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Xorbits Pandas DataFrame

This notebook goes over how to load data from a xorbits.pandas DataFrame.

#!pip install xorbits
import xorbits.pandas as pd
df = pd.read_csv("example_data/mlb_teams_2012.csv")
df.head()
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Team"Payroll (millions)""Wins"
0Nationals81.3498
1Reds82.2097
2Yankees197.9695
3Giants117.6294
4Braves83.3194
from langchain.document_loaders import XorbitsLoader
loader = XorbitsLoader(df, page_content_column="Team")
loader.load()
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[Document(page_content='Nationals', metadata={' "Payroll (millions)"': 81.34, ' "Wins"': 98}),
 Document(page_content='Reds', metadata={' "Payroll (millions)"': 82.2, ' "Wins"': 97}),
 Document(page_content='Yankees', metadata={' "Payroll (millions)"': 197.96, ' "Wins"': 95}),
 Document(page_content='Giants', metadata={' "Payroll (millions)"': 117.62, ' "Wins"': 94}),
 Document(page_content='Braves', metadata={' "Payroll (millions)"': 83.31, ' "Wins"': 94}),
 Document(page_content='Athletics', metadata={' "Payroll (millions)"': 55.37, ' "Wins"': 94}),
 Document(page_content='Rangers', metadata={' "Payroll (millions)"': 120.51, ' "Wins"': 93}),
 Document(page_content='Orioles', metadata={' "Payroll (millions)"': 81.43, ' "Wins"': 93}),
 Document(page_content='Rays', metadata={' "Payroll (millions)"': 64.17, ' "Wins"': 90}),
 Document(page_content='Angels', metadata={' "Payroll (millions)"': 154.49, ' "Wins"': 89}),
 Document(page_content='Tigers', metadata={' "Payroll (millions)"': 132.3, ' "Wins"': 88}),
 Document(page_content='Cardinals', metadata={' "Payroll (millions)"': 110.3, ' "Wins"': 88}),
 Document(page_content='Dodgers', metadata={' "Payroll (millions)"': 95.14, ' "Wins"': 86}),
 Document(page_content='White Sox', metadata={' "Payroll (millions)"': 96.92, ' "Wins"': 85}),
 Document(page_content='Brewers', metadata={' "Payroll (millions)"': 97.65, ' "Wins"': 83}),
 Document(page_content='Phillies', metadata={' "Payroll (millions)"': 174.54, ' "Wins"': 81}),
 Document(page_content='Diamondbacks', metadata={' "Payroll (millions)"': 74.28, ' "Wins"': 81}),
 Document(page_content='Pirates', metadata={' "Payroll (millions)"': 63.43, ' "Wins"': 79}),
 Document(page_content='Padres', metadata={' "Payroll (millions)"': 55.24, ' "Wins"': 76}),
 Document(page_content='Mariners', metadata={' "Payroll (millions)"': 81.97, ' "Wins"': 75}),
 Document(page_content='Mets', metadata={' "Payroll (millions)"': 93.35, ' "Wins"': 74}),
 Document(page_content='Blue Jays', metadata={' "Payroll (millions)"': 75.48, ' "Wins"': 73}),
 Document(page_content='Royals', metadata={' "Payroll (millions)"': 60.91, ' "Wins"': 72}),
 Document(page_content='Marlins', metadata={' "Payroll (millions)"': 118.07, ' "Wins"': 69}),
 Document(page_content='Red Sox', metadata={' "Payroll (millions)"': 173.18, ' "Wins"': 69}),
 Document(page_content='Indians', metadata={' "Payroll (millions)"': 78.43, ' "Wins"': 68}),
 Document(page_content='Twins', metadata={' "Payroll (millions)"': 94.08, ' "Wins"': 66}),
 Document(page_content='Rockies', 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 memory
for i in loader.lazy_load():
    print(i)
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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}
page_content='Mariners' metadata={' "Payroll (millions)"': 81.97, ' "Wins"': 75}
page_content='Mets' metadata={' "Payroll (millions)"': 93.35, ' "Wins"': 74}
page_content='Blue Jays' metadata={' "Payroll (millions)"': 75.48, ' "Wins"': 73}
page_content='Royals' metadata={' "Payroll (millions)"': 60.91, ' "Wins"': 72}
page_content='Marlins' metadata={' "Payroll (millions)"': 118.07, ' "Wins"': 69}
page_content='Red Sox' metadata={' "Payroll (millions)"': 173.18, ' "Wins"': 69}
page_content='Indians' metadata={' "Payroll (millions)"': 78.43, ' "Wins"': 68}
page_content='Twins' metadata={' "Payroll (millions)"': 94.08, ' "Wins"': 66}
page_content='Rockies' metadata={' "Payroll (millions)"': 78.06, ' "Wins"': 64}
page_content='Cubs' metadata={' "Payroll (millions)"': 88.19, ' "Wins"': 61}
page_content='Astros' metadata={' "Payroll (millions)"': 60.65, ' "Wins"': 55}