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bfe8aa2 eb20fbf bfe8aa2 eb20fbf bfe8aa2 2c49246 bfe8aa2 abb74cd bfe8aa2 66148e9 abb74cd 66148e9 bfe8aa2 66148e9 bfe8aa2 abb74cd bfe8aa2 abb74cd bfe8aa2 eb20fbf bfe8aa2 075cdf1 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 | import polars as pl
import numpy as np
import pandas as pd
import api_scraper
scrape = api_scraper.MLB_Scrape()
import requests
from shiny import App, reactive, ui, render
from shiny.ui import h2, tags
from shiny import App, reactive, ui, render
from shiny.ui import h2, tags
from shiny import App, ui, render
# Import the MLB_Scrape class from the module
from api_scraper import MLB_Scrape
# Initialize the scraper
scraper = MLB_Scrape()
# Call the get_teams method
teams = scraper.get_teams()
print(teams)
df_player = pl.concat([
scraper.get_players(sport_id=1,season=2025,game_type=['R']),
scraper.get_players(sport_id=1,season=2024,game_type=['R']),
scraper.get_players(sport_id=1,season=2023,game_type=['R']),
scraper.get_players(sport_id=11,season=2025,game_type=['R']),
scraper.get_players(sport_id=12,season=2025,game_type=['R']),
scraper.get_players(sport_id=13,season=2025,game_type=['R']),
scraper.get_players(sport_id=14,season=2025,game_type=['R']),
scraper.get_players(sport_id=22,season=2024,game_type=['R'])
]).unique(subset=['player_id'])
teams_mlb = teams.filter(pl.col("league_id").is_in([103,104])).sort("abbreviation")
teams_dict = dict(zip(teams_mlb['team_id'],teams_mlb['abbreviation']))
teams_name_dict = dict(zip(teams_mlb['team_id'],teams_mlb['franchise']))
app_ui = ui.page_sidebar(
ui.sidebar(
ui.input_select(
"team_id",
"Select Team",
choices=teams_dict
),
ui.input_switch("nri_only", "NRI Only"),
ui.div(
ui.div({"style": "font-size:1.2em;"}, ui.markdown("Legend")),
ui.div(
style="display: inline-block; width: 20px; height: 20px; background-color: #b7e1cd; margin-right: 10px;"
),
ui.span("NRI", style="vertical-align: top;"),
style="padding: 10px;"
),
),
ui.card(
ui.div({"style": "font-size:2em;"}, ui.output_text("card_title")),
ui.div({"style": "font-size:1.2em;"}, ui.markdown("By: [@TJStats](https://x.com/TJStats), Data: MLB")),
ui.output_table("team_stats")
)
)
def server(input, output, session):
@render.text
def card_title():
if input.nri_only():
return f"{teams_name_dict[int(input.team_id())]} — Spring Training Roster Non-Roster Invitees"
else:
return f"{teams_name_dict[int(input.team_id())]} — Spring Training Roster"
@render.table
def team_stats():
# Get the selected team's data
i = int(input.team_id())
url = f'https://statsapi.mlb.com/api/v1/teams/{i}/roster/40man?season=2025'
data = requests.get(url).json()
# Normalize the roster data
roster_df = pd.json_normalize(data['roster'])
roster_df['nri'] = False
roster_df['status.code'] = ''
roster_df = roster_df.fillna('')
url = f'https://statsapi.mlb.com/api/v1/teams/{i}/roster/nonRosterInvitees?season=2025'
data = requests.get(url).json()
# Normalize the roster data
nri_roster_df = pd.json_normalize(data['roster'])
nri_roster_df['nri'] = True
nri_roster_df['parentTeamId'] = i
nri_roster_df = nri_roster_df.fillna('')
df_output = pd.concat([roster_df,nri_roster_df])
df_output.loc[df_output['position.abbreviation'] == 'DH', 'position.code'] = '6.5'
df_output.loc[df_output['position.abbreviation'] == 'IF', 'position.code'] = '6.5'
df_output.loc[df_output['position.abbreviation'] == 'TWP', 'position.code'] = '1'
df_output = df_output.sort_values(by=['position.code', 'status.code']).reset_index(drop=True)
if input.nri_only():
df_output = df_output[df_output['status.code'] == 'NRI']
df_output = df_output.merge(df_player.to_pandas(),left_on='person.id',right_on='player_id',how='left')
conditions = [
(df_output['position.abbreviation'].isin(['P'])) & (~df_output.duplicated(subset=['position.abbreviation'], keep='first')),
(df_output['position.abbreviation'] == 'C') & (~df_output.duplicated(subset=['position.abbreviation'], keep='first')),
(df_output['position.abbreviation'].isin(['LF','CF','RF','OF'])) & (~df_output.duplicated(subset=['position.abbreviation'], keep='first'))
]
choices = ['Pitchers', 'Infielders', 'Outfielders']
df_output['position_group'] = np.select(conditions, choices, default='')
df_output.loc[df_output.duplicated(subset=['position_group'], keep='first'), 'position_group'] = ''
df_output['team'] = df_output['parentTeamId'].map(teams_dict)
