James McCool
commited on
Commit
·
730a147
1
Parent(s):
3deb246
Enhance name matching process in app.py: streamline the handling of player names by implementing a more efficient matching algorithm, updating session state management, and improving debug output for better traceability of matches.
Browse files
app.py
CHANGED
@@ -135,101 +135,60 @@ with tab1:
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projections = projections.apply(lambda x: x.replace(player_wrong_names_mlb, player_right_names_mlb))
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st.dataframe(projections.head(10))
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if has_nickname and has_id:
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mapping.update(dict(zip(site_csv['Nickname'], site_csv['Id'])))
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return mapping
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def standardize_names(df, name_columns, site_mapping):
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"""
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Standardize names across a dataframe using the site mapping.
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Args:
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df: DataFrame containing player names
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name_columns: List of column names containing player names
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site_mapping: Dictionary mapping names to IDs from site CSV
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df.loc[mask, col] = df.loc[mask, col].map(lambda x: site_mapping.get(fuzzy_matches.get(x, x), x))
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return df
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projections_df: DataFrame containing projections
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"""
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# Create site mapping
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site_mapping = create_site_mapping(site_csv)
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# Get portfolio columns that contain player names
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portfolio_name_cols = [col for col in portfolio_df.columns
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if col not in ['salary', 'median', 'Own']]
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# Get projections column name
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projections_name_col = 'player_names' # adjust if different
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# Standardize names in both dataframes
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portfolio_df = standardize_names(portfolio_df, portfolio_name_cols, site_mapping)
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projections_df = standardize_names(projections_df, [projections_name_col], site_mapping)
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return portfolio_df, projections_df
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if portfolio_file and projections_file and csv_file:
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# Process all files
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portfolio_df, projections_df = process_uploads(csv_file, st.session_state['portfolio'], projections)
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# Store in session state
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st.session_state['portfolio'] = portfolio_df
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st.session_state['projections_df'] = projections_df
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# with tab2:
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# if st.button('Clear data', key='reset2'):
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projections = projections.apply(lambda x: x.replace(player_wrong_names_mlb, player_right_names_mlb))
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st.dataframe(projections.head(10))
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if portfolio_file and projections_file:
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if st.session_state['portfolio'] is not None and projections is not None:
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st.subheader("Name Matching Analysis")
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# Initialize projections_df in session state if it doesn't exist
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if 'projections_df' not in st.session_state:
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st.session_state['projections_df'] = projections.copy()
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st.session_state['projections_df']['salary'] = (st.session_state['projections_df']['salary'].astype(str).str.replace(',', '').astype(float).astype(int))
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# Update projections_df with any new matches
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st.session_state['projections_df'] = find_name_mismatches(st.session_state['portfolio'], st.session_state['projections_df'])
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try:
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name_id_map = dict(zip(
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st.session_state['csv_file']['Name'],
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st.session_state['csv_file']['Name + ID']
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))
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print("Using Name + ID mapping")
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except:
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name_id_map = dict(zip(
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st.session_state['csv_file']['Nickname'],
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st.session_state['csv_file']['Id']
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))
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print("Using Nickname + Id mapping")
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# Get all names at once
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names = projections['player_names'].tolist()
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choices = list(name_id_map.keys())
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# Create a dictionary to store matches
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match_dict = {}
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# Process each name individually but more efficiently
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for name in names:
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# Use extractOne with score_cutoff for efficiency
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match = process.extractOne(
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name,
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choices,
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score_cutoff=85
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)
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if match:
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match_dict[name] = name_id_map[match[0]]
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else:
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match_dict[name] = name
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print(f"Number of entries in match_dict: {len(match_dict)}")
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print("Sample of match_dict:", list(match_dict.items())[:3])
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# Apply the matches
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projections['upload_match'] = projections['player_names'].map(match_dict)
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st.session_state['export_dict'] = match_dict
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st.write(st.session_state['export_dict'])
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st.session_state['origin_portfolio'] = st.session_state['portfolio'].copy()
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# with tab2:
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# if st.button('Clear data', key='reset2'):
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