ˀh#"ddlmZddlZddlmZmZmZddlmZm Z ddl m Z ddl m Z ddlmZer ddlmZdd lmZ dd Z dd Z dd Ze d dddedd ddZy)) annotationsN)IterableMappingSequence) TYPE_CHECKINGAny)unstable) DataFrame)N_INFER_DEFAULT) JSONEncoder)Schemac |dkDrSi}t|trt||||}|St|tr|Dcgc]}t ||||}}|S|S|Scc}w)Nr)data separator max_levelencoderrrr) isinstancedict_normalize_json_orderedlist_simple_json_normalize)rrrrnormalized_json_objectrownormalized_json_lists t/var/www/html/wine-match-dev/backend/winematch-backend/venv/lib/python3.12/site-packages/polars/convert/normalize.pyrrs 1}!# dD !%<## & ""&%d # $ '''# $ $( '%% $sAc t|trV|dkDrD|r||nd}|dz }|jD] \}} |r||n|} t| | ||||"|S||||<|S|||<|S)a Main recursive function. Designed for the most basic use case of `pl.json_normalize(data)`, intended as a performance improvement. Parameters ---------- data : Any Type dependent on types contained within nested Json key_string : str New key (with separator(s) in) for data normalized_dict : dict The new normalized/flattened Json dict separator : str, default '.' Nested records will generate names separated by sep, e.g., for sep='.', { 'foo' : { 'bar' : 0 } } -> foo.bar max_level recursion depth encoder Custom JSON encoder; if not given, `json.dumps` is used. rr key_stringnormalized_dictrrr)rritems_normalize_json) rr!r"rrrkey_rootnested_max_levelkeyvaluenew_keys rr$r$2s<$ q=5?*i[1RH(1} "jjl  U08XJse,c&$3'.#    +2$-OJ '" "&* # cii}}|jD] \}}t|tr|||<|||<"t|di|||}i||S)a Order the top level keys and then recursively go to depth. Parameters ---------- data Deserialized JSON objects (dict or list of dicts) separator Nested records will generate names separated by sep. e.g., for `separator=".", {"foo": {"bar": 0}}` -> foo.bar. max_level Max number of levels(depth of dict) to normalize. encoder Custom JSON encoder; if not given, `json.dumps` is used. Returns ------- dict or list of dicts, matching `normalized_json_object` rr )r#rrr$) rrrrtop_ nested_datakvnested_s rrrgsr2B+D 1 a KNDG    G d g r*.T)rrschemastrictinfer_schema_lengthrc^|d}|dz }t|trt|dk(r t|St|tr|g}n9t|t rt|t s t|}n d}t||tj}tt|||||||S)u Normalize semi-structured deserialized JSON data into a flat table. Dictionary objects that will not be unnested/normalized are encoded as json string data. Unlike it pandas' counterpart, this function will not encode dictionaries as objects at any level. .. warning:: This functionality is considered **unstable**. It may be changed at any point without it being considered a breaking change. Parameters ---------- data Deserialized JSON objects. separator Nested records will generate names separated by sep. e.g., for `separator=".", {"foo": {"bar": 0}}` -> foo.bar. max_level Max number of levels(depth of dict) to normalize. If None, normalizes all levels. schema Overwrite the `Schema` when the normalized data is passed to the `DataFrame` constructor. strict Whether Polars should be strict when constructing the DataFrame. infer_schema_length Number of rows to take into consideration to determine the schema. encoder Custom JSON encoder function; if not given, `json.dumps` is used. Examples -------- >>> data = [ ... { ... "id": 1, ... "name": "Cole Volk", ... "fitness": {"height": 180, "weight": 85}, ... }, ... { ... "id": 2, ... "name": "Faye Raker", ... "fitness": {"height": 155, "weight": 58}, ... }, ... { ... "name": "Mark Reg", ... "fitness": {"height": 170, "weight": 78}, ... }, ... ] >>> pl.json_normalize(data, max_level=1) shape: (3, 4) ┌──────┬────────────┬────────────────┬────────────────┐ │ id ┆ name ┆ fitness.height ┆ fitness.weight │ │ --- ┆ --- ┆ --- ┆ --- │ │ i64 ┆ str ┆ i64 ┆ i64 │ ╞══════╪════════════╪════════════════╪════════════════╡ │ 1 ┆ Cole Volk ┆ 180 ┆ 85 │ │ 2 ┆ Faye Raker ┆ 155 ┆ 58 │ │ null ┆ Mark Reg ┆ 170 ┆ 78 │ └──────┴────────────┴────────────────┴────────────────┘ Normalize to a specific depth, using a custom JSON encoder (note that `orson.dumps` encodes to bytes, not str). >>> import orjson >>> pl.json_normalize(data, max_level=0, encoder=orjson.dumps) shape: (3, 3) ┌──────┬────────────┬───────────────────────────────┐ │ id ┆ name ┆ fitness │ │ --- ┆ --- ┆ --- │ │ i64 ┆ str ┆ binary │ ╞══════╪════════════╪═══════════════════════════════╡ │ 1 ┆ Cole Volk ┆ b"{"height":180,"weight":85}" │ │ 2 ┆ Faye Raker ┆ b"{"height":155,"weight":58}" │ │ null ┆ Mark Reg ┆ b"{"height":170,"weight":78}" │ └──────┴────────────┴───────────────────────────────┘ lrr)r2z expected list or dict of objectsr)r2r3r4) rrlenr rrstrr ValueErrorjsondumpsr)rrrr2r3r4rmsgs rjson_normalizer<sp  NI$!c$i1n'' D' "v D( #JtS,ADz0o**    /  r*) r/dict[Any, Any] | Sequence[dict[Any, Any] | Any]rr7rintrr returnz+dict[Any, Any] | list[dict[Any, Any]] | Any)rrr!r7r"dict[str, Any]rr7rr>rr r?r@) rr@rr7rr>rr r?r@)rr=rr7r int | Noner2z Schema | Noner3boolr4rArzJSONEncoder | Noner?r ) __future__rr9collections.abcrrrtypingrrpolars._utils.unstabler polars.dataframer polars.datatypes.constantsr polars._typingr polars.schemar rr$rr<r*rrLsZ# 77%+&6*$ 9  1 @2 22$2 2  2  22j( ((( (  (V   &5"&r 9rr r  r  r$r rr rr*