Hugging Face's logo

Spaces:
Multichem
/
NFL_Portfolio_Manager
Runtime error

App Files Community
Multichem commited on
Commit
70daf01
·
1 Parent(s): 969926f

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +93 -172
app.py CHANGED
@@ -19,7 +19,6 @@ with tab1:
19
  col1, col2 = st.columns([3, 3])
20
 
21
  with col1:
22
- st.info("The Projections file can have any columns in any order, but must contain columns explicitly named: 'Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', and 'Own'. Upload your projections first to avoid an error message.")
23
  proj_file = st.file_uploader("Upload Projections File", key = 'proj_uploader')
24
 
25
  if proj_file is not None:
@@ -46,7 +45,6 @@ with tab1:
46
  player_own_dict = dict(zip(proj_dataframe.Player, proj_dataframe.Own))
47
 
48
  with col2:
49
- st.info("The Portfolio file must contain only columns in order and explicitly named: 'PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', and 'UTIL'. Upload your projections first to avoid an error message.")
50
  portfolio_file = st.file_uploader("Upload Portfolio File", key = 'portfolio_uploader')
51
 
52
  if portfolio_file is not None:
@@ -56,146 +54,65 @@ with tab1:
56
  except:
57
  portfolio_dataframe = pd.read_excel(portfolio_file)
58
 
