb
@@ -0,0 +1,784 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-12-02T08:23:39.858809Z",
+     "start_time": "2022-12-02T08:23:39.839859Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "from pathlib import Path\n",
+    "sys.path.append(str(Path(os.getcwd()).parent))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.0.22\n"
+     ]
+    }
+   ],
+   "source": [
+    "import PublicDataReader as pdr\n",
+    "from config import API_KEY_INFO\n",
+    "print(pdr.__version__)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# FRED API Instance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from PublicDataReader import Fred\n",
+    "api = Fred(API_KEY_INFO[\"fred\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 시리즈 검색"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "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>CPIAUCSL</td>\n",
+       "      <td>2023-06-14</td>\n",
+       "      <td>2023-06-14</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>95</td>\n",
+       "      <td>96</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>2023-06-14</td>\n",
+       "      <td>2023-06-14</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>74</td>\n",
+       "      <td>96</td>\n",
+       "      <td>Handbook of Methods (https://www.bls.gov/opub/...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>CUUS0000SA0</td>\n",
+       "      <td>2023-06-14</td>\n",
+       "      <td>2023-06-14</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>42</td>\n",
+       "      <td>96</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>CORESTICKM159SFRBATL</td>\n",
+       "      <td>2023-06-14</td>\n",
+       "      <td>2023-06-14</td>\n",
+       "      <td>Sticky Price Consumer Price Index less Food an...</td>\n",
+       "      <td>1967-12-01</td>\n",
+       "      <td>2023-05-01</td>\n",
+       "      <td>Monthly</td>\n",
+       "      <td>M</td>\n",
+       "      <td>Percent Change from Year Ago</td>\n",
+       "      <td>% Chg. from Yr. Ago</td>\n",
+       "      <td>Seasonally Adjusted</td>\n",
+       "      <td>SA</td>\n",
+       "      <td>2023-06-13 12:01:01-05</td>\n",
+       "      <td>85</td>\n",
+       "      <td>86</td>\n",
+       "      <td>The Sticky Price Consumer Price Index (CPI) is...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>FPCPITOTLZGUSA</td>\n",
+       "      <td>2023-06-14</td>\n",
+       "      <td>2023-06-14</td>\n",
+       "      <td>Inflation, consumer prices for the United States</td>\n",
+       "      <td>1960-01-01</td>\n",
+       "      <td>2022-01-01</td>\n",
+       "      <td>Annual</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Percent</td>\n",
+       "      <td>%</td>\n",
+       "      <td>Not Seasonally Adjusted</td>\n",
+       "      <td>NSA</td>\n",
+       "      <td>2023-05-09 14:09:01-05</td>\n",
+       "      <td>85</td>\n",
+       "      <td>85</td>\n",
+       "      <td>Inflation as measured by the consumer price in...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     id realtime_start realtime_end  \\\n",
+       "0              CPIAUCSL     2023-06-14   2023-06-14   \n",
+       "1              CPIAUCNS     2023-06-14   2023-06-14   \n",
+       "2           CUUS0000SA0     2023-06-14   2023-06-14   \n",
+       "3  CORESTICKM159SFRBATL     2023-06-14   2023-06-14   \n",
+       "4        FPCPITOTLZGUSA     2023-06-14   2023-06-14   \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  Consumer Price Index for All Urban Consumers: ...        1984-01-01   \n",
+       "3  Sticky Price Consumer Price Index less Food an...        1967-12-01   \n",
+       "4   Inflation, consumer prices for the United States        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      2022-07-01  Semiannual              SA           Index 1982-1984=100   \n",
+       "3      2023-05-01     Monthly               M  Percent Change from Year Ago   \n",
+       "4      2022-01-01      Annual               A                       Percent   \n",
+       "\n",
+       "           units_short      seasonal_adjustment seasonal_adjustment_short  \\\n",
+       "0  Index 1982-1984=100      Seasonally Adjusted                        SA   \n",
+       "1  Index 1982-1984=100  Not Seasonally Adjusted                       NSA   \n",
+       "2  Index 1982-1984=100  Not Seasonally Adjusted                       NSA   \n",
+       "3  % Chg. from Yr. Ago      Seasonally Adjusted                        SA   \n",
+       "4                    %  Not Seasonally Adjusted                       NSA   \n",
+       "\n",
+       "             last_updated  popularity  group_popularity  \\\n",
+       "0  2023-06-13 07:44:03-05          95                96   \n",
+       "1  2023-06-13 07:44:06-05          74                96   \n",
+       "2  2023-01-12 07:38:17-06          42                96   \n",
+       "3  2023-06-13 12:01:01-05          85                86   \n",
+       "4  2023-05-09 14:09:01-05          85                85   \n",
