Spaces:
Running
Running
James McCool
commited on
Commit
·
66b0ac2
1
Parent(s):
96799c9
Refactor tab2 layout with expanded display options and improved data management
Browse files
app.py
CHANGED
@@ -416,250 +416,252 @@ with tab1:
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with tab2:
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if player_var1 == 'Specific Players':
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elif player_var1 == 'Full Slate':
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elif
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id_dict = dict(zip(ROO_slice.Player, ROO_slice.player_ID))
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min_own = np.min(fd_lineups[:,15])
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max_own = np.max(fd_lineups[:,15])
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column_names = fd_columns
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player_var1 = st.radio("Do you want a frame with specific Players?", ('Full Slate', 'Specific Players'), key='player_var1')
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if player_var1 == 'Specific Players':
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elif player_var1 == 'Full Slate':
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player_var2 = fd_raw.Player.values.tolist()
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if st.button("Prepare data export", key='data_export'):
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data_export = st.session_state.working_seed.copy()
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if site_var1 == 'Draftkings':
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for col_idx in range(8):
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data_export[:, col_idx] = np.array([id_dict.get(player, player) for player in data_export[:, col_idx]])
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elif site_var1 == 'Fanduel':
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for col_idx in range(9):
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data_export[:, col_idx] = np.array([id_dict.get(player, player) for player in data_export[:, col_idx]])
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st.download_button(
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label="Export optimals set",
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data=convert_df(data_export),
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file_name='NBA_optimals_export.csv',
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mime='text/csv',
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)
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with col2:
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if site_var1 == 'Draftkings':
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if 'working_seed' in st.session_state:
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st.session_state.working_seed = st.session_state.working_seed
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if player_var1 == 'Specific Players':
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st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
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elif player_var1 == 'Full Slate':
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st.session_state.working_seed = dk_lineups.copy()
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st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
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elif 'working_seed' not in st.session_state:
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st.session_state.working_seed = dk_lineups.copy()
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st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
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elif 'working_seed' not in st.session_state:
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st.session_state.working_seed = fd_lineups.copy()
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if site_var1 == 'Draftkings':
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st.session_state.
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elif site_var1 == 'Fanduel':
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st.session_state.
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if 'working_seed' in st.session_state:
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# Create a new dataframe with summary statistics
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if site_var1 == 'Draftkings':
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'Metric': ['Min', 'Average', 'Max', 'STDdev'],
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'Salary': [
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np.min(st.session_state.working_seed[:,8]),
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np.mean(st.session_state.working_seed[:,8]),
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np.max(st.session_state.working_seed[:,8]),
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np.std(st.session_state.working_seed[:,8])
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],
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'Proj': [
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np.min(st.session_state.working_seed[:,9]),
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np.mean(st.session_state.working_seed[:,9]),
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np.max(st.session_state.working_seed[:,9]),
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np.std(st.session_state.working_seed[:,9])
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],
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'Own': [
