Category Archives: How to Fix

Ambiguous reference to member ‘dataTask(with:completionHandler:)’

Ambiguous reference to member ‘dataTask(with:completionHandler:)’
let request = NSMutableURLRequest(url: URL(string: “Your API URL here” ,param: param))!,
cachePolicy: .useProtocolCachePolicy,
timeoutInterval:”Your request timeout time in Seconds”)
request.httpMethod = “POST”
request.allHTTPHeaderFields = headers as? [String : String]
let dataTask = URLSession.shared.dataTask(with: request ) {data,response,error in
let httpResponse = response as? HTTPURLResponse

    if (error != nil) {
     print(error)
     } else {
     print(httpResponse)
     }

    DispatchQueue.main.async {
       //Update UI here
    }

}
dataTask.resume()

Solution: just make request as URLRequest
let dataTask = URLSession.shared.dataTask(with: request as URLRequest)

Something‘s wrong–perhaps a missing \item. \begin{thebibliography}{1}

Using textstudio, we encountered a small error

Source code:

\begin{thebibliography}{}
\bibliographystyle{aaa} % template
\bibliography{references} % references
%
% and use \bibitem to create references. Consult the Instructions
% for authors for reference list style.
%
\item{a}
% Format for Journal Reference
Author, Article title, Journal, Volume, page numbers (year)
% Format for books
%\bibitem{b}
Author, Book title, page numbers. Publisher, place (year)
% etc
\end{thebibliography}

report errors:

Something’s wrong–perhaps a missing \item. \begin{thebibliography}{1}

After checking for a long time, I didn’t find the reason, and the online method was also wrong. Later, I found that I made a low-level mistake: the template file and the bib file were misplaced.

Solution: will

\bibliographystyle{spbasic} % template
\bibliography{references} % references

Put in

\begin{thebibliography}{}

\End {the bibliography}
just ahead of this interval.

Must have equal len keys and value when setting with an Iterable

Error type

When writing the crawler program, the following prompt appears:

Must have equal len keys and value when setting with an iterable

The types of data read are as follows:

Error code:

df.loc[m,'geo']=s['geo']

reason

The reason is that the object crawled is a list, and the key values on the left and right sides are not equal, so an error message appears!

modify

if s['geo']!=None:
    df.loc[m,'geo']=str(s['geo']['coordinates'][1])+','+str(s['geo']['coordinates'][0])
else:
    df.loc[m,'geo']=''

The problem was solved successfully after modification

 

java.lang.IllegalArgumentException: column ‘_id’ does not exist

About java.lang.IllegalArgumentException Error in simplecursoradapter ‘column’_ ID ‘does not exist problem:

Simplecursoradapter inherits from the cursoradapter. This class has an implicit rule that there must be a_ The ID field.

The way to solve this problem is:

1. Double name the database table, and name the self growing primary key as_ id

2. When using database query language, use db.rawQuery (select * from Biao, null) —- there are_ ID field

3. Use db.rawQuery (select id as _ ID, name from Biao)_ ID.

That is to say, you must find it when querying_ ID column.

Python json.dumps () json.dump The difference between ()

Dumps is to convert dict to STR format, loads is to convert STR to dict format. Dump and load are similar functions, but they are combined with file operation.

Code example:

import json

dic_a = {'name': 'wang', 'age': 29}
str_b = json.dumps(dic_a)
print(type(str_b),str_b) #<class 'str'> {"name": "wang", "age": 29}


dic_c = json.loads(str_b)
print(type(dic_c),dic_c) #<class 'dict'> {'name': 'wang', 'age': 29}

Then we will see the difference between dump and dumps. See the code:

import json

dic_a = {'name': 'wang', 'age': 29}
str_b = json.dumps(dic_a)
print(type(str_b),str_b) #<class 'str'> {"name": "wang", "age": 29}

#c = json.dump(dic_a)
# TypeError: dump() missing 1 required positional argument: 'fp'

c = json.dump(dic_a)

Dump needs a parameter similar to a file pointer (not a real pointer, it can be called a file like object), which can be combined with file operation, that is, you can convert dict into STR and then store it in a file; dumps directly gives STR, that is, you can convert dictionary into str.

See the code for an example (pay attention to some small details of file operation)

import json

dic_a = {'name': 'wang', 'age': 29}
str_b = json.dumps(dic_a)
print(type(str_b),str_b) #<class 'str'> {"name": "wang", "age": 29}

#c = json.dump(dic_a)
# TypeError: dump() missing 1 required positional argument: 'fp'

c = json.dump(dic_a,open('str_b.txt','w'))

Note: dump is rarely used in practice.  

Reproduced in: https://my.oschina.net/u/3486061/blog/3065779

PackageNotInstalledError: Package is not installed in prefix

use

conda create -f environment.yml

After creating a new environment, you can see all the installation packages by using CONDA list in the new environment, but you will report an error when opening Python import

according to

https://stackoverflow.com/questions/51712693/packagenotinstallederror-package-is-not-installed-in-prefix

I tried the suggestions in it

conda update --name base conda

Invalid, no attempt

conda update --all

Because all installed packages will be upgraded

Then try

conda install anaconda

A lot of new bags, but still invalid, finally

conda activate base
conda update --all

solve the problem

Mixing iteration and read methods would lose data

Linux environment, the following figure 1 operation error, change to figure 2 form, in windows will not report an error, running well, the reason remains to be explored!

ValueError: Mixing iteration and read methods would lose data

lines = fr.readline ()

for line in lines[1:]

For large documents, read them line by line to extract the desired part to prevent too much memory and insufficient memory;

TypeError: int() can’t convert non-string with explicit base

1. Error description

>>> int(67,8);
		  
Traceback (most recent call last):
  File "<pyshell#172>", line 1, in <module>
    int(67,8);
TypeError: int() can't convert non-string with explicit base

2. Cause of error

The int() function is used to convert a string or number type to an integer. If there is only one parameter value, the value can be string or number. However, two parameters are passed in, the first parameter is string, and the second parameter is hexadecimal (binary, octal, decimal or hexadecimal). Now in the above example, the first parameter is a number, and the second parameter is octal, so an error will be reported

3. Solutions

If you want to convert an octal number to decimal, you can do this:

>>> int('67',8);
		  
55
>>>