+       "\n",
+       "                                               notes  \n",
+       "0  The Consumer Price Index for All Urban Consume...  \n",
+       "1  Handbook of Methods (https://www.bls.gov/opub/...  \n",
+       "2                                                NaN  \n",
+       "3  The Sticky Price Consumer Price Index (CPI) is...  \n",
+       "4  Inflation as measured by the consumer price in...  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "search_text = \"consumer price index\"\n",
+    "result = api.get_data(api_name=\"series_search\", search_text=search_text)\n",
+    "result.head()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Consumer Price Index for All Urban Consumers: All Items in U.S. City Average (CPIAUCNS)\n",
+    "\n",
+    "- https://fred.stlouisfed.org/series/CPIAUCNS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "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>realtime_start</th>\n",
+       "      <th>realtime_end</th>\n",
+       "      <th>value</th>\n",
+       "      <th>value_last_year</th>\n",
+       "      <th>change_rate</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2023-01-01</th>\n",
+       "      <td>2023-06-14</td>\n",
+       "      <td>2023-06-14</td>\n",
+       "      <td>299.170</td>\n",
+       "      <td>281.148</td>\n",
+       "      <td>6.410147</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-02-01</th>\n",
+       "      <td>2023-06-14</td>\n",
+       "      <td>2023-06-14</td>\n",
+       "      <td>300.840</td>\n",
+       "      <td>283.716</td>\n",
+       "      <td>6.035613</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-03-01</th>\n",
+       "      <td>2023-06-14</td>\n",
+       "      <td>2023-06-14</td>\n",
+       "      <td>301.836</td>\n",
+       "      <td>287.504</td>\n",
+       "      <td>4.984974</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-04-01</th>\n",
+       "      <td>2023-06-14</td>\n",
+       "      <td>2023-06-14</td>\n",
+       "      <td>303.363</td>\n",
+       "      <td>289.109</td>\n",
+       "      <td>4.930320</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-05-01</th>\n",
+       "      <td>2023-06-14</td>\n",
+       "      <td>2023-06-14</td>\n",
+       "      <td>304.127</td>\n",
+       "      <td>292.296</td>\n",
+       "      <td>4.047609</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           realtime_start realtime_end    value  value_last_year  change_rate\n",
+       "date                                                                         \n",
+       "2023-01-01     2023-06-14   2023-06-14  299.170          281.148     6.410147\n",
+       "2023-02-01     2023-06-14   2023-06-14  300.840          283.716     6.035613\n",
+       "2023-03-01     2023-06-14   2023-06-14  301.836          287.504     4.984974\n",
+       "2023-04-01     2023-06-14   2023-06-14  303.363          289.109     4.930320\n",
+       "2023-05-01     2023-06-14   2023-06-14  304.127          292.296     4.047609"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 계절에 따라 조정되지 않음 - CPIAUCNS - https://fred.stlouisfed.org/series/CPIAUCNS\n",
+    "series_id = \"CPIAUCNS\"\n",
+    "\n",
+    "df = api.get_data(api_name=\"series_observations\", series_id=series_id)\n",
+    "df['value'] = pd.to_numeric(df['value'])\n",
+    "df['date'] = pd.to_datetime(df['date'])\n",
+    "\n",
+    "df = df.set_index(\"date\")\n",
+    "df['value_last_year'] = df['value'].shift(12)\n",
+    "df['change_rate'] = (df['value'] - df['value_last_year']) / df['value_last_year'] * 100\n",
+    "\n",
+    "df.tail()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Federal Funds Effective Rate (DFF)\n",
+    "\n",
+    "- https://fred.stlouisfed.org/series/DFF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "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>realtime_start</th>\n",
+       "      <th>realtime_end</th>\n",
+       "      <th>value</th>\n",
+       "      <th>value_last_year</th>\n",
+       "      <th>change_rate</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2023-06-08</th>\n",
+       "      <td>2023-06-14</td>\n",
+       "      <td>2023-06-14</td>\n",
+       "      <td>5.08</td>\n",
+       "      <td>5.08</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-06-09</th>\n",
+       "      <td>2023-06-14</td>\n",
+       "      <td>2023-06-14</td>\n",
+       "      <td>5.08</td>\n",
+       "      <td>5.08</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-06-10</th>\n",
+       "      <td>2023-06-14</td>\n",
+       "      <td>2023-06-14</td>\n",
+       "      <td>5.08</td>\n",
+       "      <td>5.08</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-06-11</th>\n",
+       "      <td>2023-06-14</td>\n",
+       "      <td>2023-06-14</td>\n",
+       "      <td>5.08</td>\n",
+       "      <td>5.08</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-06-12</th>\n",
