o ۭj foo.bar level: int, optional, default: 0 The number of levels in the json string. max_level: int, optional, default: None The max depth to normalize. Returns ------- d - dict or list of dicts, matching `ds` Examples -------- >>> nested_to_record( ... dict(flat1=1, dict1=dict(c=1, d=2), nested=dict(e=dict(c=1, d=2), d=2)) ... ) {'flat1': 1, 'dict1.c': 1, 'dict1.d': 2, 'nested.e.c': 1, 'nested.e.d': 2, 'nested.d': 2} FTrNr) isinstancedictcopydeepcopyitemsrpopupdatenested_to_recordappend) dsrrrr singletonnew_dsdnew_dkvnewkeyrrrr'-s2,        r'datar key_stringnormalized_dictdict[str, Any] separatorcCsZt|tr'|D]\}}|||}|s||}t||||dq |S|||<|S)a3 Main recursive function Designed for the most basic use case of pd.json_normalize(data) intended as a performance improvement, see #15621 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 r1r2r3r5)r r!r$ removeprefix_normalise_json)r1r2r3r5keyvaluenew_keyrrrr8~s  r8cCs<dd|D}tdd|Ddi|d}i||S)aw Order the top level keys and then recursively go to depth Parameters ---------- data : dict or list of dicts separator : str, default '.' Nested records will generate names separated by sep, e.g., for sep='.', { 'foo' : { 'bar' : 0 } } -> foo.bar Returns ------- dict or list of dicts, matching `normalised_json_object` cSs i|] \}}t|ts||qSrr r!.0r.r/rrr  z+_normalise_json_ordered..cSs i|] \}}t|tr||qSrr<r=rrrr?r@rr6)r$r8)r1r5 top_dict_ nested_dict_rrr_normalise_json_ordereds rCr)dict | list[dict]dict | list[dict] | AnycsBi}t|trt|d}|St|trfdd|D}|S|S)a A optimized basic json_normalize Converts a nested dict into a flat dict ("record"), unlike json_normalize and nested_to_record it doesn't do anything clever. But for the most basic use cases it enhances performance. E.g. pd.json_normalize(data) Parameters ---------- ds : dict or list of dicts sep : str, default '.' Nested records will generate names separated by sep, e.g., for sep='.', { 'foo' : { 'bar' : 0 } } -> foo.bar Returns ------- frame : DataFrame d - dict or list of dicts, matching `normalised_json_object` Examples -------- >>> _simple_json_normalize( ... { ... "flat1": 1, ... "dict1": {"c": 1, "d": 2}, ... "nested": {"e": {"c": 1, "d": 2}, "d": 2}, ... } ... ) {'flat1': 1, 'dict1.c': 1, 'dict1.d': 2, 'nested.e.c': 1, 'nested.e.d': 2, 'nested.d': 2} )r1r5csg|]}t|dqS)r)_simple_json_normalize)r>rowrFrr sz*_simple_json_normalize..)r r!rClist)r)rnormalised_json_objectnormalised_json_listrrFrrGs+   rGraise record_pathstr | list | Nonemeta"str | list[str | list[str]] | None meta_prefix str | None record_prefixerrorsr r c s d%d&fd d d'fd d t|tr|stSt|tr#|g}nt|tjr3t|ts3t|}nt|durQ|durQ|durQ durQdurQtt| dS|durit dd|Dret | d}t|St|tsq|g}|durxg}nt|ts|g}dd|Dg gt t fddDd(d) f dd ||iddt } dur|j fddd } D]C\} } |dur|| } | |vrtd!| d"tj| td#} | jd$krtjt| ftd#} t| D]\} } | | | <q| || <q|S)*a Normalize semi-structured JSON data into a flat table. Parameters ---------- data : dict or list of dicts Unserialized JSON objects. record_path : str or list of str, default None Path in each object to list of records. If not passed, data will be assumed to be an array of records. meta : list of paths (str or list of str), default None Fields to use as metadata for each record in resulting table. meta_prefix : str, default None If True, prefix records with dotted (?) path, e.g. foo.bar.field if meta is ['foo', 'bar']. record_prefix : str, default None If True, prefix records with dotted (?) path, e.g. foo.bar.field if path to records is ['foo', 'bar']. errors : {'raise', 'ignore'}, default 'raise' Configures error handling. * 'ignore' : will ignore KeyError if keys listed in meta are not always present. * 'raise' : will raise KeyError if keys listed in meta are not always present. sep : str, default '.' Nested records will generate names separated by sep. e.g., for sep='.', {'foo': {'bar': 0}} -> foo.bar. max_level : int, default None Max number of levels(depth of dict) to normalize. if None, normalizes all levels. Returns ------- frame : DataFrame Normalize semi-structured JSON data into a flat table. Examples -------- >>> data = [ ... {"id": 