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np.min(st.session_state.working_seed[:,14]),
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np.mean(st.session_state.working_seed[:,14]),
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np.max(st.session_state.working_seed[:,14]),
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np.std(st.session_state.working_seed[:,14])
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]
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})
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elif site_var1 == 'Fanduel':
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'Metric': ['Min', 'Average', 'Max', 'STDdev'],
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'Salary': [
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np.min(st.session_state.working_seed[:,9]),
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np.mean(st.session_state.working_seed[:,9]),
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np.max(st.session_state.working_seed[:,9]),
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np.std(st.session_state.working_seed[:,9])
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],
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'Proj': [
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np.min(st.session_state.working_seed[:,10]),
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np.mean(st.session_state.working_seed[:,10]),
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np.max(st.session_state.working_seed[:,10]),
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np.std(st.session_state.working_seed[:,10])
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],
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'Own': [
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np.min(st.session_state.working_seed[:,15]),
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np.mean(st.session_state.working_seed[:,15]),
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np.max(st.session_state.working_seed[:,15]),
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np.std(st.session_state.working_seed[:,15])
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]
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})
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# Set the index of the summary dataframe as the "Metric" column
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summary_df = summary_df.set_index('Metric')
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# Display the summary dataframe
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st.subheader("Optimal Statistics")
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st.dataframe(summary_df.style.format({
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'Salary': '{:.2f}',
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'Proj': '{:.2f}',
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'Own': '{:.2f}'
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}).background_gradient(cmap='RdYlGn', axis=0, subset=['Salary', 'Proj', 'Own']), use_container_width=True)
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with st.container():
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tab1, tab2 = st.tabs(["Display Frequency", "Seed Frame Frequency"])
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with tab1:
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if 'data_export_display' in st.session_state:
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if site_var1 == 'Draftkings':
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player_columns = st.session_state.data_export_display.iloc[:, :8]
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elif site_var1 == 'Fanduel':
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player_columns = st.session_state.data_export_display.iloc[:, :9]
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# Flatten the DataFrame and count unique values
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value_counts = player_columns.values.flatten().tolist()
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value_counts = pd.Series(value_counts).value_counts()
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percentages = (value_counts / lineup_num_var * 100).round(2)
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# Create a DataFrame with the results
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summary_df = pd.DataFrame({
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'Player': value_counts.index,
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'Salary': [salary_dict.get(player, player) for player in value_counts.index],
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'Frequency': value_counts.values,
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'Percentage': percentages.values
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})
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# Sort by frequency in descending order
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summary_df = summary_df.sort_values('Frequency', ascending=False)
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# Display the table
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st.write("Player Frequency Table:")
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st.dataframe(summary_df.style.format({'Percentage': '{:.2f}%'}, precision=2), height=500, use_container_width=True)
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file_name='NBA_player_frequency.csv',
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mime='text/csv',
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)
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with tab2:
