扩展python json包功能
jsonextended的Python项目详细描述
json扩展
扩展python json包功能的模块:
- 将目录结构视为嵌套字典:
- 轻量级插件系统:为 分析不同的文件扩展名(在框中:.json、.csv、.hdf5) 以及编码/解码对象
- 延迟加载:仅当文件被索引到
- tab completion:索引为选项卡,用于快速浏览数据
- 嵌套词典的操作:
- 增强的漂亮打印机
- jupyter笔记本中的javascript呈现的可扩展树
- 函数包括:filter、merge、flatten、unflatten、diff
- 输出到目录结构(属于n文件夹级别)
<> LI>大型JSON文件的磁盘索引选项(使用IJSON包) - 应用和转换物理单位的单位模式概念(使用PINT软件包)
文档:https://jsonextended.readthedocs.io
内容
安装
来自conda(推荐):
conda install -c conda-forge jsonextended
来自PYPI:
pip install jsonextended
jsonextended不依赖于python 3.x并且只依赖于python3.x
pathlib2
在2.7上,但是,为了获得完整的功能,建议安装
以下软件包:
conda install -c conda-forge ijson numpy pint h5py pandas
基本示例
from jsonextended import edict, plugins, example_mockpaths
采用目录结构,可能包含多个文件类型:
datadir = example_mockpaths.directory1 print(datadir.to_string(indentlvl=3,file_content=True))
Folder("dir1")
File("file1.json") Contents:
{"key2": {"key3": 4, "key4": 5}, "key1": [1, 2, 3]}
Folder("subdir1")
File("file1.csv") Contents:
# a csv file
header1,header2,header3
val1,val2,val3
val4,val5,val6
val7,val8,val9
File("file1.literal.csv") Contents:
# a csv file with numbers
header1,header2,header3
1,1.1,string1
2,2.2,string2
3,3.3,string3
Folder("subdir2")
Folder("subsubdir21")
File("file1.keypair") Contents:
# a key-pair file
key1 val1
key2 val2
key3 val3
key4 val4
可以为解析每种文件类型定义插件(请参见Creating Plugins部分):
plugins.load_builtin_plugins('parsers') plugins.view_plugins('parsers')
{'csv.basic': 'read *.csv delimited file with headers to {header:[column_values]}',
'csv.literal': 'read *.literal.csv delimited files with headers to {header:column_values}, with number strings converted to int/float',
'hdf5.read': 'read *.hdf5 (in read mode) files using h5py',
'json.basic': 'read *.json files using json.load',
'keypair': "read *.keypair, where each line should be; '<key> <pair>'"}
然后lazyload获取一个路径名、类似路径的对象或类似dict的对象, 它会懒洋洋地用一个兼容的插件加载每个文件。
lazy = edict.LazyLoad(datadir) lazy
{file1.json:..,subdir1:..,subdir2:..}
然后可以将lazyload视为字典,或按tab索引 完成时间:
list(lazy.keys())
['subdir1', 'subdir2', 'file1.json']
lazy[['file1.json','key1']]
[1, 2, 3]
lazy.subdir1.file1_literal_csv.header2
[1.1, 2.2, 3.3]
为了漂亮地打印字典:
edict.pprint(lazy,depth=2)
file1.json:
key1: [1, 2, 3]
key2: {...}
subdir1:
file1.csv: {...}
file1.literal.csv: {...}
subdir2:
subsubdir21: {...}
存在许多函数来操作嵌套字典:
edict.flatten(lazy.subdir1)
{('file1.csv', 'header1'): ['val1', 'val4', 'val7'],
('file1.csv', 'header2'): ['val2', 'val5', 'val8'],
('file1.csv', 'header3'): ['val3', 'val6', 'val9'],
('file1.literal.csv', 'header1'): [1, 2, 3],
('file1.literal.csv', 'header2'): [1.1, 2.2, 3.3],
('file1.literal.csv', 'header3'): ['string1', 'string2', 'string3']}
lazyload将plugins.decode
函数解析为解析器插件的
read_file
方法(关键字“object_hook”)。因此,定制解码器
可以为特定的字典密钥签名设置插件:
print(example_mockpaths.jsonfile2.to_string())
File("file2.json") Contents:
{"key1":{"_python_set_": [1, 2, 3]},"key2":{"_numpy_ndarray_": {"dtype": "int64", "value": [1, 2, 3]}}}
edict.LazyLoad(example_mockpaths.jsonfile2).to_dict()
{u'key1': {u'_python_set_': [1, 2, 3]},
u'key2': {u'_numpy_ndarray_': {u'dtype': u'int64', u'value': [1, 2, 3]}}}
plugins.load_builtin_plugins('decoders') plugins.view_plugins('decoders')
{'decimal.Decimal': 'encode/decode Decimal type',
'numpy.ndarray': 'encode/decode numpy.ndarray',
'pint.Quantity': 'encode/decode pint.Quantity object',
'python.set': 'decode/encode python set'}
dct = edict.LazyLoad(example_mockpaths.jsonfile2).to_dict() dct
{u'key1': {1, 2, 3}, u'key2': array([1, 2, 3])}
使用编码器插件,可以反转此过程:
plugins.load_builtin_plugins('encoders') plugins.view_plugins('encoders')
{'decimal.Decimal': 'encode/decode Decimal type',
'numpy.ndarray': 'encode/decode numpy.ndarray',
'pint.Quantity': 'encode/decode pint.Quantity object',
'python.set': 'decode/encode python set'}
import json json.dumps(dct,default=plugins.encode)
'{"key2": {"_numpy_ndarray_": {"dtype": "int64", "value": [1, 2, 3]}}, "key1": {"_python_set_": [1, 2, 3]}}'
创建和加载插件
from jsonextended import plugins, utils
插件被识别为具有最小属性集的类 匹配插件类别界面:
plugins.view_interfaces()
