Okay, here’s a structured representation of the data you provided, assuming it’s a table of draft picks. I’ll present it as a list of dictionaries, where each dictionary represents a row in the table. I’ll also clean up some of the whitespace and characters.
“`python
draft_picks =[
{
“Round”: 2,
“Pick Number”: 35,
“Team”: “Buffalo Sabres”,
“Player Name”: “Jiri Kulich“,
“Position”: “F”,
“nationality”: “CZE”,
“League”: “Czechia”,
“Team/Programme”: “HC Karlovy Vary”
},
{
“Round”: 2,
“Pick Number”: 36,
“Team”: “Los Angeles Kings”,
“Player Name”: “Kenny Connors“,
“Position”: “F”,
“Nationality”: “USA”,
“League”: “USHL”,
“Team/Program”: “Lincoln”
},
{
“Round”: 2,
“Pick Number”: 38,
“Team”: “Philadelphia Flyers”,
“Player Name”: “Carter Amico”,
“Position”: “D”,
“Nationality”: “USA”,
“League”: “USHL”,
“Team/Program”: “NTDP”
},
{
“round”: 2,
“Pick Number”: 40,
“Team”: “Philadelphia Flyers”,
“Player Name”: “Jack Murtagh“,
“Position”: “F”,
“Nationality”: “USA”,
“League”: “USHL”,
“Team/Program”: “NTDP”
},
{
“Round”: 2,
“Pick Number”: 48,
“Team”: “Philadelphia Flyers”,
“Player Name”: “Shane Vansaghi“,
“Position”: “F”,
“Nationality”: “USA”,
“League”: “Big Ten”,
“Team/Program”: “Michigan State”
},
{
“Round”: 2,
“Pick Number”: 49,
“Team”: “Carolina Hurricanes”,
“Player Name”: “Charlie Cerrato”,
“Position”: “F”,
“Nationality”: “USA”,
“League”: “Big Ten”,
“Team/Program”: “Penn State”
},
{
“round”: 2,
“Pick Number”: 50,
“team”: “New Jersey Devils”,
“Player Name”: “Conrad Fondrk”,
“Position”: “F”,
“Nationality”: “USA”,
“League”: “USHL”,
“Team/Program”: “NTDP”
},
{
“Round”: 2,
“Pick Number”: 51,
“Team”: “Boston Bruins”,
“player name”: “Will Moore”,
“Position”: “F”,
“Nationality”: “USA”,
“League”: “USHL”,
“Team/Program”: “NTDP”
},
{
“Round”: 2,
“Pick Number”: 53,
“Team”: “San Jose Sharks”,
“Player Name”: “Cole McKinney”,
“Position”: “F”,
“Nationality”: “USA”,
“League”: “USHL”,
