bigframes.extensions.core.series_accessor.BigQuerySeriesAccessor#

class bigframes.extensions.core.series_accessor.BigQuerySeriesAccessor(obj: S)[source]#

Series accessor for BigQuery functions.

abstract property aead: AeadSeriesAccessor[S]#

Accessor for BigQuery aead functions.

array_concat(array_expression_2: Series | Expression | Any | Literal[Sentinel.ARGUMENT_DEFAULT], *, session: Session | None = None) → S[source]#

Concatenates one or more arrays with the same element type into a single array.

array_first(*, session: Session | None = None) → S[source]#

Takes an array and returns the first element in the array.

array_first_n(n: Series | Expression | Literal[Sentinel.ARGUMENT_DEFAULT] | int, *, session: Session | None = None) → S[source]#

Returns a prefix of input_array consisting of the first n elements.

array_includes(search_value: Series | Expression | Any | Literal[Sentinel.ARGUMENT_DEFAULT], *, session: Session | None = None) → S[source]#

Takes an array and returns TRUE if there is an element in the array that is equal to the search_value.

array_includes_all(search_values: Series | Expression | Any | Literal[Sentinel.ARGUMENT_DEFAULT], *, session: Session | None = None) → S[source]#

Takes an array to search and an array of search values. Returns TRUE if all search values are in the array to search, otherwise returns FALSE.

array_includes_any(search_values: Series | Expression | Any | Literal[Sentinel.ARGUMENT_DEFAULT], *, session: Session | None = None) → S[source]#

Takes an array to search and an array of search values. Returns TRUE if any search values are in the array to search, otherwise returns FALSE.

array_is_distinct(*, session: Session | None = None) → S[source]#

Returns TRUE if the array contains no repeated elements, using the same equality comparison logic as SELECT DISTINCT.

array_last(*, session: Session | None = None) → S[source]#

Takes an array and returns the last element in the array.

array_length(*, session: Session | None = None) → S[source]#

Compute the length of each array element in the Series.

Examples:

>>> import bigframes.pandas as bpd
>>> import bigframes.bigquery as bbq

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

You can call this function using the Series bigquery accessor.

>>> s.bigquery.array_length()
0    4
1    0
2    2
dtype: Int64

You can also use this accessor on a pandas Series after importing bigframes.

>>> import bigframes
>>> import pandas as pd
>>> ps = pd.Series([[1, 2, 8, 3], [], [3, 4]])
>>> ps.bigquery.array_length()
0    4
1    0
2    2
dtype: Int64

You can also apply this function directly to Series using apply.

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

series (bigframes.series.Series) – A Series with array columns.

Returns:

A Series of integer values indicating

the length of each element in the Series.

Return type:

bigframes.series.Series

array_reverse(*, session: Session | None = None) → S[source]#

Returns the input ARRAY with elements in reverse order.

array_slice(start_offset: Series | Expression | Literal[Sentinel.ARGUMENT_DEFAULT] | int, end_offset: Series | Expression | Literal[Sentinel.ARGUMENT_DEFAULT] | int, *, session: Session | None = None) → S[source]#

Returns an array containing zero or more consecutive elements from the input array.

array_to_string(delimiter: Series | Expression | Literal[Sentinel.ARGUMENT_DEFAULT] | bytes | str, null_text: Series | Expression | Literal[Sentinel.ARGUMENT_DEFAULT] | bytes | str = Sentinel.ARGUMENT_DEFAULT, *, session: Session | None = None) → S[source]#

Converts array elements within a Series into delimited strings.

Examples:

>>> import bigframes.pandas as bpd
>>> import bigframes.bigquery as bbq

>>> 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

You can call this function using the Series bigquery accessor.

>>> s.bigquery.array_to_string(delimiter=", ")
0         H, i, !
1    Hello, World
2
3
4              Hi
dtype: string

You can also use this accessor on a pandas Series after importing bigframes.

>>> import bigframes
>>> import pandas as pd
>>> ps = pd.Series([["H", "i", "!"], ["Hello", "World"], None, [], ["Hi"]])
>>> ps.bigquery.array_to_string(delimiter=", ")
0         H, i, !
1    Hello, World
2
3
4              Hi
dtype: string
Parameters:

  • series (bigframes.series.Series) – A Series containing arrays.

  • delimiter (str) – The string used to separate array elements.

  • null_text (str, optional) – The string to replace any NULL values in the array with.

Returns:

A Series containing delimited strings.

Return type:

bigframes.series.Series

deterministic_decrypt_bytes(ciphertext: Series | Expression | Literal[Sentinel.ARGUMENT_DEFAULT] | bytes, additional_data: Series | Expression | Literal[Sentinel.ARGUMENT_DEFAULT] | bytes, *, session: Session | None = None) → S[source]#

Uses the matching key from keyset to decrypt ciphertext and verifies the integrity of the data using additional_data. Returns an error if decryption fails.

deterministic_decrypt_string(ciphertext: Series | Expression | Literal[Sentinel.ARGUMENT_DEFAULT] | bytes, additional_data: Series | Expression | Literal[Sentinel.ARGUMENT_DEFAULT] | str, *, session: Session | None = None) → S[source]#

Like DETERMINISTIC_DECRYPT_BYTES, but where plaintext is of type STRING.

deterministic_encrypt(plaintext: Series | Expression | Literal[Sentinel.ARGUMENT_DEFAULT] | bytes | str, additional_data: Series | Expression | Literal[Sentinel.ARGUMENT_DEFAULT] | bytes | str, *, session: Session | None = None) → S[source]#

Encrypts plaintext using the primary cryptographic key in keyset using deterministic AEAD. The algorithm of the primary key must be DETERMINISTIC_AEAD_AES_SIV_CMAC_256. Binds the ciphertext to the context defined by additional_data. Returns NULL if any input is NULL.

flatten(depth: Series | Expression | Literal[Sentinel.ARGUMENT_DEFAULT] | int = Sentinel.ARGUMENT_DEFAULT, *, session: Session | None = None) → S[source]#

Takes an array of nested data and flattens a specific part of it into a single, flat array with the [array elements field access operator][array-el-field-operator]. Returns NULL if the input value is NULL.