James McCool
commited on
Commit
·
3deb246
1
Parent(s):
d209a5b
Refactor name standardization and mapping logic in app.py: introduce functions for creating site mappings and standardizing player names, improving code organization and efficiency in handling name variations across dataframes.
Browse files
app.py
CHANGED
@@ -135,60 +135,101 @@ 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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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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# 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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def create_site_mapping(site_csv):
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"""
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Create a mapping dictionary from the site CSV that handles both Name and Nickname cases.
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Args:
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site_csv: DataFrame containing site data with either Name/Nickname and Name+ID/Id columns
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Returns:
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dict: Mapping of all possible name variations to their ID
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"""
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mapping = {}
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# Check which columns we have
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has_name = 'Name' in site_csv.columns
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has_nickname = 'Nickname' in site_csv.columns
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has_name_id = 'Name + ID' in site_csv.columns
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has_id = 'Id' in site_csv.columns
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# Create mappings for all possible combinations
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if has_name and has_name_id:
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mapping.update(dict(zip(site_csv['Name'], site_csv['Name + ID'])))
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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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Returns:
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DataFrame: Updated dataframe with standardized names
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"""
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df = df.copy()
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# First try exact matches
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for col in name_columns:
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df[col] = df[col].map(lambda x: site_mapping.get(x, x))
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# Then try fuzzy matching for any remaining unmatched names
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unmatched = df[name_columns].apply(lambda x: x.isin(site_mapping.keys())).any(axis=1)
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if unmatched.any():
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for col in name_columns:
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# Only process unmatched names
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mask = ~df[col].isin(site_mapping.keys())
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if mask.any():
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# Get fuzzy matches for unmatched names
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fuzzy_matches = {
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name: process.extractOne(name, list(site_mapping.keys()), score_cutoff=90)[0]
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for name in df.loc[mask, col].unique()
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if process.extractOne(name, list(site_mapping.keys()), score_cutoff=90)
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}
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# Apply fuzzy matches
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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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def process_uploads(site_csv, portfolio_df, projections_df):
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"""
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Process all three files and ensure name consistency.
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Args:
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site_csv: DataFrame from site CSV
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portfolio_df: DataFrame containing portfolio data
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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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