Package bigquery (1.24.0)

API documentation for bigquery package.

Packages Functions

approx_top_count

approx_top_count(
    series: bigframes.series.Series, number: int
) -> bigframes.series.Series

Returns the approximate top elements of expression as an array of STRUCTs. The number parameter specifies the number of elements returned.

Each STRUCT contains two fields. The first field (named value) contains an input value. The second field (named count) contains an INT64 specifying the number of times the value was returned.

Returns NULL if there are zero input rows.

Examples:

>>> import bigframes.pandas as bpd
>>> import bigframes.bigquery as bbq
>>> bpd.options.display.progress_bar = None
>>> s = bpd.Series(["apple", "apple", "pear", "pear", "pear", "banana"])
>>> bbq.approx_top_count(s, number=2)
[{'value': 'pear', 'count': 3}, {'value': 'apple', 'count': 2}]

Parameters
Name	Description
`series`	The Series with any data type that the `GROUP BY` clause supports.
`number`	An integer specifying the number of times the value was returned.

array_agg

array_agg(
    obj: groupby.SeriesGroupBy | groupby.DataFrameGroupBy,
) -> series.Series | dataframe.DataFrame

Group data and create arrays from selected columns, omitting NULLs to avoid BigQuery errors (NULLs not allowed in arrays).

Examples:

>>> import bigframes.pandas as bpd
>>> import bigframes.bigquery as bbq
>>> import numpy as np
>>> bpd.options.display.progress_bar = None

For a SeriesGroupBy object:

>>> lst = ['a', 'a', 'b', 'b', 'a']
>>> s = bpd.Series([1, 2, 3, 4, np.nan], index=lst)
>>> bbq.array_agg(s.groupby(level=0))
a    [1. 2.]
b    [3. 4.]
dtype: list<item: double>[pyarrow]

For a DataFrameGroupBy object:

>>> l = [[1, 2, 3], [1, None, 4], [2, 1, 3], [1, 2, 2]]
>>> df = bpd.DataFrame(l, columns=["a", "b", "c"])
>>> bbq.array_agg(df.groupby(by=["b"]))
         a      c
b
1.0    [2]    [3]
2.0  [1 1]  [3 2]
<BLANKLINE>
[2 rows x 2 columns]

Parameter
Name	Description
`obj`	A GroupBy object to be applied the function.

array_length

array_length(series: bigframes.series.Series) -> bigframes.series.Series

Compute the length of each array element in the Series.

Examples:

>>> import bigframes.pandas as bpd
>>> import bigframes.bigquery as bbq
>>> bpd.options.display.progress_bar = None

>>> s = bpd.Series([[1, 2, 8, 3], [], [3, 4]])
>>> bbq.array_length(s)
0    4
1    0
2    2
dtype: Int64

You can also apply this function directly to Series.

>>> s.apply(bbq.array_length, by_row=False)
0    4
1    0
2    2
dtype: Int64

Parameter
Name	Description
`series`	A Series with array columns.

array_to_string

array_to_string(
    series: bigframes.series.Series, delimiter: str
) -> bigframes.series.Series

Converts array elements within a Series into delimited strings.

Examples:

>>> import bigframes.pandas as bpd
>>> import bigframes.bigquery as bbq
>>> import numpy as np
>>> bpd.options.display.progress_bar = None

>>> s = bpd.Series([["H", "i", "!"], ["Hello", "World"], np.nan, [], ["Hi"]])
>>> bbq.array_to_string(s, delimiter=", ")
    0         H, i, !
    1    Hello, World
    2
    3
    4              Hi
    dtype: string

Parameters
Name	Description
`series`	A Series containing arrays.
`delimiter`	The string used to separate array elements.

create_vector_index

create_vector_index(
    table_id: str,
    column_name: str,
    *,
    replace: bool = False,
    index_name: typing.Optional[str] = None,
    distance_type="cosine",
    stored_column_names: typing.Collection[str] = (),
    index_type: str = "ivf",
    ivf_options: typing.Optional[typing.Mapping] = None,
    tree_ah_options: typing.Optional[typing.Mapping] = None,
    session: typing.Optional[bigframes.session.Session] = None
) -> None

Creates a new vector index on a column of a table.

