metadata={' "Payroll (millions)"': 78.06, ' "Wins"': 64}),     Document(page_content='Cubs', metadata={' "Payroll (millions)"': 88.19, ' "Wins"': 61}),     Document(page_content='Astros', metadata={' "Payroll (millions)"': 60.65, ' "Wins"': 55})]# Use lazy load for larger table, which won't read the full table into memoryfor i in loader.lazy_load():    print(i)      0%|          |   0.00/100 [00:00<?, ?it/s]    page_content='Nationals' metadata={' "Payroll (millions)"': 81.34, ' "Wins"': 98}    page_content='Reds' metadata={' "Payroll (millions)"': 82.2, ' "Wins"': 97}    page_content='Yankees' metadata={' "Payroll (millions)"': 197.96, ' "Wins"': 95}    page_content='Giants' metadata={' "Payroll (millions)"': 117.62, ' "Wins"': 94}    page_content='Braves' metadata={' "Payroll (millions)"': 83.31, ' "Wins"': 94}    page_content='Athletics' metadata={' "Payroll (millions)"': 55.37, ' "Wins"': 94}    page_content='Rangers' metadata={' "Payroll (millions)"': 120.51, ' "Wins"': 93}    page_content='Orioles' metadata={' "Payroll (millions)"': 81.43, ' "Wins"': 93}    page_content='Rays' metadata={' "Payroll (millions)"': 64.17, ' "Wins"': 90}    page_content='Angels' metadata={' "Payroll (millions)"': 154.49, ' "Wins"': 89}    page_content='Tigers' metadata={' "Payroll (millions)"': 132.3, ' "Wins"': 88}    page_content='Cardinals' metadata={' "Payroll (millions)"': 110.3, ' "Wins"': 88}    page_content='Dodgers' metadata={' "Payroll (millions)"': 95.14, ' "Wins"': 86}    page_content='White Sox' metadata={' "Payroll (millions)"': 96.92, ' "Wins"': 85}    page_content='Brewers' metadata={' "Payroll (millions)"': 97.65, ' "Wins"': 83}    page_content='Phillies' metadata={' "Payroll (millions)"': 174.54, ' "Wins"': 81}    page_content='Diamondbacks' metadata={' "Payroll (millions)"': 74.28, ' "Wins"': 81}    page_content='Pirates' metadata={' "Payroll (millions)"': 63.43, ' "Wins"': 79}    page_content='Padres' metadata={' "Payroll (millions)"': 55.24, ' "Wins"': 76}
This notebook goes over how to load data from a xorbits.pandas DataFrame.
This notebook goes over how to load data from a xorbits.pandas DataFrame. ->: metadata={' "Payroll (millions)"': 78.06, ' "Wins"': 64}),     Document(page_content='Cubs', metadata={' "Payroll (millions)"': 88.19, ' "Wins"': 61}),     Document(page_content='Astros', metadata={' "Payroll (millions)"': 60.65, ' "Wins"': 55})]# Use lazy load for larger table, which won't read the full table into memoryfor i in loader.lazy_load():    print(i)      0%|          |   0.00/100 [00:00<?, ?it/s]    page_content='Nationals' metadata={' "Payroll (millions)"': 81.34, ' "Wins"': 98}    page_content='Reds' metadata={' "Payroll (millions)"': 82.2, ' "Wins"': 97}    page_content='Yankees' metadata={' "Payroll (millions)"': 197.96, ' "Wins"': 95}    page_content='Giants' metadata={' "Payroll (millions)"': 117.62, ' "Wins"': 94}    page_content='Braves' metadata={' "Payroll (millions)"': 83.31, ' "Wins"': 94}    page_content='Athletics' metadata={' "Payroll (millions)"': 55.37, ' "Wins"': 94}    page_content='Rangers' metadata={' "Payroll (millions)"': 120.51, ' "Wins"': 93}    page_content='Orioles' metadata={' "Payroll (millions)"': 81.43, ' "Wins"': 93}    page_content='Rays' metadata={' "Payroll (millions)"': 64.17, ' "Wins"': 90}    page_content='Angels' metadata={' "Payroll (millions)"': 154.49, ' "Wins"': 89}    page_content='Tigers' metadata={' "Payroll (millions)"': 132.3, ' "Wins"': 88}    page_content='Cardinals' metadata={' "Payroll (millions)"': 110.3, ' "Wins"': 88}    page_content='Dodgers' metadata={' "Payroll (millions)"': 95.14, ' "Wins"': 86}    page_content='White Sox' metadata={' "Payroll (millions)"': 96.92, ' "Wins"': 85}    page_content='Brewers' metadata={' "Payroll (millions)"': 97.65, ' "Wins"': 83}    page_content='Phillies' metadata={' "Payroll (millions)"': 174.54, ' "Wins"': 81}    page_content='Diamondbacks' metadata={' "Payroll (millions)"': 74.28, ' "Wins"': 81}    page_content='Pirates' metadata={' "Payroll (millions)"': 63.43, ' "Wins"': 79}    page_content='Padres' metadata={' "Payroll (millions)"': 55.24, ' "Wins"': 76}