+ xȇi}PP^RIHt^RIHtHt^RIt^RIHtHtH t H t ^RI t ^RI Ht^RIHt^RIt^RIHtHt]'d^RIHt^R IHtHtR R lt] RR R ll4t] RRRll4tRRRlltRRltRRltRRRlltRRlt]!R4R RRll4t R#)!) annotations)abc defaultdictN) TYPE_CHECKINGAny DefaultDictoverloadconvert_json_to_lines) set_module) DataFrameSeries)Iterable) IgnoreRaiseScalarc V^8dQhRRRR/#)sstrreturn)formats"i/Users/max/.openclaw/workspace/postharvest/venv/lib/python3.14/site-packages/pandas/io/json/_normalize.py __annotate__r%s $ $ $ $c^V^,R8XgVR,R8XdV#V^Rp\V4#)zB Helper function that converts JSON lists to line delimited JSON. []r )rs&rconvert_to_line_delimitsr %s4 Q43;1R5C< !BA  ##rc 0V^8dQhRRRRRRRRRR R R /#) rdsdictprefixrseplevelint max_level int | Nonerdict[str, Any]r)rs"rrr3sD         rcR#Nrr"r$r%r&r(s&&&&&rnested_to_recordr.2src 0V^8dQhRRRRRRRRRR R R /#) rr"z list[dict]r$rr%r&r'r(r)rzlist[dict[str, Any]]r)rs"rrr=sD        rcR#r,rr-s&&&&&rr.r.<src 0V^8dQhRRRRRRRRRR R R /#) rr"dict | list[dict]r$rr%r&r'r(r)rz%dict[str, Any] | list[dict[str, Any]]r)rs"rrrFsLNNN N N  N  N + Nrc PRp\V\4'dV.pRp.pVFp\P!V4pVP 4Fwr\V \ 4'g \ V 4p V^8XdT p MW,V ,p \V \4'd Ve%W48dV^8wdVP V 4p WV &K}VP V 4p VP\WW#^,V44K VPV4K V'd V^,#V#)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} FT) isinstancer#copydeepcopyitemsrpopupdater.append) r"r$r%r&r( singletonnew_dsdnew_dkvnewkeys &&&&& rr.r.FsXI"dT F  a GGIDAa%%Fz) a&&%%*<A: ! A$%&M ! A LL)!S!)YO P-.  e36ay Mrc ,V^8dQhRRRRRRRRRR/#) rdatar key_stringrnormalized_dictr* separatorrr)rs"rrrs:&& &&$& &  &rc\V\4'dLVP4F5wrEV V V 2pV'gVPV4p\ VVVVR7K7 V#WV&V#)a Main recursive function Designed for the most basic use case of pd.json_normalize(data) intended as a performance improvement, see #15621 Parameters ---------- data : Any Type dependent on types contained within nested Json key_string : str New key (with separator(s) in) for data normalized_dict : dict The new normalized/flattened Json dict separator : str, default '.' Nested records will generate names separated by sep, e.g., for sep='.', { 'foo' : { 'bar' : 0 } } -> foo.bar rCrDrErF)r4r#r7 removeprefix_normalize_json)rCrDrErFkeyvaluenew_keys&&&& rrJrJst.$**,JC# YKu5G!..y9 " /#   ' '+ # rc$V^8dQhRRRRRR/#)rrCr*rFrrr)rs"rrrs!)).)S)^)rc .VP4UUu/uFwr#\V\4'dKW#bK! ppp\VP4UUu/uFwr#\V\4'gKW#bK! uppR/VR7p/VCVC#uuppiuuppi)aK Order the top level keys and then recursively go to depth Parameters ---------- data : dict or list of dicts separator : str, default '.' Nested records will generate names separated by sep, e.g., for sep='.', { 'foo' : { 'bar' : 0 } } -> foo.bar Returns ------- dict or list of dicts, matching `normalized_json_object` rH)r7r4r#rJ)rCrFr?r@ top_dict_ nested_dict_s&& r_normalize_json_orderedrSs#'**,J,$!jD6I,IJ"#zz| C|tqz!T/Bdad| C L )i (< ((K CsB B B 3B c$V^8dQhRRRRRR/#)rr"r2r%rrzdict | list[dict] | Anyr)rs"rrrs&2"2"2" 2"2"rc/p\V\4'd\WR7pV#\V\4'dVUu.uFp\ W1R7NK ppV#V#uupi)a1 An optimized basic json_normalize Converts a nested dict into a flat dict ("record"), unlike json_normalize and nested_to_record it doesn't do anything clever. But for the most basic use cases it enhances performance. E.g. pd.json_normalize(data) Parameters ---------- ds : dict or list of dicts sep : str, default '.' Nested records will generate names separated by sep, e.g., for sep='.', { 'foo' : { 'bar' : 0 } } -> foo.bar Returns ------- frame : DataFrame d - dict or list of dicts, matching `normalized_json_object` Examples -------- >>> _simple_json_normalize( ... { ... "flat1": 1, ... "dict1": {"c": 1, "d": 2}, ... "nested": {"e": {"c": 1, "d": 2}, "d": 2}, ... } ... ) {'flat1': 1, 'dict1.c': 1, 'dict1.d': 2, 'nested.e.c': 1, 'nested.e.d': 2, 'nested.d': 