Article | carefree huan \

Source: Python Technology “ID: pythonall”

To do a good job, you must sharpen your tools. We need to learn some “advanced” skills if we want to develop more easily and efficiently. Not long ago I saw a Python monk code, which used a short and concise module, I think it is quite useful, today to share with you.

This module, called Glom, is a small Python module that handles data and has the following features:

  • Nested structure and path-based access
  • Use the lightweight Pythonic specification for declarative data transformation
  • Readable and meaningful error messages
  • Built-in data detection and debugging capabilities

It’s kind of abstract, right? Let’s use an example to show you.

The installation

As a built-in Python module, you must know how to install it:

pip3 install glom

In seconds!

Simple to use

Let’s look at the simplest use:


d = {"a": {"b": {"c": 1}}}
print(glom(d, "a.b.c")) # 1
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Here, we have a nested three layers of JSON structure, we want to get the value of the innermost layer of C, the normal writing should be:


print(d["a"] ["b"] ["c"])
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Would you scoff if I said glom is better than the traditional way because you don’t have to write layer upon layer of brackets and quotes?

Ok, let’s look at the following situation:


d = {"a": {"b": None}}
print(d["a"] ["b"] ["c"])
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Iterating through a None object, you get the following error:


Traceback (most recent call last):
  File "/Users/cxhuan/Documents/python_workspace/mypy/pmodules/pglom/glomstudy.py", line 10, in <module>
    print(d["a"] ["b"] ["c"])
TypeError: 'NoneType' object is not subscriptable
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Let’s take a look at glom’s handling:


from glom import glom

d = {"a": {"b": None}}
print(glom(d, "a.b.c"))
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Likewise, glom cannot pair the error output, and you get the following error:


Traceback (most recent call last):
  File "/Users/cxhuan/Documents/python_workspace/mypy/pmodules/pglom/glomstudy.py", line 11, in <module>
    print(glom(d, "a.b.c"))
  File "/ Library/Frameworks/Python framework Versions / 3.7 / lib/python3.7 / site - packages/glom/core. Py." ", line 2181, in glom
    raise err
glom.core.PathAccessError: error raised while processing, details below.
 Target-spec trace (most recent last):
 - Target: {'a': {'b': None}}
 - Spec: 'a.b.c'
glom.core.PathAccessError: could not access 'c', part 2 of Path('a'.'b'.'c'), got error: AttributeError("'NoneType' object has no attribute 'c'")
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If you look carefully at the error content, you will find that the error content is extremely detailed, at a glance, this is a magic tool for finding bugs in the program!

Complex usage

Just a simple example, let us have an intuitive understanding of glom, next let’s look at the definition of glom method:

glom(target, spec, **kwargs)

Let’s look at what parameters mean:

  • Target: Target data, which can be dict, list, or any other object
  • Spec: is what we want to output

Let’s use this method.

Let’s start with an example. We have a dict, and if we want to get all the names, we can do it with glom:


data = {"student": {"info": [{"name": "Zhang"}, {"name": "Bill"}]}}
info = glom(data, ("student.info"["name"]))
print(info) # ['Joe'.'bill']
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In the traditional way, we might have to iterate to get it, but with glom, we only need one line of code, and the output is an array.

If you want a dict instead of an array, that’s easy:


info = glom(data, {"info": ("student.info"["name"])})
print(info) # {'info': ['Joe'.'bill']
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We just need to assign the original array to a dictionary to receive it.

Deal with troublesome needs

If I now have two sets of data, I want to fetch the value of name:


data_1 = {"school": {"student": [{"name": "Zhang"}, {"name": "Bill"}]}}
data_2 = {"school": {"teacher": [{"name": "Miss Wang"}, {"name": "Miss Zhao"}]}}

spec_1 = {"name": ("school.student"["name"])}
spec_2 = {"name": ("school.teacher"["name"])}
print(glom(data_1, spec_1)) # {'name': ['Joe'.'bill']}
print(glom(data_2, spec_2)) # {'name': ['Miss Wang'.'Miss Zhao']}
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That’s how we usually write it, right? What if we had multiple sets of data, and each set was taken in a similar way? We can use the Coalesce method to avoid repeating N rows of parameters: Coalesce: Coalesce: Coalesce: Coalesce: Coalesce: Coalesce: Coalesce: Coalesce: Coalesce: Coalesce


data_1 = {"school": {"student": [{"name": "Zhang"}, {"name": "Bill"}]}}
data_2 = {"school": {"teacher": [{"name": "Miss Wang"}, {"name": "Miss Zhao"}]}}

spec = {"name": (Coalesce("school.student"."school.teacher"),"name"])}
 
print(glom(data_1, spec)) # {'name': ['Joe'.'bill']}
print(glom(data_2, spec)) # {'name': ['Miss Wang'.'Miss Zhao']}
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You can use Coalesce to aggregate multiple requirements and value them against the same spec.

Here’s another big kill — value calculation. Glom can also perform simple calculations of values. Let’s take an example:


data = {"school": {"student": [{"name": "Zhang"."age": 8}, {"name": "Bill"."age": 10}]}}
spec = {"sum_age": ("school.student"["age"], sum)}
print(glom(data, spec)) # {'sum_age': 18}
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conclusion

Introduced so much, we should know the fierce place of Glom, it is said that many big guys like to use it. In fact, it has a lot of other practical functions to be explored, here will not be introduced. If you find the modules shared today useful, click “Watching” to support!

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