add example

CEDA/economic/ECB.py

@@ -0,0 +1,33 @@
+import requests
+import pandas as pd
+from bs4 import BeautifulSoup
+
+url = {
+    "ECB": "https://sdw.ecb.europa.eu/",
+    "ECB-API": "http://sdw-wsrest.ecb.europa.eu/service/data/"
+}
+
+class ECBData(object):
+    def __init__(self) -> None:
+        pass
+
+    def toc(self):
+        r = requests.get(url["ECB"] + "browse.do?node=9689727")
+        dataset_list = BeautifulSoup(r.text, "html.parser").find_all("div", {"id": "currentMaximizeNode0"})
+        uls = dataset_list[0].find_all("ul")
+        lis = [li for ul in uls for li in ul.find_all("li")]
+        li_text = [li.text.strip() for li in lis]
+        name, metadata = [], []
+        for i in range(0, len(li_text)):
+            name.append(li_text[i].split("-")[0])
+            metadata.append(li_text[i].split("-")[1])
+
+        li_urls = [url["ECB"] + li.a.get("href") for li in lis]
+        toc = pd.DataFrame({"name": name, "metadata":metadata, "url":li_urls})
+        return toc
+
+    
+    def download_data(self, datasetname:str=None):
+        tmp_url = url["ECB-API"] + "{}?format=csvdata".format(datasetname)
+        data = pd.read_csv(tmp_url)
+        return data

CEDA/economic/ECBData.py

@@ -1,13 +0,0 @@
-import pandas as pd
-
-url = {
-    "ECB": "http://sdw-wsrest.ecb.europa.eu/service/data/"
-}
-
-class ECBData(object):
-    def __init__(self) -> None:
-        pass
-    def get_data(dataset:str=None):
-        tmp_url = url["ECB"] + "{}?format=csvdata".format(dataset)
-        data = pd.read_csv(tmp_url)
-        return data

CEDA/economic/EPU.py

@@ -9,12 +9,7 @@ url = {
     "EPU": "https://www.policyuncertainty.com/"
 }
 
-
-class EPUData(object):
-    def __init__(self, country:str=None):
-        self.country = country
-
-    def country_list(self):
+def country_list():
     country_list = ["Global", "USA", "Australia", "Belgium", 
             "Brazil", "Canada", "Chile", "China", 
             "Colombia", "Croatia", "Denmark", "France", 
@@ -25,6 +20,10 @@ class EPUData(object):
     annotations = "Disambiguation: the word 'Korea' in here stands for 'South Korea'"
     return country_list, annotations
 
+class EPUData(object):
+    def __init__(self, country:str=None):
+        self.country = country
+    
     def download(self):
         if self.country == "China":
             r = requests.get(url["EPU-China"])

CEDA/economic/Eurostat.py

@@ -50,24 +50,24 @@ class EurostatData(object):
         if geo != None and unit != None:
             data = data.loc[(data["geo"] == geo) & (data["unit"] == unit)]
             for i in range(4, len(list(data.columns))):
-                data[data.columns[i]] = data[data.columns[i]].str.extract(r'(\d+.\d+)').astype("float")
+                data[data.columns[i]] = data[data.columns[i]].astype(str).str.extract(r'(\d+.\d+)').astype("float")
             return data
 
         elif geo != None and unit == None:
             data = data.loc[(data["geo"] == geo)]
             for i in range(4, len(list(data.columns))):
-                data[data.columns[i]] = data[data.columns[i]].str.extract(r'(\d+.\d+)').astype("float")
+                data[data.columns[i]] = data[data.columns[i]].astype(str).str.extract(r'(\d+.\d+)').astype("float")
             return data
 
         elif geo == None and unit != None:
             data = data.loc[(data["geo"] == geo)]
             for i in range(4, len(list(data.columns))):
-                data[data.columns[i]] = data[data.columns[i]].str.extract(r'(\d+.\d+)').astype("float")
+                data[data.columns[i]] = data[data.columns[i]].astype(str).str.extract(r'(\d+.\d+)').astype("float")
             return data
         
         elif geo == None and unit == None:
             for i in range(4, len(list(data.columns))):
-                data[data.columns[i]] = data[data.columns[i]].str.extract(r'(\d+.\d+)').astype("float")
+                data[data.columns[i]] = data[data.columns[i]].astype(str).str.extract(r'(\d+.\d+)').astype("float")
             return data
 
     def download_dic(self, category:str=None):
@@ -77,7 +77,7 @@ class EurostatData(object):
 
 
 if __name__ == "__main__":
-    eu = EurostatData(language="en", version=2.1)
+    eu = EurostatData(language="en")
     
     
         