This method calls the CREATE VECTOR INDEX DDL statement <https://cloud.google.com/bigquery/docs/reference/standard-sql/data-definition-language#create_vector_index_statement>_.

json_extract

json_extract(
    series: bigframes.series.Series, json_path: str
) -> bigframes.series.Series

Extracts a JSON value and converts it to a SQL JSON-formatted STRING or JSON value. This function uses single quotes and brackets to escape invalid JSONPath characters in JSON keys.

Examples:

>>> import bigframes.pandas as bpd
>>> import bigframes.bigquery as bbq
>>> bpd.options.display.progress_bar = None

>>> s = bpd.Series(['{"class": {"students": [{"id": 5}, {"id": 12}]}}'])
>>> bbq.json_extract(s, json_path="$.class")
0    {"students":[{"id":5},{"id":12}]}
dtype: string

Parameters
Name	Description
`series`	The Series containing JSON data (as native JSON objects or JSON-formatted strings).
`json_path`	The JSON path identifying the data that you want to obtain from the input.

json_extract_array

json_extract_array(
    series: bigframes.series.Series, json_path: str = "$"
) -> bigframes.series.Series

Extracts a JSON array and converts it to a SQL array of JSON-formatted STRING or JSON values. This function uses single quotes and brackets to escape invalid JSONPath characters in JSON keys.

Examples:

>>> import bigframes.pandas as bpd
>>> import bigframes.bigquery as bbq
>>> bpd.options.display.progress_bar = None

>>> s = bpd.Series(['[1, 2, 3]', '[4, 5]'])
>>> bbq.json_extract_array(s)
0    ['1' '2' '3']
1        ['4' '5']
dtype: list<item: string>[pyarrow]

Parameters
Name	Description
`series`	The Series containing JSON data (as native JSON objects or JSON-formatted strings).
`json_path`	The JSON path identifying the data that you want to obtain from the input.

json_set

json_set(
    series: bigframes.series.Series,
    json_path_value_pairs: typing.Sequence[typing.Tuple[str, typing.Any]],
) -> bigframes.series.Series

Produces a new JSON value within a Series by inserting or replacing values at specified paths.

Examples:

>>> import bigframes.pandas as bpd
>>> import bigframes.bigquery as bbq
>>> import numpy as np
>>> bpd.options.display.progress_bar = None

>>> s = bpd.read_gbq("SELECT JSON '{\"a\": 1}' AS data")["data"]
>>> bbq.json_set(s, json_path_value_pairs=[("$.a", 100), ("$.b", "hi")])
    0    {"a":100,"b":"hi"}
    Name: data, dtype: string

Parameters
Name	Description
`series`	The Series containing JSON data (as native JSON objects or JSON-formatted strings).
`json_path_value_pairs`	Pairs of JSON path and the new value to insert/replace.

struct

struct(value: dataframe.DataFrame) -> series.Series

Takes a DataFrame and converts it into a Series of structs with each struct entry corresponding to a DataFrame row and each struct field corresponding to a DataFrame column

Examples:

>>> import bigframes.pandas as bpd
>>> import bigframes.bigquery as bbq
>>> import bigframes.series as series
>>> bpd.options.display.progress_bar = None

>>> srs = series.Series([{"version": 1, "project": "pandas"}, {"version": 2, "project": "numpy"},])
>>> df = srs.struct.explode()
>>> bbq.struct(df)
0    {'project': 'pandas', 'version': 1}
1     {'project': 'numpy', 'version': 2}
dtype: struct<project: string, version: int64>[pyarrow]

Args:
    value (bigframes.dataframe.DataFrame):
        The DataFrame to be converted to a Series of structs

Returns:
    bigframes.series.Series: A new Series with struct entries representing rows of the original DataFrame

vector_search

vector_search(
    base_table: str,
    column_to_search: str,
    query: Union[dataframe.DataFrame, series.Series],
    *,
    query_column_to_search: Optional[str] = None,
    top_k: Optional[int] = 10,
    distance_type: Literal["euclidean", "cosine"] = "euclidean",
    fraction_lists_to_search: Optional[float] = None,
    use_brute_force: bool = False
) -> dataframe.DataFrame

Conduct vector search which searches embeddings to find semantically similar entities.

This method calls the VECTOR_SEARCH() SQL function <https://cloud.google.com/bigquery/docs/reference/standard-sql/search_functions#vector_search>_.

