def get(request):
score_df = get_player_data()
picks_df = read_frame(
GolfPicks.objects.all(), [f'player{i}' for i in range(1, 7)],
'name').rename(columns=lambda c: c.replace('player', 'Tier '))
out_dict = {}
for name in picks_df.index:
df = picks_df.loc[[name]].T.merge(score_df,
how='left',
left_on=name,
right_index=True)
df.rename(columns={name: 'Picks'}, inplace=True)
cols = ['TO PAR', 'TODAY', 'R1', 'R2', 'R3', 'R4', 'TOTAL']
df.loc['Total', [col for col in cols if col in df.columns]] = df[[
col for col in cols if col in df.columns
]].apply(
lambda x: sum([int(float(s)) for s in x if is_numeric(s)]))
df[['TO PAR', 'TODAY']] = df[['TO PAR',
'TODAY']].applymap(clean_up_scores)
out_dict[name] = df.to_json(orient='index')
return Response(out_dict)
def clean_up_scores(s_or_t):
if re.match(TIME_REGEX, str(s_or_t)) or not is_numeric(s_or_t):
return s_or_t
elif int(float(s_or_t)) > 0:
return f'+{s_or_t}'
elif int(float(s_or_t)) == 0:
return 'E'
else:
return s_or_t
def sum_scores(s):
sum_vals = 0
for x in s:
x = str(x)
if x.startswith('CUT:'):
val = int(x.replace('CUT:', ''))
elif is_numeric(x):
val = int(float(x))
else:
val = 0
sum_vals += val
return sum_vals
def to_par_or_tee_time(x):
thru = score_df.loc[x,
'THRU'] if 'THRU' in score_df.columns else 'F'
cut_ = score_df.loc[x, 'POSITION'] in ('WD', 'DQ', 'CUT')
if re.match(TIME_REGEX,
thru) and not is_numeric(score_df.loc[x, 'R1']):
return thru
else:
if not cut_:
return score_df.loc[x, 'TO PAR']
else:
return f"CUT:{score_df.loc[x, 'TO PAR']}"
def get(request):
soup = get_soup()
score_df = get_player_data(soup)
picks_df = read_frame(
GolfPicks.objects.all(), [f'player{i}' for i in range(1, 7)],
'name').rename(columns=lambda c: c.replace('player', 'Tier '))
if 'TO PAR' not in score_df.columns:
return Response(picks_df.reset_index().to_json(orient='index'))
def to_par_or_tee_time(x):
thru = score_df.loc[x,
'THRU'] if 'THRU' in score_df.columns else 'F'
cut_ = score_df.loc[x, 'POSITION'] in ('WD', 'DQ', 'CUT')
if re.match(TIME_REGEX,
thru) and not is_numeric(score_df.loc[x, 'R1']):
return thru
else:
if not cut_:
return score_df.loc[x, 'TO PAR']
else:
return f"CUT:{score_df.loc[x, 'TO PAR']}"
picks_df = picks_df.applymap(to_par_or_tee_time)
def sum_scores(s):
sum_vals = 0
for x in s:
x = str(x)
if x.startswith('CUT:'):
val = int(x.replace('CUT:', ''))
elif is_numeric(x):
val = int(float(x))
else:
val = 0
sum_vals += val
return sum_vals
picks_df['TOTAL'] = picks_df.apply(sum_scores, axis=1)
picks_df.sort_values('TOTAL', inplace=True)
picks_df['RANK'] = picks_df['TOTAL'].rank(method='min').astype(int)
picks_df.loc[picks_df['RANK'].duplicated(keep=False), 'RANK'] = \
picks_df.loc[picks_df['RANK'].duplicated(keep=False), 'RANK'].apply(lambda x: f'T{x}')
projected, cut = get_cut(soup)
cut_col = '# Projected to Make Cut' if projected else "# Made the Cut"
if cut is not None and projected:
cut = 0 if cut == 'E' else int(cut)
picks_df[cut_col] = picks_df.apply(lambda x: (x.replace(
'E', 0).loc[x.index.str.match(r'Tier\s\d')] <= cut).sum(),
axis=1)
elif cut is not None or is_numeric(score_df.iloc[0].loc['R4']):
picks_df[cut_col] = picks_df.apply(
lambda x: 6 - (x.loc[x.index.str.match(r'Tier\s\d')].astype(
str).str.startswith('CUT:')).sum(),
axis=1)
picks_df = picks_df.applymap(lambda x: x if not isinstance(x, str)
else x.replace('CUT:', ''))
picks_df.loc[:, ~picks_df.columns.isin(['RANK', 'name', cut_col])] = \
picks_df.loc[:, ~picks_df.columns.isin(['RANK', 'name', cut_col])].applymap(clean_up_scores)
col_order = ['RANK', 'name', 'TOTAL']
col_order.extend(
picks_df.loc[:, picks_df.columns.str.match(r'Tier\s\d')].columns)
col_order.extend(
[col for col in picks_df.columns if col not in col_order])
picks_df = picks_df.reset_index()[col_order]
return Response(picks_df.to_json(orient='index'))