59
- try:
60
- try:
61
- portfolio_dataframe.columns=['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'UTIL']
62
- split_portfolio = portfolio_dataframe
63
- split_portfolio[['PG', 'PG_ID']] = split_portfolio.PG.str.split("(", n=1, expand = True)
64
- split_portfolio[['SG', 'SG_ID']] = split_portfolio.SG.str.split("(", n=1, expand = True)
65
- split_portfolio[['SF', 'SF_ID']] = split_portfolio.SF.str.split("(", n=1, expand = True)
66
- split_portfolio[['PF', 'PF_ID']] = split_portfolio.PF.str.split("(", n=1, expand = True)
67
- split_portfolio[['C', 'C_ID']] = split_portfolio.C.str.split("(", n=1, expand = True)
68
- split_portfolio[['G', 'G_ID']] = split_portfolio.G.str.split("(", n=1, expand = True)
69
- split_portfolio[['F', 'F_ID']] = split_portfolio.F.str.split("(", n=1, expand = True)
70
- split_portfolio[['UTIL', 'UTIL_ID']] = split_portfolio.UTIL.str.split("(", n=1, expand = True)
71
-
72
- split_portfolio['PG'] = split_portfolio['PG'].str.strip()
73
- split_portfolio['SG'] = split_portfolio['SG'].str.strip()
74
- split_portfolio['SF'] = split_portfolio['SF'].str.strip()
75
- split_portfolio['PF'] = split_portfolio['PF'].str.strip()
76
- split_portfolio['C'] = split_portfolio['C'].str.strip()
77
- split_portfolio['G'] = split_portfolio['G'].str.strip()
78
- split_portfolio['F'] = split_portfolio['F'].str.strip()
79
- split_portfolio['UTIL'] = split_portfolio['UTIL'].str.strip()
80
-
81
- split_portfolio['Salary'] = sum([split_portfolio['PG'].map(player_salary_dict),
82
- split_portfolio['SG'].map(player_salary_dict),
83
- split_portfolio['SF'].map(player_salary_dict),
84
- split_portfolio['PF'].map(player_salary_dict),
85
- split_portfolio['C'].map(player_salary_dict),
86
- split_portfolio['G'].map(player_salary_dict),
87
- split_portfolio['F'].map(player_salary_dict),
88
- split_portfolio['UTIL'].map(player_salary_dict)])
89
-
90
- split_portfolio['Projection'] = sum([split_portfolio['PG'].map(player_proj_dict),
91
- split_portfolio['SG'].map(player_proj_dict),
92
- split_portfolio['SF'].map(player_proj_dict),
93
- split_portfolio['PF'].map(player_proj_dict),
94
- split_portfolio['C'].map(player_proj_dict),
95
- split_portfolio['G'].map(player_proj_dict),
96
- split_portfolio['F'].map(player_proj_dict),
97
- split_portfolio['UTIL'].map(player_proj_dict)])
98
-
99
- split_portfolio['Ownership'] = sum([split_portfolio['PG'].map(player_own_dict),
100
- split_portfolio['SG'].map(player_own_dict),
101
- split_portfolio['SF'].map(player_own_dict),
102
- split_portfolio['PF'].map(player_own_dict),
103
- split_portfolio['C'].map(player_own_dict),
104
- split_portfolio['G'].map(player_own_dict),
105
- split_portfolio['F'].map(player_own_dict),
106
- split_portfolio['UTIL'].map(player_own_dict)])
107
-
108
- st.table(split_portfolio.head(10))
109
-
110
-
111
- except:
112
- portfolio_dataframe.columns=['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'UTIL']
113
-
114
- split_portfolio = portfolio_dataframe
115
- split_portfolio[['PG_ID', 'PG']] = split_portfolio.PG.str.split(":", n=1, expand = True)
116
- split_portfolio[['SG_ID', 'SG']] = split_portfolio.SG.str.split(":", n=1, expand = True)
117
- split_portfolio[['SF_ID', 'SF']] = split_portfolio.SF.str.split(":", n=1, expand = True)
118
- split_portfolio[['PF_ID', 'PF']] = split_portfolio.PF.str.split(":", n=1, expand = True)
119
- split_portfolio[['C_ID', 'C']] = split_portfolio.C.str.split(":", n=1, expand = True)
120
- split_portfolio[['G_ID', 'G']] = split_portfolio.G.str.split(":", n=1, expand = True)
121
- split_portfolio[['F_ID', 'F']] = split_portfolio.F.str.split(":", n=1, expand = True)
122
- split_portfolio[['UTIL_ID', 'UTIL']] = split_portfolio.UTIL.str.split(":", n=1, expand = True)
123
-
124
- split_portfolio['PG'] = split_portfolio['PG'].str.strip()
125
- split_portfolio['SG'] = split_portfolio['SG'].str.strip()
126
- split_portfolio['SF'] = split_portfolio['SF'].str.strip()
127
- split_portfolio['PF'] = split_portfolio['PF'].str.strip()
128
- split_portfolio['C'] = split_portfolio['C'].str.strip()
129
- split_portfolio['G'] = split_portfolio['G'].str.strip()
130
- split_portfolio['F'] = split_portfolio['F'].str.strip()
131
- split_portfolio['UTIL'] = split_portfolio['UTIL'].str.strip()
132
-
133
- split_portfolio['Salary'] = sum([split_portfolio['PG'].map(player_salary_dict),
134
- split_portfolio['SG'].map(player_salary_dict),
135
- split_portfolio['SF'].map(player_salary_dict),
136
- split_portfolio['PF'].map(player_salary_dict),
137
- split_portfolio['C'].map(player_salary_dict),
138
- split_portfolio['G'].map(player_salary_dict),
139
- split_portfolio['F'].map(player_salary_dict),
140
- split_portfolio['UTIL'].map(player_salary_dict)])
141
-
142
- split_portfolio['Projection'] = sum([split_portfolio['PG'].map(player_proj_dict),
143
- split_portfolio['SG'].map(player_proj_dict),
144
- split_portfolio['SF'].map(player_proj_dict),
145
- split_portfolio['PF'].map(player_proj_dict),
146
- split_portfolio['C'].map(player_proj_dict),
147
- split_portfolio['G'].map(player_proj_dict),
148
- split_portfolio['F'].map(player_proj_dict),
149
- split_portfolio['UTIL'].map(player_proj_dict)])
150
-
151
-
152
- split_portfolio['Ownership'] = sum([split_portfolio['PG'].map(player_own_dict),
153
- split_portfolio['SG'].map(player_own_dict),
154
- split_portfolio['SF'].map(player_own_dict),
155
- split_portfolio['PF'].map(player_own_dict),
156
- split_portfolio['C'].map(player_own_dict),
157
- split_portfolio['G'].map(player_own_dict),
158
- split_portfolio['F'].map(player_own_dict),
159
- split_portfolio['UTIL'].map(player_own_dict)])
160
-
161
- st.table(split_portfolio.head(10))
162
-
163
- except:
164
- split_portfolio = portfolio_dataframe
165
-
166
- split_portfolio['Salary'] = sum([split_portfolio['PG'].map(player_salary_dict),
167
- split_portfolio['SG'].map(player_salary_dict),
168
- split_portfolio['SF'].map(player_salary_dict),
169
- split_portfolio['PF'].map(player_salary_dict),
170
- split_portfolio['C'].map(player_salary_dict),
171
- split_portfolio['G'].map(player_salary_dict),
172
- split_portfolio['F'].map(player_salary_dict),
173
- split_portfolio['UTIL'].map(player_salary_dict)])
174
-
175
- split_portfolio['Projection'] = sum([split_portfolio['PG'].map(player_proj_dict),
176
- split_portfolio['SG'].map(player_proj_dict),
177
- split_portfolio['SF'].map(player_proj_dict),
178
- split_portfolio['PF'].map(player_proj_dict),
179
- split_portfolio['C'].map(player_proj_dict),
180
- split_portfolio['G'].map(player_proj_dict),
181
- split_portfolio['F'].map(player_proj_dict),
182
- split_portfolio['UTIL'].map(player_proj_dict)])
183
-
184
-
185
- split_portfolio['Ownership'] = sum([split_portfolio['PG'].map(player_own_dict),
186
- split_portfolio['SG'].map(player_own_dict),
187
- split_portfolio['SF'].map(player_own_dict),
188
- split_portfolio['PF'].map(player_own_dict),
189
- split_portfolio['C'].map(player_own_dict),
190
- split_portfolio['G'].map(player_own_dict),
191
- split_portfolio['F'].map(player_own_dict),
192
- split_portfolio['UTIL'].map(player_own_dict)])
193
 
