]j=QSSKJr SSKJrJr SSKrSSKJrJrJ r J r SSK r SSK Jr SSKJr SSKJr SSKrSSKJrJr \(aSS KJr SS KJrJr SS jr\ SSS jj5r\ SSS jj5rSSSjjrSSjrSSjrSSSjjr SSjr!\"S5S S!Sjj5r"g)") annotations)abc defaultdictN) TYPE_CHECKINGAny DefaultDictoverloadconvert_json_to_lines) set_module) is_scalar) DataFrameSeries)Iterable) IgnoreRaiseScalarcJUSS:Xd USS:XaU$USSn[U5$)zB Helper function that converts JSON lists to line delimited JSON. r[]r )ss V/home/mande/repo/quber/.venv/lib/python3.13/site-packages/pandas/io/json/_normalize.pyconvert_to_line_delimitsr's4 Q43;1R5C< !BA  ##cgNdsprefixseplevel max_levels rnested_to_recordr%4srcgrrrs rr%r%>src  Sn[U[5(aU/nSn/nUHn[R"U5nUR 5Hup[U [ 5(d [ U 5n US:XaU n OX-U -n [U [5(aUb"X4:aUS:waUR U 5n XU 'MoUR U 5n UR[XX#S-U55 M URU5 M U(aUS$U$)a0 A simplified json_normalize Converts a nested dict into a flat dict ("record"), unlike json_normalize, it does not attempt to extract a subset of the data. Parameters ---------- ds : dict or list of dicts prefix: the prefix, optional, default: "" sep : str, default '.' Nested records will generate names separated by sep, e.g., for sep='.', { 'foo' : { 'bar' : 0 } } -> 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) isinstancedictcopydeepcopyitemsstrpopupdater%append) r r!r"r#r$ singletonnew_dsdnew_dkvnewkeys rr%r%HsXI"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$)a Main recursive function Designed for the most basic use case of pd.json_normalize(data) intended as a performance improvement, see #15621 Parameters ---------- data : Any Type dependent on types contained within nested Json key_string : str New key (with separator(s) in) for data normalized_dict : dict The new normalized/flattened Json dict separator : str, default '.' Nested records will generate names separated by sep, e.g., for sep='.', { 'foo' : { 'bar' : 0 } } -> foo.bar data key_stringnormalized_dict separator)r(r)r, removeprefix_normalize_json)r:r;r<r=keyvaluenew_keys rr?r?st.$**,JC# YKu5G!..y9 " /#   ' '+ # rc (UR5VVs0sHup#[U[5(aMX#_M nnn[UR5VVs0sHup#[U[5(dMX#_M snnS0US9n0UEUE$s snnfs snnf)aK Order the top level keys and then recursively go to depth Parameters ---------- data : dict or list of dicts separator : str, default '.' Nested records will generate names separated by sep, e.g., for sep='.', { 'foo' : { 'bar' : 0 } } -> foo.bar Returns ------- dict or list of dicts, matching `normalized_json_object` r9)r,r(r)r?)r:r=r5r6 top_dict_ nested_dict_s r_normalize_json_orderedrGs#'**,J,$!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$s snf)a1 An optimized basic json_normalize Converts a nested dict into a flat dict ("record"), unlike json_normalize and nested_to_record it doesn't do anything clever. But for the most basic use cases it enhances performance. E.g. pd.json_normalize(data) Parameters ---------- ds : dict or list of dicts sep : str, default '.' Nested records will generate names separated by sep, e.g., for sep='.', { 'foo' : { 'bar' : 0 } } -> foo.bar Returns ------- frame : DataFrame d - dict or list of dicts, matching `normalized_json_object` Examples -------- >>> _simple_json_normalize( ... { ... "flat1": 1, ... "dict1": {"c": 1, "d": 2}, ... "nested": {"e": {"c": 1, "d": 2}, "d": 2}, ... } ... ) {'flat1': 1, 'dict1.c': 1, 'dict1.d': 2, 'nested.e.c': 1, 'nested.e.d': 2, 'nested.d': 2} )r:r=r")r(r)rGlist_simple_json_normalize)r r"normalized_json_objectrownormalized_json_lists rrKrKsiV "d!8b!P "! B  PRSPR 6s DPRS## !! TsAcrUcg[U[5(agUHn[U[5(aEUH=n[U[5(aM[S[ U5R SU<35e M][U[5(aMt[S[ U5R SU<35e g)a Validate that meta parameter contains only strings or lists of strings. Parameters ---------- meta : str or list of str or list of list of str or None The meta parameter to validate. Raises ------ TypeError If meta contains elements that are not strings or lists of strings. Nz9All elements in nested meta paths must be strings. Found z: zBAll elements in 'meta' must be strings or lists of strings. Found )r(r-rJ TypeErrortype__name__)metaitemsubitems r_validate_metarVs |$ dD ! !!'3//#!!