def test_draft_player_to_empty_roster_fail_to_find():
avail = df.copy()
empty_roster = pd.DataFrame()
mean_player = sfb.mean_player(avail)
single_player = sfb.find_player("Damian Lillard", avail)
player_name = "Steven Curry"
assert sfb.draft_player(player_name, empty_roster, avail).equals(
empty_roster
) # empty unchanged
assert sfb.draft_player(player_name, mean_player, avail).equals(
mean_player
) # mean unchanged
assert sfb.draft_player(player_name, single_player, avail).equals(
single_player
) # single unchanged
assert avail.equals(df) # avail unchanged
def test_means_player_produces_means():
avail = df.copy() # generate duplicate data
mean_player = sfb.mean_player(avail)
assert len(mean_player.values) == 1 # produces a single player
assert mean_player["Player"].values[0] == "Mr Mean" # name is correct
assert mean_player["G"].values[0] == avail["G"].mean() # g is correct
assert mean_player["FGM"].values[0] == avail["FGM"].mean() # fgm is correct
assert mean_player["FGA"].values[0] == avail["FGA"].mean() # fga is correct
assert mean_player["3PT"].values[0] == avail["3PT"].mean() # 3pt is correct
assert mean_player["FTM"].values[0] == avail["FTM"].mean() # ftm is correct
assert mean_player["FTA"].values[0] == avail["FTA"].mean() # fta is correct
assert mean_player["REB"].values[0] == avail["REB"].mean() # reb is correct
assert mean_player["AST"].values[0] == avail["AST"].mean() # ast is correct
assert mean_player["STL"].values[0] == avail["STL"].mean() # stl is correct
assert mean_player["BLK"].values[0] == avail["BLK"].mean() # blk is correct
assert mean_player["TOV"].values[0] == avail["TOV"].mean() # tov is correct
assert mean_player["PTS"].values[0] == avail["PTS"].mean() # pts is correct
def test_draft_player_to_mean_player_roster():
avail = df.copy()
mean_player = sfb.mean_player(avail)
player_name = "Stephen Curry"
new_roster = sfb.draft_player(player_name, mean_player, avail)
assert (
len(avail.values) == len(df.values) - 1
) # size of available players decreases by 1
assert (
sfb.find_player(player_name, avail).empty == True
) # drafted player no longer available
assert len(new_roster.values) == 1 # new roster has exactly one player in it
assert (
new_roster["Player"].values[0] == player_name
) # that player is the one that was drafted
assert (
sfb.find_player("Mr Mean", new_roster).empty == True
) # mean player no longer on roster
import stat_funcs_basics as sfb
import stat_funcs_net as sfn
import stat_funcs_z as sfz
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import colors
from matplotlib.ticker import PercentFormatter
# Generate z score data
df = pd.read_csv("data/espn_data_set.csv")
roster = sfb.mean_player(df)
net_change_table = sfn.generate_net_change_table(roster, df)
z_score_table = sfz.generate_z_score_table(net_change_table)
# Select data parameter & print table
stat_str = "Z_AVG"
z_score_table.sort_values(by=[stat_str], ascending=False, inplace=True)
# z_score_table.to_csv("data/z_score_table.csv", index=False)
print(net_change_table)
print(z_score_table)
for stat_str in z_score_table:
# Create matplotlib histogram
if stat_str == "Player":
continue
z_avg_array = z_score_table[stat_str].to_numpy()
num_bins = 25
n, bins, patches = plt.hist(z_avg_array, num_bins)
ax = plt.gca()
def __init__(self, owner_name, team_name, df):
self.owner_name = owner_name
self.team_name = team_name
self.roster = sfb.mean_player(df)