194
- display_portfolio = split_portfolio[['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'UTIL', 'Salary', 'Projection', 'Ownership']]
195
  st.session_state.display_portfolio = display_portfolio
196
  hold_portfolio = display_portfolio.sort_values(by='Projection', ascending=False)
197
 
198
- st.session_state.player_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.display_portfolio.iloc[:,0:8].values, return_counts=True)),
199
  columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
200
  st.session_state.player_freq['Freq'] = st.session_state.player_freq['Freq'] / len(st.session_state.display_portfolio)
201
  st.session_state.player_freq = st.session_state.player_freq.set_index('Player')
@@ -237,22 +154,27 @@ with tab2:
237
  with hold_container:
238
 
239
  working_roo = proj_dataframe
240
- working_roo.rename(columns={"Minutes Proj": "Minutes_Proj"}, inplace = True)
241
  own_dict = dict(zip(working_roo.Player, working_roo.Own))
242
- min_dict = dict(zip(working_roo.Player, working_roo.Minutes_Proj))
243
  team_dict = dict(zip(working_roo.Player, working_roo.Team))
 
244
  total_sims = 1000
245
 
246
  player_var = working_roo.loc[working_roo['Player'] == player_check]
247
  player_var = player_var.reset_index()
248
 
249
- working_roo = working_roo.loc[(working_roo['Salary'] >= player_var['Salary'][0] - 300) & (working_roo['Salary'] <= player_var['Salary'][0] + 300)]
250
- working_roo = working_roo.loc[(working_roo['Median'] >= player_var['Median'][0] - 3) & (working_roo['Median'] <= player_var['Median'][0] + 3)]
251
-
252
- flex_file = working_roo[['Player', 'Position', 'Salary', 'Median', 'Minutes_Proj']]
253
- flex_file['Floor'] = (flex_file['Median'] * .25) + (flex_file['Minutes_Proj'] * .25)
254
- flex_file['Ceiling'] = flex_file['Median'] + 10 + (flex_file['Minutes_Proj'] * .25)
255
- flex_file['STD'] = (flex_file['Median']/4)
 
 
 
 
 
 
256
  flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']]
257
  hold_file = flex_file
258
  overall_file = flex_file
@@ -284,42 +206,41 @@ with tab2:
284
 