{'decoders': ['plugin_name', 'plugin_descript', 'dict_signature'],
'encoders': ['plugin_name', 'plugin_descript', 'objclass'],
'parsers': ['plugin_name', 'plugin_descript', 'file_regex', 'read_file']}
plugins.unload_all_plugins() plugins.view_plugins()
{'decoders': {}, 'encoders': {}, 'parsers': {}}
例如,一个简单的解析器插件是:
class ParserPlugin(object): plugin_name = 'example' plugin_descript = 'a parser for *.example files, that outputs (line_number:line)' file_regex = '*.example' def read_file(self, file_obj, **kwargs): out_dict = {} for i, line in enumerate(file_obj): out_dict[i] = line.strip() return out_dict
插件可以作为类加载:
plugins.load_plugin_classes([ParserPlugin],'parsers') plugins.view_plugins()
{'decoders': {},
'encoders': {},
'parsers': {'example': 'a parser for *.example files, that outputs (line_number:line)'}}
或按目录(加载所有.py文件):
fobj = utils.MockPath('example.py',is_file=True,content=""" class ParserPlugin(object): plugin_name = 'example.other' plugin_descript = 'a parser for *.example.other files, that outputs (line_number:line)' file_regex = '*.example.other' def read_file(self, file_obj, **kwargs): out_dict = {} for i, line in enumerate(file_obj): out_dict[i] = line.strip() return out_dict """) dobj = utils.MockPath(structure=[fobj]) plugins.load_plugins_dir(dobj,'parsers') plugins.view_plugins()
{'decoders': {},
'encoders': {},
'parsers': {'example': 'a parser for *.example files, that outputs (line_number:line)',
'example.other': 'a parser for *.example.other files, that outputs (line_number:line)'}}
有关更复杂的解析器示例,请参见
jsonextended.complex_parsers
接口规范
- 分析器:
- file_regex属性,一个str,指示应用它的文件 到。一个文件将被它匹配的最长正则表达式解析。
- read_file方法,它接受(打开的)文件对象和 Kwargs作为参数
- 解码器:
- dict_signature属性,表示
字典必须有,例如dict_signature=('a','b')解码
{'a':1,'b':2}
- 来自…方法,将dict对象作为参数。
plugins.decode
函数将使用 intype参数,例如如果intype='json',则from_json
将 被召唤。
- dict_signature属性,表示
字典必须有,例如dict_signature=('a','b')解码
- 编码器:
- objClass属性,应用编码的对象类, 例如objClass=decimal.decimal对该类型的对象进行编码
- 到…方法,将dict对象作为参数。这个
plugins.encode
函数将使用 outtype参数,例如如果outtype='json',则将调用to_json
。
扩展示例
有关详细信息,所有函数都包含 例子。
数据文件夹初始化
from jsonextended import ejson, edict, utils
path = utils.get_test_path() ejson.jkeys(path)
['dir1', 'dir2', 'dir3']
jdict1 = ejson.to_dict(path) edict.pprint(jdict1,depth=2)
dir1:
dir1_1: {...}
file1: {...}
file2: {...}
dir2:
file1: {...}
dir3:
edict.to_html(jdict1,depth=2)
要尝试呈现的json树,请在jupyter笔记本中输出,请转到: https://chrisjsewell.github.io/
嵌套字典操作
jdict2 = ejson.to_dict(path,['dir1','file1']) edict.pprint(jdict2,depth=1)
initial: {...}
meta: {...}
optimised: {...}
units: {...}
filtered = edict.filter_keys(jdict2,['vol*'],use_wildcards=True) edict.pprint(filtered)
initial:
crystallographic:
volume: 924.62752781
primitive:
volume: 462.313764
optimised:
crystallographic:
volume: 1063.98960509
primitive:
volume: 531.994803
edict.pprint(edict.flatten(filtered))
(initial, crystallographic, volume): 924.62752781
(initial, primitive, volume): 462.313764
(optimised, crystallographic, volume): 1063.98960509
(optimised, primitive, volume): 531.994803
单位模式
from jsonextended.units import apply_unitschema, split_quantities withunits = apply_unitschema(filtered,{'volume':'angstrom^3'}) edict.pprint(withunits)
initial:
crystallographic:
volume: 924.62752781 angstrom ** 3
primitive:
volume: 462.313764 angstrom ** 3
optimised:
crystallographic:
volume: 1063.98960509 angstrom ** 3
primitive:
volume: 531.994803 angstrom ** 3
newunits = apply_unitschema(withunits,{'volume':'nm^3'}) edict.pprint(newunits)
initial:
crystallographic:
volume: 0.92462752781 nanometer ** 3
primitive:
volume: 0.462313764 nanometer ** 3
optimised:
crystallographic:
volume: 1.06398960509 nanometer ** 3
primitive:
volume: 0.531994803 nanometer ** 3
edict.pprint(split_quantities(newunits),depth=4)
initial:
crystallographic:
volume:
magnitude: 0.92462752781
units: nanometer ** 3
primitive:
volume:
magnitude: 0.462313764
units: nanometer ** 3
optimised:
crystallographic:
volume:
magnitude: 1.06398960509
units: nanometer ** 3
primitive:
volume:
magnitude: 0.531994803
units: nanometer ** 3