Skip to main content
Pre-release

This release is a pre-release and may not be stable for production use.

from xmlmanip import XMLSchema, SearchableList
string = """
<breakfast_menu>
<food tag="waffles">
    <name>Belgian Waffles</name>
    <price>$5.95</price>
    <description>
   Two of our famous Belgian Waffles with plenty of real maple syrup
   </description>
    <calories>650</calories>
</food>
<food tag="waffles">
    <name >Strawberry Belgian Waffles</name>
    <price>$7.95</price>
    <description>
    Light Belgian waffles covered with strawberries and whipped cream
    </description>
    <calories>900</calories>
</food>
<food tag="waffles">
    <name>Berry-Berry Belgian Waffles</name>
    <price>$8.95</price>
    <description>
    Belgian waffles covered with assorted fresh berries and whipped cream
    </description>
    <calories>900</calories>
</food>
<food tag="toast">
    <name>French Toast</name>
    <price>$4.50</price>
    <description>
    Thick slices made from our homemade sourdough bread
    </description>
    <calories>600</calories>
</food>
<food tag="classic">
    <name>Homestyle Breakfast</name>
    <price>$6.95</price>
    <description>
    Two eggs, bacon or sausage, toast, and our ever-popular hash browns
    </description>
    <calories>950</calories>
</food>
</breakfast_menu>
"""

You can import your XML string to convert it to a dict. (dict conversion handled by https://github.com/martinblech/xmltodict).

schema = XMLSchema(string)
schema
XMLSchema([('breakfast_menu',
            OrderedDict([('food',
                          [OrderedDict([('@tag', 'waffles'),
                                        ('name', 'Belgian Waffles'),
                                        ('price', '$5.95'),
                                        ('description',
                                         'Two of our famous Belgian Waffles with plenty of real maple syrup'),
                                        ('calories', '650')]),
                           OrderedDict([('@tag', 'waffles'),
                                        ('name', 'Strawberry Belgian Waffles'),
                                        ('price', '$7.95'),
                                        ('description',
                                         'Light Belgian waffles covered with strawberries and whipped cream'),
                                        ('calories', '900')]),
                           OrderedDict([('@tag', 'waffles'),
                                        ('name',
                                         'Berry-Berry Belgian Waffles'),
                                        ('price', '$8.95'),
                                        ('description',
                                         'Belgian waffles covered with assorted fresh berries and whipped cream'),
                                        ('calories', '900')]),
                           OrderedDict([('@tag', 'toast'),
                                        ('name', 'French Toast'),
                                        ('price', '$4.50'),
                                        ('description',
                                         'Thick slices made from our homemade sourdough bread'),
                                        ('calories', '600')]),
                           OrderedDict([('@tag', 'classic'),
                                        ('name', 'Homestyle Breakfast'),
                                        ('price', '$6.95'),
                                        ('description',
                                         'Two eggs, bacon or sausage, toast, and our ever-popular hash browns'),
                                        ('calories', '950')])])]))])

Use .search() to search for data of interest.

schema.search(name="Homestyle Breakfast")
[SchemaInnerDict([('@tag', 'classic'),
                  ('name', 'Homestyle Breakfast'),
                  ('price', '$6.95'),
                  ('description',
                   'Two eggs, bacon or sausage, toast, and our ever-popular hash browns'),
                  ('calories', '950')])]

The SearchAbleList class will also allow you to easily search through lists of dicts.

example_list = [{"thing": 1, "other_thing": 2}, {"thing": 2, "other_thing": 2}]
searchable_list = SearchableList(example_list)
print(searchable_list.search(thing__ne=2)) # thing != 2
print(searchable_list.search(other_thing=2))
[{'thing': 1, 'other_thing': 2}]
[{'thing': 1, 'other_thing': 2}, {'thing': 2, 'other_thing': 2}]

Use .locate() if you are interested in the “path” to your data of interest and .retrieve() to get an object from its “path.”

schema.locate(name="Homestyle Breakfast")
['__breakfast_menu__food__4__name']
schema.retrieve('__breakfast_menu__food__4__name')
'Homestyle Breakfast'
schema.retrieve('__breakfast_menu__food__4')
SchemaInnerDict([('@tag', 'classic'),
                 ('name', 'Homestyle Breakfast'),
                 ('price', '$6.95'),
                 ('description',
                  'Two eggs, bacon or sausage, toast, and our ever-popular hash browns'),
                 ('calories', '950')])