1, "name": {"first": "Coleen", "last": "Volk"}}, ... {"name": {"given": "Mark", "family": "Regner"}}, ... {"id": 2, "name": "Faye Raker"}, ... ] >>> pd.json_normalize(data) id name.first name.last name.given name.family name 0 1.0 Coleen Volk NaN NaN NaN 1 NaN NaN NaN Mark Regner NaN 2 2.0 NaN NaN NaN NaN Faye Raker >>> data = [ ... { ... "id": 1, ... "name": "Cole Volk", ... "fitness": {"height": 130, "weight": 60}, ... }, ... {"name": "Mark Reg", "fitness": {"height": 130, "weight": 60}}, ... { ... "id": 2, ... "name": "Faye Raker", ... "fitness": {"height": 130, "weight": 60}, ... }, ... ] >>> pd.json_normalize(data, max_level=0) id name fitness 0 1.0 Cole Volk {'height': 130, 'weight': 60} 1 NaN Mark Reg {'height': 130, 'weight': 60} 2 2.0 Faye Raker {'height': 130, 'weight': 60} Normalizes nested data up to level 1. >>> data = [ ... { ... "id": 1, ... "name": "Cole Volk", ... "fitness": {"height": 130, "weight": 60}, ... }, ... {"name": "Mark Reg", "fitness": {"height": 130, "weight": 60}}, ... { ... "id": 2, ... "name": "Faye Raker", ... "fitness": {"height": 130, "weight": 60}, ... }, ... ] >>> pd.json_normalize(data, max_level=1) id name fitness.height fitness.weight 0 1.0 Cole Volk 130 60 1 NaN Mark Reg 130 60 2 2.0 Faye Raker 130 60 >>> data = [ ... { ... "state": "Florida", ... "shortname": "FL", ... "info": {"governor": "Rick Scott"}, ... "counties": [ ... {"name": "Dade", "population": 12345}, ... {"name": "Broward", "population": 40000}, ... {"name": "Palm Beach", "population": 60000}, ... ], ... }, ... { ... "state": "Ohio", ... "shortname": "OH", ... "info": {"governor": "John Kasich"}, ... "counties": [ ... {"name": "Summit", "population": 1234}, ... {"name": "Cuyahoga", "population": 1337}, ... ], ... }, ... ] >>> result = pd.json_normalize( ... data, "counties", ["state", "shortname", ["info", "governor"]] ... ) >>> result name population state shortname info.governor 0 Dade 12345 Florida FL Rick Scott 1 Broward 40000 Florida FL Rick Scott 2 Palm Beach 60000 Florida FL Rick Scott 3 Summit 1234 Ohio OH John Kasich 4 Cuyahoga 1337 Ohio OH John Kasich >>> data = {"A": [1, 2]} >>> pd.json_normalize(data, "A", record_prefix="Prefix.") Prefix.0 0 1 1 2 Returns normalized data with columns prefixed with the given string. Fjsr4spec list | strextract_recordboolrScalar | Iterablec s|}z t|tr|D]}|durt|||}q W|S||}W|StyR}z$|r5td|d|dkrBtjWYd}~Std|d|d|d}~ww)zInternal function to pull fieldNzKey zS not found. If specifying a record_path, all elements of data should have the path.ignorez) not found. To replace missing values of z% with np.nan, pass in errors='ignore')r rJKeyErrornpnan)rVrWrYresultfielde)rUrr _pull_fields6    z#json_normalize.._pull_fieldrJcsH||dd}t|ts"t|rg}|St|d|d|d|S)z Internal function to pull field for records, and similar to _pull_field, but require to return list. And will raise error if has non iterable value. T)rYz has non list value z for path z. Must be list or null.)r rJpdisnull TypeError)rVrWr`)rcrr _pull_recordss  z%json_normalize.._pull_recordsNrFcss"|] }dd|DVqdS)cSsg|]}t|tqSrr<)r>xrrrrIz,json_normalize...N)values)r>yrrr s z!json_normalize..rrcSs g|] }t|tr |n|gqSr)r rJ)r>mrrrrIr@z"json_normalize..csg|]}|qSr)join)r>valrFrrrIrirrrNonec st|tr|g}t|dkrB|D]/}tD]\}}|dt|kr,||d||<q||d|dd||ddqdS|D]F}||d} fdd|D}t|tD]!\}}|dt|krt||}n |||d}||qc|qDdS)Nrrrrcs(g|]}t|trt|dn|qS)rm)r r!r')r>r)rrrrrIs z>json_normalize.._recursive_extract..)r r!lenzipr(extend) r1path seen_metarobjrpr9recsmeta_val) _metarcrg_recursive_extractlengthsr meta_keys meta_valsrecordsrrrr}s.  &   z*json_normalize.._recursive_extractrrcs |S)Nr)rh)rTrr s z json_normalize..)columnszConflicting metadata name z, need distinguishing prefix )dtyper)F)rVr4rWrXrYrZrr[)rVr4rWrXrrJ)r)rrrrq)r rJr r!rr rNotImplementedErrorrGanyr'rrenamer$ ValueErrorr^arrayobjectndimemptyrt enumeraterepeat) r1rNrPrRrTrUrrr`r.r/rjir) r|rcrgr}rUr~rrrrTrrrjson_normalizesf    "   r)rrrr)rrrN)rrrrrrrr) r1rr2rr3r4r5rrr4)r1r4r5rrr4)r)r)rDrrrrE)NNNNrMrN)r1rDrNrOrPrQrRrSrTrSrUr rrrrrr ) __future__r collectionsrrr"typingrrrnumpyr^pandas._libs.writersr pandasrdr collections.abcr pandas._typingr r rr'r8rCrGrrrrrs:      Q ) 7