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Running
James McCool
commited on
Commit
·
54f0cae
1
Parent(s):
ca50776
Refactor database connection in app.py to remove unused database reference and streamline data retrieval for DraftKings and FanDuel lineups, enhancing code clarity and maintainability.
Browse files
app.py
CHANGED
@@ -11,11 +11,10 @@ def init_conn():
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uri = st.secrets['mongo_uri']
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client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000)
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db = client["MLB_Database"]
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-
db2 = client["MLB_DFS"]
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return db
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-
db
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game_format = {'Win Percentage': '{:.2%}','First Inning Lead Percentage': '{:.2%}', 'Top Score': '{:.2%}',
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'Fifth Inning Lead Percentage': '{:.2%}', '8+ runs': '{:.2%}', 'DK LevX': '{:.2%}', 'FD LevX': '{:.2%}'}
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@@ -143,19 +142,19 @@ def init_DK_lineups(type_var, slate_var):
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raw_display[dict_columns] = raw_display[dict_columns].apply(lambda x: x.map(names_dict))
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elif type_var == 'Showdown':
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if slate_var == 'Main':
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-
collection =
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cursor = collection.find().limit(10000)
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raw_display = pd.DataFrame(list(cursor))
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raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
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elif slate_var == 'Secondary':
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-
collection =
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cursor = collection.find().limit(10000)
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raw_display = pd.DataFrame(list(cursor))
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raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
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elif slate_var == 'Auxiliary':
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collection =
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cursor = collection.find().limit(10000)
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raw_display = pd.DataFrame(list(cursor))
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@@ -214,19 +213,19 @@ def init_FD_lineups(type_var,slate_var):
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elif type_var == 'Showdown':
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if slate_var == 'Main':
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-
collection =
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cursor = collection.find().limit(10000)
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raw_display = pd.DataFrame(list(cursor))
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raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
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elif slate_var == 'Secondary':
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collection =
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cursor = collection.find().limit(10000)
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raw_display = pd.DataFrame(list(cursor))
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raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
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elif slate_var == 'Auxiliary':
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-
collection =
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cursor = collection.find().limit(10000)
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raw_display = pd.DataFrame(list(cursor))
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uri = st.secrets['mongo_uri']
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client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000)
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db = client["MLB_Database"]
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return db
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db = init_conn()
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game_format = {'Win Percentage': '{:.2%}','First Inning Lead Percentage': '{:.2%}', 'Top Score': '{:.2%}',
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'Fifth Inning Lead Percentage': '{:.2%}', '8+ runs': '{:.2%}', 'DK LevX': '{:.2%}', 'FD LevX': '{:.2%}'}
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raw_display[dict_columns] = raw_display[dict_columns].apply(lambda x: x.map(names_dict))
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elif type_var == 'Showdown':
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if slate_var == 'Main':
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collection = db['DK_MLB_SD1_seed_frame']
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cursor = collection.find().limit(10000)
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raw_display = pd.DataFrame(list(cursor))
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raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
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elif slate_var == 'Secondary':
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+
collection = db['DK_MLB_SD2_seed_frame']
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cursor = collection.find().limit(10000)
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raw_display = pd.DataFrame(list(cursor))
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raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
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elif slate_var == 'Auxiliary':
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collection = db['DK_MLB_SD3_seed_frame']
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cursor = collection.find().limit(10000)
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raw_display = pd.DataFrame(list(cursor))
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elif type_var == 'Showdown':
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if slate_var == 'Main':
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collection = db['FD_MLB_SD1_seed_frame']
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cursor = collection.find().limit(10000)
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raw_display = pd.DataFrame(list(cursor))
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raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
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elif slate_var == 'Secondary':
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collection = db['FD_MLB_SD2_seed_frame']
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cursor = collection.find().limit(10000)
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raw_display = pd.DataFrame(list(cursor))
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raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
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elif slate_var == 'Auxiliary':
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collection = db['FD_MLB_SD3_seed_frame']
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cursor = collection.find().limit(10000)
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raw_display = pd.DataFrame(list(cursor))
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