285
  players_only=players_only.drop(['Player'], axis=1)
286
  players_only.astype('int').dtypes
287
-
288
- salary_2x_check = (overall_file - (salary_file*4))
289
- salary_3x_check = (overall_file - (salary_file*5))
290
- salary_4x_check = (overall_file - (salary_file*6))
291
- gpp_check = (overall_file - ((salary_file*5)+10))
292
-
293
  players_only['Average_Rank'] = players_only.mean(axis=1)
294
  players_only['Top_finish'] = players_only[players_only == 1].count(axis=1)/total_sims
295
  players_only['Top_5_finish'] = players_only[players_only <= 5].count(axis=1)/total_sims
296
  players_only['Top_10_finish'] = players_only[players_only <= 10].count(axis=1)/total_sims
297
  players_only['20+%'] = overall_file[overall_file >= 20].count(axis=1)/float(total_sims)
298
- players_only['3x%'] = salary_2x_check[salary_2x_check >= 1].count(axis=1)/float(total_sims)
299
- players_only['4x%'] = salary_3x_check[salary_3x_check >= 1].count(axis=1)/float(total_sims)
300
- players_only['5x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims)
301
- players_only['GPP%'] = salary_4x_check[gpp_check >= 1].count(axis=1)/float(total_sims)
302
-
303
  players_only['Player'] = hold_file[['Player']]
304
-
305
- final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '3x%', '4x%', '5x%', 'GPP%']]
306
 
307
  final_Proj = pd.merge(hold_file, final_outcomes, on="Player")
308
- final_Proj = final_Proj[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '3x%', '4x%', '5x%', 'GPP%']]
309
-
310
  final_Proj['Own'] = final_Proj['Player'].map(own_dict)
311
- final_Proj['Minutes Proj'] = final_Proj['Player'].map(min_dict)
312
  final_Proj['Team'] = final_Proj['Player'].map(team_dict)
313
- final_Proj['Own'] = final_Proj['Own'].astype('float')
314
- final_Proj['Projection Rank'] = final_Proj.Top_finish.rank(pct = True)
 
315
  final_Proj['Own Rank'] = final_Proj.Own.rank(pct = True)
316
- final_Proj['LevX'] = (final_Proj['Projection Rank'] - final_Proj['Own Rank']) * 100
317
- final_Proj['ValX'] = ((final_Proj[['4x%', '5x%']].mean(axis=1))*100) + final_Proj['LevX']
318
- final_Proj['ValX'] = np.where(final_Proj['ValX'] > 100, 100, final_Proj['ValX'])
319
- final_Proj['ValX'] = np.where(final_Proj['ValX'] < 0, 0, final_Proj['ValX'])
320
-
321
- final_Proj = final_Proj[['Player', 'Minutes Proj', 'Position', 'Team', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '3x%', '4x%', '5x%', 'GPP%', 'Own', 'LevX', 'ValX']]
322
- final_Proj = final_Proj.sort_values(by='Median', ascending=False)
 
 
323
  final_Proj['Player_swap'] = player_check
324
  st.session_state.final_Proj = final_Proj
325
 
 
19
  col1, col2 = st.columns([3, 3])
20
 
21
  with col1:
 
22
  proj_file = st.file_uploader("Upload Projections File", key = 'proj_uploader')
23
 
24
  if proj_file is not None:
 
45
  player_own_dict = dict(zip(proj_dataframe.Player, proj_dataframe.Own))
46
 
47
  with col2:
 
48
  portfolio_file = st.file_uploader("Upload Portfolio File", key = 'portfolio_uploader')
49
 
50
  if portfolio_file is not None:
 
