Delete ETF_sector_data_prep.py

ETF_sector_data_prep.py

@@ -1,44 +0,0 @@
-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(%)'})