James McCool commited on
Commit
f4dd7c9
·
1 Parent(s): e3896eb

Transferred DFS data to mongo, established connections

Browse files
Files changed (1) hide show
  1. app.py +40 -22
app.py CHANGED
@@ -55,50 +55,68 @@ fd_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C1', 'sal
55
 
56
  @st.cache_data(ttl=300)
57
  def load_overall_stats():
58
- try:
59
- sh = gcservice_account.open_by_url(NBA_Data)
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- except:
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- sh = gcservice_account2.open_by_url(NBA_Data)
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-
63
- worksheet = sh.worksheet('DK_Build_Up')
64
- raw_display = pd.DataFrame(worksheet.get_all_records())
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- raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"}, inplace = True)
66
- raw_display = raw_display.loc[raw_display['Salary'] > 0]
67
  raw_display = raw_display.loc[raw_display['Median'] > 0]
68
  raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
69
  dk_raw = raw_display.sort_values(by='Median', ascending=False)
70
 
71
- worksheet = sh.worksheet('FD_Build_Up')
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- raw_display = pd.DataFrame(worksheet.get_all_records())
73
- raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"}, inplace = True)
 
 
 
 
74
  raw_display = raw_display.loc[raw_display['Median'] > 0]
75
  raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
76
  fd_raw = raw_display.sort_values(by='Median', ascending=False)
77
 
78
- worksheet = sh.worksheet('Secondary_DK_Build')
79
- raw_display = pd.DataFrame(worksheet.get_all_records())
80
- raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"}, inplace = True)
 
 
 
 
81
  raw_display = raw_display.loc[raw_display['Median'] > 0]
82
  raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
83
  dk_raw_sec = raw_display.sort_values(by='Median', ascending=False)
84
 
85
- worksheet = sh.worksheet('Secondary_FD_Build')
86
- raw_display = pd.DataFrame(worksheet.get_all_records())
87
- raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"}, inplace = True)
 
 
 
 
88
  raw_display = raw_display.loc[raw_display['Median'] > 0]
89
  raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
90
  fd_raw_sec = raw_display.sort_values(by='Median', ascending=False)
91
 
92
- worksheet = sh.worksheet('Player_Level_ROO')
93
- raw_display = pd.DataFrame(worksheet.get_all_records())
 
 
 
 
94
  raw_display = raw_display.loc[raw_display['Median'] > 0]
95
  raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
96
  roo_raw = raw_display.sort_values(by='Median', ascending=False)
97
 
98
  timestamp = raw_display['timestamp'].values[0]
99
 
100
- worksheet = sh.worksheet('ROO_Backlog')
101
- raw_display = pd.DataFrame(worksheet.get_all_records())
 
 
 
 
102
  roo_backlog = raw_display.sort_values(by='Date', ascending=False)
103
  roo_backlog = roo_backlog[roo_backlog['slate'] == 'Main Slate']
104
 
 
55
 
56
  @st.cache_data(ttl=300)
57
  def load_overall_stats():
58
+ collection = db["DK_Player_Stats"]
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+ cursor = collection.find()
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+
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+ raw_display = pd.DataFrame(list(cursor))
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+ raw_display = raw_display[['Name', 'Salary', 'Position', 'Team', 'Opp', 'Minutes', 'FGM', 'FGA', 'FG2M', 'FG2A', 'Threes', 'FG3A', 'FTM', 'FTA', 'TRB', 'AST', 'STL', 'BLK', 'TOV', '2P', '3P', 'FT',
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+ 'Points', 'Rebounds', 'Assists', 'PRA', 'PR', 'PA', 'RA', 'Steals', 'Blocks', 'Turnovers', 'Fantasy', 'Raw', 'Own']]
64
+ raw_display = raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"})
 
 
65
  raw_display = raw_display.loc[raw_display['Median'] > 0]
66
  raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
67
  dk_raw = raw_display.sort_values(by='Median', ascending=False)
68
 
