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6424951 cea381d 6424951 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 | """Pytest fixtures for NBA Streamlit application tests."""
from typing import Any
import pandas as pd
import pytest
@pytest.fixture
def sample_player_data() -> list[tuple[Any, ...]]:
"""Create sample player data matching database schema.
Returns:
List of tuples with sample player data
"""
return [
(
"LeBron James", # FULL_NAME
10141, # AST
1107, # BLK
5972, # DREB
2891, # FG3A
1043, # FG3M
0.361, # FG3_PCT
24856, # FGA
12621, # FGM
0.508, # FG_PCT
11067, # FTA
7938, # FTM
0.717, # FT_PCT
1421, # GP
1421, # GS
54218, # MIN
1663, # OREB
2159, # PF
39223, # PTS
10988, # REB
2219, # STL
5015, # TOV
"LeBron", # FIRST_NAME
"James", # LAST_NAME
"lebron james", # FULL_NAME_LOWER
"lebron", # FIRST_NAME_LOWER
"james", # LAST_NAME_LOWER
True, # IS_ACTIVE
),
(
"Michael Jordan",
5633,
893,
4578,
1778,
581,
0.327,
24537,
12192,
0.497,
8772,
7327,
0.835,
1072,
1039,
41011,
1463,
2783,
32292,
6672,
2514,
2924,
"Michael",
"Jordan",
"michael jordan",
"michael",
"jordan",
False,
),
]
@pytest.fixture
def sample_player_df(sample_player_data: list[tuple]) -> pd.DataFrame:
"""Create sample player DataFrame.
Args:
sample_player_data: List of player tuples
Returns:
DataFrame with sample player data
"""
from src.config import PLAYER_COLUMNS
return pd.DataFrame(sample_player_data, columns=PLAYER_COLUMNS)
@pytest.fixture
def sample_team_stats() -> list[list[float]]:
"""Create sample team stats for ML model input.
Returns:
List of player stat lists (5 players x 10 stats)
"""
return [
[1500.0, 100.0, 200.0, 300.0, 50.0, 30.0, 100.0, 0.35, 0.80, 500.0],
[1200.0, 80.0, 180.0, 250.0, 40.0, 25.0, 90.0, 0.38, 0.75, 450.0],
[1000.0, 60.0, 150.0, 200.0, 35.0, 20.0, 80.0, 0.40, 0.82, 400.0],
[800.0, 50.0, 120.0, 150.0, 30.0, 15.0, 70.0, 0.33, 0.78, 350.0],
[600.0, 40.0, 100.0, 100.0, 25.0, 10.0, 60.0, 0.36, 0.85, 300.0],
]
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