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if 'working_seed' in st.session_state:
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if site_var1 == 'Draftkings':
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player_columns = st.session_state.working_seed[:, :8]
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elif site_var1 == 'Fanduel':
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player_columns = st.session_state.working_seed[:, :9]
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# Flatten the DataFrame and count unique values
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value_counts = player_columns.flatten().tolist()
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value_counts = pd.Series(value_counts).value_counts()
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percentages = (value_counts / len(st.session_state.working_seed) * 100).round(2)
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# Create a DataFrame with the results
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summary_df = pd.DataFrame({
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'Player': value_counts.index,
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'Salary': [salary_dict.get(player, player) for player in value_counts.index],
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'Frequency': value_counts.values,
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'Percentage': percentages.values
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})
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# Sort by frequency in descending order
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summary_df = summary_df.sort_values('Frequency', ascending=False)
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# Display the table
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st.write("Seed Frame Frequency Table:")
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st.dataframe(summary_df.style.format({'Percentage': '{:.2f}%'}, precision=2), height=500, use_container_width=True)
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)
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with tab2:
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if st.button("Load/Reset Data", key='reset2'):
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st.cache_data.clear()
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dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, timestamp, roo_backlog = load_overall_stats()
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dk_lineups = init_DK_lineups()
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fd_lineups = init_FD_lineups()
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t_stamp = f"Last Update: " + str(timestamp) + f" CST"
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for key in st.session_state.keys():
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del st.session_state[key]
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with st.expander("Display Options"):
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col1, col2, col3, col4, col5 = st.columns(5)
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with col1:
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slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Just the Main Slate'))
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with col2:
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site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'))
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with col3:
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lineup_num_var = st.number_input("How many lineups do you want to display?", min_value=1, max_value=1000, value=150, step=1)
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with col4:
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if site_var1 == 'Draftkings':
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raw_baselines = dk_raw
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ROO_slice = roo_raw[roo_raw['site'] == 'Draftkings']
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id_dict = dict(zip(ROO_slice.Player, ROO_slice.player_ID))
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# Get the minimum and maximum ownership values from dk_lineups
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min_own = np.min(dk_lineups[:,14])
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max_own = np.max(dk_lineups[:,14])
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column_names = dk_columns
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player_var1 = st.radio("Do you want a frame with specific Players?", ('Full Slate', 'Specific Players'), key='player_var1')
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if player_var1 == 'Specific Players':
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player_var2 = st.multiselect('Which players do you want?', options = dk_raw['Player'].unique())
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elif player_var1 == 'Full Slate':
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player_var2 = dk_raw.Player.values.tolist()
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elif site_var1 == 'Fanduel':
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raw_baselines = fd_raw
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ROO_slice = roo_raw[roo_raw['site'] == 'Fanduel']
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id_dict = dict(zip(ROO_slice.Player, ROO_slice.player_ID))
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min_own = np.min(fd_lineups[:,15])
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max_own = np.max(fd_lineups[:,15])