CEDA/economic/Fred.py

@@ -18,7 +18,8 @@ ssl._create_default_https_context = ssl._create_unverified_context
 url = {
     "fred_econ": "https://fred.stlouisfed.org/graph/fredgraph.csv?",
     "fred_series": "https://fred.stlouisfed.org/series/",
-    "philfed": "https://www.philadelphiafed.org/surveys-and-data/real-time-data-research/",
+    "philfed":
+    "https://www.philadelphiafed.org/surveys-and-data/real-time-data-research/",
     "chicagofed": "https://www.chicagofed.org/~/media/publications/",
     "OECD": "https://stats.oecd.org/sdmx-json/data/DP_LIVE/"
 }
@@ -41,12 +42,29 @@ def get_metadata(id: str = None) -> dict:
     tmp_url = url["fred_series"] + id
     r = requests.get(tmp_url)
     metadata = {
-        "name": (" ".join(BeautifulSoup(r.text, "html.parser").find_all('div', {"class": "page-title"})[0].span.text.split())),
-        "id": id,
-        "update_time": datetime.strftime(dparser.parse(BeautifulSoup(r.text, "html.parser").find_all('div', {"class": "pull-left meta-col"})[0].find_all('span')[3].text, fuzzy=True), format="%Y-%m-%d"),
-        "units": BeautifulSoup(r.text, "html.parser").find_all('div', {"class": "pull-left meta-col"})[1].find_all('span')[0].text.split("        ")[0],
-        "frequency": BeautifulSoup(r.text, "html.parser").find_all('div', {"class": "pull-left meta-col"})[2].find_all('span')[0].text.split("            ")[1].split("    ")[1],
-        "tags": get_tag(id)
+        "name": (" ".join(
+            BeautifulSoup(r.text, "html.parser").find_all(
+                'div', {"class": "page-title"})[0].span.text.split())),
+        "id":
+        id,
+        "update_time":
+        datetime.strftime(dparser.parse(
+            BeautifulSoup(r.text, "html.parser").find_all(
+                'div',
+                {"class": "pull-left meta-col"})[0].find_all('span')[3].text,
+            fuzzy=True),
+                          format="%Y-%m-%d"),
+        "units":
+        BeautifulSoup(r.text, "html.parser").find_all(
+            'div', {"class": "pull-left meta-col"
+                    })[1].find_all('span')[0].text.split("        ")[0],
+        "frequency":
+        BeautifulSoup(r.text,
+                      "html.parser").find_all('div',
+                                              {"class": "pull-left meta-col"})
+        [2].find_all('span')[0].text.split("            ")[1].split("    ")[1],
+        "tags":
+        get_tag(id)
     }
     return metadata
 
@@ -54,17 +72,20 @@ def get_metadata(id: str = None) -> dict:
 def date_transform(df, format_origin, format_after):
     return_list = []
     for i in range(0, len(df)):
-        return_list.append(datetime.strptime(
-            df[i], format_origin).strftime(format_after))
+        return_list.append(
+            datetime.strptime(df[i], format_origin).strftime(format_after))
     return return_list
 
 
 class FredData(object):
+
     def __init__(self, country: str = "usa"):
         self.country = country
 
-    __annotations__ = {"name": "Main Economic Indicators",
-                       "url": "https://fred.stlouisfed.org/tags/series?t=mei"}
+    __annotations__ = {
+        "name": "Main Economic Indicators",
+        "url": "https://fred.stlouisfed.org/tags/series?t=mei"
+    }
 
     def get_id(self, url: str) -> list:
         id_list = []
@@ -93,6 +114,20 @@ class FredData(object):
         id_list = list(set(id_list))
         return id_list
 
+    def toc(self):
+        sid = self.extract_id()
+        name = []
+        for i in range(0, len(sid)):
+            name.append(get_metadata(id=sid[i])["name"])
+            time.sleep(2)
+            
+        toc = pd.DataFrame({"name": name, "id": sid})
+        return toc
+
+    def download_data(self, sid: str = None):
+        data = pd.read_csv(url["fred_econ"] + "id={}".format(sid))
+        return data
+
 
 if __name__ == "__main__":
     usa = FredData(country="usa")

example/economic.md

@@ -0,0 +1,79 @@
+## Fred
+
+For the ST.Louis Fred data, we mainly focus on the ["Main Economic Indicators" Series](https://fred.stlouisfed.org/tags/series?t=mei)
+
+```python
+from CEDA.economic.Fred import *
+usa = FredData(country="usa")
+usa_toc = usa.toc()
+data = usa.download_data(sid="LFAC24FEUSM647N")
+```
+
+## Eurostat
+
+```python
+from CEDA.economic.Eurostat import *
+eurostat = EurostatData(language="en")
+eurostat_toc = eurostat.toc()
+GDP_related = eurostat.search_toc(query="GDP")
+nama_10_gdp = eurostat.download_data(datasetcode="nama_10_gdp")
+tet00004 = eurostat.download_data(datasetcode="tet00004")
+```
+
+## ECB
+
+```python
+from CEDA.economic.ECB import *
+ecb = ECBData()
+ecb_toc = ecb.toc()
+AME = ecb.download_data(datasetname="AME")
+```
+
+## OECD
+
+```python
+from CEDA.economic.OECD import *
+oecd = OECDData()
+oecd_toc = oecd.toc()
+oecd_tos = oecd.tos(dataset="QNA")
+data = oecd.download_data(dataset="QNA", query="QNA/CAN.B1_GE.CQRSA.Q")
+```
+
+## NBSC
+
+```python
+from CEDA.economic.NBSC import *
+nbsc = NBSCData(language="en")
+nbsc_nodes = nbsc.tree_generation()
+nbsc_toc = nbsc.toc(nodes=nbsc_nodes)
+nbsc_toc[nbsc_toc["name"].str.contains("GDP")]
+A0203 = nbsc.download_data(nid="A0203")
+```
+
+## Xinhua
+```python
+from CEDA.economic.XinHua import *
+xhdata = XHData()
+toc = xhdata.toc()
+data = xhdata.download_data(iid=12006) # GDP
+```
+
+## BOJ
+
+```python
+from CEDA.economic.BOJ import *
+boj = BOJData()
+boj_toc = boj.toc()
+survey = boj.download_data("Survey")
+```
+
+## EPU
+
+```python
+from CEDA.economic.EPU import * 
+country_list, annotations = country_list()
+can_epu = EPUData(country="Canada")
+mainland_china_epu = EPUData(country="China")
+can_data, can_reference = can_epu.download()
+mainland_china_data, cn_reference = mainland_china_epu.download()
+```