54
  except:
55
  portfolio_dataframe = pd.read_excel(portfolio_file)
56
 
57
+ portfolio_dataframe.columns=["QB", "RB1", "RB2", "WR1", "WR2", "WR3", "TE", "FLEX", "DST"]
58
+ split_portfolio = portfolio_dataframe
59
+ split_portfolio[['QB', 'QB_ID']] = split_portfolio.QB.str.split("(", n=1, expand = True)
60
+ split_portfolio[['RB1', 'RB1_ID']] = split_portfolio.RB1.str.split("(", n=1, expand = True)
61
+ split_portfolio[['RB2', 'RB2_ID']] = split_portfolio.RB2.str.split("(", n=1, expand = True)
62
+ split_portfolio[['WR1', 'WR1_ID']] = split_portfolio.WR1.str.split("(", n=1, expand = True)
63
+ split_portfolio[['WR2', 'WR2_ID']] = split_portfolio.WR2.str.split("(", n=1, expand = True)
64
+ split_portfolio[['WR3', 'WR3_ID']] = split_portfolio.WR3.str.split("(", n=1, expand = True)
65
+ split_portfolio[['TE', 'TE_ID']] = split_portfolio.TE.str.split("(", n=1, expand = True)
66
+ split_portfolio[['FLEX', 'FLEX_ID']] = split_portfolio.FLEX.str.split("(", n=1, expand = True)
67
+ split_portfolio[['DST', 'DST_ID']] = split_portfolio.DST.str.split("(", n=1, expand = True)
68
+
69
+ split_portfolio['QB'] = split_portfolio['QB'].str.strip()
70
+ split_portfolio['RB1'] = split_portfolio['RB1'].str.strip()
71
+ split_portfolio['RB2'] = split_portfolio['RB2'].str.strip()
72
+ split_portfolio['WR1'] = split_portfolio['WR1'].str.strip()
73
+ split_portfolio['WR2'] = split_portfolio['WR2'].str.strip()
74
+ split_portfolio['WR3'] = split_portfolio['WR3'].str.strip()
75
+ split_portfolio['TE'] = split_portfolio['TE'].str.strip()
76
+ split_portfolio['FLEX'] = split_portfolio['FLEX'].str.strip()
77
+ split_portfolio['DST'] = split_portfolio['DST'].str.strip()
78
+
79
+ st.table(split_portfolio.head(10))
80
+
81
+ split_portfolio['Salary'] = sum([split_portfolio['QB'].map(player_salary_dict),
82
+ split_portfolio['RB1'].map(player_salary_dict),
83
+ split_portfolio['RB2'].map(player_salary_dict),
84
+ split_portfolio['WR1'].map(player_salary_dict),
85
+ split_portfolio['WR2'].map(player_salary_dict),
86
+ split_portfolio['WR3'].map(player_salary_dict),
87
+ split_portfolio['TE'].map(player_salary_dict),
88
+ split_portfolio['FLEX'].map(player_salary_dict),
89
+ split_portfolio['DST'].map(player_salary_dict)])
90
+
91
+ split_portfolio['Projection'] = sum([split_portfolio['QB'].map(player_proj_dict),
92
+ split_portfolio['RB1'].map(player_proj_dict),
93
+ split_portfolio['RB2'].map(player_proj_dict),
94
+ split_portfolio['WR1'].map(player_proj_dict),
95
+ split_portfolio['WR2'].map(player_proj_dict),
96
+ split_portfolio['WR3'].map(player_proj_dict),
97
+ split_portfolio['TE'].map(player_proj_dict),
98
+ split_portfolio['FLEX'].map(player_proj_dict),
99
+ split_portfolio['DST'].map(player_proj_dict)])
100
+
101
+ split_portfolio['Ownership'] = sum([split_portfolio['QB'].map(player_own_dict),
102
+ split_portfolio['RB1'].map(player_own_dict),
103
+ split_portfolio['RB2'].map(player_own_dict),
104
+ split_portfolio['WR1'].map(player_own_dict),
105
+ split_portfolio['WR2'].map(player_own_dict),
106
+ split_portfolio['WR3'].map(player_own_dict),
107
+ split_portfolio['TE'].map(player_own_dict),
108
+ split_portfolio['FLEX'].map(player_own_dict),
109
+ split_portfolio['DST'].map(player_own_dict)])
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
110
 
111
+ display_portfolio = split_portfolio[["QB", "RB1", "RB2", "WR1", "WR2", "WR3", "TE", "FLEX", "DST", 'Salary', 'Projection', 'Ownership']]
112
  st.session_state.display_portfolio = display_portfolio
113
  hold_portfolio = display_portfolio.sort_values(by='Projection', ascending=False)
114
 
115
+ st.session_state.player_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.display_portfolio.iloc[:,0:9].values, return_counts=True)),
116
  columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
117
  st.session_state.player_freq['Freq'] = st.session_state.player_freq['Freq'] / len(st.session_state.display_portfolio)
118
  st.session_state.player_freq = st.session_state.player_freq.set_index('Player')
 
154
  with hold_container:
155
 
156
  working_roo = proj_dataframe
 
157
  own_dict = dict(zip(working_roo.Player, working_roo.Own))
 
158
  team_dict = dict(zip(working_roo.Player, working_roo.Team))
159
+ opp_dict = dict(zip(working_roo.Player, working_roo.Opp))
160
  total_sims = 1000
161
 
162
  player_var = working_roo.loc[working_roo['Player'] == player_check]
163
  player_var = player_var.reset_index()
164
 
