Create ETF_sector_data_prep.py

ETF_sector_data_prep.py

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+import yahooquery as yq
+import pandas as pd
+
+tickers = ['EWA', 'EWH', 'EWJ', 'EWM', 'EWS', 'EWT', 'EWY', 'THD']
+countries = ['Australia', 'Hong Kong', 'Japan', 'Malaysia', 'Singapore', 'Taiwan', 'South Korea', 'Thailand']
+
+
+all_dataframes = []
+
+for ticker, country in zip(tickers, countries):
+    t = yq.Ticker(ticker)
+    # sector weightings, returns pandas DataFrame with one column containing the weights, indexed by the sector names
+    df = t.fund_sector_weightings
+    # give a name to the first column
+    df.columns = ['Weight']
+    # turn the index column into a dataframe column
+    df['Sector'] = df.index
+    
+    # Add the Country column
+    df['Country'] = country
+    
+    # Append the dataframe to the list
+    all_dataframes.append(df)
+
+# Concatenate all dataframes into one
+sector_df = pd.concat(all_dataframes, ignore_index=True)
+
+# dictionary with keys equal to the elements of the sectors and values as specified in the assignment
+sector_dict = {'realestate': 'Real Estate', 'consumer_cyclical': 'Consumer Cyclical', 'basic_materials': 'Basic Materials', 'consumer_defensive': 'Consumer Defensive', 'technology': 'Technology', 'communication_services': 'Communication Services', 'financial_services': 'Financial Services', 'utilities': 'Utilities', 'industrials': 'Industrials', 'energy': 'Energy', 'healthcare': 'Healthcare'}
+
+# create a new column in the dataframe caled 'Sector_Name' and fill it with the values from the sector_dict
+sector_df['Sector_Name'] = sector_df['Sector'].map(sector_dict)
+
+# Reorder the columns
+new_order = ['Country', 'Sector_Name', 'Weight', 'Sector']
+sector_df = sector_df[new_order]
+
+# Drop the 'Sector' column
+sector_df = sector_df.drop('Sector', axis=1)
+
+# Convert 'Weight' column to percentages
+sector_df['Weight'] = sector_df['Weight'] * 100
+# Rename the 'Weight' column
+sector_df = sector_df.rename(columns={'Weight': 'Weight(%)'})