You have access to all of the standard comparison methods.

paths = schema.locate(name__contains="Waffles")
paths
['__breakfast_menu__food__0__name',
 '__breakfast_menu__food__1__name',
 '__breakfast_menu__food__2__name']
schema.search(name__contains="Waffles")
[SchemaInnerDict([('@tag', 'waffles'),
                  ('name', 'Belgian Waffles'),
                  ('price', '$5.95'),
                  ('description',
                   'Two of our famous Belgian Waffles with plenty of real maple syrup'),
                  ('calories', '650')]),
 SchemaInnerDict([('@tag', 'waffles'),
                  ('name', 'Berry-Berry Belgian Waffles'),
                  ('price', '$8.95'),
                  ('description',
                   'Belgian waffles covered with assorted fresh berries and whipped cream'),
                  ('calories', '900')]),
 SchemaInnerDict([('@tag', 'waffles'),
                  ('name', 'Strawberry Belgian Waffles'),
                  ('price', '$7.95'),
                  ('description',
                   'Light Belgian waffles covered with strawberries and whipped cream'),
                  ('calories', '900')])]
schema.search(calories__lt="700")
[SchemaInnerDict([('@tag', 'toast'),
                  ('name', 'French Toast'),
                  ('price', '$4.50'),
                  ('description',
                   'Thick slices made from our homemade sourdough bread'),
                  ('calories', '600')]),
 SchemaInnerDict([('@tag', 'waffles'),
                  ('name', 'Belgian Waffles'),
                  ('price', '$5.95'),
                  ('description',
                   'Two of our famous Belgian Waffles with plenty of real maple syrup'),
                  ('calories', '650')])]

Warning, all types are compared as strings, which may have undesirable results.

schema.search(calories__lt="700") == schema.search(calories__lt="70")
True

Some attributes cannot be accessed via keyword arguements, unfortunately.

schema.search(@tag__ne="waffles")
  File "<ipython-input-13-da95e3095c41>", line 1
    schema.search(@tag__ne="waffles")
                  ^
SyntaxError: invalid syntax

You will need to pass the desired attribute and comparison method as strings in this case.

schema.search('@tag', 'waffles') # default comparison is __eq__
[SchemaInnerDict([('@tag', 'waffles'),
                  ('name', 'Belgian Waffles'),
                  ('price', '$5.95'),
                  ('description',
                   'Two of our famous Belgian Waffles with plenty of real maple syrup'),
                  ('calories', '650')]),
 SchemaInnerDict([('@tag', 'waffles'),
                  ('name', 'Strawberry Belgian Waffles'),
                  ('price', '$7.95'),
                  ('description',
                   'Light Belgian waffles covered with strawberries and whipped cream'),
                  ('calories', '900')]),
 SchemaInnerDict([('@tag', 'waffles'),
                  ('name', 'Berry-Berry Belgian Waffles'),
                  ('price', '$8.95'),
                  ('description',
                   'Belgian waffles covered with assorted fresh berries and whipped cream'),
                  ('calories', '900')])]
schema.search('@tag', 'waffles', comparison='ne')
[SchemaInnerDict([('@tag', 'classic'),
                  ('name', 'Homestyle Breakfast'),
                  ('price', '$6.95'),
                  ('description',
                   'Two eggs, bacon or sausage, toast, and our ever-popular hash browns'),
                  ('calories', '950')]),
 SchemaInnerDict([('@tag', 'toast'),
                  ('name', 'French Toast'),
                  ('price', '$4.50'),
                  ('description',
                   'Thick slices made from our homemade sourdough bread'),
                  ('calories', '600')])]

Metadata

Release files for xmlmanip 1.1.8.dev0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for xmlmanip 1.1.8.dev0
File Size Uploaded Provenance
xmlmanip-1.1.8.dev0.tar.gz 9.2 kB Without provenance Details

Release files / xmlmanip-1.1.8.dev0.tar.gz

Download URL xmlmanip-1.1.8.dev0.tar.gz
Size 9.2 kB
Tags Source
SHA-256 checksum
How to use checksums
4cb41572d3076dfbc20b14210de3f875fb1abe26cb49dfbde7f0c41c4ecebe6d
BLAKE2b-256 checksum
How to use checksums
7d13e86bd7acf9403552d2f9e4cc817bca06f0e87377ee61313e55b427d2a7d1
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No

Security for xmlmanip 1.1.8.dev0

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page