Documentation Index
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Table.load(data, /)
Load data into the table.
This is a blocking operation: the method will not return until all the data is loaded.
Example
>>> from datetime import date
>>> table = session.create_table(
... "Sales",
... data_types={
... "ID": "String",
... "Product": "String",
... "Price": "int",
... "Quantity": "int",
... "Date": "LocalDate",
... },
... keys={"ID"},
... )
>>> import pyarrow as pa
>>> arrow_table = pa.Table.from_pydict(
... {
... "ID": pa.array(["ab", "cd"]),
... "Product": pa.array(["phone", "watch"]),
... "Price": pa.array([699, 349]),
... "Quantity": pa.array([1, 2]),
... "Date": pa.array([date(2024, 3, 5), date(2024, 12, 12)]),
... }
... )
>>> table.load(arrow_table)
>>> table.head().sort_index()
Product Price Quantity Date
ID
ab phone 699 1 2024-03-05
cd watch 349 2 2024-12-12
>>> import pandas as pd
>>> pandas_dataframe = pd.DataFrame(
... {
... "ID": ["ef", "gh"],
... "Product": ["laptop", "book"],
... "Price": [2599, 19],
... "Quantity": [3, 5],
... "Date": [date(2023, 8, 10), date(2024, 1, 13)],
... }
... )
>>> table.load(pandas_dataframe)
>>> table.head().sort_index()
Product Price Quantity Date
ID
ab phone 699 1 2024-03-05
cd watch 349 2 2024-12-12
ef laptop 2599 3 2023-08-10
gh book 19 5 2024-01-13
>>> import numpy as np
>>> numpy_array = np.asarray(
... [
... ["ij", "watch", 299, 1, date(2022, 7, 20)],
... ["kl", "keyboard", 69, 1, date(2023, 5, 8)],
... ],
... dtype=object,
... )
>>> table.load(pd.DataFrame(numpy_array, columns=list(table)))
>>> table.head(10).sort_index()
Product Price Quantity Date
ID
ab phone 699 1 2024-03-05
cd watch 349 2 2024-12-12
ef laptop 2599 3 2023-08-10
gh book 19 5 2024-01-13
ij watch 299 1 2022-07-20
kl keyboard 69 1 2023-05-08
>>> from pyspark.sql import Row, SparkSession
>>> spark = SparkSession.builder.getOrCreate()
>>> spark_dataframe = spark.createDataFrame(
... [
... Row(
... ID="mn",
... Product="glasses",
... Price=129,
... Quantity=2,
... Date=date(2021, 3, 3),
... ),
... Row(
... ID="op",
... Product="battery",
... Price=49,
... Quantity=2,
... Date=date(2024, 11, 7),
... ),
... ]
... )
>>> table.load(spark_dataframe.toPandas())
>>> spark.stop()
>>> table.head(10).sort_index()
Product Price Quantity Date
ID
ab phone 699 1 2024-03-05
cd watch 349 2 2024-12-12
ef laptop 2599 3 2023-08-10
gh book 19 5 2024-01-13
ij watch 299 1 2022-07-20
kl keyboard 69 1 2023-05-08
mn glasses 129 2 2021-03-03
op battery 49 2 2024-11-07
tuple or a Mapping:
>>> table += ("qr", "mouse", 29, 3, date(2024, 11, 7))
>>> table.head(10).sort_index()
Product Price Quantity Date
ID
ab phone 699 1 2024-03-05
cd watch 349 2 2024-12-12
ef laptop 2599 3 2023-08-10
gh book 19 5 2024-01-13
ij watch 299 1 2022-07-20
kl keyboard 69 1 2023-05-08
mn glasses 129 2 2021-03-03
op battery 49 2 2024-11-07
qr mouse 29 3 2024-11-07
>>> table += { # The order of the keys does not matter.
... "Product": "screen",
... "Quantity": 1,
... "Price": 599,
... "Date": date(2023, 5, 8),
... "ID": "st",
... }
>>> table.head(10).sort_index()
Product Price Quantity Date
ID
ab phone 699 1 2024-03-05
cd watch 349 2 2024-12-12
ef laptop 2599 3 2023-08-10
gh book 19 5 2024-01-13
ij watch 299 1 2022-07-20
kl keyboard 69 1 2023-05-08
mn glasses 129 2 2021-03-03
op battery 49 2 2024-11-07
qr mouse 29 3 2024-11-07
st screen 599 1 2023-05-08