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column_names = fd_columns
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player_var1 = st.radio("Do you want a frame with specific Players?", ('Full Slate', 'Specific Players'), key='player_var1')
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if player_var1 == 'Specific Players':
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player_var2 = st.multiselect('Which players do you want?', options = fd_raw['Player'].unique())
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elif player_var1 == 'Full Slate':
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player_var2 = fd_raw.Player.values.tolist()
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with col5:
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if st.button("Prepare data export", key='data_export'):
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data_export = st.session_state.working_seed.copy()
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if site_var1 == 'Draftkings':
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for col_idx in range(8):
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data_export[:, col_idx] = np.array([id_dict.get(player, player) for player in data_export[:, col_idx]])
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elif site_var1 == 'Fanduel':
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for col_idx in range(9):
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data_export[:, col_idx] = np.array([id_dict.get(player, player) for player in data_export[:, col_idx]])
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st.download_button(
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label="Export optimals set",
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data=convert_df(data_export),
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file_name='NBA_optimals_export.csv',
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mime='text/csv',
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)
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if site_var1 == 'Draftkings':
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if 'working_seed' in st.session_state:
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st.session_state.working_seed = st.session_state.working_seed
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if player_var1 == 'Specific Players':
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st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
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elif player_var1 == 'Full Slate':
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st.session_state.working_seed = dk_lineups.copy()
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st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
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elif 'working_seed' not in st.session_state:
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st.session_state.working_seed = dk_lineups.copy()
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st.session_state.working_seed = st.session_state.working_seed
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if player_var1 == 'Specific Players':
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st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
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elif player_var1 == 'Full Slate':
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st.session_state.working_seed = dk_lineups.copy()
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st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
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elif site_var1 == 'Fanduel':
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if 'working_seed' in st.session_state:
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500 |
+
st.session_state.working_seed = st.session_state.working_seed
|
501 |
+
if player_var1 == 'Specific Players':
|
502 |
+
st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
|
503 |
+
elif player_var1 == 'Full Slate':
|
504 |
+
st.session_state.working_seed = fd_lineups.copy()
|
505 |
+
st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
|
506 |
+
elif 'working_seed' not in st.session_state:
|
507 |
+
st.session_state.working_seed = fd_lineups.copy()
|
508 |
+
st.session_state.working_seed = st.session_state.working_seed
|
509 |
+
if player_var1 == 'Specific Players':
|
510 |
+
st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
|
511 |
+
elif player_var1 == 'Full Slate':
|
512 |
+
st.session_state.working_seed = fd_lineups.copy()
|
513 |
+
st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
|
514 |
+
|
515 |
+
export_file = st.session_state.data_export_display.copy()
|
516 |
+
if site_var1 == 'Draftkings':
|
517 |
+
for col_idx in range(8):
|
518 |
+
export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(id_dict)
|
519 |
+
elif site_var1 == 'Fanduel':
|
520 |
+
for col_idx in range(9):
|
521 |
+
export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(id_dict)
|
522 |
|
523 |
+
with st.container():
|
524 |
+
if st.button("Reset Optimals", key='reset3'):
|
525 |
+
for key in st.session_state.keys():
|
526 |
+
del st.session_state[key]
|
527 |
+
if site_var1 == 'Draftkings':
|
528 |
+
st.session_state.working_seed = dk_lineups.copy()
|
529 |
+
elif site_var1 == 'Fanduel':
|
|
|
|
|
530 |
st.session_state.working_seed = fd_lineups.copy()
|
531 |
+
if 'data_export_display' in st.session_state:
|
532 |
+
st.dataframe(st.session_state.data_export_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), height=500, use_container_width = True)
|
533 |
+
st.download_button(
|
534 |
+
label="Export display optimals",
|
535 |