Examples:

>>> import bigframes.pandas as bpd
>>> import bigframes.bigquery as bbq
>>> bpd.options.display.progress_bar = None

DataFrame embeddings for which to find nearest neighbors. The ARRAY<FLOAT64> column is used as the search query:

>>> search_query = bpd.DataFrame({"query_id": ["dog", "cat"],
...                               "embedding": [[1.0, 2.0], [3.0, 5.2]]})
>>> bbq.vector_search(
...             base_table="bigframes-dev.bigframes_tests_sys.base_table",
...             column_to_search="my_embedding",
...             query=search_query,
...             top_k=2)
  query_id  embedding  id my_embedding  distance
1      cat  [3.  5.2]   5    [5.  5.4]  2.009975
0      dog    [1. 2.]   1      [1. 2.]       0.0
0      dog    [1. 2.]   4    [1.  3.2]       1.2
1      cat  [3.  5.2]   2      [2. 4.]   1.56205
<BLANKLINE>
[4 rows x 5 columns]

Series embeddings for which to find nearest neighbors:

>>> search_query = bpd.Series([[1.0, 2.0], [3.0, 5.2]],
...                            index=["dog", "cat"],
...                            name="embedding")
>>> bbq.vector_search(
...             base_table="bigframes-dev.bigframes_tests_sys.base_table",
...             column_to_search="my_embedding",
...             query=search_query,
...             top_k=2)
     embedding  id my_embedding  distance
dog    [1. 2.]   1      [1. 2.]       0.0
cat  [3.  5.2]   5    [5.  5.4]  2.009975
dog    [1. 2.]   4    [1.  3.2]       1.2
cat  [3.  5.2]   2      [2. 4.]   1.56205
<BLANKLINE>
[4 rows x 4 columns]

You can specify the name of the column in the query DataFrame embeddings and distance type. If you specify query_column_to_search_value, it will use the provided column which contains the embeddings for which to find nearest neighbors. Otherwiese, it uses the column_to_search value.

>>> search_query = bpd.DataFrame({"query_id": ["dog", "cat"],
...                               "embedding": [[1.0, 2.0], [3.0, 5.2]],
...                               "another_embedding": [[0.7, 2.2], [3.3, 5.2]]})
>>> bbq.vector_search(
...             base_table="bigframes-dev.bigframes_tests_sys.base_table",
...             column_to_search="my_embedding",
...             query=search_query,
...             distance_type="cosine",
...             query_column_to_search="another_embedding",
...             top_k=2)
  query_id  embedding another_embedding  id my_embedding  distance
1      cat  [3.  5.2]         [3.3 5.2]   2      [2. 4.]  0.005181
0      dog    [1. 2.]         [0.7 2.2]   4    [1.  3.2]  0.000013
1      cat  [3.  5.2]         [3.3 5.2]   1      [1. 2.]  0.005181
0      dog    [1. 2.]         [0.7 2.2]   3    [1.5 7. ]  0.004697
<BLANKLINE>
[4 rows x 6 columns]

Parameters
Name	Description
`base_table`	The table to search for nearest neighbor embeddings.
`column_to_search`	The name of the base table column to search for nearest neighbor embeddings. The column must have a type of `ARRAY`. All elements in the array must be non-NULL.
`query`	A Series or DataFrame that provides the embeddings for which to find nearest neighbors.
`query_column_to_search`	Specifies the name of the column in the query that contains the embeddings for which to find nearest neighbors. The column must have a type of `ARRAY`. All elements in the array must be non-NULL and all values in the column must have the same array dimensions as the values in the `column_to_search` column. Can only be set when query is a DataFrame.
`top_k`	Sepecifies the number of nearest neighbors to return. Default to 10.
`distance_type`	Specifies the type of metric to use to compute the distance between two vectors. Possible values are "euclidean" and "cosine". Default to "euclidean".
`fraction_lists_to_search`	Specifies the percentage of lists to search. Specifying a higher percentage leads to higher recall and slower performance, and the converse is true when specifying a lower percentage. It is only used when a vector index is also used. You can only specify `fraction_lists_to_search` when `use_brute_force` is set to False.
`use_brute_force`	Determines whether to use brute force search by skipping the vector index if one is available. Default to False.