%g!7!7 87+G D#&&d,,-Rx9 rpandasc ^^^^^^^^^^^^[U5 SSU4SjjjmSU4Sjjm[U[5(a URnOSn[U[5(aU(d [ 5$[U[ 5(aU/nO[U[R5(a[U[5(d[ U5n[U5Hnup[U [ 5(aM[U 5(a![R"U 5(a0X 'MMS[U 5R3n [!U 5e O["eUcUcUcTcTc[ [%UTS9US9$UcC['SU55(a [)UTTS9n[ XS9n TbU R+U4S jS 9n U $[U[5(dU/nUc/nO[U[5(dU/nUV s/sHn [U [5(aU OU /PM sn m/m/m[-[5mTVs/sHnTR/U5PM snmSSUUUUUUUUUU4 S jjjmT"X0S S 9 [ T5n TbU R+U4SjS 9n TR15HunnUbX?-nX;a[3SUS35e[4R6"U[8S9nUR:S:a<[4R<"[?U54[8S9n[U5H upUUU 'M URAT5X'M UbURAT5U lU $s sn fs snf)a Normalize semi-structured JSON data into a flat table. This method is designed to transform semi-structured JSON data, such as nested dictionaries or lists, into a flat table. This is particularly useful when handling JSON-like data structures that contain deeply nested fields. Parameters ---------- data : dict, list of dicts, or Series 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 String to prefix records with dotted path, e.g. foo.bar.field if meta is ['foo', 'bar']. record_prefix : str, default None String to 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 ------- DataFrame The normalized data, represented as a pandas DataFrame. See Also -------- DataFrame : Two-dimensional, size-mutable, potentially heterogeneous tabular data. Series : One-dimensional ndarray with axis labels (including time series). 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 = [ ... { ... "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}, ... }, ... ] >>> series = pd.Series(data, index=pd.Index(["a", "b", "c"])) >>> pd.json_normalize(series) id name fitness.height fitness.weight a 1.0 Cole Volk 130 60 b NaN Mark Reg 130 60 c 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')r(rJKeyErrornpnan)jsspecextract_recordresultfieldeerrorss r _pull_field#json_normalize.._pull_fields $%%!E~&uo-#]F"(    1#12!vv 1#FqcJ67 s%0>> B,B 4B:B  Bc>T"XSS9n[U[5(dD[R"U5(a/nU$[ S[ U5R SU<35eU$)z Internal function to pull field for records, and similar to _pull_field, but require to return list. And will raise error if has non iterable value. T)r`z(Path must contain list or null, but got z at )r(rJpdisnullrPrQrR)r^r_rares r _pull_records%json_normalize.._pull_recordssr Rd;&$''yy     #F|445T$C rNz9All items in data must be of type dict or NA-like, found rI)indexc3# UH3oR5Vs/sHn[U[5PM snv M5 gs snf7fr)valuesr(r)).0yxs r !json_normalize..5s-G$QXXZ8Z 1d#Z8$8sA=Ar"r$c>TU3$rrrq record_prefixs r json_normalize..@s qc5Jr)columnsrc x> [U[5(aU/n[U5S:aRUHKn[T TSS9H&upVUS-[U5:XdMT "XES5X&'M( T "XASUSSX#S-S9 MM gUHnT "XAS5nUVs/sH%n[U[5(a [ UTTS9OUPM' nnTR [U55 [T TSS9H;upVUS-[U5:aX&n O T "XEUS5n TUR U 5 M= TR U5 M gs snf)NrT)strictrrr#rt)r(r)lenzipr%r0extend)r:path seen_metar#objvalr@recsrmeta_val_metarerj_recursive_extractlengthsr$ meta_keys meta_valsrecordsr"s rr*json_normalize.._recursive_extractSsU dD ! !6D t9q= #E9T BHCqyCH,)4Sb')B !C#3Aw<ab9TUIV $Sq'2 " ""!T**%QC9E" s4y) #E9T BHCqy3s8+#,>#.sK#@cN))(3 !C t$#s,D7r}c>TU3$rrrvs rrxryvsM?1#1FrzConflicting metadata name z, need distinguishing prefix )dtyper)F)r^dict[str, Any]r_ list | strr`boolreturnzScalar | Iterable)r^rr_rrrJ)r)r#intrNone)!rVr(rrlrJrr)rrr- enumerater rhisnarQrRrPNotImplementedErrorrKanyr%renamerjoinr, ValueErrorr\arrayobjectndimemptyr~repeat)r: record_pathrS meta_prefixrwrdr"r$rlirTmsgramrr5r6rnrrerjrrrrrs ```` @@@@@@@@rjson_normalizer/s7T4FK ",>B :($ $d{ D$  v D#,, ' ' 40E0EDz GA$%%2774==!$Z0013 n$'"!  L    !  /#>eLL G$G G G$DcYGD4-  $]]+J]KF  T * *"m  | d # #v8< =1*Q%%QA3 . =EGG(.I*/0%3#%0I%%%<t"A6 w F 'FG!1  "A ;,QC/LM  !6* ;;?XXs1viv6F#A,q 'MM'* %"& ||G, MI >1s 9$M7M)rr-rr-)....) r r)r!r-r"r-r#rr$ int | Nonerr) r z list[dict]r!r-r"r-r#rr$rrzlist[dict[str, Any]])rD.rN) r dict | list[dict]r!r-r"r-r#rr$rrz%dict[str, Any] | list[dict[str, Any]]) r:rr;r-r<rr=r-rr)r:rr=r-rr)r)r rr"r-rzdict | list[dict] | Any)rS"str | list[str | list[str]] | Nonerr)NNNNraiserN)r:zdict | list[dict] | Seriesrzstr | list | NonerSrr str | Nonerwrrdrr"r-r$rrr)# __future__r collectionsrrr*typingrrrr numpyr\pandas._libs.writersr pandas.util._decoratorsr pandas.core.dtypes.commonr rWrhrrcollections.abcrpandas._typingrrrr%r?rGrKrVrrrrrsA# 6./ ( $                        NN N N  N  N + Nb& &&$& &  &R)62"2" 2"2"j> H&*/3" $! ^ $^"^ -^ ^  ^  ^ ^^^^r