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} FTrr) isinstancedictcopydeepcopyitemsstrpopupdatenested_to_recordappend) dsprefixseplevel max_level singletonnew_dsdnew_dkvnewkeys rr!r!-sXI"dT F  a GGIDAa%%Fz) a&&%%*<A: ! A$%&M ! A LL)!S!)YO P-.  e36ay Mrc[U[5(aGUR5H1upEUUU3nU(dURU5n[ UUUUS9 M3 U$XU'U$)a Main recursive function Designed for the most basic use case of pd.json_normalize(data) intended as a performance improvement, see #15621 Parameters ---------- data : Any Type dependent on types contained within nested Json key_string : str New key (with separator(s) in) for data normalized_dict : dict The new normalized/flattened Json dict separator : str, default '.' Nested records will generate names separated by sep, e.g., for sep='.', { 'foo' : { 'bar' : 0 } } -> foo.bar data key_stringnormalized_dict separator)rrr removeprefix_normalise_json)r1r2r3r4keyvaluenew_keys rr6r6~st.$**,JC# YKu5G!..y9 " /#   ' '+ # rc (UR5VVs0sHup#[U[5(aMX#_M nnn[UR5VVs0sHup#[U[5(dMX#_M snnS0US9n0UEUE$s snnfs snnf)aK Order the top level keys and then recursively go to depth Parameters ---------- data : dict or list of dicts separator : str, default '.' Nested records will generate names separated by sep, e.g., for sep='.', { 'foo' : { 'bar' : 0 } } -> foo.bar Returns ------- dict or list of dicts, matching `normalised_json_object` r0)rrrr6)r1r4r,r- top_dict_ nested_dict_s r_normalise_json_orderedr>s#'**,J,$!jD6I,IJ"#zz| C|tqz!T/Bdad| C L )i (< ((K CsBBB 3B c0n[U[5(a [XS9nU$[U[5(aUVs/sH n[ X1S9PM nnU$U$s snf)a0 A optimized basic json_normalize Converts a nested dict into a flat dict ("record"), unlike json_normalize and nested_to_record it doesn't do anything clever. But for the most basic use cases it enhances performance. E.g. pd.json_normalize(data) Parameters ---------- ds : dict or list of dicts sep : str, default '.' Nested records will generate names separated by sep, e.g., for sep='.', { 'foo' : { 'bar' : 0 } } -> foo.bar Returns ------- frame : DataFrame d - dict or list of dicts, matching `normalised_json_object` Examples -------- >>> _simple_json_normalize( ... { ... "flat1": 1, ... "dict1": {"c": 1, "d": 2}, ... "nested": {"e": {"c": 1, "d": 2}, "d": 2}, ... } ... ) {'flat1': 1, 'dict1.c': 1, 'dict1.d': 2, 'nested.e.c': 1, 'nested.e.d': 2, 'nested.d': 2} )r1r4r%)rrr>list_simple_json_normalize)r#r%normalised_json_objectrownormalised_json_lists rrBrBsiV "d!8b!P "! B  PRSPR 6s DPRS## !! TsAc ^^^^^^^^^^^^SSU4SjjjmSU4Sjjm[U[5(aU(d [5$[U[5(aU/nOF[U[R 5(a![U[ 5(d [U5nO[eUcUcUcTcTc[[UTS95$Uc-[SU55(a [UTTS9n[U5$[U[5(dU/nUc/nO[U[5(dU/nUVs/sHn[U[5(aUOU/PM snm/m/m[[5mTV s/sHn TRU 5PM sn mSSUUUUUUUUUU4 SjjjmT"X0SS9 [T5n TbU RU4S jS 9n TR5HupUbX;-n X;a[S U S 35e[ R""U [$S 9n U R&S:a;[ R("[+U 54[$S 9n [-U 5H upXU'M U R/T5X'M U $s snfs sn f)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. c*>Un[U[5(aUHnUc [U5eX4nM U$X1nU$![aJnU(a[SUS35UeTS:Xa[RsSnA$[SUSUS35UeSnAff=f)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')rrAKeyErrornpnan)jsspecextract_recordresultfieldeerrorss r _pull_field#json_normalize.._pull_fields $%%!E~&uo-#]F"(    1#12!vv 1#FqcJ67 s%0>> B,B 4B:B  Bc>T"XSS9n[U[5(d3[R"U5(a/nU$[ USUSUS35eU$)z Internal function to pull field for records, and similar to _pull_field, but require to return list. And will raise error if has non iterable value. T)rNz has non list value z for path z. Must be list or null.)rrApdisnull TypeError)rLrMrOrSs r _pull_records%json_normalize.._pull_recordssn Rd;&$''yy     d.vhjG,, rr@c3# UH3oR5Vs/sHn[U[5PM snv M5 gs snf7fN)valuesrr).0yxs r !json_normalize..s-G$QXXZ8Z 1d#Z8$8sA=Ar%r'rc |> [U[5(aU/n[U5S:aSUHLn[T T5H&upVUS-[U5:XdMT "XES5X&'M( T "XASUSSX#S-S9 MN gUHnT "XAS5nUVs/sH%n[U[5(a [ UTTS9OUPM' nnTR [U55 [T T5H;upVUS-[U5:aX&n O T "XEUS5n TUR U 5 M= TR U5 M gs snf)Nrrrr&rc)rrlenzipr!r"extend)r1path seen_metar&objvalr7recsrmeta_val_metarSrY_recursive_extractlengthsr' meta_keys meta_valsrecordsr%s rrq*json_normalize.._recursive_extractsO dD ! !6D t9q= #E9 5HCqyCH,)4Sb')B !6#3Aw<ab9TUIV $Sq'2 " ""!T**%QC9E" s4y) #E9 5HCqy3s8+#,>#.sK#@cN))(3 !6 t$#s,D9rec>TU3$r\)r` record_prefixs r json_normalize.. sM?1#1Fr)columnszConflicting metadata name z, need distinguishing prefix )dtyper)F)rLdict[str, Any]rM list | strrNboolreturnzScalar | Iterable)rLr~rMrrrA)r)r&intrNone)rrAr rrr rNotImplementedErrorrBanyr!rjoinrenamer ValueErrorrJarrayobjectndimemptyrf enumeraterepeat)r1 record_pathmeta meta_prefixryrRr%r'mrlrOr,r-r]irprSrYrqrrrsrtrus ```` @@@@@@@@rjson_normalizersq\FK ",>B :($d{ D$  v D#,, ' ' 40E0EDz!!  L    !  /#>?? G$G G G$DcYGD  T * *"m  | d # #v8< =1*Q%%QA3 . =EGG(.I*/0%3#%0I%%%<t"A6 w F 'FG!  "A ;,QC/LM  !6* ;;?XXs1viv6F!! q %MM'* %"& ME >1s 8$I=6J)rrrr)r;.rN)r$rr%rr&rr' int | None) r1rr2rr3r~r4rrr~)r1r~r4rrr~)r)r#dict | list[dict]r%rrzdict | list[dict] | Any)NNNNraiserN)r1rrzstr | list | Nonerz"str | list[str | list[str]] | Noner str | NoneryrrRr r%rr'rrr ) __future__r collectionsrrrtypingrrrnumpyrJpandas._libs.writersr pandasrVr collections.abcr pandas._typingr rrr!r6r>rBrrxrrrsU#  6( $ N N N  N  Nb& &&$& &  &R)62"2" 2"2"n&*/3" $! k k"k -k k  k  k kkkr