+       "      <td>2023-06-14</td>\n",
+       "      <td>2023-06-14</td>\n",
+       "      <td>5.08</td>\n",
+       "      <td>5.08</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           realtime_start realtime_end  value  value_last_year  change_rate\n",
+       "date                                                                       \n",
+       "2023-06-08     2023-06-14   2023-06-14   5.08             5.08          0.0\n",
+       "2023-06-09     2023-06-14   2023-06-14   5.08             5.08          0.0\n",
+       "2023-06-10     2023-06-14   2023-06-14   5.08             5.08          0.0\n",
+       "2023-06-11     2023-06-14   2023-06-14   5.08             5.08          0.0\n",
+       "2023-06-12     2023-06-14   2023-06-14   5.08             5.08          0.0"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "series_id = \"DFF\"\n",
+    "\n",
+    "df2 = api.get_data(api_name=\"series_observations\", series_id=series_id)\n",
+    "df2['value'] = pd.to_numeric(df2['value'])\n",
+    "df2['date'] = pd.to_datetime(df2['date'])\n",
+    "\n",
+    "df2 = df2.set_index('date')\n",
+    "df2['value_last_year'] = df2['value'].shift(12)\n",
+    "df2['change_rate'] = (df2['value'] - df2['value_last_year']) / df2['value_last_year'] * 100\n",
+    "\n",
+    "df2.tail()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Unemployment Rate (UNRATE)\n",
+    "\n",
+    "- https://fred.stlouisfed.org/series/UNRATE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "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>realtime_start</th>\n",
+       "      <th>realtime_end</th>\n",
+       "      <th>value</th>\n",
+       "      <th>value_last_year</th>\n",
+       "      <th>change_rate</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2023-01-01</th>\n",
+       "      <td>2023-06-14</td>\n",
+       "      <td>2023-06-14</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>-15.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-02-01</th>\n",
+       "      <td>2023-06-14</td>\n",
+       "      <td>2023-06-14</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>-5.263158</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-03-01</th>\n",
+       "      <td>2023-06-14</td>\n",
+       "      <td>2023-06-14</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>-2.777778</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-04-01</th>\n",
+       "      <td>2023-06-14</td>\n",
+       "      <td>2023-06-14</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>-5.555556</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-05-01</th>\n",
+       "      <td>2023-06-14</td>\n",
+       "      <td>2023-06-14</td>\n",
+       "      <td>3.7</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>2.777778</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           realtime_start realtime_end  value  value_last_year  change_rate\n",
+       "date                                                                       \n",
+       "2023-01-01     2023-06-14   2023-06-14    3.4              4.0   -15.000000\n",
+       "2023-02-01     2023-06-14   2023-06-14    3.6              3.8    -5.263158\n",
+       "2023-03-01     2023-06-14   2023-06-14    3.5              3.6    -2.777778\n",
+       "2023-04-01     2023-06-14   2023-06-14    3.4              3.6    -5.555556\n",
+       "2023-05-01     2023-06-14   2023-06-14    3.7              3.6     2.777778"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "series_id = \"UNRATE\"\n",
+    "\n",
+    "df3 = api.get_data(api_name=\"series_observations\", series_id=series_id)\n",
+    "df3['value'] = pd.to_numeric(df3['value'])\n",
+    "df3['date'] = pd.to_datetime(df3['date'])\n",
+    "\n",
+    "df3 = df3.set_index('date')\n",
+    "df3['value_last_year'] = df3['value'].shift(12)\n",
+    "df3['change_rate'] = (df3['value'] - df3['value_last_year']) / df3['value_last_year'] * 100\n",
+    "\n",
+    "df3.tail()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 시각화"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "plt.rcParams['axes.unicode_minus'] = False\n",
+    "plt.rcParams['font.family'] = 'Malgun Gothic'\n",
+    "\n",
+    "# Filter data for years 2020 and after\n",
+    "df = df.loc['2020':]\n",
+    "df2 = df2.loc['2020':]\n",
+    "df3 = df3.loc['2020':]\n",
+    "\n",
+    "# Create the plot\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "\n",
+    "# Add each line\n",
+    "plt.plot(df.index, df['change_rate'], label='CPI (YoY)')\n",
+    "plt.plot(df2.index, df2['value'], label='기준금리')\n",
+    "plt.plot(df3.index, df3['value'], label='실업률')\n",
+    "\n",
+    "# Add labels and legend\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Value')\n",
+    "plt.legend()\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "hide_input": false,
+  "kernelspec": {
+   "display_name": "Python 3.9.12 ('venv': venv)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
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