ó šxŠ\c@sˆddlmZddlZddlZddlmZddlmZm Z d„Z dddd „Z ddddd dd „Z dS( iÿÿÿÿ(t defaultdictN(tconvert_json_to_lines(t DataFrametcompatcCs<|ddk r%|ddkr%|S|dd!}t|ƒS(sJ Helper function that converts JSON lists to line delimited JSON. it[iÿÿÿÿt]i(R(ts((s7lib/python2.7/site-packages/pandas/io/json/normalize.pyt_convert_to_line_delimitss! tt.ic Cs9t}t|tƒr'|g}t}ng}x÷|D]ï}tj|ƒ}xÊ|jƒD]¼\}} t|tjƒsƒt |ƒ}n|dkr˜|} n|||} t| tƒsã|dkrV|j |ƒ} | || foo.bar .. versionadded:: 0.20.0 level: the number of levels in the jason string, optional, default: 0 Returns ------- d - dict or list of dicts, matching `ds` Examples -------- IN[52]: nested_to_record(dict(flat1=1,dict1=dict(c=1,d=2), nested=dict(e=dict(c=1,d=2),d=2))) Out[52]: {'dict1.c': 1, 'dict1.d': 2, 'flat1': 1, 'nested.d': 2, 'nested.e.c': 1, 'nested.e.d': 2} ii(tFalset isinstancetdicttTruetcopytdeepcopytitemsRt string_typeststrtpoptupdatetnested_to_recordtappend( tdstprefixtseptlevelt singletontnew_dstdtnew_dtktvtnewkey((s7lib/python2.7/site-packages/pandas/io/json/normalize.pyRs0$       $traisec sLd„‰t|tƒr&| r&tƒSt|tƒrA|g}n|d kr‚td„|Dƒƒrxt|d|ƒ}nt|ƒSt|tƒs|g}nˆd kr²g‰ntˆtƒs͈g‰ngˆD]$}t|tƒrï|n|g^qÔ‰g‰g‰ttƒ‰t|tj ƒs7t |ƒ}ngˆD]}|j |ƒ^q>‰d‡‡‡‡‡‡‡‡fd†‰ˆ||iddƒtˆƒ} ˆd k rÌ| j d‡fd†ƒ} nxytj ˆƒD]h\} } |d k r|| } n| | kr(td jd | ƒƒ‚ntj| ƒjˆƒ| | foo.bar .. versionadded:: 0.20.0 Returns ------- frame : DataFrame Examples -------- >>> from pandas.io.json import json_normalize >>> data = [{'id': 1, 'name': {'first': 'Coleen', 'last': 'Volk'}}, ... {'name': {'given': 'Mose', 'family': 'Regner'}}, ... {'id': 2, 'name': 'Faye Raker'}] >>> json_normalize(data) id name name.family name.first name.given name.last 0 1.0 NaN NaN Coleen NaN Volk 1 NaN NaN Regner NaN Mose NaN 2 2.0 Faye Raker NaN NaN NaN NaN >>> 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 = json_normalize(data, 'counties', ['state', 'shortname', ... ['info', 'governor']]) >>> result name population info.governor state shortname 0 Dade 12345 Rick Scott Florida FL 1 Broward 40000 Rick Scott Florida FL 2 Palm Beach 60000 Rick Scott Florida FL 3 Summit 1234 John Kasich Ohio OH 4 Cuyahoga 1337 John Kasich Ohio OH >>> data = {'A': [1, 2]} >>> json_normalize(data, 'A', record_prefix='Prefix.') Prefix.0 0 1 1 2 cSsA|}t|tƒr3x%|D]}||}qWn ||}|S(N(R tlist(tjstspectresulttfield((s7lib/python2.7/site-packages/pandas/io/json/normalize.pyt _pull_field·s   css:|]0}gtj|ƒD]}t|tƒ^qVqdS(N(Rt itervaluesR R (t.0tytx((s7lib/python2.7/site-packages/pandas/io/json/normalize.pys ÉsRic s°t|tƒr|g}nt|ƒdkr·x||D]|}xMtˆˆƒD]<\}}|dt|ƒkrJˆ||dƒ||ss=Conflicting metadata name {name}, need distinguishing prefix tnameN(R R#RR tNonetanyRRRRRtjointrenamet iteritemst ValueErrorR4R2tarraytrepeat( R6t record_pathRBt meta_prefixRIR@RtmR:R&RR (( R(R?R@RARBRCRDRIREs7lib/python2.7/site-packages/pandas/io/json/normalize.pytjson_normalizecsJT          1 "'$        (t collectionsRRtnumpyR2tpandas._libs.writersRtpandasRRRRRLRW(((s7lib/python2.7/site-packages/pandas/io/json/normalize.pyts   G