2} )rCrFr%)r4r#rSlist_simple_json_normalize)r"r%normalized_json_objectrownormalized_json_lists&& rrXrXsiV "d!8b!P "! B  PRSPR 6s DPRS## !! TsAc V^8dQhRRRR/#)rmeta"str | list[str | list[str]] | NonerNoner)rs"rrrs;rcVfR#\V\4'dR#VFp\V\4'dGVF>p\V\4'dK\R\ V4P RV: 24h K_\V\4'dKw\R\ V4P RV: 24h R#)a Validate that meta parameter contains only strings or lists of strings. Parameters ---------- meta : str or list of str or list of list of str or None The meta parameter to validate. Raises ------ TypeError If meta contains elements that are not strings or lists of strings. Nz9All elements in nested meta paths must be strings. Found z: zBAll elements in 'meta' must be strings or lists of strings. Found )r4rrW TypeErrortype__name__)r]itemsubitems& r_validate_metarfs |$ dD ! !!'3//#!!%g!7!7 87+G D#&&d,,-Rx9 rpandasc<V^8dQhRRRRRRRRR RR R R R RRRR/ #)rrCzdict | list[dict] | Series record_pathzstr | list | Noner]r^ meta_prefixz str | None record_prefixerrorsrr%rr(r)rr r)rs"rrr.smZZ $Z"Z -Z Z  Z  Z ZZZrc aaaaaaaaaaaa\V4RRV3RllloRV3Rllo\V\4'dVPpMRp\V\4'dV'g \ 4#\V\ 4'dV.pM\V\P4'de\V\4'gO\ V4pVF<p \V \ 4'dKR\V 4P 2p \V 4h M\hVf)Vf%Vf!SfSf\ \VSR7VR 7#Vfq\;QJdR V4F 'gK R M RM !R V44'd\!VSSR 7p\ WR 7p SeV P#V3R lR7p V #\V\4'gV.pVf.pM\V\4'gV.pVU u.uFp \V \4'dT MV .NK up o.o.o\%\4oSU u.uFp SP'V 4NK up oRRVVVVVVVVVV3 RllloS!W/^R7\ S4p SeV P#V3RlR7p SP)4FwrVe W>,pW9d\+RV R24h\,P.!V\0R7pVP2^8dA\,P4!\7V43\0R7p\9V4F wpp V VV&K VP;S4W&K VeVP;S4V nV #uup iuup i)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. Fc(V^8dQhRRRRRRRR/#) rjsr*spec list | strextract_recordboolrzScalar | Iterabler)rs"rr$json_normalize..__annotate__s, ",>B rcD<Tp\V\4'd#VFpVf \V4hW4,pK V#W1,pV# \dMpT'd\RT R24ThSR8Xd\PuRp?#\RT RT R24ThRp?ii;i)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')r4rWKeyErrornpnan)rorprrresultfielderls&&& r _pull_field#json_normalize.._pull_fields $%%!E~&uo-#]F"(    1#12!vv 1#FqcJ67 s-A AA B.BBBBc$V^8dQhRRRRRR/#)rror*rprqrrWr)rs"rrrts!. trc<S!WRR7p\V\4'gF\P!V4'd.pV#\ R\ V4P RV: 24hV#)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)rrz(Path must contain list or null, but got z at )r4rWpdisnullrarbrc)rorprzr}s&& r _pull_records%json_normalize.._pull_recordssr Rd;&$''yy     #F|445T$C rNz.All items in data must be of type dict, found rV)indexc3"TF2qP4Uu.uFp\V\4NK upxK4 R#uupi5ir,)valuesr4r#).0yxs& r !json_normalize../s-G$QXXZ8Z 1d#Z8$8sA<ATr%r(c<S V 2#r,rrrks&r json_normalize..:s qc5Jr)columnsc V^8dQhRRRR/#)rr&r'rr_r)rs"rrrtMs%%%T%rc < \V\4'dV.p\V4^8dsVFjp\S SRR7F/wrVV^,\V48XgKS !WER,4W&&K1 S !WA^,,VR,W#^,R7Kl R#VFpS !WA^,4pVUu.uF)p\V\4'd\ VSSR7MTNK+ ppSP \V44\S SRR7FHwrVV^,\V48d W&,p M S !WEVR4p SV,P V 4KJ SP V4K R#uupi)T)strict:rNNr&rNr)r4r#lenzipr.r:extend)rCpath seen_metar&objvalrKrecsrmeta_val_metar}r_recursive_extractlengthsr( meta_keys meta_valsrecordsr%s&&&& rr*json_normalize.._recursive_extractMsS dD ! !6D t9q= #E9T BHCqyCH,)4Sb')B !C#3Aw<b9TUIV $Sq'2 " ""!T**%QC9E" s4y) #E9T BHCqy3s8+#,>#.sK#@cN))(3 !C t$#s5/E1rc<S V 2#r,rrs&rrrpsM?1#1FrzConflicting metadata name z, need distinguishing prefix )dtype)F)r)rfr4rrrWr r#rrrrbrcraNotImplementedErrorrXanyr.renamerjoinr7 ValueErrorrxarrayobjectndimemptyr enumeraterepeat)rCrir]rjrkrlr%r(rrdmsgrzmrr?r@rirr}rrrrrrs&&&&ffff @@@@@@@@rjson_normalizer-sT4:($ $d{ D$  v D#,, ' ' 40E0EDzDdD))!$Z0013 n$ "!  L    !  /#>eLL 3G$G333G$G G G$DcYGD4-  $]]+J]KF  T * *"m  | d # #v8< =1*Q%%QA3 . =EGG(.I*/0%3#%0I%%%<t"A6 w F 'FG!  "A ;,QC/LM  !6* ;;?XXs1viv6F#A,3q 'MM'* %"& ||G, MI >1s :$M/8M4)....)rP.rN)r)NNNNraiserN)! __future__r collectionsrrr5typingrrrr numpyrxpandas._libs.writersr pandas.util._decoratorsr rgrr rcollections.abcrpandas._typingrrr r.rJrSrXrfrrrrrs# 6. ( $    Nb&R)22"j> HZZr