69
+ collection = db["FD_Player_Stats"]
70
+ cursor = collection.find()
71
+
72
+ raw_display = pd.DataFrame(list(cursor))
73
+ raw_display = raw_display[['Name', 'Salary', 'Position', 'Team', 'Opp', 'Minutes', 'FGM', 'FGA', 'FG2M', 'FG2A', 'Threes', 'FG3A', 'FTM', 'FTA', 'TRB', 'AST', 'STL', 'BLK', 'TOV', '2P', '3P', 'FT',
74
+ 'Points', 'Rebounds', 'Assists', 'PRA', 'PR', 'PA', 'RA', 'Steals', 'Blocks', 'Turnovers', 'Fantasy', 'Raw', 'Own']]
75
+ raw_display = raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"})
76
  raw_display = raw_display.loc[raw_display['Median'] > 0]
77
  raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
78
  fd_raw = raw_display.sort_values(by='Median', ascending=False)
79
 
80
+ collection = db["Secondary_DK_Player_Stats"]
81
+ cursor = collection.find()
82
+
83
+ raw_display = pd.DataFrame(list(cursor))
84
+ raw_display = raw_display[['Name', 'Salary', 'Position', 'Team', 'Opp', 'Minutes', 'FGM', 'FGA', 'FG2M', 'FG2A', 'Threes', 'FG3A', 'FTM', 'FTA', 'TRB', 'AST', 'STL', 'BLK', 'TOV', '2P', '3P', 'FT',
85
+ 'Points', 'Rebounds', 'Assists', 'PRA', 'PR', 'PA', 'RA', 'Steals', 'Blocks', 'Turnovers', 'Fantasy', 'Raw', 'Own']]
86
+ raw_display = raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"})
87
  raw_display = raw_display.loc[raw_display['Median'] > 0]
88
  raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
89
  dk_raw_sec = raw_display.sort_values(by='Median', ascending=False)
90
 
91
+ collection = db["Secondary_FD_Player_Stats"]
92
+ cursor = collection.find()
93
+
94
+ raw_display = pd.DataFrame(list(cursor))
95
+ raw_display = raw_display[['Name', 'Salary', 'Position', 'Team', 'Opp', 'Minutes', 'FGM', 'FGA', 'FG2M', 'FG2A', 'Threes', 'FG3A', 'FTM', 'FTA', 'TRB', 'AST', 'STL', 'BLK', 'TOV', '2P', '3P', 'FT',
96
+ 'Points', 'Rebounds', 'Assists', 'PRA', 'PR', 'PA', 'RA', 'Steals', 'Blocks', 'Turnovers', 'Fantasy', 'Raw', 'Own']]
97
+ raw_display = raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"})
98
  raw_display = raw_display.loc[raw_display['Median'] > 0]
99
  raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
100
  fd_raw_sec = raw_display.sort_values(by='Median', ascending=False)
101
 
102
+ collection = db["Player_Range_Of_Outcomes"]
103
+ cursor = collection.find()
104
+
105
+ raw_display = pd.DataFrame(list(cursor))
106
+ raw_display = raw_display[['Player', 'Minutes', 'Proj', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Cei', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%',
107
+ 'Own', 'Small_Own', 'Large_Own', 'Cash_Own', 'CPT_Own', 'LevX', 'ValX', 'site', 'version', 'slate', 'timestamp']]
108
  raw_display = raw_display.loc[raw_display['Median'] > 0]
109
  raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
110
  roo_raw = raw_display.sort_values(by='Median', ascending=False)
111
 
112
  timestamp = raw_display['timestamp'].values[0]
113
 
114
+ collection = db["Range_Of_Outcomes_Backlog"]
115
+ cursor = collection.find()
116
+
117
+ raw_display = pd.DataFrame(list(cursor))
118
+ raw_display = raw_display[['Player', 'Minutes', 'Proj', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Cei', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%',
119
+ 'Own', 'Small_Own', 'Large_Own', 'Cash_Own', 'CPT_Own', 'LevX', 'ValX', 'site', 'version', 'slate', 'timestamp', 'Date']]
120
  roo_backlog = raw_display.sort_values(by='Date', ascending=False)
121
  roo_backlog = roo_backlog[roo_backlog['slate'] == 'Main Slate']
122