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import pandas as pd | ||
import requests | ||
from bs4 import BeautifulSoup | ||
import xmltodict, json | ||
import matplotlib.pyplot as plt | ||
import os | ||
import numpy as np | ||
import matplotlib.pyplot as plt | ||
import imageio.v2 as imageio | ||
import textwrap | ||
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# read database | ||
database_csv = pd.read_csv('https://raw.githubusercontent.com/thanhqtran/gso-macro-monitor/refs/heads/main/dsbb_indicator_desc.csv') | ||
database_df = pd.DataFrame(database_csv) | ||
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# parse data from xml to python dictionary | ||
def get_data(url): | ||
r = requests.get(url) | ||
soup = BeautifulSoup(r.text, 'xml') | ||
data = xmltodict.parse(str(soup)) | ||
return data | ||
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def get_obs_data(dataframe): | ||
x_dict = [] | ||
y_dict = [] | ||
for i in range(0,len(dataframe['Obs'])): | ||
x = dataframe['Obs'][i]['@TIME_PERIOD'] | ||
y = dataframe['Obs'][i]['@OBS_VALUE'] | ||
x = pd.to_datetime(x) #convert x to datetime | ||
try: | ||
y = float(y) | ||
except: | ||
y = np.nan | ||
x_dict.append(x) | ||
y_dict.append(y) | ||
return x_dict, y_dict | ||
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def get_meta_data(dataframe): | ||
meta_data = {} | ||
meta_data['REF_AREA'] = dataframe['@REF_AREA'] | ||
meta_data['INDICATOR'] = dataframe['@INDICATOR'] | ||
meta_data['FREQ'] = dataframe['@FREQ'] | ||
meta_data['DATA_DOMAIN'] = dataframe['@DATA_DOMAIN'] | ||
return meta_data | ||
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# return unique database and database_link pair | ||
database_df = database_df.drop_duplicates(subset=['database', 'database_link', 'database_link_archive']).reset_index(drop=True) | ||
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# extract data from database_link_archive | ||
extracted_database = [] | ||
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# archived | ||
for i in range(0, len(database_df)): | ||
database = database_df['database'][i] | ||
url = database_df['database_link_archive'][i] | ||
data = get_data(url) | ||
database_raw = data['message:StructureSpecificData']['message:DataSet']['Series'] | ||
extracted_database.append(database_raw) | ||
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# save extracted data to json | ||
with open('extracted_database.json', 'w') as f: | ||
json.dump(extracted_database, f) |