nested dict copies the keys of the other key?
def add_vocabulary(vokabulary): #{"12:11:2023": {"english": "german", "english1": "german1"}} dater = datetime.datetime.now().strftime("%d:%m:%Y") dater="13:11:2023" new_vocabulary = {dater: {"german3": 'english3'}} data = dict(pd.read_json("German-English_wordlist.json")) round=0 for key in data.keys(): print(data) round+=1 if key == dater: print(dater, key) data[dater]["english3"] = "gnglih" else: if len(data.keys()) == round: data[dater] = {} data[dater]["english3"] = "gngh" dateframe = pd.DataFrame( data ).to_json("German-English_wordlist.json") print(data)Original: Jason datai content:
{"12:11:2023":{"englis":"gnglish","english3":"gnglis\\h","germanr":"englishz"}}When I create a new key code:
data[dater] = {} :Occurrence:
All keys from "12:11:2023" are copied to "13:11:2023" 🙁
output
{"12:11:2023":{"englis":"gnglish","english3":"gnglis\\h","germanr":"englishz"},"13:11:2023":{"englis":null,"english3":null,"germanr":null}}Expectation:
{"12:11:2023":{"englis":"gnglish","english3":"gnglis\\h","germanr":"englishz"},"13:11:2023":{}}on it
Occurrence:
in key "13:11:2023" create the key and value {"english3":"gngh"} (in my case overwrite) code:
data[dater]["english3"] = "gngh"output:
{"12:11:2023":{"englis":"gnglish","english3":"gnglis\\h","germanr":"englishz"},"13:11:2023":{"englis":null,"english3":gngh,"germanr":null}}
(1 votes)
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Where in your code is this to take place?
That’s commented.
row 17
Dumb question why do you even know about the keys()?
I’m looking at the Doku for a second.
https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html
If you go down, there’s a play, then NaN is apparently generated with missing values, i.e. the Pandas object forces a tabular form.
This is the real part you stumble on. You should read in more detail and see what you actually want to do.
And a JSON file you are recovering from the dict, do I suppose?
Could it be quite possible that the data structure is based on a table and therefore does not necessarily supplement existing fields as NULL to have uniform tuples? So that to_json() this supplements the so ne PAnda story?
Only as a marginal note, Python itself also has a JSON module, so as long as you only want to process JSON, you might also use it.
Aha:
So according to the print of Data, last line, everything sparks like it should.
Could also not display the keys with a value “zero”
Terminal print:
Content of the Jason file
Right, that’s the real question.
Only you are remarkably further and also find inefficient code^
Are you sure this is what you want? I have keien experience with Pandas, but according to Doku, the to_dict() MEthode should create a dictionary, so more
something?
I’m missing the context, somewhere the Stale Entries have to come. The thing is:
In the event that there is data in data udn is not just generated, in the existing dict the key english3 is set to gngh, the output also shows that. So the question is where the other keys come from?
While your approach is more efficient, where I am grateful to you,
but the main problem remains
If you are only concerned with the problem that a more unprecedented key should be created.
Function:
creates the ubergeortneten key like: “12:11:2023”
Function:
creates/ prescribes the key in “12:11:2023”
Answer:
if I don’t create “12:11:2023” first, I get a KeysError
I have not found anything definitive on the fast, but my feeling tells me that it is a bad idea to change a dictionary (especially in length) while you are iterating over an ikeys() view.
If, then you would probably have to iterate over the keys() a stable copy to execute the modifications on the original.
But as I said, I don’t see why you’re expensive about the keys if you can make a lookup directly.