!h 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_dkvnewkeys rr*r*-sPXI"dT F  a  GGII Q QDAqa%% FFzz#) a&& %%9*<*<A:: ! A$%E&M ! A LL)!VS%!)YOO P P P P eay Mrdatar key_stringnormalized_dictdict[str, Any] separatorct|trM|D]7\}}|||}|s||}t ||||8n|||<|S)a3 Main recursive function Designed for the most basic use case of pd.json_normalize(data) intended as a performance improvement, see #15621 Parameters ---------- data : Any Type dependent on types contained within nested Json key_string : str New key (with separator(s) in) for data normalized_dict : dict The new normalized/flattened Json dict separator : str, default '.' Nested records will generate names separated by sep, e.g., for sep='.', { 'foo' : { 'bar' : 0 } } -> foo.bar r4r5r6r8)r#r$r' removeprefix_normalise_json)r4r5r6r8keyvaluenew_keys rr<r<~s.$+**,,  JC#5Y555G :!..y99 " /#       '+ # rcd|D}td|Ddi|}i||S)aw Order the top level keys and then recursively go to depth Parameters ---------- data : dict or list of dicts separator : str, default '.' Nested records will generate names separated by sep, e.g., for sep='.', { 'foo' : { 'bar' : 0 } } -> foo.bar Returns ------- dict or list of dicts, matching `normalised_json_object` cDi|]\}}t|t||Sr#r$.0r1r2s r z+_normalise_json_ordered..s-JJJ$!QjD6I6IJAJJJrcDi|]\}}t|t||SrBrCrDs rrFz+_normalise_json_ordered..s- C C Ctq!z!T/B/B Ca C C Crrr:)r'r<)r4r8 top_dict_ nested_dict_s r_normalise_json_orderedrJshKJ$**,,JJJI" C Ctzz|| C C C L )i (< ((rr,dict | list[dict]dict | list[dict] | Anyci}t|trt|}n%t|trfd|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} )r4r8c2g|]}t|S)r)_simple_json_normalize)rErowrs r z*_simple_json_normalize..s'SSS 6s D D DSSSr)r#r$rJlist)r,rnormalised_json_objectnormalised_json_lists ` rrPrPsmV "d$!8bC!P!P!P B  $SSSSPRSSS## !!rraise record_pathstr | list | Nonemeta"str | list[str | list[str]] | None meta_prefix str | None record_prefixerrorsr r c   dd fd d!fd t|tr|stSt|tr|g}nFt|tjr%t|t st|}nt|&|$|" tt|S|:td|Drt|}t|St|ts|g}|g}nt|ts|g}d|D ggttfd Dd"d# f d ||idt}| fd} D]\} } ||| z} | |vrtd| dtj| t"} | jdkrCtjt)| ft"} t+| D] \} } | | | < | || <|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. Fjsr7spec list | strextract_recordboolrScalar | Iterablec4|} t|tr|D]}|t|||}n||}nV#t$rI}|rtd|d|dkrtjcYd}~Std|d|d|d}~wwxYw|S)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#rSKeyErrornpnan)r`rarcresultfielder^s r _pull_fieldz#json_normalize.._pull_fields $%% &!++E~&uoo-#E]FF+      21222!!v 7177q777  s#._pull_recordss Rd;;;&$'' y   ,,v,,,,, rNrOc3RK|]"}d|DV#dS)c8g|]}t|tSrBrC)rExs rrRz,json_normalize...s"888 1d##888rN)values)rEys r z!json_normalize..s9GGQ88QXXZZ888GGGGGGrrr cBg|]}t|tr|n|gSrB)r#rS)rEms rrRz"json_normalize..s- = = =1*Q%% .QQA3 = = =rc:g|]}|SrB)join)rEvalrs rrRz"json_normalize..s#0003#000rrrrNonec t|tr|g}t|dkrn|D]i}t D]0\}}|dzt|kr ||d||<1 ||d|dd||dzjdS|D]} ||d}fd|D} t|t D]S\}}|dzt|kr ||}n |||d}||T|dS)Nrrrrcbg|]+}t|trt|n|,S)rz)r#r$r*)rErr rs rrRz>json_normalize.._recursive_extract..sR"!T**$QC9EEEEr)r#r$lenzipr+extend)r4path seen_metarobjrr=recsmeta_val_metarnrs_recursive_extractlengthsr meta_keys meta_valsrecordsrs rrz*json_normalize.._recursive_extracts dD ! ! 6D t99q== W W #E9 5 5CCHCqyCHH,,)4S#b')B)B #""3tAw<abb9ETUIVVVVV  W W % %$}S$q'22" s4yy))) #E9 5 544HCqy3s88++#,S>#.;sCK#@#@cN))(3333t$$$$# % %rrc|S)NrB)rvr]s rz json_normalize.. sM1F11F1Fr)columnszConflicting metadata name z, need distinguishing prefix )dtyper)F)r`r7rarbrcrdrre)r`r7rarbrrS)r)rrrr)r#rSr r$rr rNotImplementedErrorrPanyr*rrenamer' ValueErrorriarrayobjectndimemptyr enumeraterepeat)r4rWrYr[r]r^rr rkr1r2rwirrnrsrrrrrs ```` @@@@@@@@rjson_normalizersG\FK:($ "d "{{ D$  "v D#, ' '" 40E0E"Dzz!!  L    !  /#>>>??? GG$GGG G G H$DcYGGGD  T * *$"m  | d # #v = = = = =EGG(..I0000%000I%%%%%%%%%%%%%%%%<t["A6666 w  F 'F'F'F'FGG!!++1  "aA ;;MQMMM  !6*** ;??Xs1vviv666F!!   1q MM'**q Mr)rrrr)rrrN)rrrrrrr r!) r4rr5rr6r7r8rrr7)r4r7r8rrr7)r)r,rKrrrrL)NNNNrVrN)r4rKrWrXrYrZr[r\r]r\r^r rrr r!rr ) __future__r collectionsrrr%typingrrrnumpyripandas._libs.writersr pandasrpr collections.abcr pandas._typingr rrr*r<rJrPrrBrrrs#"""""  666666(((((( $ $ $ $ NNNNNb&&&&R))))62"2"2"2"2"n&*/3" $! kkkkkkkr