+
data=convert_df(export_file),
|
536 |
+
file_name='NBA_display_optimals.csv',
|
537 |
+
mime='text/csv',
|
538 |
+
)
|
539 |
+
|
540 |
+
with st.container():
|
541 |
+
if 'working_seed' in st.session_state:
|
542 |
+
# Create a new dataframe with summary statistics
|
543 |
+
if site_var1 == 'Draftkings':
|
544 |
+
summary_df = pd.DataFrame({
|
545 |
+
'Metric': ['Min', 'Average', 'Max', 'STDdev'],
|
546 |
+
'Salary': [
|
547 |
+
np.min(st.session_state.working_seed[:,8]),
|
548 |
+
np.mean(st.session_state.working_seed[:,8]),
|
549 |
+
np.max(st.session_state.working_seed[:,8]),
|
550 |
+
np.std(st.session_state.working_seed[:,8])
|
551 |
+
],
|
552 |
+
'Proj': [
|
553 |
+
np.min(st.session_state.working_seed[:,9]),
|
554 |
+
np.mean(st.session_state.working_seed[:,9]),
|
555 |
+
np.max(st.session_state.working_seed[:,9]),
|
556 |
+
np.std(st.session_state.working_seed[:,9])
|
557 |
+
],
|
558 |
+
'Own': [
|
559 |
+
np.min(st.session_state.working_seed[:,14]),
|
560 |
+
np.mean(st.session_state.working_seed[:,14]),
|
561 |
+
np.max(st.session_state.working_seed[:,14]),
|
562 |
+
np.std(st.session_state.working_seed[:,14])
|
563 |
+
]
|
564 |
+
})
|
565 |
+
elif site_var1 == 'Fanduel':
|
566 |
+
summary_df = pd.DataFrame({
|
567 |
+
'Metric': ['Min', 'Average', 'Max', 'STDdev'],
|
568 |
+
'Salary': [
|
569 |
+
np.min(st.session_state.working_seed[:,9]),
|
570 |
+
np.mean(st.session_state.working_seed[:,9]),
|
571 |
+
np.max(st.session_state.working_seed[:,9]),
|
572 |
+
np.std(st.session_state.working_seed[:,9])
|
573 |
+
],
|
574 |
+
'Proj': [
|
575 |
+
np.min(st.session_state.working_seed[:,10]),
|
576 |
+
np.mean(st.session_state.working_seed[:,10]),
|
577 |
+
np.max(st.session_state.working_seed[:,10]),
|
578 |
+
np.std(st.session_state.working_seed[:,10])
|
579 |
+
],
|
580 |
+
'Own': [
|
581 |
+
np.min(st.session_state.working_seed[:,15]),
|
582 |
+
np.mean(st.session_state.working_seed[:,15]),
|
583 |
+
np.max(st.session_state.working_seed[:,15]),
|
584 |
+
np.std(st.session_state.working_seed[:,15])
|
585 |
+
]
|
586 |
+
})
|
587 |
+
|
588 |
+
# Set the index of the summary dataframe as the "Metric" column
|
589 |
+
summary_df = summary_df.set_index('Metric')
|
590 |
+
|
591 |
+
# Display the summary dataframe
|
592 |
+
st.subheader("Optimal Statistics")
|
593 |
+
st.dataframe(summary_df.style.format({
|
594 |
+
'Salary': '{:.2f}',
|
595 |
+
'Proj': '{:.2f}',
|
596 |
+
'Own': '{:.2f}'
|
597 |
+
}).background_gradient(cmap='RdYlGn', axis=0, subset=['Salary', 'Proj', 'Own']), use_container_width=True)
|
598 |
+
|
599 |
+
with st.container():
|
600 |
+
tab1, tab2 = st.tabs(["Display Frequency", "Seed Frame Frequency"])
|
601 |
+
with tab1:
|
602 |
+
if 'data_export_display' in st.session_state:
|
603 |
if site_var1 == 'Draftkings':
|
604 |
+
player_columns = st.session_state.data_export_display.iloc[:, :8]
|
605 |
elif site_var1 == 'Fanduel':
|
606 |
+
player_columns = st.session_state.data_export_display.iloc[:, :9]
|
607 |
+
|
608 |
+
# Flatten the DataFrame and count unique values
|
609 |
+
value_counts = player_columns.values.flatten().tolist()
|
610 |
+
value_counts = pd.Series(value_counts).value_counts()
|
611 |
+
|
612 |
+
percentages = (value_counts / lineup_num_var * 100).round(2)
|
613 |
+
|
614 |
+
# Create a DataFrame with the results
|
615 |
+
summary_df = pd.DataFrame({
|
616 |
+
'Player': value_counts.index,
|
617 |
+
'Salary': [salary_dict.get(player, player) for player in value_counts.index],
|
618 |
+
'Frequency': value_counts.values,
|
619 |
+
'Percentage': percentages.values
|
620 |
+
})
|
621 |
+
|
622 |
+
# Sort by frequency in descending order
|
623 |
+
summary_df = summary_df.sort_values('Frequency', ascending=False)
|
624 |
+
|
625 |
+
# Display the table
|
626 |
+
st.write("Player Frequency Table:")
|
627 |
+
st.dataframe(summary_df.style.format({'Percentage': '{:.2f}%'}, precision=2), height=500, use_container_width=True)
|
628 |
+
|
629 |
+
st.download_button(
|
630 |
+
label="Export player frequency",
|
631 |
+
data=convert_df_to_csv(summary_df),
|
632 |
+
file_name='NBA_player_frequency.csv',
|
633 |
+
mime='text/csv',
|
634 |
+
)
|
635 |
+
with tab2:
|
636 |
if 'working_seed' in st.session_state:
|
|
|
637 |
if site_var1 == 'Draftkings':
|
638 |
+
player_columns = st.session_state.working_seed[:, :8]
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
639 |
elif site_var1 == 'Fanduel':
|
640 |
+
player_columns = st.session_state.working_seed[:, :9]
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
641 |
|
642 |
+
# Flatten the DataFrame and count unique values
|
643 |
+
value_counts = player_columns.flatten().tolist()
|
644 |
+
value_counts = pd.Series(value_counts).value_counts()
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
645 |
|
646 |
+
percentages = (value_counts / len(st.session_state.working_seed) * 100).round(2)
|
647 |
+
# Create a DataFrame with the results
|
648 |
+
summary_df = pd.DataFrame({
|
649 |
+
'Player': value_counts.index,
|
650 |
+
'Salary': [salary_dict.get(player, player) for player in value_counts.index],
|
651 |
+
'Frequency': value_counts.values,
|
652 |
+
'Percentage': percentages.values
|
653 |
+
})
|
654 |
+
|
655 |
+
# Sort by frequency in descending order
|
656 |
+
summary_df = summary_df.sort_values('Frequency', ascending=False)
|
657 |
+
|
658 |
+
# Display the table
|
659 |
+
st.write("Seed Frame Frequency Table:")
|
660 |
+
st.dataframe(summary_df.style.format({'Percentage': '{:.2f}%'}, precision=2), height=500, use_container_width=True)
|
661 |
+
|
662 |
+
st.download_button(
|
663 |
+
label="Export seed frame frequency",
|
664 |
+
data=convert_df_to_csv(summary_df),
|
665 |
+
file_name='NBA_seed_frame_frequency.csv',
|
666 |
+
mime='text/csv',
|
667 |
+
)
|