165
+ working_roo = working_roo[working_roo['Position'].isin(pos_var_list)]
166
+ working_roo = working_roo[working_roo['Team'].isin(team_var1)]
167
+ working_roo = working_roo.loc[(working_roo['Salary'] >= player_var['Salary'][0] - Salary_var) & (working_roo['Salary'] <= player_var['Salary'][0] + Salary_var)]
168
+ working_roo = working_roo.loc[(working_roo['Median'] >= player_var['Median'][0] - Median_var) & (working_roo['Median'] <= player_var['Median'][0] + Median_var)]
169
+
170
+ flex_file = working_roo[['Player', 'Position', 'Salary', 'Median']]
171
+ flex_file['Floor_raw'] = flex_file['Median'] * .20
172
+ flex_file['Ceiling_raw'] = flex_file['Median'] * 1.9
173
+ flex_file['Floor'] = np.where(flex_file['Position'] == 'QB', (flex_file['Median'] * .33), flex_file['Floor_raw'])
174
+ flex_file['Floor'] = np.where(flex_file['Position'] == 'RB', (flex_file['Median'] * .15), flex_file['Floor_raw'])
175
+ flex_file['Ceiling'] = np.where(flex_file['Position'] == 'QB', (flex_file['Median'] * 1.75), flex_file['Ceiling_raw'])
176
+ flex_file['Ceiling'] = np.where(flex_file['Position'] == 'RB', (flex_file['Median'] * 1.85), flex_file['Ceiling_raw'])
177
+ flex_file['STD'] = flex_file['Median'] / 4
178
  flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']]
179
  hold_file = flex_file
180
  overall_file = flex_file
 
206
 
207
  players_only=players_only.drop(['Player'], axis=1)
208
  players_only.astype('int').dtypes
209
+
210
+ salary_2x_check = (overall_file - (salary_file*2))
211
+ salary_3x_check = (overall_file - (salary_file*3))
212
+ salary_4x_check = (overall_file - (salary_file*4))
213
+
 
214
  players_only['Average_Rank'] = players_only.mean(axis=1)
215
  players_only['Top_finish'] = players_only[players_only == 1].count(axis=1)/total_sims
216
  players_only['Top_5_finish'] = players_only[players_only <= 5].count(axis=1)/total_sims
217
  players_only['Top_10_finish'] = players_only[players_only <= 10].count(axis=1)/total_sims
218
  players_only['20+%'] = overall_file[overall_file >= 20].count(axis=1)/float(total_sims)
219
+ players_only['2x%'] = salary_2x_check[salary_2x_check >= 1].count(axis=1)/float(total_sims)
220
+ players_only['3x%'] = salary_3x_check[salary_3x_check >= 1].count(axis=1)/float(total_sims)
221
+ players_only['4x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims)
222
+
 
223
  players_only['Player'] = hold_file[['Player']]
224
+
225
+ final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']]
226
 
227
  final_Proj = pd.merge(hold_file, final_outcomes, on="Player")
228
+ final_Proj = final_Proj[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']]
 
229
  final_Proj['Own'] = final_Proj['Player'].map(own_dict)
 
230
  final_Proj['Team'] = final_Proj['Player'].map(team_dict)
231
+ final_Proj['Opp'] = final_Proj['Player'].map(opp_dict)
232
+ final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own']]
233
+ final_Proj['Projection Rank'] = final_Proj.Median.rank(pct = True)
234
  final_Proj['Own Rank'] = final_Proj.Own.rank(pct = True)
235
+ final_Proj['LevX'] = 0
236
+ final_Proj['LevX'] = np.where(final_Proj['Position'] == 'QB', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
237
+ final_Proj['LevX'] = np.where(final_Proj['Position'] == 'TE', final_Proj[['Projection Rank', '2x%']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
238
+ final_Proj['LevX'] = np.where(final_Proj['Position'] == 'RB', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['20+%'] - final_Proj['Own Rank'], final_Proj['LevX'])
239
+ final_Proj['LevX'] = np.where(final_Proj['Position'] == 'WR', final_Proj[['Projection Rank', 'Top_10_finish']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
240
+ final_Proj['CPT_Own'] = final_Proj['Own'] / 4
241
+
242
+ final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'LevX']]
243
+ final_Proj = final_Proj.sort_values(by='Top_finish', ascending=False)
244
  final_Proj['Player_swap'] = player_check
245
  st.session_state.final_Proj = final_Proj
246