import pandas as pd
from great_tables import GT, md
import gt_extras as gte
= pd.DataFrame(
df
{"Category": ["Points", "Rebounds", "Assists", "Blocks", "Steals"],
"Hart": [1051, 737, 453, 27, 119],
"Brunson": [1690, 187, 475, 8, 60],
"Bridges": [1444, 259, 306, 43, 75],
}
)
= gte.img_header(
hart_header ="Josh Hart",
label="https://a.espncdn.com/combiner/i?img=/i/headshots/nba/players/full/3062679.png",
img_url
)
= gte.img_header(
brunson_header ="Jalen Brunson",
label="https://a.espncdn.com/combiner/i?img=/i/headshots/nba/players/full/3934672.png",
img_url
)
= gte.img_header(
bridges_header ="Mikal Bridges",
label="https://a.espncdn.com/combiner/i?img=/i/headshots/nba/players/full/3147657.png",
img_url
)
(="Category")
GT(df, rowname_col"Images and data courtesy of [ESPN](https://www.espn.com)"))
.tab_source_note(md(
.cols_label(
{"Hart": hart_header,
"Brunson": brunson_header,
"Bridges": bridges_header,
}
) )
![]()
Josh Hart
|
![]()
Jalen Brunson
|
![]()
Mikal Bridges
|
|
---|---|---|---|
Points | 1051 | 1690 | 1444 |
Rebounds | 737 | 187 | 259 |
Assists | 453 | 475 | 306 |
Blocks | 27 | 8 | 43 |
Steals | 119 | 60 | 75 |
Images and data courtesy of ESPN |