a é´«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) Ú isinstanceÚdictÚcopyÚdeepcopyÚitemsrÚpopÚupdateÚnested_to_recordÚappend)ÚdsrrrrZ singletonZnew_dsÚdZnew_dÚkÚvZnewkeyrrrr&-s8, ÿÿ r&rúdict[str, Any])ÚdataÚ key_stringÚnormalized_dictÚ separatorrcCsXt|tƒrL| ¡D]6\}}|›|›|›}|s8| |¡}t||||dqn|||<|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 ©r-r.r/r0)rr r#ÚremoveprefixÚ_normalise_json)r-r.r/r0ÚkeyÚvalueZnew_keyrrrr3~s ü r3)r-r0rcCs<dd„| ¡Dƒ}tdd„| ¡Dƒdi|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|tƒs||“qSr©rr ©Ú.0r*r+rrrÚ ¶óz+_normalise_json_ordered..cSs i|]\}}t|tƒr||“qSrr6r7rrrr9¸r:rr1)r#r3)r-r0Z top_dict_Znested_dict_rrrÚ_normalise_json_ordered§sür;zdict | list[dict]zdict | list[dict] | Any)r(rrcs@i}t|tƒrt|ˆd}n t|tƒr<‡fdd„|Dƒ}|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} )r-r0csg|]}t|ˆd‘qS)©r)Ú_simple_json_normalize)r8Úrowr<rrÚ ðr:z*_simple_json_normalize..)rr r;Úlist)r(rZnormalised_json_objectZnormalised_json_listrr<rr=Às+ r=Úraisezstr | list | Nonez"str | list[str | list[str]] | Nonez str | Nonerr ) r-Úrecord_pathÚmetaÚmeta_prefixÚ record_prefixÚerrorsrrrc s>d#dddddœ‡fdd„ ‰ddd d œ‡fdd„‰t|tƒrD|sDtƒSt|tƒrV|g}n$t|tjƒrvt|tƒsvt|ƒ}nt‚|d ur²|d ur²|d ur²ˆ d ur²ˆd ur²tt|ˆdƒS|d urât dd„|DƒƒrÚt |ˆˆd}t|ƒSt|tƒsò|g}|d urg}nt|tƒs|g}dd„|Dƒ‰g‰ g‰ttƒ‰‡fdd„ˆDƒ‰d$dddœ‡‡‡‡‡‡‡‡‡ ‡f dd„ ‰ˆ||iddtˆ ƒ}ˆ d ur¢|j‡ fdd„d}ˆ ¡D]Ž\} } |d urÄ|| } | |vrÞtd| ›d ƒ‚tj| td!}|jd"kr(tjt| ƒftd!}t| ƒD]\}} | ||<q| ˆ¡|| <qª|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. Fr,z list | strÚboolzScalar | Iterable)ÚjsÚspecÚextract_recordrc s°|}z:t|tƒr4|D]}|dur(t|ƒ‚||}qn||}Wnltyª}zT|rdtd|›dƒ|‚ˆdkr~tjWYd}~Std|›d|›dƒ|‚WYd}~n d}~00|S)zInternal function to pull fieldNzKey zS not found. If specifying a record_path, all elements of data should have the path.Úignorez) not found. To replace missing values of z% with np.nan, pass in errors='ignore')rr@ÚKeyErrorÚnpÚnan)rHrIrJÚresultÚfieldÚe)rFrrÚ_pull_field‚s. ÿýÿýz#json_normalize.._pull_fieldr@)rHrIrcsFˆ||dd}t|tƒsBt |¡r(g}nt|›d|›d|›dƒ‚|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)rJz has non list value z for path z. Must be list or null.)rr@ÚpdZisnullÚ TypeError)rHrIrO)rRrrÚ _pull_recordsŸs ÿz%json_normalize.._pull_recordsNr<css |]}dd„| ¡DƒVqdS)cSsg|]}t|tƒ‘qSrr6)r8Úxrrrr?Ìr:z,json_normalize...N)Úvalues)r8ÚyrrrÚ Ìr:z!json_normalize..©rrcSs g|]}t|tƒr|n|g‘qSr)rr@)r8Úmrrrr?Þr:z"json_normalize..csg|]}ˆ |¡‘qSr)Újoin)r8Úvalr<rrr?år:rrÚNone)rrc st|tƒr|g}t|ƒdkr‚|D]^}tˆˆƒD]*\}}|dt|ƒkr.ˆ||dƒ||<q.ˆ||d|dd…||ddq n’|D]Œ}ˆ||dƒ}‡‡ fdd„|Dƒ}ˆ t|ƒ¡tˆˆƒD]B\}}|dt|ƒkræ||}nˆ|||d…ƒ}ˆ| |¡qÄˆ |¡q†dS)Nrrr©rcs(g|] }t|tƒr t|ˆˆdn|‘qS)rZ)rr r&)r8Úr)rrrrr?ôsþÿz>json_normalize.._recursive_extract..)rr ÚlenÚzipr'Úextend) r-ÚpathZ seen_metarÚobjr]r4ZrecsZmeta_val) Ú_metarRrUÚ_recursive_extractÚlengthsrÚ meta_keysÚ meta_valsÚrecordsrrrrgçs( (ü z*json_normalize.._recursive_extractr_csˆ›|›S)Nr)rV)rErrÚ r:z json_normalize..)ÚcolumnszConflicting metadata name z, need distinguishing prefix )Zdtyper)F)r)rr@r r rrrÚNotImplementedErrorr=Úanyr&rÚrenamer#Ú ValueErrorrMÚarrayÚobjectÚndimÚemptyraÚ enumerateÚrepeat) r-rBrCrDrErFrrrOr*r+rWÚir)rfrRrUrgrFrhrrirjrErkrrÚjson_normalizeõspÿ ÿþýüû ( ÿry)rrrN)r)NNNNrArN)Ú __future__rÚcollectionsrrr!ÚtypingrrrÚnumpyrMZpandas._libs.writersr ZpandasrSr Úcollections.abcrZpandas._typingrr rr&r3r;r=ryrrrrÚs8ûQ)þ7ø