U mdB @sddlmZddlmZmZddlZddlZddlmZm Z m Z ddl Z ddl mZddlmZmZddlZddlmZddd d d Zd)dddddddZddddddddZddddddZd*ddddddZd+dd!d"d#d#d$ddd%d& d'd(ZdS),) annotations)abc defaultdictN)Any DefaultDictIterableconvert_json_to_lines) IgnoreRaiseScalar) DataFramestr)sreturncCs0|ddks|ddkr|S|dd}t|S)zJ Helper function that converts JSON lists to line delimited JSON. r[]r)rrR/home/sam/Atlas/atlas_env/lib/python3.8/site-packages/pandas/io/json/_normalize.pyconvert_to_line_delimitss r.intz int | None)prefixseplevel max_levelc Csd}t|tr|g}d}g}|D]}t|}|D]\} } t| tsPt| } |dkr^| } n ||| } t| tr|dk r||kr|dkr6|| } | || <q6|| } |t| | ||d|q6| |q |r|dS|S)a 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} FTrNr) isinstancedictcopydeepcopyitemsr popupdatenested_to_recordappend) dsrrrrZ singletonZnew_dsdZnew_dkvZnewkeyrrrr%*s8,        r%rdict[str, Any])data key_stringnormalized_dict separatorrcCszt|trn|D]X\}}|||}|sZtjdkrPddlm}|||}n ||}t||||dqn|||<|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 ) r) removeprefixr,r-r.r/)rrr"sys version_infoZpandas.util._str_methodsr2_normalise_json)r,r-r.r/keyvalueZnew_keyr2rrrr6{s       r6)r,r/rcCs8dd|D}tdd|Ddi|d}||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` cSs i|]\}}t|ts||qSrrr.0r)r*rrr s z+_normalise_json_ordered..cSs i|]\}}t|tr||qSrr9r:rrrr<s rr3)r"r6)r,r/Z top_dict_Z nested_dict_rrr_normalise_json_orderedsr=zdict | list[dict]zdict | list[dict] | Any)r'rrcs@i}t|trt|d}n t|tr<fdd|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} )r,r/csg|]}t|dqS)r)_simple_json_normalize)r;rowr>rr sz*_simple_json_normalize..)rrr=list)r'rZnormalised_json_objectZnormalised_json_listrr>rr?s+  r?raisezstr | list | Nonez"str | list[str | list[str]] | Nonez str | Noner r ) r, record_pathmeta meta_prefix record_prefixerrorsrrrc sd"dddddfdd ddd d fd d t|trD|sDtSt|trV|g}n$t|tjrvt|tsvt|}nt|d kr|d kr|d kr d krd krtt| dS|d krt dd|Drt | d}t|St|ts|g}|d krg}nt|ts|g}dd|Dg gt t fddDd#ddd f dd ||iddt } d k r|j fddd} D]N\} } |d k r|| } | |krtd| d tj| td!|| <q|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. Fr+z list | strboolzScalar | Iterable)jsspecextract_recordrc s|}z:t|tr4|D]}|dkr(t|||}qn||}Wnftk r}zH|rftd|d|dkr|tjWYStd|d|d|W5d}~XYnX|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. 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