bigframes.bigquery.json_query_array#

bigframes.bigquery.json_query_array(input: Series, json_path: str = '$') → Series[source]#

Extracts a JSON array and converts it to a SQL array of JSON-formatted STRING or JSON values. This function uses double quotes to escape invalid JSONPath characters in JSON keys. For example: a.b.

Examples:

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

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

>>> s = bpd.Series([
...   '{"fruits": [{"name": "apple"}, {"name": "cherry"}]}',
...   '{"fruits": [{"name": "guava"}, {"name": "grapes"}]}'
... ])
>>> bbq.json_query_array(s, "$.fruits")
0    ['{"name":"apple"}' '{"name":"cherry"}']
1    ['{"name":"guava"}' '{"name":"grapes"}']
dtype: list<item: string>[pyarrow]

>>> s = bpd.Series([
...   '{"fruits": {"color": "red",   "names": ["apple","cherry"]}}',
...   '{"fruits": {"color": "green", "names": ["guava", "grapes"]}}'
... ])
>>> bbq.json_query_array(s, "$.fruits.names")
0    ['"apple"' '"cherry"']
1    ['"guava"' '"grapes"']
dtype: list<item: string>[pyarrow]

Parameters:

• input (bigframes.series.Series) The Series containing JSON data (as native JSON objects or JSON-formatted strings).

• json_path (str) The JSON path identifying the data that you want to obtain from the input.

Returns:

A new Series with the parsed arrays from the input.

Return type:

bigframes.series.Series