df_output.loc[df_output['position.abbreviation'] == 'P', 'position.abbreviation'] = df_output['pitchHand'] + 'H' + df_output['position.abbreviation']
df_output['bat_throw'] = df_output['batSide'] + '/' + df_output['pitchHand']
df_output_small = df_output[['position_group','person.id', 'person.fullName',
'position.abbreviation','team', 'status.code', 'age','weight', 'height', 'bat_throw']]
df_output_small['age'] = df_output_small['age'].replace('', np.nan).astype('Int64')
df_output_small['weight'] = df_output_small['weight'].replace('', np.nan).astype('Int64')
# # Insert blank rows with position group indicated
# blank_rows = []
# for idx, row in df_output_small.iterrows():
# if row['position_group']:
# blank_row = pd.Series([''] * len(df_output_small.columns), index=df_output_small.columns)
# blank_row['position_group'] = row['position_group']
# blank_rows.append((idx, blank_row))
# for idx, blank_row in reversed(blank_rows):
# df_output_small = pd.concat([df_output_small.iloc[:idx], pd.DataFrame([blank_row]), df_output_small.iloc[idx:]]).reset_index(drop=True)
# df_output_small.loc[(df_output_small['position_group'] != '') & (df_output_small['person.fullName'] != ''), 'position_group'] = ''
def highlight_nri(val):
color = 'yellow' if val else ''
return f'background-color: {color}'
# Function to alternate row colors
def highlight_alternate_rows(x):
return ['background-color: #ebebeb' if i % 2 == 0 else '' for i in range(len(x))]
#
df_output_small.columns = ['Group','Player ID', 'Name', 'Pos','Team', 'Status','Age','Weight', 'Height', 'B/T']
style_df = (df_output_small.style.set_precision(1)
.set_properties(**{'border': '3 px'}, overwrite=False)
.set_table_styles([{
'selector': 'caption',
'props': [
('color', ''),
('fontname', 'Century Gothic'),
('font-size', '16px'),
('font-style', 'italic'),
('font-weight', ''),
('text-align', 'centre'),
]
}, {
'selector': 'th',
'props': [('font-size', '16px'), ('text-align', 'center'), ('Height', 'px'), ('color', 'black'), ('border', '1px black solid !important')]
}, {
'selector': 'td',
'props': [('text-align', 'center'), ('font-size', '16px'), ('color', 'black')]
}], overwrite=False)
.set_properties(**{'background-color': 'White', 'index': 'White', 'min-width': '72px'}, overwrite=False)
.set_table_styles([{'selector': 'th:first-child', 'props': [('background-color', 'white')]}], overwrite=False)
.set_table_styles([{'selector': 'tr:first-child', 'props': [('background-color', 'white')]}], overwrite=False)
.set_table_styles([{'selector': 'tr', 'props': [('line-height', '20px')]}], overwrite=False)
.set_properties(**{'Height': '8px'}, **{'text-align': 'center'}, overwrite=False)
.hide_index()
.set_properties(**{'border': '1px black solid'})
.set_table_styles([{'selector': 'thead th:nth-child(1)', 'props': [('min-width', '150px')]}], overwrite=False)
.set_table_styles([{'selector': 'thead th:nth-child(2)', 'props': [('min-width', '150px')]}], overwrite=False)
.set_table_styles([{'selector': 'thead th:nth-child(3)', 'props': [('min-width', '250px')]}], overwrite=False)
.set_table_styles([{'selector': 'thead th', 'props': [('height', '30px')]}], overwrite=False)
.apply(highlight_alternate_rows, axis=0, subset=df_output_small.columns[1:])
.applymap(lambda x: 'background-color: #bdbdbd' if x != '' else '', subset=['Group'])
.applymap(lambda x: 'background-color: #bdbdbd' if x else '', subset=['Group'])
# .apply(lambda x: ['background-color: #bdbdbd' if x['Group'] != '' else '' for _ in x], axis=1)
.set_properties(
**{'background-color':'#bdbdbd'}, # Apply only right border
subset=df_output_small.columns[0] # Only affects column 1
)
.set_properties(
**{'border-top': 'none', 'border-bottom': 'none'},
subset=df_output_small.columns[0] # Apply only to column 1
)
# .format({'Age': '{:.0f}', 'Weight': '{:.0f}'})
)
def highlight_nri(s):
return ['background-color: #b7e1cd' if s.name != 'Status' and s['Status'] == 'NRI' else '' for _ in s]
# style_df = style_df.style.apply(highlight_nri, axis=1, subset=style_df.columns[1:])
if not input.nri_only():
style_df = style_df.apply(highlight_nri, axis=1, subset=df_output_small.columns[1:])
def add_top_border(s):
return ['border-top: 3px solid black' if s['Group'] != '' else '' for _ in s]
styled_df = style_df.apply(add_top_border, axis=1)
def add_bottom_border(s):
return ['border-bottom: 3px solid black' if s.name == len(df_output_small) - 1 else '' for _ in s]
styled_df = style_df.apply(add_bottom_border, axis=1)
return style_df
app = App(app_ui, server) |