Article directory
- Preface
- 1. Dictionary comprehensions and Set comprehensions
- 2. Use Counter counting object when counting
- 3. Beautifully print out JSON
- 4. Create small, one-off, fast web services
- 5. Python’s amazing open source community
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- About Python technical reserves
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- 1. Learning routes in all directions of Python
- 2. Python basic learning video
- 3. Excellent Python learning books
- 4. Python toolkit + project source code collection
- ①Python toolkit
- ②Python practical case
- ③Python mini game source code
- 5. Interview materials
- 6. Python part-time channels
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Foreword
1. Dictionary comprehensions and Set comprehensions
Most Python programmers know and have used list comprehensions. If you’re not familiar with the concept of list comprehensions – a list comprehension is a shorter, more concise way of creating a list.
>>> some_list = [1, 2, 3, 4, 5] >>> another_list = [ x + 1 for x in some_list ] >>> another_list [2, 3, 4, 5, 6]
Since Python 3.1 (and even Python 2.7), we can use the same syntax to create sets and dictionaries:
>>> # Set Comprehensions
>>> some_list = [1, 2, 3, 4, 5, 2, 5, 1, 4, 8]
>>> even_set = {<!-- --> x for x in some_list if x % 2 == 0 }
>>> even_set
set([8, 2, 4])
>>> #Dict Comprehensions
>>> d = {<!-- --> x: x % 2 == 0 for x in range(1, 11) }
>>>d
{<!-- -->1: False, 2: True, 3: False, 4: True, 5: False, 6: True, 7: False, 8: True, 9: False, 10: True}
In the first example, we create a set with unique elements based on some_list, and the set only contains even numbers. In the example of the dictionary table, we created a key that is a non-repeating integer between 1 and 10, and the value is a Boolean type that indicates whether the key is an even number.
Another thing worth noting here is the literal representation of sets. We can simply create a collection this way:
>>> my_set = {<!-- -->1, 2, 1, 2, 3, 4}
>>> my_set
set([1, 2, 3, 4])
There is no need to use the built-in function set().
2. Use Counter counting object when counting
This sounds obvious, but is often forgotten. Counting something is a common task for most programmers, and in most cases not very challenging – here are a few ways to make it easier.
There is a built-in subclass of the dict class in Python’s collections library, which is specifically designed to do this kind of thing:
>>> from collections import Counter
>>> c = Counter( hello world )
>>>c
Counter({<!-- --> l : 3, o : 2, : 1, e : 1, d : 1, h : 1, r : 1, w : 1})
>>> c.most_common(2)
[( l , 3), ( o , 2)]
3. Beautifully print out JSON
JSON is a very good form of data serialization and is widely used by various APIs and web services today. Using Python’s built-in json processing can make the JSON string readable to a certain extent, but when encountering large data, it appears as a long, continuous line, which is difficult for the human eye to view.
In order to make JSON data more user-friendly, we can use the indent parameter to output beautiful JSON. This is especially useful when programming or logging interactively at the console:
>>> import json
>>> print(json.dumps(data)) # No indention
{<!-- -->"status": "OK", "count": 2, "results": [{<!-- -->"age": 27 , "name": "Oz", "lactose_intolerant": true}, {<!-- -->"age": 29, "name": "Joe", "lactose_intolerant": false}]}
>>> print(json.dumps(data, indent=2)) # With indention
{<!-- -->
"status": "OK",
"count": 2,
"results": [
{<!-- -->
"age": 27,
"name": "Oz",
"lactose_intolerant": true
},
{<!-- -->
"age": 29,
"name": "Joe",
"lactose_intolerant": false
}
]
}
Similarly, using the built-in pprint module can make anything else print more beautifully.
4. Create a one-time, fast small web service
Sometimes, we need to do some simple, very basic RPC-like interactions between two machines or services. We want to use program B to call a method in program A in a simple way – sometimes on another machine. Internal use only.
I do not encourage the use of the methods described here for non-internal, one-off programming. We can use a protocol called XML-RPC (corresponding to this Python library) to do this kind of thing.
The following is an example of using the SimpleXMLRPCServer module to build a fast small file reading server:
from SimpleXMLRPCServer import SimpleXMLRPCServer
def file_reader(file_name):
with open(file_name, r ) as f:
return f.read()
server = SimpleXMLRPCServer(( localhost , 8000))
server.register_introspection_functions()
server.register_function(file_reader)
server.serve_forever()
client
import xmlrpclib proxy = xmlrpclib.ServerProxy( http://localhost:8000/ ) proxy.file_reader(/tmp/secret.txt)
In this way, we get a remote file reading tool with no external dependencies and only a few lines of code (of course, without any security measures, so you can only do this at home).
5. Python’s amazing open source community
Several of the things I mentioned here are in the Python standard library. If you have Python installed, you can already use it in this way. For many other types of tasks, there are a large number of community-maintained third-party libraries you can use.
The following list is what I consider necessary for a useful and robust open source library:
A good open source library must…
- Include a clear permission statement that applies to your use case.
- Development and maintenance work is active (or, you can participate in developing and maintaining it.)
- Can be easily installed or deployed repeatedly using pip.
- Have a test suite with adequate test coverage.
If you find a good library that meets your requirements, don’t be embarrassed – most open source projects welcome code donations and help – even if you are not a Python expert.
About Python technical reserves
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② Route corresponding learning video
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